Face the Future

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Face the Future

We need not be afraid of the future, for the future will be in our own hands.

—Thomas E. Dewey

Your SHM board recently spent some time on the most comprehensive strategic planning that we have undertaken. Our last strategic planning meeting was almost three years ago. It is reassuring to review those minutes and see that we have accomplished a number of things that we set out to do. We have:

  • Enhanced our chapters by making more resources and staff assistance available to them;
  • Expanded our leadership training offerings;
  • Established relationships with other organizations, such as the American Hospital Association, the Joint Commission on Accreditation of Healthcare Organizations, the Society of Critical Care Medicine, and many others; and
  • Explored a credential for the hospitalist that distinguishes our work from other practitioners.

Planning to Plan

In these exciting times, however, we decided it was important to stop and take stock to either confirm that we are on the right track or adjust our direction. To prepare for the meeting, we hired an outside facilitator. We invited all board members and our staff from Philadelphia. Our staff has grown from several people to more than 20. They are a diverse group with a tremendous amount of talent. Their perspective and input remain crucial to our success.

We included some of our key committee chairs as well. These individuals have regular contact with other agencies, our members, and their employees. We surveyed our membership and hospitalist leaders to determine their perspective on the dilemmas that they face. We interviewed 13 “futurists” to obtain their opinions about key trends that will affect hospitalists, including:

  • The current environment for hospitalists;
  • The implications of future trends in patient populations;
  • The regulatory and political environment;
  • The competitive forces; and
  • Advancements in science, technology, and pharmaceuticals.

Our members and their leaders seem to feel adequately prepared for clinical decision-making to deliver high quality care, but they see a gap when it comes to how they are equipped to provide leadership in a number of areas.

Bang for the Buck

The SHM board, when surveyed, expressed a strong interest in better understanding SHM’s customer groups, what they value, and what we can offer to them. We conduct many activities and support many projects through our staff, our volunteer leadership, and our members. We need to know if we are spending our resources in a way that optimizes our impact on our members and our field. Each participant spent two to three hours reviewing materials in preparation for the meeting.

When we gathered for two days, our facilitator worked us hard. We began by reviewing what we are doing and checking that against the needs and directions identified by our members and others. We then attempted to prioritize new initiatives so that we could focus on “bang for the buck.”

As we continued the process of refining our findings and designing our action plans, a few things become apparent. Among them:

  • There is and will continue to be a shortage of qualified hospitalists;
  • The demands of an aging population, in conjunction with the expectations of healthcare givers, will be a source of pressure;
  • It will take more time to deliver care to our incoming group of patients than it did for their grandparents;
  • The technology and options that are available continue to expand, as does the need to stay abreast of ongoing changes; and
  • There will be more medical information to absorb and more to communicate and organize.
 

 

As this pressure increases, the facilities’ search for solutions to the impact on cost will increase. In addition, the transparency of hospital results, as well as pay for performance, will drive a desire to improve quality results. The process improvement changes that will be needed cannot be accomplished without a committed medical staff. Hospitalists are uniquely positioned to take on this role. Thus, the demands on hospitalists for participation and leadership will increase.

Labor Shortage a Key Issue

It appears from our membership survey that the labor shortage is a key worry. Because we have no control over demand and we expect demand to increase, we will need to be creative about impacting supply. SHM may be able to address this issue. One approach is to increase supply in the following ways:

1. Design programs that attract individuals who want to be hospitalists into the primary residencies for hospitalists: internal medicine, pediatrics, and family medicine.

An example:

  • Influencing training programs and educators to develop positive experiences for residents.

2. Create a model that includes nursing and physician assistants, as well as others, who can extend physicians’ capabilities

Another approach is to improve retention, a goal that might be accomplished by:

  • Educating hospitals on their roles in creating a good working environment with excellent support systems for their hospitalists;
  • Training group leaders to manage their programs for success;
  • Creating alternative delivery models that enhance the physician lifestyle and practice experience; and
  • Training individuals to matching their career goals with the right program.

Leadership Gap

Our members and their leaders seem to feel adequately prepared for clinical decision-making to deliver high quality care, but they see a gap when it comes to how they are equipped to provide leadership in a number of areas. These areas include transitions and coordination of care, resource utilization, and collaboration with multidisciplinary teams. SHM can respond to this need with training and mentoring. Perhaps we can also influence training programs and their curriculum.

Caring for the Uninsured

As many of you experience, hospitalists are increasingly called on to provide care for those without funds. When caring for the uninsured, physicians experience special challenges that create job dissatisfaction and affect the sustainability of the practice. In many cases, hospitals are willing to pay for this care because they are required by government regulation to provide it. As they see their profit margins erode, however, they are reluctant to compensate this work. It is important for SHM to be positioned to participate in these discussions as the payment and care of the uninsured gets increasing attention. Our public policy committee will continue to try to identify our best opportunities to impact this issue. How their mission will change is unclear, but this issue continues to be identified by our members as an important one.

This article only touches briefly on the many topics that SHM continues to explore as we try to see the future and take it into our hands. TH

Dr. Gorman is the president of SHM.

Issue
The Hospitalist - 2007(02)
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We need not be afraid of the future, for the future will be in our own hands.

—Thomas E. Dewey

Your SHM board recently spent some time on the most comprehensive strategic planning that we have undertaken. Our last strategic planning meeting was almost three years ago. It is reassuring to review those minutes and see that we have accomplished a number of things that we set out to do. We have:

  • Enhanced our chapters by making more resources and staff assistance available to them;
  • Expanded our leadership training offerings;
  • Established relationships with other organizations, such as the American Hospital Association, the Joint Commission on Accreditation of Healthcare Organizations, the Society of Critical Care Medicine, and many others; and
  • Explored a credential for the hospitalist that distinguishes our work from other practitioners.

Planning to Plan

In these exciting times, however, we decided it was important to stop and take stock to either confirm that we are on the right track or adjust our direction. To prepare for the meeting, we hired an outside facilitator. We invited all board members and our staff from Philadelphia. Our staff has grown from several people to more than 20. They are a diverse group with a tremendous amount of talent. Their perspective and input remain crucial to our success.

We included some of our key committee chairs as well. These individuals have regular contact with other agencies, our members, and their employees. We surveyed our membership and hospitalist leaders to determine their perspective on the dilemmas that they face. We interviewed 13 “futurists” to obtain their opinions about key trends that will affect hospitalists, including:

  • The current environment for hospitalists;
  • The implications of future trends in patient populations;
  • The regulatory and political environment;
  • The competitive forces; and
  • Advancements in science, technology, and pharmaceuticals.

Our members and their leaders seem to feel adequately prepared for clinical decision-making to deliver high quality care, but they see a gap when it comes to how they are equipped to provide leadership in a number of areas.

Bang for the Buck

The SHM board, when surveyed, expressed a strong interest in better understanding SHM’s customer groups, what they value, and what we can offer to them. We conduct many activities and support many projects through our staff, our volunteer leadership, and our members. We need to know if we are spending our resources in a way that optimizes our impact on our members and our field. Each participant spent two to three hours reviewing materials in preparation for the meeting.

When we gathered for two days, our facilitator worked us hard. We began by reviewing what we are doing and checking that against the needs and directions identified by our members and others. We then attempted to prioritize new initiatives so that we could focus on “bang for the buck.”

As we continued the process of refining our findings and designing our action plans, a few things become apparent. Among them:

  • There is and will continue to be a shortage of qualified hospitalists;
  • The demands of an aging population, in conjunction with the expectations of healthcare givers, will be a source of pressure;
  • It will take more time to deliver care to our incoming group of patients than it did for their grandparents;
  • The technology and options that are available continue to expand, as does the need to stay abreast of ongoing changes; and
  • There will be more medical information to absorb and more to communicate and organize.
 

 

As this pressure increases, the facilities’ search for solutions to the impact on cost will increase. In addition, the transparency of hospital results, as well as pay for performance, will drive a desire to improve quality results. The process improvement changes that will be needed cannot be accomplished without a committed medical staff. Hospitalists are uniquely positioned to take on this role. Thus, the demands on hospitalists for participation and leadership will increase.

Labor Shortage a Key Issue

It appears from our membership survey that the labor shortage is a key worry. Because we have no control over demand and we expect demand to increase, we will need to be creative about impacting supply. SHM may be able to address this issue. One approach is to increase supply in the following ways:

1. Design programs that attract individuals who want to be hospitalists into the primary residencies for hospitalists: internal medicine, pediatrics, and family medicine.

An example:

  • Influencing training programs and educators to develop positive experiences for residents.

2. Create a model that includes nursing and physician assistants, as well as others, who can extend physicians’ capabilities

Another approach is to improve retention, a goal that might be accomplished by:

  • Educating hospitals on their roles in creating a good working environment with excellent support systems for their hospitalists;
  • Training group leaders to manage their programs for success;
  • Creating alternative delivery models that enhance the physician lifestyle and practice experience; and
  • Training individuals to matching their career goals with the right program.

Leadership Gap

Our members and their leaders seem to feel adequately prepared for clinical decision-making to deliver high quality care, but they see a gap when it comes to how they are equipped to provide leadership in a number of areas. These areas include transitions and coordination of care, resource utilization, and collaboration with multidisciplinary teams. SHM can respond to this need with training and mentoring. Perhaps we can also influence training programs and their curriculum.

Caring for the Uninsured

As many of you experience, hospitalists are increasingly called on to provide care for those without funds. When caring for the uninsured, physicians experience special challenges that create job dissatisfaction and affect the sustainability of the practice. In many cases, hospitals are willing to pay for this care because they are required by government regulation to provide it. As they see their profit margins erode, however, they are reluctant to compensate this work. It is important for SHM to be positioned to participate in these discussions as the payment and care of the uninsured gets increasing attention. Our public policy committee will continue to try to identify our best opportunities to impact this issue. How their mission will change is unclear, but this issue continues to be identified by our members as an important one.

This article only touches briefly on the many topics that SHM continues to explore as we try to see the future and take it into our hands. TH

Dr. Gorman is the president of SHM.

We need not be afraid of the future, for the future will be in our own hands.

—Thomas E. Dewey

Your SHM board recently spent some time on the most comprehensive strategic planning that we have undertaken. Our last strategic planning meeting was almost three years ago. It is reassuring to review those minutes and see that we have accomplished a number of things that we set out to do. We have:

  • Enhanced our chapters by making more resources and staff assistance available to them;
  • Expanded our leadership training offerings;
  • Established relationships with other organizations, such as the American Hospital Association, the Joint Commission on Accreditation of Healthcare Organizations, the Society of Critical Care Medicine, and many others; and
  • Explored a credential for the hospitalist that distinguishes our work from other practitioners.

Planning to Plan

In these exciting times, however, we decided it was important to stop and take stock to either confirm that we are on the right track or adjust our direction. To prepare for the meeting, we hired an outside facilitator. We invited all board members and our staff from Philadelphia. Our staff has grown from several people to more than 20. They are a diverse group with a tremendous amount of talent. Their perspective and input remain crucial to our success.

We included some of our key committee chairs as well. These individuals have regular contact with other agencies, our members, and their employees. We surveyed our membership and hospitalist leaders to determine their perspective on the dilemmas that they face. We interviewed 13 “futurists” to obtain their opinions about key trends that will affect hospitalists, including:

  • The current environment for hospitalists;
  • The implications of future trends in patient populations;
  • The regulatory and political environment;
  • The competitive forces; and
  • Advancements in science, technology, and pharmaceuticals.

Our members and their leaders seem to feel adequately prepared for clinical decision-making to deliver high quality care, but they see a gap when it comes to how they are equipped to provide leadership in a number of areas.

Bang for the Buck

The SHM board, when surveyed, expressed a strong interest in better understanding SHM’s customer groups, what they value, and what we can offer to them. We conduct many activities and support many projects through our staff, our volunteer leadership, and our members. We need to know if we are spending our resources in a way that optimizes our impact on our members and our field. Each participant spent two to three hours reviewing materials in preparation for the meeting.

When we gathered for two days, our facilitator worked us hard. We began by reviewing what we are doing and checking that against the needs and directions identified by our members and others. We then attempted to prioritize new initiatives so that we could focus on “bang for the buck.”

As we continued the process of refining our findings and designing our action plans, a few things become apparent. Among them:

  • There is and will continue to be a shortage of qualified hospitalists;
  • The demands of an aging population, in conjunction with the expectations of healthcare givers, will be a source of pressure;
  • It will take more time to deliver care to our incoming group of patients than it did for their grandparents;
  • The technology and options that are available continue to expand, as does the need to stay abreast of ongoing changes; and
  • There will be more medical information to absorb and more to communicate and organize.
 

 

As this pressure increases, the facilities’ search for solutions to the impact on cost will increase. In addition, the transparency of hospital results, as well as pay for performance, will drive a desire to improve quality results. The process improvement changes that will be needed cannot be accomplished without a committed medical staff. Hospitalists are uniquely positioned to take on this role. Thus, the demands on hospitalists for participation and leadership will increase.

Labor Shortage a Key Issue

It appears from our membership survey that the labor shortage is a key worry. Because we have no control over demand and we expect demand to increase, we will need to be creative about impacting supply. SHM may be able to address this issue. One approach is to increase supply in the following ways:

1. Design programs that attract individuals who want to be hospitalists into the primary residencies for hospitalists: internal medicine, pediatrics, and family medicine.

An example:

  • Influencing training programs and educators to develop positive experiences for residents.

2. Create a model that includes nursing and physician assistants, as well as others, who can extend physicians’ capabilities

Another approach is to improve retention, a goal that might be accomplished by:

  • Educating hospitals on their roles in creating a good working environment with excellent support systems for their hospitalists;
  • Training group leaders to manage their programs for success;
  • Creating alternative delivery models that enhance the physician lifestyle and practice experience; and
  • Training individuals to matching their career goals with the right program.

Leadership Gap

Our members and their leaders seem to feel adequately prepared for clinical decision-making to deliver high quality care, but they see a gap when it comes to how they are equipped to provide leadership in a number of areas. These areas include transitions and coordination of care, resource utilization, and collaboration with multidisciplinary teams. SHM can respond to this need with training and mentoring. Perhaps we can also influence training programs and their curriculum.

Caring for the Uninsured

As many of you experience, hospitalists are increasingly called on to provide care for those without funds. When caring for the uninsured, physicians experience special challenges that create job dissatisfaction and affect the sustainability of the practice. In many cases, hospitals are willing to pay for this care because they are required by government regulation to provide it. As they see their profit margins erode, however, they are reluctant to compensate this work. It is important for SHM to be positioned to participate in these discussions as the payment and care of the uninsured gets increasing attention. Our public policy committee will continue to try to identify our best opportunities to impact this issue. How their mission will change is unclear, but this issue continues to be identified by our members as an important one.

This article only touches briefly on the many topics that SHM continues to explore as we try to see the future and take it into our hands. TH

Dr. Gorman is the president of SHM.

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TH NewsAlerts

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Hospitalists Prone to Evidence-Based Treatments

Researchers from Harvard Medical School (Boston) and other institutions surveyed 213 pediatric hospitalists and a random sample of 352 community pediatricians. They found that, overall, the hospitalist group showed greater adherence to evidence-based therapies and tests for common pediatric illnesses in hospitalized patients, and relied less on those therapies and tests of unproven benefits.

Hourly Rounding Pays Off

According to a study by the Studer Group (Gulf Breeze, Fl.) and the Alliance for Health Care Research, having nurses round every hour results in an average 8.9-point increase in patient satisfaction, a 50% decrease in patient falls, a reduction in the use of call lights, and an undocumented improvement in clinical outcomes.

SHM Makes Acute Heart Failure a High Priority

SHM, supported in part by funding from Scios, Inc., will undertake a major outreach campaign on quality improvement related to acute heart failure. These initiatives will focus on the “front end” of the acutely decompensated heart failure patient admission, beginning with early diagnosis and aggressive treatment and intervention including emergency department and observation unit care to optimize outcomes.

“Hospitalists manage a significant number of heart failure patients across the hospital continuum,” says Larry Wellikson, MD, CEO of SHM. “Our goal is to improve heart failure care by disseminating the necessary tools and knowledge to hospitalists.”

Google as Diagnostic Tool

The popular Web site Google (Mountain View, Calif.) can help physicians with diagnoses, according to researchers from Princess Alexandria Hospital in Brisbane, Australia. The team “googled” symptoms of 26 mystery cases, and a Web search resulted in a correct diagnosis for 15 of them. The researchers call Google a useful aid for physicians.

SHM Participates in IHI Campaign

When the Institute for Healthcare Improvement (IHI) introduced its “5 Million Lives” campaign recently, SHM was one of the few organizations asked to speak about their part in the campaign.

“We are ready … and could not be more enthusiastic about participating in this landmark campaign,” said Russell Holman, M.D., SHM’s president-elect.

A Winning Team Reaps Rewards

Caritas Good Samaritan Medical Center in Brockton, Mass., and hospital medicine provider Cogent Healthcare (Irvine, Calif.) collaborated to achieve quality of care goals that earned the facility pay-for-performance bonuses from major commercial payers. Cogent used best practices that also helped reduce length of stay and unnecessary re-admissions, and improve patient and physician satisfaction.

A Prevalence of Persistent Pain

The “Health United States 2006” report issued by the National Center for Health Statistics (Hyattsville, Md.) finds a prevalence of pain in the U.S. One in four adults reported a daylong bout of pain in the last month, and one in 10 say they’ve suffered pain for at least a year.

Palliative Medicine Now Official Subspecialty

The American Board of Medical Specialties (ABMS) will establish a subspecialty certificate in Hospice and Palliative Medicine. “[This certification] affirms the concept that specialized knowledge and skills must be learned to care for the increasingly complex-care needs of the dying and their families,” says David Weissman, MD, FACP, editor-in-chief of the Journal of Palliative Medicine.

Busy Shifts Put Elderly Admissions at Risk

Older patients admitted during busy hospital shifts may have a greater mortality rate, according to a study by the University of California San Francisco’s Moffitt-Long Hospital. The retrospective cohort analysis included 5,742 adults admitted to the general medicine service and intensive care unit without cardiovascular, neurologic, or cancer- related primary diagnoses. TH

Issue
The Hospitalist - 2007(02)
Publications
Sections

Hospitalists Prone to Evidence-Based Treatments

Researchers from Harvard Medical School (Boston) and other institutions surveyed 213 pediatric hospitalists and a random sample of 352 community pediatricians. They found that, overall, the hospitalist group showed greater adherence to evidence-based therapies and tests for common pediatric illnesses in hospitalized patients, and relied less on those therapies and tests of unproven benefits.

Hourly Rounding Pays Off

According to a study by the Studer Group (Gulf Breeze, Fl.) and the Alliance for Health Care Research, having nurses round every hour results in an average 8.9-point increase in patient satisfaction, a 50% decrease in patient falls, a reduction in the use of call lights, and an undocumented improvement in clinical outcomes.

SHM Makes Acute Heart Failure a High Priority

SHM, supported in part by funding from Scios, Inc., will undertake a major outreach campaign on quality improvement related to acute heart failure. These initiatives will focus on the “front end” of the acutely decompensated heart failure patient admission, beginning with early diagnosis and aggressive treatment and intervention including emergency department and observation unit care to optimize outcomes.

“Hospitalists manage a significant number of heart failure patients across the hospital continuum,” says Larry Wellikson, MD, CEO of SHM. “Our goal is to improve heart failure care by disseminating the necessary tools and knowledge to hospitalists.”

Google as Diagnostic Tool

The popular Web site Google (Mountain View, Calif.) can help physicians with diagnoses, according to researchers from Princess Alexandria Hospital in Brisbane, Australia. The team “googled” symptoms of 26 mystery cases, and a Web search resulted in a correct diagnosis for 15 of them. The researchers call Google a useful aid for physicians.

SHM Participates in IHI Campaign

When the Institute for Healthcare Improvement (IHI) introduced its “5 Million Lives” campaign recently, SHM was one of the few organizations asked to speak about their part in the campaign.

“We are ready … and could not be more enthusiastic about participating in this landmark campaign,” said Russell Holman, M.D., SHM’s president-elect.

A Winning Team Reaps Rewards

Caritas Good Samaritan Medical Center in Brockton, Mass., and hospital medicine provider Cogent Healthcare (Irvine, Calif.) collaborated to achieve quality of care goals that earned the facility pay-for-performance bonuses from major commercial payers. Cogent used best practices that also helped reduce length of stay and unnecessary re-admissions, and improve patient and physician satisfaction.

A Prevalence of Persistent Pain

The “Health United States 2006” report issued by the National Center for Health Statistics (Hyattsville, Md.) finds a prevalence of pain in the U.S. One in four adults reported a daylong bout of pain in the last month, and one in 10 say they’ve suffered pain for at least a year.

Palliative Medicine Now Official Subspecialty

The American Board of Medical Specialties (ABMS) will establish a subspecialty certificate in Hospice and Palliative Medicine. “[This certification] affirms the concept that specialized knowledge and skills must be learned to care for the increasingly complex-care needs of the dying and their families,” says David Weissman, MD, FACP, editor-in-chief of the Journal of Palliative Medicine.

Busy Shifts Put Elderly Admissions at Risk

Older patients admitted during busy hospital shifts may have a greater mortality rate, according to a study by the University of California San Francisco’s Moffitt-Long Hospital. The retrospective cohort analysis included 5,742 adults admitted to the general medicine service and intensive care unit without cardiovascular, neurologic, or cancer- related primary diagnoses. TH

Hospitalists Prone to Evidence-Based Treatments

Researchers from Harvard Medical School (Boston) and other institutions surveyed 213 pediatric hospitalists and a random sample of 352 community pediatricians. They found that, overall, the hospitalist group showed greater adherence to evidence-based therapies and tests for common pediatric illnesses in hospitalized patients, and relied less on those therapies and tests of unproven benefits.

Hourly Rounding Pays Off

According to a study by the Studer Group (Gulf Breeze, Fl.) and the Alliance for Health Care Research, having nurses round every hour results in an average 8.9-point increase in patient satisfaction, a 50% decrease in patient falls, a reduction in the use of call lights, and an undocumented improvement in clinical outcomes.

SHM Makes Acute Heart Failure a High Priority

SHM, supported in part by funding from Scios, Inc., will undertake a major outreach campaign on quality improvement related to acute heart failure. These initiatives will focus on the “front end” of the acutely decompensated heart failure patient admission, beginning with early diagnosis and aggressive treatment and intervention including emergency department and observation unit care to optimize outcomes.

“Hospitalists manage a significant number of heart failure patients across the hospital continuum,” says Larry Wellikson, MD, CEO of SHM. “Our goal is to improve heart failure care by disseminating the necessary tools and knowledge to hospitalists.”

Google as Diagnostic Tool

The popular Web site Google (Mountain View, Calif.) can help physicians with diagnoses, according to researchers from Princess Alexandria Hospital in Brisbane, Australia. The team “googled” symptoms of 26 mystery cases, and a Web search resulted in a correct diagnosis for 15 of them. The researchers call Google a useful aid for physicians.

SHM Participates in IHI Campaign

When the Institute for Healthcare Improvement (IHI) introduced its “5 Million Lives” campaign recently, SHM was one of the few organizations asked to speak about their part in the campaign.

“We are ready … and could not be more enthusiastic about participating in this landmark campaign,” said Russell Holman, M.D., SHM’s president-elect.

A Winning Team Reaps Rewards

Caritas Good Samaritan Medical Center in Brockton, Mass., and hospital medicine provider Cogent Healthcare (Irvine, Calif.) collaborated to achieve quality of care goals that earned the facility pay-for-performance bonuses from major commercial payers. Cogent used best practices that also helped reduce length of stay and unnecessary re-admissions, and improve patient and physician satisfaction.

A Prevalence of Persistent Pain

The “Health United States 2006” report issued by the National Center for Health Statistics (Hyattsville, Md.) finds a prevalence of pain in the U.S. One in four adults reported a daylong bout of pain in the last month, and one in 10 say they’ve suffered pain for at least a year.

Palliative Medicine Now Official Subspecialty

The American Board of Medical Specialties (ABMS) will establish a subspecialty certificate in Hospice and Palliative Medicine. “[This certification] affirms the concept that specialized knowledge and skills must be learned to care for the increasingly complex-care needs of the dying and their families,” says David Weissman, MD, FACP, editor-in-chief of the Journal of Palliative Medicine.

Busy Shifts Put Elderly Admissions at Risk

Older patients admitted during busy hospital shifts may have a greater mortality rate, according to a study by the University of California San Francisco’s Moffitt-Long Hospital. The retrospective cohort analysis included 5,742 adults admitted to the general medicine service and intensive care unit without cardiovascular, neurologic, or cancer- related primary diagnoses. TH

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Proceedings of the Heart-Brain Summit

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Proceedings of the Heart-Brain Summit

Supplement Editor:
Marc S. Penn, MD, PhD

Contents

Introduction
Heart-brain medicine: Where we go from here and why
Marc S. Penn, MD, PhD, Cleveland Clinic, Cleveland, Ohio, and Earl E. Bakken, DSc (hon), Medtronic, Inc., Minneapolis, Minnesota, and North Hawaii Community Hospital, Kamuela, Hawaii

Opening remarks
The dream behind the summit
Earl E. Bakken, DSc (hon), Medtronic, Inc., Minneapolis, Minnesota, and North Hawaii Community Hospital, Kamuela, Hawaii

Keynote address
'Voodoo' death revisited: The modern lessons of neurocardiology
Martin A. Samuels, MD, DSc (hon), Brigham and Women's Hospital, Boston, Massachusetts

The broken heart syndrome
Ilan S. Wittstein, MD, Johns Hopkins University School of Medicine, Baltimore, Maryland

Brain imaging in cardiovascular disease: State of the art
Michael Phillips, MD, Cleveland Clinic, Cleveland, Ohio

Cortical control of the heart
Stephen Oppenheimer, MD, PhD, Sentient Medical Systems, Cockeysville, Maryland

Neurological mechanisms of chest pain and cardiac disease
Robert D. Foreman, PhD, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma

Hypertension in sleep apnea: The role of the sympathetic pathway
Diana L. Kunze, PhD; David Kline, PhD; and Angelina Ramirez-Navarro, PhD, MetroHealth Medical Center and Case Western Reserve University, Cleveland, Ohio

Inflammation: Implications for understanding the heart-brain connection
Mehdi H. Shishehbor, DO; Carlos Alves, MD; and Vivek Rajagopal, MD, Cleveland Clinic, Cleveland, Ohio

The anti-ischemic effects of electrical neurostimulation in the heart
Jessica de Vries, MD, University of Groningen, The Netherlands; Robert D. Foreman, PhD, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma; and Mike J.L.  DeJongste, MD, PhD, University of Groningen, The Netherlands

The little brain on the heart
J. Andrew Armour, MD, PhD, Hôpital du Sacré-Coeur and Université de Montréal, Montréal, Québec, Canada

Open heart surgery and cognitive decline
Mark F. Newman, MD, Duke University Medical Center, Durham, North Carolina

The heart and the brain within the broader context of wellness
Michael O’Donnell, PhD, MBA, MPH, Cleveland Clinic, Cleveland, Ohio

The heart-brain interaction during emotionally provoked myocardial ischemia: Implications of cortical hyperactivation in CAD and gender interactions
Robert Soufer, MD, Yale University, New Haven, Connecticut, and Matthew M. Burg, PhD, Yale University, New Haven, Connecticut, and Columbia University, New York, New York

Depression and heart disease
François Lespérance, MD, Montréal Heart Institute, and Nancy Frasure-Smith, PhD, McGill University and Montréal Heart Institute, Montréal, Québec, Canada

Sick at heart: The pathophysiology of negative emotions
Laura D. Kubzansky, PhD, MPH, Harvard School of Public Health, Boston, Massachusetts

Role of the brain in ventricular fibrillation and hypertension: From animal models to early human studies
James E. Skinner, PhD, Vicor Technologies, Inc., Boca Raton, Florida

New paradigms in heart-brain medicine: Nonlinear physiology, state-dependent proteomics
James E. Skinner, PhD, Vicor Technologies, Inc., Boca Raton, Florida

Subarachnoid hemorrhage: A model for heart-brain interactions
J. Javier Provencio, MD, Cleveland Clinic, Cleveland, Ohio

Cardiac denervation in patients with Parkinson disease
David S. Goldstein, MD, PhD, National Institutes of Health, Bethesda, Maryland

Aging and the brain renin-angiotensin system: Insights from studies in transgenic rats
Debra I. Diz, PhD; Sherry O. Kasper, PhD; Atsushi Sakima, MD; and Carlos M. Ferrario, MD, Wake Forest University School of Medicine, Winston-Salem, North Carolina

Contextual cardiology: What modern medicine can learn from ancient Hawaiian wisdom
Paul Pearsall, PhD, University of Hawaii at Manoa and Hawaii State Consortium for Integrative Medicine, Honolulu, Hawaii

Cardiocerebral resuscitation: The optimal approach to cardiac arrest
Gordon A. Ewy, MD, University of Arizona College of Medicine, Tucson, Arizona

Heart transplantation: A magnified model of heart-brain interactions
Mohamad H. Yamani, MD, and Randall C. Starling, MD, MPH, Cleveland Clinic, Cleveland, Ohio

Patent foramen ovale and migraine
Gian Paolo Anzola, MD, S. Orsola Hospital FBF, Brescia, Italy

Patent foramen ovale and stroke: To close or not to close?
Anthony J. Furlan, MD, Cleveland Clinic, Cleveland, Ohio

Sudden unexplained death in epilepsy: The role of the heart
Stephan U. Schuele, MD, Cleveland Clinic, Cleveland, Ohio, and Northwestern University, Chicago, Illinois; Peter Widdess-Walsh, MD, Adriana Bermeo, MD, and Hans O. Lüders, MD, PhD, Cleveland Clinic, Cleveland, Ohio

Hydrocephalus and the heart: Interactions of the first and third circulations
Mark Luciano, MD, PhD, and Stephen Dombrowski, PhD, Cleveland Clinic, Cleveland, Ohio

Cognitive impairment in chronic heart failure
Cathy A. Sila, MD, Cleveland Clinic, Cleveland, Ohio

Cardiac events and brain injury: Ethical implications
Paul J. Ford, PhD, Cleveland Clinic, Cleveland, Ohio

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Cleveland Clinic Journal of Medicine - 74(2)
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Contents

Introduction
Heart-brain medicine: Where we go from here and why
Marc S. Penn, MD, PhD, Cleveland Clinic, Cleveland, Ohio, and Earl E. Bakken, DSc (hon), Medtronic, Inc., Minneapolis, Minnesota, and North Hawaii Community Hospital, Kamuela, Hawaii

Opening remarks
The dream behind the summit
Earl E. Bakken, DSc (hon), Medtronic, Inc., Minneapolis, Minnesota, and North Hawaii Community Hospital, Kamuela, Hawaii

Keynote address
'Voodoo' death revisited: The modern lessons of neurocardiology
Martin A. Samuels, MD, DSc (hon), Brigham and Women's Hospital, Boston, Massachusetts

The broken heart syndrome
Ilan S. Wittstein, MD, Johns Hopkins University School of Medicine, Baltimore, Maryland

Brain imaging in cardiovascular disease: State of the art
Michael Phillips, MD, Cleveland Clinic, Cleveland, Ohio

Cortical control of the heart
Stephen Oppenheimer, MD, PhD, Sentient Medical Systems, Cockeysville, Maryland

Neurological mechanisms of chest pain and cardiac disease
Robert D. Foreman, PhD, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma

Hypertension in sleep apnea: The role of the sympathetic pathway
Diana L. Kunze, PhD; David Kline, PhD; and Angelina Ramirez-Navarro, PhD, MetroHealth Medical Center and Case Western Reserve University, Cleveland, Ohio

Inflammation: Implications for understanding the heart-brain connection
Mehdi H. Shishehbor, DO; Carlos Alves, MD; and Vivek Rajagopal, MD, Cleveland Clinic, Cleveland, Ohio

The anti-ischemic effects of electrical neurostimulation in the heart
Jessica de Vries, MD, University of Groningen, The Netherlands; Robert D. Foreman, PhD, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma; and Mike J.L.  DeJongste, MD, PhD, University of Groningen, The Netherlands

The little brain on the heart
J. Andrew Armour, MD, PhD, Hôpital du Sacré-Coeur and Université de Montréal, Montréal, Québec, Canada

Open heart surgery and cognitive decline
Mark F. Newman, MD, Duke University Medical Center, Durham, North Carolina

The heart and the brain within the broader context of wellness
Michael O’Donnell, PhD, MBA, MPH, Cleveland Clinic, Cleveland, Ohio

The heart-brain interaction during emotionally provoked myocardial ischemia: Implications of cortical hyperactivation in CAD and gender interactions
Robert Soufer, MD, Yale University, New Haven, Connecticut, and Matthew M. Burg, PhD, Yale University, New Haven, Connecticut, and Columbia University, New York, New York

Depression and heart disease
François Lespérance, MD, Montréal Heart Institute, and Nancy Frasure-Smith, PhD, McGill University and Montréal Heart Institute, Montréal, Québec, Canada

Sick at heart: The pathophysiology of negative emotions
Laura D. Kubzansky, PhD, MPH, Harvard School of Public Health, Boston, Massachusetts

Role of the brain in ventricular fibrillation and hypertension: From animal models to early human studies
James E. Skinner, PhD, Vicor Technologies, Inc., Boca Raton, Florida

New paradigms in heart-brain medicine: Nonlinear physiology, state-dependent proteomics
James E. Skinner, PhD, Vicor Technologies, Inc., Boca Raton, Florida

Subarachnoid hemorrhage: A model for heart-brain interactions
J. Javier Provencio, MD, Cleveland Clinic, Cleveland, Ohio

Cardiac denervation in patients with Parkinson disease
David S. Goldstein, MD, PhD, National Institutes of Health, Bethesda, Maryland

Aging and the brain renin-angiotensin system: Insights from studies in transgenic rats
Debra I. Diz, PhD; Sherry O. Kasper, PhD; Atsushi Sakima, MD; and Carlos M. Ferrario, MD, Wake Forest University School of Medicine, Winston-Salem, North Carolina

Contextual cardiology: What modern medicine can learn from ancient Hawaiian wisdom
Paul Pearsall, PhD, University of Hawaii at Manoa and Hawaii State Consortium for Integrative Medicine, Honolulu, Hawaii

Cardiocerebral resuscitation: The optimal approach to cardiac arrest
Gordon A. Ewy, MD, University of Arizona College of Medicine, Tucson, Arizona

Heart transplantation: A magnified model of heart-brain interactions
Mohamad H. Yamani, MD, and Randall C. Starling, MD, MPH, Cleveland Clinic, Cleveland, Ohio

Patent foramen ovale and migraine
Gian Paolo Anzola, MD, S. Orsola Hospital FBF, Brescia, Italy

Patent foramen ovale and stroke: To close or not to close?
Anthony J. Furlan, MD, Cleveland Clinic, Cleveland, Ohio

Sudden unexplained death in epilepsy: The role of the heart
Stephan U. Schuele, MD, Cleveland Clinic, Cleveland, Ohio, and Northwestern University, Chicago, Illinois; Peter Widdess-Walsh, MD, Adriana Bermeo, MD, and Hans O. Lüders, MD, PhD, Cleveland Clinic, Cleveland, Ohio

Hydrocephalus and the heart: Interactions of the first and third circulations
Mark Luciano, MD, PhD, and Stephen Dombrowski, PhD, Cleveland Clinic, Cleveland, Ohio

Cognitive impairment in chronic heart failure
Cathy A. Sila, MD, Cleveland Clinic, Cleveland, Ohio

Cardiac events and brain injury: Ethical implications
Paul J. Ford, PhD, Cleveland Clinic, Cleveland, Ohio

Supplement Editor:
Marc S. Penn, MD, PhD

Contents

Introduction
Heart-brain medicine: Where we go from here and why
Marc S. Penn, MD, PhD, Cleveland Clinic, Cleveland, Ohio, and Earl E. Bakken, DSc (hon), Medtronic, Inc., Minneapolis, Minnesota, and North Hawaii Community Hospital, Kamuela, Hawaii

Opening remarks
The dream behind the summit
Earl E. Bakken, DSc (hon), Medtronic, Inc., Minneapolis, Minnesota, and North Hawaii Community Hospital, Kamuela, Hawaii

Keynote address
'Voodoo' death revisited: The modern lessons of neurocardiology
Martin A. Samuels, MD, DSc (hon), Brigham and Women's Hospital, Boston, Massachusetts

The broken heart syndrome
Ilan S. Wittstein, MD, Johns Hopkins University School of Medicine, Baltimore, Maryland

Brain imaging in cardiovascular disease: State of the art
Michael Phillips, MD, Cleveland Clinic, Cleveland, Ohio

Cortical control of the heart
Stephen Oppenheimer, MD, PhD, Sentient Medical Systems, Cockeysville, Maryland

Neurological mechanisms of chest pain and cardiac disease
Robert D. Foreman, PhD, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma

Hypertension in sleep apnea: The role of the sympathetic pathway
Diana L. Kunze, PhD; David Kline, PhD; and Angelina Ramirez-Navarro, PhD, MetroHealth Medical Center and Case Western Reserve University, Cleveland, Ohio

Inflammation: Implications for understanding the heart-brain connection
Mehdi H. Shishehbor, DO; Carlos Alves, MD; and Vivek Rajagopal, MD, Cleveland Clinic, Cleveland, Ohio

The anti-ischemic effects of electrical neurostimulation in the heart
Jessica de Vries, MD, University of Groningen, The Netherlands; Robert D. Foreman, PhD, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma; and Mike J.L.  DeJongste, MD, PhD, University of Groningen, The Netherlands

The little brain on the heart
J. Andrew Armour, MD, PhD, Hôpital du Sacré-Coeur and Université de Montréal, Montréal, Québec, Canada

Open heart surgery and cognitive decline
Mark F. Newman, MD, Duke University Medical Center, Durham, North Carolina

The heart and the brain within the broader context of wellness
Michael O’Donnell, PhD, MBA, MPH, Cleveland Clinic, Cleveland, Ohio

The heart-brain interaction during emotionally provoked myocardial ischemia: Implications of cortical hyperactivation in CAD and gender interactions
Robert Soufer, MD, Yale University, New Haven, Connecticut, and Matthew M. Burg, PhD, Yale University, New Haven, Connecticut, and Columbia University, New York, New York

Depression and heart disease
François Lespérance, MD, Montréal Heart Institute, and Nancy Frasure-Smith, PhD, McGill University and Montréal Heart Institute, Montréal, Québec, Canada

Sick at heart: The pathophysiology of negative emotions
Laura D. Kubzansky, PhD, MPH, Harvard School of Public Health, Boston, Massachusetts

Role of the brain in ventricular fibrillation and hypertension: From animal models to early human studies
James E. Skinner, PhD, Vicor Technologies, Inc., Boca Raton, Florida

New paradigms in heart-brain medicine: Nonlinear physiology, state-dependent proteomics
James E. Skinner, PhD, Vicor Technologies, Inc., Boca Raton, Florida

Subarachnoid hemorrhage: A model for heart-brain interactions
J. Javier Provencio, MD, Cleveland Clinic, Cleveland, Ohio

Cardiac denervation in patients with Parkinson disease
David S. Goldstein, MD, PhD, National Institutes of Health, Bethesda, Maryland

Aging and the brain renin-angiotensin system: Insights from studies in transgenic rats
Debra I. Diz, PhD; Sherry O. Kasper, PhD; Atsushi Sakima, MD; and Carlos M. Ferrario, MD, Wake Forest University School of Medicine, Winston-Salem, North Carolina

Contextual cardiology: What modern medicine can learn from ancient Hawaiian wisdom
Paul Pearsall, PhD, University of Hawaii at Manoa and Hawaii State Consortium for Integrative Medicine, Honolulu, Hawaii

Cardiocerebral resuscitation: The optimal approach to cardiac arrest
Gordon A. Ewy, MD, University of Arizona College of Medicine, Tucson, Arizona

Heart transplantation: A magnified model of heart-brain interactions
Mohamad H. Yamani, MD, and Randall C. Starling, MD, MPH, Cleveland Clinic, Cleveland, Ohio

Patent foramen ovale and migraine
Gian Paolo Anzola, MD, S. Orsola Hospital FBF, Brescia, Italy

Patent foramen ovale and stroke: To close or not to close?
Anthony J. Furlan, MD, Cleveland Clinic, Cleveland, Ohio

Sudden unexplained death in epilepsy: The role of the heart
Stephan U. Schuele, MD, Cleveland Clinic, Cleveland, Ohio, and Northwestern University, Chicago, Illinois; Peter Widdess-Walsh, MD, Adriana Bermeo, MD, and Hans O. Lüders, MD, PhD, Cleveland Clinic, Cleveland, Ohio

Hydrocephalus and the heart: Interactions of the first and third circulations
Mark Luciano, MD, PhD, and Stephen Dombrowski, PhD, Cleveland Clinic, Cleveland, Ohio

Cognitive impairment in chronic heart failure
Cathy A. Sila, MD, Cleveland Clinic, Cleveland, Ohio

Cardiac events and brain injury: Ethical implications
Paul J. Ford, PhD, Cleveland Clinic, Cleveland, Ohio

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The dream behind the summit

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Pediatric Hospitalist Comanagement

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Pediatric hospitalist comanagement of spinal fusion surgery patients

As the field of pediatric hospital medicine has emerged, so too has evidence that hospitalist management of pediatric medical patients in the tertiary‐care setting is associated with decreased length of stay (LOS).13 The American Academy of Pediatrics Committee on Hospital Care has recommended hospitalist consultation for pediatric surgical patients being managed by adult surgeons.4 In one survey of pediatric hospitalists, 66% of community hospitalists and 47% of academic hospitalists reported comanaging surgical patients.5 However, little work has been published on the effect of hospitalist comanagement of pediatric surgical patients.

Since June 2000 patients undergoing spinal fusion surgery at the Children's Hospital in Denver, Colorado have been screened by a spine nurse for medical complexity. Medically complex patients undergoing preoperative multispecialty evaluation and their perioperative care are coordinated by the spine nurse.6 Introduction of a general pediatric hospitalist to aid with pre‐ and perioperative management of the most complicated patients in December 2004 provided us with an opportunity to study hospitalist comanagement of medically complex pediatric patients undergoing spine fusion surgery.

Our objectives were (1) to describe comanagement activities and (2) to determine the association of hospitalist comanagement on LOS following spinal fusion surgery. We hypothesized that by addressing a variety of pre‐ and perioperative medical issues, hospitalist comanagement would be associated with a decreased LOS for medically complex pediatric patients undergoing spinal fusion surgery.

METHODS

Design and Population

A retrospective analysis of the orthopedic surgeons' log at the Children's Hospital in Denver, Colorado, a tertiary‐care academic pediatric hospital serving the Rocky Mountain region, was performed. Patients included were those underwent their first episode of spinal fusion surgery between July 1, 2000, and October 1, 2005 (n = 759); exclusion criteria included diagnoses of spondylolisthesis or spondylolysis. The study was approved by the Colorado Multiple Institutional Review Board and exempted from ongoing review, and informed consent was not required.

Intervention: Pre‐ and Perioperative High‐Risk Pathway

Since June 2000 medically complex patients undergoing spinal fusion surgery at the Children's Hospital in Denver, Colorado have been referred by either the orthopedic surgeon or the rehabilitation physician to the spine surgery nurse. This nurse, an RN, BSN with more than a decade of experience with spinal fusion patients, then coordinates preoperative multispecialty evaluation (Fig. 1, column 2). Patients are seen by a pulmonologist pre‐operatively, and undergo pulmonary function tests, chest radiograph, venous blood gas, and, at times, a polysomnogram and electrocardiography. A cardiology consult is obtained for patients with muscle disease. Gastroenterology and neurology may be consulted if there are significant feeding and/or neurological issues that the primary care physician needs assistance addressing preoperatively. Shortly before the scheduled surgery, the patient is evaluated at a discharge planning meeting by a nutritionist, respiratory therapist, physical therapist, social worker (at times), and the spinal surgery nurse for discharge equipment planning. Immediately before surgery, in addition to teaching, surgical consent and a surgical history and physical are obtained, as well as laboratory studies that include a complete blood cell count, type, and cross, coagulation studies, and spine radiographs.5 Medically complex patients are managed in the intensive care unit for at least 24 hours after surgery. In addition, standardized order sets developed in 2001 and edited in July 2005 are used to streamline management of the intensive care unit and the orthopedic ward.

Figure 1
Multispecialty evaluation of patients undergoing spinal fusion surgery

Since December 2004, a hospitalist has aided pre‐ and perioperative evaluation and management (Fig. 1, column 3). The patients seen and comanaged by the hospitalist have been those who have significant medical issues in addition to neuromuscular disease, including multiple medications, seizure disorders, nutritional concerns, and/or significant social concerns. Whereas patients with a multitude of different diagnoses were referred to the spinal surgery nurse for a variety of reasons, patients comanaged by hospitalists were generally children with multiple medical conditions who had neuromuscular scoliosis.

Data Sources

The primary data source used was the surgeons' log. The surgeons' log is a record of patients undergoing spinal fusion surgery maintained concurrently by 2 individuals (the spine surgery nurse and her assistant). From January 2000 on, patient data were manually input into an Excel spreadsheet regularly and were cross‐referenced weekly with the surgery schedule. Data entered include: patient name, medical record number, date of surgery, date of discharge (from hospital admission information), underlying diagnosis, type of procedure, primary surgeon (from operative and/or discharge summaries), and LOS (calculated from dates of discharge and surgery). If either underlying diagnosis or type of procedure needed clarification, the spine surgery nurse discussed it with the primary surgeon. We verified the completeness of the surgeons' log for first spinal fusion surgeries by cross‐referencing with billing records; of 572 surgeries performed by 5 surgeons, 571 were recorded (99.8%) in the surgeons' log.

To perform the descriptive analysis of hospitalist activities, the first author (T.S.) performed a retrospective review of the charts of patients she had seen in her role as hospitalist from December 1, 2004, to October 1, 2005. Prepared in advance was a checklist of pre‐ and perioperative activities, modeled on prior work reporting clinical activities.7 Activities either mentioned in the daily progress note or ordered were recorded as completed and entered into an Excel spreadsheet.

Data Collection

The outcome measure was LOS, log‐transformed for analyses. Covariates included were: patient age, underlying diagnosis, procedure type, and surgeon. Underlying diagnoses were subdivided on the basis of the manually input Excel spreadsheet entries into 5 categories: idiopathic, congenital, neuromuscular, osteogenic, and other. The major diagnoses in the idiopathic category were infantile, juvenile, and adolescent idiopathic scoliosis; in the congenital category were congenital scoliosis, congenital kyphoscoliosis; in the neuromuscular category were cerebral palsy, Duchenne's muscular dystrophy, spina bifida, brain injury, spinal cord injury, and chromosomal anomalies; and in the osteogenic category were Scheuermann's kyphosis, trauma, tumor, kyphoscoliosis, and bone disease. Procedures were subdivided according to the manually input Excel spreadsheet entries into 3 categories: posterior only, anterior/ posterior, and anterior spinal fusion only.

After our initial analysis demonstrated a decline in LOS after December 2004 in both idiopathic and neuromuscular patients, we asked the orthopedic surgeons and spine surgery nurse to determine cointerventions that may have occurred around December 2004. We attempted to contact each surgeon who performed surgery during the study period and asked, What changes did you make on or around November 2004 in your management of spinal fusion patients? We received e‐mail and verbal responses from the spine surgery nurse and from 6 surgeons who had performed 646 of the procedures (78%) over the study period.

Increased use of intrathecal morphine was raised as a possible confounding cointervention. To characterize use of intrathecal morphine, we reviewed the charts of all the patients categorized as idiopathic or neuromuscular patients who underwent surgery after December 2004 and a random sample of 20% of these patients who underwent surgery before December 2004.

Analyses

All quantitative analyses (ie, those of the surgeons' log) were performed in a blinded manner, whereas the chart review was not blinded. Univariate analyses of hospitalist activities and univariate and bivariate analyses of the surgeons' log were performed using SAS 9.1. Mean LOS after log back‐transformation along with 95% confidence interval is reported. The chi square test of equality of variance was used to analyze whether the variances differed.

The multifaceted approach to the care of spinal fusion patients is a tiered approach, with 3 major patient groups (Fig. 1): (1) patients with scoliosis, generally idiopathic, and no or minimal medical conditions, who receive care by the usual pathway and do not receive care by the high‐risk pathway or do not have a hospitalist; (2) patients with scoliosis with any underlying diagnosis and some medical conditions, who receive care by high‐risk pathway; and (3) patients with scoliosis, usually neuromuscular scoliosis, and multiple medical conditions, who receive care by the high‐risk pathway and have hospitalist comanagement. Because of selection bias in the receipt of hospitalist comanagement (ie, the most complicated patients), we cannot reasonably compare hospitalist patients to nonhospitalist patients after December 2004. Instead, we compared all neuromuscular patients before and after hospitalist comanagement with a control group of idiopathic patients.

Initial examination of mean monthly LOS from June 2000 to October 2005 (Fig. 2) suggested a possible decline in both mean LOS and variability in LOS after December 2004, when hospitalist comanagement was initiated. To determine the trend in LOS over time before and after December 2004, we performed a mixed‐effects piecewise Poisson regression, adjusting for patient covariates (patient age, underlying diagnosis, procedure type, and intrathecal morphine [for idiopathic and neuromuscular patients]) and clustering by surgeon (as a random effect). We used the model to estimate 2 slopes to represent the linear trend before and after December 2004 (when hospitalist comanagement started). After regression modeling generated beta coefficients for each covariate, the average covariates were entered into the model to generate an average adjusted LOS as shown in Figure 3.

Figure 2
Mean monthly LOS for all spinal fusion surgeries from July 2000 to October 2005. Error bars represent standard deviation.
Figure 3
Adjusted LOS for initial spinal fusion surgeries among idiopathic and neuromuscular patient from July 2000 to October 2005. Adjusted for patient age, surgeon, procedure, and intrathecal morphine use. For slopes: all P values prior to December 2004 were not significant; after December 2004, idiopathic P = .0007, neuromuscular P = .0075.

RESULTS

A total of 759 patients underwent initial spinal fusion surgery between July 1, 2000, and October 1, 2005644 before and 115 after December 2004, when hospitalist involvement started. After December 2004, 12% (14 of 115) of all spinal fusion surgery patients were comanaged by a hospitalist. Most comanaged patients (14 of 15, 93%) had neuromuscular scoliosis, and comanaged patients represented 37% (13 of 35) of all neuromuscular patients (Table 1). Over the course of the study, the number of more invasive and complicated anterior/posterior spinal fusion surgeries declined, whereas the number of posterior spinal fusion surgeries increased significantly because of the introduction of new technology (data not shown).

Patient Characteristics
 LOS Days (95% CI)
All SurgeriesPreintervention (July 2000December 2004)Postintervention (December 2004September 2005)Hospitalist Comanaged (December 2004September 2005)
  • One patient described in hospitalist activities was not included here as it was not a first surgery.

Number of surgeries75964411514*
Age (years), mean (SD)13.6 (3.4)13.7 (3.4)13.1 (3.4)12.6 (4.0)
Diagnosis    
Idiopathic328 (43%)277 (43%)51 (44%)1 (7%)
Neuromuscular247 (32%)212 (33%)35 (30%)13 (93%)
Congenital66 (9%)55 (8%)11 (10%) 
Osteogenic96 (13%)81 (13%)15 (13%) 
Other22 (3%)19 (3%)3 (3%) 
Procedure    
Posterior470 (62%)365 (57%)105 (91%)13 (93%)
Ant/post227 (30%)217 (34%)10 (8%)1 (7%)
Anterior62 (8%)62 (9%)  
Intrathecal morphine use    
Idiopathic 30/50 (60%)45/51 (88%)0 (0%)
Neuromuscular 10/42 (24%)21/35 (62%)5/13 (38%)

The 15 patients seen by the hospitalist received a total of 60 visits by the hospitalist. The hospitalist saw 9 patients preoperatively. Of the 15 patients comanaged in the hospital, 5 (33%) were seen once, 8 (53%) were seen between 2 and 5 times, and 2 (14%) were seen more than 10 times. Patients were seen both in the ICU and on the surgical ward. Among the patients seen preoperatively, the hospitalist recommended nutritional interventions for 5 patients (33%), bowel regimens for 4 patients (27%), and preoperative hospitalization for 1 patient for 5 days to optimize nutritional intake, address reflux, and modify bowel regimen, as well as facilitate multispecialty evaluation. Postoperative involvement generally addressed a variety of issues, but 20% of patients had no changes in their management (Table 2).

Hospitalist Activities
Hospitalist ActivityNumber of Patients (%) (n = 15)
  • Other medical issues included: new labs (6), new medications (5), pulmonary equipment (5), new radiology (4), swallow study (2), sleep study (1).

Care coordination 
Updated family11 (73%)
Coordinated discharge8 (53%)
Updated PCP7 (47%)
Transfer facilitated4 (27%)
Consulted pulmonary3 (20%)
Consulted GI2 (13%)
Type of recommendation 
Home medications reviewed14 (93%)
Nutritional (ie, feed changes)11 (73%)
Pain medications reviewed11 (73%)
Bowel regimen10 (67%)
New medical issues*10 (67%)
Pain medications modified9 (60%)
Foley removed7 (46%)
Unnecessary medication removed6 (40%)
Central line removed4 (27%)
No changes in management3 (20%)
TPN2 (13%)
Harmful medications removed0 (0%)

Initial examination of mean monthly LOS from June 2000 to October 2005 suggested a possible decline in both mean LOS and variability in LOS after hospitalist comanagement was initiated (Fig. 2). Mean LOS for all initial spinal fusion surgeries decreased from 6.5 days (95% CI: 6.26.7) to 4.8 days (95% CI: 4.55.1) after December 2004. The standard deviation in LOS for all initial spinal fusion surgeries decreased from 1.64 to 1.39 days (P < .0001; Table 3). In the 52 months prior to hospitalist comanagement, there was no change in adjusted LOS over time (slope = 0.009 days/month, P = .3997). After December 2004, there was a significant decline in average adjusted LOS (slope = 0.2 days/month; P < .0001).

Mean Length of Stay (LOS) and Standard Deviation in LOS For Spine Fusion Surgery Patients, Before and After Hospitalist Comanagement
 Before Hospitalist 7/0012/04 n=644After Hospitalist After 12/04 n=115p value
LOS Days (95% CI)   
All Initial Spinal Fusion Surgeries6.5 (6.26.7)4.8 (4.55.1) 
Idiopathic5.2 (5.05.4)4.1(3.94.4) 
Neuromuscular8.6 (8.09.2)6.25 (5.56.9) 
Standard Deviation Days   
All Initial Spinal Fusion Surgeries1.641.39<0.0001
Idiopathic1.351.260.03
Neuromuscular1.701.410.002

Mean and adjusted LOS of patients in the 2 main diagnostic categories, idiopathic and neuromuscular scoliosis, decreased. The absolute mean LOS decreased more for neuromuscular patients (8.6 days [95% CI: 8.09.2] to 6.2 days [95% CI: 5.56.9]) than for idiopathic patients (5.2 days [95% CI: 5.05.4] to 4.1 days [95% CI: 3.94.4]). The standard deviation in LOS decreased more for the neuromuscular patients, from 1.70 to 1.41 days (P = .002), as shown in Table 3. In the 52 months prior to hospitalist comanagement, there was no change in adjusted LOS over time (neuromuscular slope = 0.024 to 0.027 days/month, P = .49; idiopathic slope = 0.0005 days/month, P = .96). After December 2004, there was a significant decline in average adjusted LOS (neuromuscular slope = 0.23 to 0.31 days/month, P = .0075; idiopathic slope = 0.10 to 0.12 days/month; P = .0007), as demonstrated in Figure 3. A survey of the orthopedic surgical staff demonstrated no known specific changes in surgical or postoperative management initiated around December 2004 other than intrathecal morphine use. Some surgeons performed fewer surgeries, particularly of idiopathic patients.

DISCUSSION

The introduction of hospitalist comanagement to ongoing multispecialty evaluation for medically complex spinal fusion surgery patients was associated with a decrease in mean LOS among all patients undergoing initial spinal fusion surgery. A greater magnitude of decline in LOS was seen among children with neuromuscular scoliosis, who were often comanaged, than among children with idiopathic scoliosis, who were rarely comanaged. Variability in LOS also decreased following initiation of hospitalist comanagement, particularly in the more complex patients. The decreases in LOS persisted after adjustment for patient age, diagnosis, procedure type, intrathecal morphine use, and surgeon. This study provides support for the hypothesis that selective hospitalist comanagement of pediatric surgical patients in the tertiary‐care setting is associated with decreased LOS and decreased variability in LOS.

Analysis of a nationally representative data set demonstrated that 4504 children with idiopathic scoliosis and 1570 children with neuromuscular scoliosis underwent spinal fusion surgery in the United States in 2000.8 The average LOS for children with neuromuscular scoliosis was 9.2 days versus 6.1 days for those with idiopathic scoliosis. The LOS of both our patient populations, those before hospitalist comanagement and those after hospitalist comanagement, was less than the national estimates. Multidisciplinary management strategies with or without hospitalist comanagement may be associated with decreases in LOS for neuromuscular scoliosis patients undergoing spinal fusion surgery.

The hospitalist performed a variety of activities in comanaging the medically complex pediatric orthopedic patients. Hospitalist comanagement may have been associated with reduction in LOS for several reasons: preoperative prevention of medical problems, early postoperative identification of and intervention on medical complications, improved coordination of care, or simply consistency of postoperative medical care.

These findings are consistent with the pediatric nonsurgical literature, which suggests that hospitalist management of pediatric medical patients in the tertiary‐care setting is associated with decreased LOS.13 Hospitalist comanagement of adult orthopedic patients has been better studied than has been comanagement of pediatric patients. Elderly patients undergoing elective hip or knee arthroplasty were randomized to hospitalist care versus traditional orthopedic care after surgery. Both sets of patients were managed by the same nursing staff according to standardized care pathways. The mean LOS did not differ between the 2 groups, but the adjusted LOS was lower in the group that received hospitalist care.9 When the same center examined outcomes in hip fracture patients before and after implementation of a hospitalist care model, there was a decrease in LOS and no change in readmission or deaths.10 As in these studies, spinal fusion surgery management is highly standardized in our center. Nonetheless, hospitalist comanagement still was associated with a decreased LOS.

This study found a decline in LOS among all patients undergoing spinal fusion surgery, even among children with idiopathic scoliosis, of whom only 1 was comanaged. This finding may suggest hospitalist comanagement had a global, or indirect, effect on the management of all postoperative patients. However, the time‐series design could have been biased by a cointervention implemented at the same time as hospitalist care. Some surgeons performed fewer surgeries on their idiopathic patients over the course of the study; however, we adjusted for that surgeon in our analysis. Intrathecal morphine use is the only known change in postoperative management that may have affected care starting in December 2004; we also adjusted for intrathecal morphine use in our analysis. There may be other changes of which we are unaware. Nonetheless, the decline in LOS seen in the idiopathic population was exceeded by the decline in LOS in the comanaged neuromuscular population.

Unlike earlier reported studies, which examined hospitalist management among pediatric medical patients, this study did not assess complications (such as pneumonia, respiratory failure, urinary tract infection, gastric ulcers, pathologic fractures, poor wound healing, nutritional compromise, and readmission),7, 11, 12 costs, or patient and provider satisfaction with hospitalist comanagement.13 This assessment is critical to defining the value of these services to patients and providers. In addition, because we did not collect information on severity of disability, we were unable to control for disability. These are other covariates and outcomes of interest that should be assessed in future studies. Furthermore, there was potential bias introduced by having the lead author both conducting the study and performing the intervention; this was minimized by having different individuals responsible for primary data collection and having the analyses performed in a blinded fashion. In addition, although this study provided promising initial evidence that selective hospitalist comanagement along with multispecialty evaluation of spinal fusion surgery patients may lead to a significant decrease in LOS, this evidence needs to be replicated in other surgical patient populations and hospital settings. Ideally, the impact of hospitalist comanagement should be more fully evaluated in a randomized controlled trial. Hospitalist comanagement is a promising technique for improving the care of children undergoing spinal fusion surgery, particularly those with complex medical conditions.

Acknowledgements

The authors acknowledge the contributions of Heidi Gullord of University Physician, Incorporated, for assistance with obtaining billing records. We also appreciate the ongoing input of the Children's Hospital Department of Epidemiology, including Lorna Dyk, BSN, MBA; Michael Rannie, RN, MS; Meghan Birkholz, MSPH; and Michael Kahn, MD, PhD. We appreciate the willingness of the Department of Orthopedics to cooperate with this study, including (but not limited to) Mark Erickson, MD; Frank Chang, MD; and Gaia Georgopoulos, MD. In addition, we acknowledge the ongoing efforts of the care providers involved in the High Risk Pathway at the Children's Hospital, including Carol Page, PT; Alice Radic, PTA; Sarah Hack Baltazar, RD; Monte Leidholm, RRT; Cloy Vaneman, RRT; Gail Shattuck, MSW; and Lynn Katz, MSW, as well as the Divisions of Pulmonary Medicine and Intensive Care of the Department of Pediatrics. We also thank Heather Ramey, BS, BA, for her assistance with organizing the multispecialty evaluation of patients undergoing surgery. We also appreciate the efforts of the fellows and faculty of the Primary Care Research Fellowship and appreciate their assistance in crafting this research.

References
  1. Bellet PS,Whitaker RC.Evaluation of a pediatric hospitalist service: impact on length of stay and hospital charges.Pediatrics.2000;105:478484.
  2. Landrigan CP,Srivastava R,Muret‐Wagstaff S, et al.Impact of a health maintenance organization hospitalist system in academic pediatrics.Pediatrics.2002;110:720728.
  3. Dwight P,MacArthur C,Friedman JN,Parkin PC.Evaluation of a staff‐only hospitalist system in a tertiary care, academic children's hospital.Pediatrics.2004;114:15451549.
  4. Percelay JM and theCommittee on Hospital Care.Physicians' roles in coordinating care of hospitalized children.Pediatrics.2003;111:707709.
  5. Conway PH andLandrigan CL.Differences in work environment, responsibilities, and training need between community hospital and academic center hospitalists. E‐PAS2006:59:4128.3.
  6. Benefield E,Erickson M.Development and implementation of a spine fusion high risk pathway. 5th Annual Pre‐Brandon Carrell Pediatric Orthopaedic Symposium for Nursing and Allied Healthcare Professionals, Texas Scottish Rite Hospital for Children, Dallas, Texas, June 26,2003.
  7. Marcantonio ER,Flacker JM,Wright RJ,Resnick NM.Reducing delirium after hip fracture: a randomized trial.J Am Geriatr Soc.2001;49:516522.
  8. Murphy NA,Firth S,Jorgensen T, et al.Spinal fusion in children with idiopathic and neuromuscular scoliosis: what's the difference?J Pediatr Orthop.2006;26(2):216220.
  9. Huddleston JM,Long KH,Naessens JM, et al.Medical and surgical comanagement after elective hip and knee arthroplasty.Ann Intern Med.2004;141:2838.
  10. Phy MP,Vanness DJ,Melton J, et al.Effects of a hospitalist model on elderly patients with hip fracture.Arch Intern Med2005;165:796801.
  11. Lipton GE,Miller F,Dabney KW, et al.Factors predicting post‐operative complications following spinal fusion in children with cerebral palsy.J Spinal Disord.1999;12:297305.
  12. Pruijs JE,van Tol MJ,van Kesteren RG, et al.Neuromuscular scoliosis: clinical evaluation pre‐ and post‐operative.J Pediatr Orthop..2000;9(4):217220.
Article PDF
Issue
Journal of Hospital Medicine - 2(1)
Page Number
23-30
Legacy Keywords
comanagement, pediatrics, orthopedics, spine fusion
Sections
Article PDF
Article PDF

As the field of pediatric hospital medicine has emerged, so too has evidence that hospitalist management of pediatric medical patients in the tertiary‐care setting is associated with decreased length of stay (LOS).13 The American Academy of Pediatrics Committee on Hospital Care has recommended hospitalist consultation for pediatric surgical patients being managed by adult surgeons.4 In one survey of pediatric hospitalists, 66% of community hospitalists and 47% of academic hospitalists reported comanaging surgical patients.5 However, little work has been published on the effect of hospitalist comanagement of pediatric surgical patients.

Since June 2000 patients undergoing spinal fusion surgery at the Children's Hospital in Denver, Colorado have been screened by a spine nurse for medical complexity. Medically complex patients undergoing preoperative multispecialty evaluation and their perioperative care are coordinated by the spine nurse.6 Introduction of a general pediatric hospitalist to aid with pre‐ and perioperative management of the most complicated patients in December 2004 provided us with an opportunity to study hospitalist comanagement of medically complex pediatric patients undergoing spine fusion surgery.

Our objectives were (1) to describe comanagement activities and (2) to determine the association of hospitalist comanagement on LOS following spinal fusion surgery. We hypothesized that by addressing a variety of pre‐ and perioperative medical issues, hospitalist comanagement would be associated with a decreased LOS for medically complex pediatric patients undergoing spinal fusion surgery.

METHODS

Design and Population

A retrospective analysis of the orthopedic surgeons' log at the Children's Hospital in Denver, Colorado, a tertiary‐care academic pediatric hospital serving the Rocky Mountain region, was performed. Patients included were those underwent their first episode of spinal fusion surgery between July 1, 2000, and October 1, 2005 (n = 759); exclusion criteria included diagnoses of spondylolisthesis or spondylolysis. The study was approved by the Colorado Multiple Institutional Review Board and exempted from ongoing review, and informed consent was not required.

Intervention: Pre‐ and Perioperative High‐Risk Pathway

Since June 2000 medically complex patients undergoing spinal fusion surgery at the Children's Hospital in Denver, Colorado have been referred by either the orthopedic surgeon or the rehabilitation physician to the spine surgery nurse. This nurse, an RN, BSN with more than a decade of experience with spinal fusion patients, then coordinates preoperative multispecialty evaluation (Fig. 1, column 2). Patients are seen by a pulmonologist pre‐operatively, and undergo pulmonary function tests, chest radiograph, venous blood gas, and, at times, a polysomnogram and electrocardiography. A cardiology consult is obtained for patients with muscle disease. Gastroenterology and neurology may be consulted if there are significant feeding and/or neurological issues that the primary care physician needs assistance addressing preoperatively. Shortly before the scheduled surgery, the patient is evaluated at a discharge planning meeting by a nutritionist, respiratory therapist, physical therapist, social worker (at times), and the spinal surgery nurse for discharge equipment planning. Immediately before surgery, in addition to teaching, surgical consent and a surgical history and physical are obtained, as well as laboratory studies that include a complete blood cell count, type, and cross, coagulation studies, and spine radiographs.5 Medically complex patients are managed in the intensive care unit for at least 24 hours after surgery. In addition, standardized order sets developed in 2001 and edited in July 2005 are used to streamline management of the intensive care unit and the orthopedic ward.

Figure 1
Multispecialty evaluation of patients undergoing spinal fusion surgery

Since December 2004, a hospitalist has aided pre‐ and perioperative evaluation and management (Fig. 1, column 3). The patients seen and comanaged by the hospitalist have been those who have significant medical issues in addition to neuromuscular disease, including multiple medications, seizure disorders, nutritional concerns, and/or significant social concerns. Whereas patients with a multitude of different diagnoses were referred to the spinal surgery nurse for a variety of reasons, patients comanaged by hospitalists were generally children with multiple medical conditions who had neuromuscular scoliosis.

Data Sources

The primary data source used was the surgeons' log. The surgeons' log is a record of patients undergoing spinal fusion surgery maintained concurrently by 2 individuals (the spine surgery nurse and her assistant). From January 2000 on, patient data were manually input into an Excel spreadsheet regularly and were cross‐referenced weekly with the surgery schedule. Data entered include: patient name, medical record number, date of surgery, date of discharge (from hospital admission information), underlying diagnosis, type of procedure, primary surgeon (from operative and/or discharge summaries), and LOS (calculated from dates of discharge and surgery). If either underlying diagnosis or type of procedure needed clarification, the spine surgery nurse discussed it with the primary surgeon. We verified the completeness of the surgeons' log for first spinal fusion surgeries by cross‐referencing with billing records; of 572 surgeries performed by 5 surgeons, 571 were recorded (99.8%) in the surgeons' log.

To perform the descriptive analysis of hospitalist activities, the first author (T.S.) performed a retrospective review of the charts of patients she had seen in her role as hospitalist from December 1, 2004, to October 1, 2005. Prepared in advance was a checklist of pre‐ and perioperative activities, modeled on prior work reporting clinical activities.7 Activities either mentioned in the daily progress note or ordered were recorded as completed and entered into an Excel spreadsheet.

Data Collection

The outcome measure was LOS, log‐transformed for analyses. Covariates included were: patient age, underlying diagnosis, procedure type, and surgeon. Underlying diagnoses were subdivided on the basis of the manually input Excel spreadsheet entries into 5 categories: idiopathic, congenital, neuromuscular, osteogenic, and other. The major diagnoses in the idiopathic category were infantile, juvenile, and adolescent idiopathic scoliosis; in the congenital category were congenital scoliosis, congenital kyphoscoliosis; in the neuromuscular category were cerebral palsy, Duchenne's muscular dystrophy, spina bifida, brain injury, spinal cord injury, and chromosomal anomalies; and in the osteogenic category were Scheuermann's kyphosis, trauma, tumor, kyphoscoliosis, and bone disease. Procedures were subdivided according to the manually input Excel spreadsheet entries into 3 categories: posterior only, anterior/ posterior, and anterior spinal fusion only.

After our initial analysis demonstrated a decline in LOS after December 2004 in both idiopathic and neuromuscular patients, we asked the orthopedic surgeons and spine surgery nurse to determine cointerventions that may have occurred around December 2004. We attempted to contact each surgeon who performed surgery during the study period and asked, What changes did you make on or around November 2004 in your management of spinal fusion patients? We received e‐mail and verbal responses from the spine surgery nurse and from 6 surgeons who had performed 646 of the procedures (78%) over the study period.

Increased use of intrathecal morphine was raised as a possible confounding cointervention. To characterize use of intrathecal morphine, we reviewed the charts of all the patients categorized as idiopathic or neuromuscular patients who underwent surgery after December 2004 and a random sample of 20% of these patients who underwent surgery before December 2004.

Analyses

All quantitative analyses (ie, those of the surgeons' log) were performed in a blinded manner, whereas the chart review was not blinded. Univariate analyses of hospitalist activities and univariate and bivariate analyses of the surgeons' log were performed using SAS 9.1. Mean LOS after log back‐transformation along with 95% confidence interval is reported. The chi square test of equality of variance was used to analyze whether the variances differed.

The multifaceted approach to the care of spinal fusion patients is a tiered approach, with 3 major patient groups (Fig. 1): (1) patients with scoliosis, generally idiopathic, and no or minimal medical conditions, who receive care by the usual pathway and do not receive care by the high‐risk pathway or do not have a hospitalist; (2) patients with scoliosis with any underlying diagnosis and some medical conditions, who receive care by high‐risk pathway; and (3) patients with scoliosis, usually neuromuscular scoliosis, and multiple medical conditions, who receive care by the high‐risk pathway and have hospitalist comanagement. Because of selection bias in the receipt of hospitalist comanagement (ie, the most complicated patients), we cannot reasonably compare hospitalist patients to nonhospitalist patients after December 2004. Instead, we compared all neuromuscular patients before and after hospitalist comanagement with a control group of idiopathic patients.

Initial examination of mean monthly LOS from June 2000 to October 2005 (Fig. 2) suggested a possible decline in both mean LOS and variability in LOS after December 2004, when hospitalist comanagement was initiated. To determine the trend in LOS over time before and after December 2004, we performed a mixed‐effects piecewise Poisson regression, adjusting for patient covariates (patient age, underlying diagnosis, procedure type, and intrathecal morphine [for idiopathic and neuromuscular patients]) and clustering by surgeon (as a random effect). We used the model to estimate 2 slopes to represent the linear trend before and after December 2004 (when hospitalist comanagement started). After regression modeling generated beta coefficients for each covariate, the average covariates were entered into the model to generate an average adjusted LOS as shown in Figure 3.

Figure 2
Mean monthly LOS for all spinal fusion surgeries from July 2000 to October 2005. Error bars represent standard deviation.
Figure 3
Adjusted LOS for initial spinal fusion surgeries among idiopathic and neuromuscular patient from July 2000 to October 2005. Adjusted for patient age, surgeon, procedure, and intrathecal morphine use. For slopes: all P values prior to December 2004 were not significant; after December 2004, idiopathic P = .0007, neuromuscular P = .0075.

RESULTS

A total of 759 patients underwent initial spinal fusion surgery between July 1, 2000, and October 1, 2005644 before and 115 after December 2004, when hospitalist involvement started. After December 2004, 12% (14 of 115) of all spinal fusion surgery patients were comanaged by a hospitalist. Most comanaged patients (14 of 15, 93%) had neuromuscular scoliosis, and comanaged patients represented 37% (13 of 35) of all neuromuscular patients (Table 1). Over the course of the study, the number of more invasive and complicated anterior/posterior spinal fusion surgeries declined, whereas the number of posterior spinal fusion surgeries increased significantly because of the introduction of new technology (data not shown).

Patient Characteristics
 LOS Days (95% CI)
All SurgeriesPreintervention (July 2000December 2004)Postintervention (December 2004September 2005)Hospitalist Comanaged (December 2004September 2005)
  • One patient described in hospitalist activities was not included here as it was not a first surgery.

Number of surgeries75964411514*
Age (years), mean (SD)13.6 (3.4)13.7 (3.4)13.1 (3.4)12.6 (4.0)
Diagnosis    
Idiopathic328 (43%)277 (43%)51 (44%)1 (7%)
Neuromuscular247 (32%)212 (33%)35 (30%)13 (93%)
Congenital66 (9%)55 (8%)11 (10%) 
Osteogenic96 (13%)81 (13%)15 (13%) 
Other22 (3%)19 (3%)3 (3%) 
Procedure    
Posterior470 (62%)365 (57%)105 (91%)13 (93%)
Ant/post227 (30%)217 (34%)10 (8%)1 (7%)
Anterior62 (8%)62 (9%)  
Intrathecal morphine use    
Idiopathic 30/50 (60%)45/51 (88%)0 (0%)
Neuromuscular 10/42 (24%)21/35 (62%)5/13 (38%)

The 15 patients seen by the hospitalist received a total of 60 visits by the hospitalist. The hospitalist saw 9 patients preoperatively. Of the 15 patients comanaged in the hospital, 5 (33%) were seen once, 8 (53%) were seen between 2 and 5 times, and 2 (14%) were seen more than 10 times. Patients were seen both in the ICU and on the surgical ward. Among the patients seen preoperatively, the hospitalist recommended nutritional interventions for 5 patients (33%), bowel regimens for 4 patients (27%), and preoperative hospitalization for 1 patient for 5 days to optimize nutritional intake, address reflux, and modify bowel regimen, as well as facilitate multispecialty evaluation. Postoperative involvement generally addressed a variety of issues, but 20% of patients had no changes in their management (Table 2).

Hospitalist Activities
Hospitalist ActivityNumber of Patients (%) (n = 15)
  • Other medical issues included: new labs (6), new medications (5), pulmonary equipment (5), new radiology (4), swallow study (2), sleep study (1).

Care coordination 
Updated family11 (73%)
Coordinated discharge8 (53%)
Updated PCP7 (47%)
Transfer facilitated4 (27%)
Consulted pulmonary3 (20%)
Consulted GI2 (13%)
Type of recommendation 
Home medications reviewed14 (93%)
Nutritional (ie, feed changes)11 (73%)
Pain medications reviewed11 (73%)
Bowel regimen10 (67%)
New medical issues*10 (67%)
Pain medications modified9 (60%)
Foley removed7 (46%)
Unnecessary medication removed6 (40%)
Central line removed4 (27%)
No changes in management3 (20%)
TPN2 (13%)
Harmful medications removed0 (0%)

Initial examination of mean monthly LOS from June 2000 to October 2005 suggested a possible decline in both mean LOS and variability in LOS after hospitalist comanagement was initiated (Fig. 2). Mean LOS for all initial spinal fusion surgeries decreased from 6.5 days (95% CI: 6.26.7) to 4.8 days (95% CI: 4.55.1) after December 2004. The standard deviation in LOS for all initial spinal fusion surgeries decreased from 1.64 to 1.39 days (P < .0001; Table 3). In the 52 months prior to hospitalist comanagement, there was no change in adjusted LOS over time (slope = 0.009 days/month, P = .3997). After December 2004, there was a significant decline in average adjusted LOS (slope = 0.2 days/month; P < .0001).

Mean Length of Stay (LOS) and Standard Deviation in LOS For Spine Fusion Surgery Patients, Before and After Hospitalist Comanagement
 Before Hospitalist 7/0012/04 n=644After Hospitalist After 12/04 n=115p value
LOS Days (95% CI)   
All Initial Spinal Fusion Surgeries6.5 (6.26.7)4.8 (4.55.1) 
Idiopathic5.2 (5.05.4)4.1(3.94.4) 
Neuromuscular8.6 (8.09.2)6.25 (5.56.9) 
Standard Deviation Days   
All Initial Spinal Fusion Surgeries1.641.39<0.0001
Idiopathic1.351.260.03
Neuromuscular1.701.410.002

Mean and adjusted LOS of patients in the 2 main diagnostic categories, idiopathic and neuromuscular scoliosis, decreased. The absolute mean LOS decreased more for neuromuscular patients (8.6 days [95% CI: 8.09.2] to 6.2 days [95% CI: 5.56.9]) than for idiopathic patients (5.2 days [95% CI: 5.05.4] to 4.1 days [95% CI: 3.94.4]). The standard deviation in LOS decreased more for the neuromuscular patients, from 1.70 to 1.41 days (P = .002), as shown in Table 3. In the 52 months prior to hospitalist comanagement, there was no change in adjusted LOS over time (neuromuscular slope = 0.024 to 0.027 days/month, P = .49; idiopathic slope = 0.0005 days/month, P = .96). After December 2004, there was a significant decline in average adjusted LOS (neuromuscular slope = 0.23 to 0.31 days/month, P = .0075; idiopathic slope = 0.10 to 0.12 days/month; P = .0007), as demonstrated in Figure 3. A survey of the orthopedic surgical staff demonstrated no known specific changes in surgical or postoperative management initiated around December 2004 other than intrathecal morphine use. Some surgeons performed fewer surgeries, particularly of idiopathic patients.

DISCUSSION

The introduction of hospitalist comanagement to ongoing multispecialty evaluation for medically complex spinal fusion surgery patients was associated with a decrease in mean LOS among all patients undergoing initial spinal fusion surgery. A greater magnitude of decline in LOS was seen among children with neuromuscular scoliosis, who were often comanaged, than among children with idiopathic scoliosis, who were rarely comanaged. Variability in LOS also decreased following initiation of hospitalist comanagement, particularly in the more complex patients. The decreases in LOS persisted after adjustment for patient age, diagnosis, procedure type, intrathecal morphine use, and surgeon. This study provides support for the hypothesis that selective hospitalist comanagement of pediatric surgical patients in the tertiary‐care setting is associated with decreased LOS and decreased variability in LOS.

Analysis of a nationally representative data set demonstrated that 4504 children with idiopathic scoliosis and 1570 children with neuromuscular scoliosis underwent spinal fusion surgery in the United States in 2000.8 The average LOS for children with neuromuscular scoliosis was 9.2 days versus 6.1 days for those with idiopathic scoliosis. The LOS of both our patient populations, those before hospitalist comanagement and those after hospitalist comanagement, was less than the national estimates. Multidisciplinary management strategies with or without hospitalist comanagement may be associated with decreases in LOS for neuromuscular scoliosis patients undergoing spinal fusion surgery.

The hospitalist performed a variety of activities in comanaging the medically complex pediatric orthopedic patients. Hospitalist comanagement may have been associated with reduction in LOS for several reasons: preoperative prevention of medical problems, early postoperative identification of and intervention on medical complications, improved coordination of care, or simply consistency of postoperative medical care.

These findings are consistent with the pediatric nonsurgical literature, which suggests that hospitalist management of pediatric medical patients in the tertiary‐care setting is associated with decreased LOS.13 Hospitalist comanagement of adult orthopedic patients has been better studied than has been comanagement of pediatric patients. Elderly patients undergoing elective hip or knee arthroplasty were randomized to hospitalist care versus traditional orthopedic care after surgery. Both sets of patients were managed by the same nursing staff according to standardized care pathways. The mean LOS did not differ between the 2 groups, but the adjusted LOS was lower in the group that received hospitalist care.9 When the same center examined outcomes in hip fracture patients before and after implementation of a hospitalist care model, there was a decrease in LOS and no change in readmission or deaths.10 As in these studies, spinal fusion surgery management is highly standardized in our center. Nonetheless, hospitalist comanagement still was associated with a decreased LOS.

This study found a decline in LOS among all patients undergoing spinal fusion surgery, even among children with idiopathic scoliosis, of whom only 1 was comanaged. This finding may suggest hospitalist comanagement had a global, or indirect, effect on the management of all postoperative patients. However, the time‐series design could have been biased by a cointervention implemented at the same time as hospitalist care. Some surgeons performed fewer surgeries on their idiopathic patients over the course of the study; however, we adjusted for that surgeon in our analysis. Intrathecal morphine use is the only known change in postoperative management that may have affected care starting in December 2004; we also adjusted for intrathecal morphine use in our analysis. There may be other changes of which we are unaware. Nonetheless, the decline in LOS seen in the idiopathic population was exceeded by the decline in LOS in the comanaged neuromuscular population.

Unlike earlier reported studies, which examined hospitalist management among pediatric medical patients, this study did not assess complications (such as pneumonia, respiratory failure, urinary tract infection, gastric ulcers, pathologic fractures, poor wound healing, nutritional compromise, and readmission),7, 11, 12 costs, or patient and provider satisfaction with hospitalist comanagement.13 This assessment is critical to defining the value of these services to patients and providers. In addition, because we did not collect information on severity of disability, we were unable to control for disability. These are other covariates and outcomes of interest that should be assessed in future studies. Furthermore, there was potential bias introduced by having the lead author both conducting the study and performing the intervention; this was minimized by having different individuals responsible for primary data collection and having the analyses performed in a blinded fashion. In addition, although this study provided promising initial evidence that selective hospitalist comanagement along with multispecialty evaluation of spinal fusion surgery patients may lead to a significant decrease in LOS, this evidence needs to be replicated in other surgical patient populations and hospital settings. Ideally, the impact of hospitalist comanagement should be more fully evaluated in a randomized controlled trial. Hospitalist comanagement is a promising technique for improving the care of children undergoing spinal fusion surgery, particularly those with complex medical conditions.

Acknowledgements

The authors acknowledge the contributions of Heidi Gullord of University Physician, Incorporated, for assistance with obtaining billing records. We also appreciate the ongoing input of the Children's Hospital Department of Epidemiology, including Lorna Dyk, BSN, MBA; Michael Rannie, RN, MS; Meghan Birkholz, MSPH; and Michael Kahn, MD, PhD. We appreciate the willingness of the Department of Orthopedics to cooperate with this study, including (but not limited to) Mark Erickson, MD; Frank Chang, MD; and Gaia Georgopoulos, MD. In addition, we acknowledge the ongoing efforts of the care providers involved in the High Risk Pathway at the Children's Hospital, including Carol Page, PT; Alice Radic, PTA; Sarah Hack Baltazar, RD; Monte Leidholm, RRT; Cloy Vaneman, RRT; Gail Shattuck, MSW; and Lynn Katz, MSW, as well as the Divisions of Pulmonary Medicine and Intensive Care of the Department of Pediatrics. We also thank Heather Ramey, BS, BA, for her assistance with organizing the multispecialty evaluation of patients undergoing surgery. We also appreciate the efforts of the fellows and faculty of the Primary Care Research Fellowship and appreciate their assistance in crafting this research.

As the field of pediatric hospital medicine has emerged, so too has evidence that hospitalist management of pediatric medical patients in the tertiary‐care setting is associated with decreased length of stay (LOS).13 The American Academy of Pediatrics Committee on Hospital Care has recommended hospitalist consultation for pediatric surgical patients being managed by adult surgeons.4 In one survey of pediatric hospitalists, 66% of community hospitalists and 47% of academic hospitalists reported comanaging surgical patients.5 However, little work has been published on the effect of hospitalist comanagement of pediatric surgical patients.

Since June 2000 patients undergoing spinal fusion surgery at the Children's Hospital in Denver, Colorado have been screened by a spine nurse for medical complexity. Medically complex patients undergoing preoperative multispecialty evaluation and their perioperative care are coordinated by the spine nurse.6 Introduction of a general pediatric hospitalist to aid with pre‐ and perioperative management of the most complicated patients in December 2004 provided us with an opportunity to study hospitalist comanagement of medically complex pediatric patients undergoing spine fusion surgery.

Our objectives were (1) to describe comanagement activities and (2) to determine the association of hospitalist comanagement on LOS following spinal fusion surgery. We hypothesized that by addressing a variety of pre‐ and perioperative medical issues, hospitalist comanagement would be associated with a decreased LOS for medically complex pediatric patients undergoing spinal fusion surgery.

METHODS

Design and Population

A retrospective analysis of the orthopedic surgeons' log at the Children's Hospital in Denver, Colorado, a tertiary‐care academic pediatric hospital serving the Rocky Mountain region, was performed. Patients included were those underwent their first episode of spinal fusion surgery between July 1, 2000, and October 1, 2005 (n = 759); exclusion criteria included diagnoses of spondylolisthesis or spondylolysis. The study was approved by the Colorado Multiple Institutional Review Board and exempted from ongoing review, and informed consent was not required.

Intervention: Pre‐ and Perioperative High‐Risk Pathway

Since June 2000 medically complex patients undergoing spinal fusion surgery at the Children's Hospital in Denver, Colorado have been referred by either the orthopedic surgeon or the rehabilitation physician to the spine surgery nurse. This nurse, an RN, BSN with more than a decade of experience with spinal fusion patients, then coordinates preoperative multispecialty evaluation (Fig. 1, column 2). Patients are seen by a pulmonologist pre‐operatively, and undergo pulmonary function tests, chest radiograph, venous blood gas, and, at times, a polysomnogram and electrocardiography. A cardiology consult is obtained for patients with muscle disease. Gastroenterology and neurology may be consulted if there are significant feeding and/or neurological issues that the primary care physician needs assistance addressing preoperatively. Shortly before the scheduled surgery, the patient is evaluated at a discharge planning meeting by a nutritionist, respiratory therapist, physical therapist, social worker (at times), and the spinal surgery nurse for discharge equipment planning. Immediately before surgery, in addition to teaching, surgical consent and a surgical history and physical are obtained, as well as laboratory studies that include a complete blood cell count, type, and cross, coagulation studies, and spine radiographs.5 Medically complex patients are managed in the intensive care unit for at least 24 hours after surgery. In addition, standardized order sets developed in 2001 and edited in July 2005 are used to streamline management of the intensive care unit and the orthopedic ward.

Figure 1
Multispecialty evaluation of patients undergoing spinal fusion surgery

Since December 2004, a hospitalist has aided pre‐ and perioperative evaluation and management (Fig. 1, column 3). The patients seen and comanaged by the hospitalist have been those who have significant medical issues in addition to neuromuscular disease, including multiple medications, seizure disorders, nutritional concerns, and/or significant social concerns. Whereas patients with a multitude of different diagnoses were referred to the spinal surgery nurse for a variety of reasons, patients comanaged by hospitalists were generally children with multiple medical conditions who had neuromuscular scoliosis.

Data Sources

The primary data source used was the surgeons' log. The surgeons' log is a record of patients undergoing spinal fusion surgery maintained concurrently by 2 individuals (the spine surgery nurse and her assistant). From January 2000 on, patient data were manually input into an Excel spreadsheet regularly and were cross‐referenced weekly with the surgery schedule. Data entered include: patient name, medical record number, date of surgery, date of discharge (from hospital admission information), underlying diagnosis, type of procedure, primary surgeon (from operative and/or discharge summaries), and LOS (calculated from dates of discharge and surgery). If either underlying diagnosis or type of procedure needed clarification, the spine surgery nurse discussed it with the primary surgeon. We verified the completeness of the surgeons' log for first spinal fusion surgeries by cross‐referencing with billing records; of 572 surgeries performed by 5 surgeons, 571 were recorded (99.8%) in the surgeons' log.

To perform the descriptive analysis of hospitalist activities, the first author (T.S.) performed a retrospective review of the charts of patients she had seen in her role as hospitalist from December 1, 2004, to October 1, 2005. Prepared in advance was a checklist of pre‐ and perioperative activities, modeled on prior work reporting clinical activities.7 Activities either mentioned in the daily progress note or ordered were recorded as completed and entered into an Excel spreadsheet.

Data Collection

The outcome measure was LOS, log‐transformed for analyses. Covariates included were: patient age, underlying diagnosis, procedure type, and surgeon. Underlying diagnoses were subdivided on the basis of the manually input Excel spreadsheet entries into 5 categories: idiopathic, congenital, neuromuscular, osteogenic, and other. The major diagnoses in the idiopathic category were infantile, juvenile, and adolescent idiopathic scoliosis; in the congenital category were congenital scoliosis, congenital kyphoscoliosis; in the neuromuscular category were cerebral palsy, Duchenne's muscular dystrophy, spina bifida, brain injury, spinal cord injury, and chromosomal anomalies; and in the osteogenic category were Scheuermann's kyphosis, trauma, tumor, kyphoscoliosis, and bone disease. Procedures were subdivided according to the manually input Excel spreadsheet entries into 3 categories: posterior only, anterior/ posterior, and anterior spinal fusion only.

After our initial analysis demonstrated a decline in LOS after December 2004 in both idiopathic and neuromuscular patients, we asked the orthopedic surgeons and spine surgery nurse to determine cointerventions that may have occurred around December 2004. We attempted to contact each surgeon who performed surgery during the study period and asked, What changes did you make on or around November 2004 in your management of spinal fusion patients? We received e‐mail and verbal responses from the spine surgery nurse and from 6 surgeons who had performed 646 of the procedures (78%) over the study period.

Increased use of intrathecal morphine was raised as a possible confounding cointervention. To characterize use of intrathecal morphine, we reviewed the charts of all the patients categorized as idiopathic or neuromuscular patients who underwent surgery after December 2004 and a random sample of 20% of these patients who underwent surgery before December 2004.

Analyses

All quantitative analyses (ie, those of the surgeons' log) were performed in a blinded manner, whereas the chart review was not blinded. Univariate analyses of hospitalist activities and univariate and bivariate analyses of the surgeons' log were performed using SAS 9.1. Mean LOS after log back‐transformation along with 95% confidence interval is reported. The chi square test of equality of variance was used to analyze whether the variances differed.

The multifaceted approach to the care of spinal fusion patients is a tiered approach, with 3 major patient groups (Fig. 1): (1) patients with scoliosis, generally idiopathic, and no or minimal medical conditions, who receive care by the usual pathway and do not receive care by the high‐risk pathway or do not have a hospitalist; (2) patients with scoliosis with any underlying diagnosis and some medical conditions, who receive care by high‐risk pathway; and (3) patients with scoliosis, usually neuromuscular scoliosis, and multiple medical conditions, who receive care by the high‐risk pathway and have hospitalist comanagement. Because of selection bias in the receipt of hospitalist comanagement (ie, the most complicated patients), we cannot reasonably compare hospitalist patients to nonhospitalist patients after December 2004. Instead, we compared all neuromuscular patients before and after hospitalist comanagement with a control group of idiopathic patients.

Initial examination of mean monthly LOS from June 2000 to October 2005 (Fig. 2) suggested a possible decline in both mean LOS and variability in LOS after December 2004, when hospitalist comanagement was initiated. To determine the trend in LOS over time before and after December 2004, we performed a mixed‐effects piecewise Poisson regression, adjusting for patient covariates (patient age, underlying diagnosis, procedure type, and intrathecal morphine [for idiopathic and neuromuscular patients]) and clustering by surgeon (as a random effect). We used the model to estimate 2 slopes to represent the linear trend before and after December 2004 (when hospitalist comanagement started). After regression modeling generated beta coefficients for each covariate, the average covariates were entered into the model to generate an average adjusted LOS as shown in Figure 3.

Figure 2
Mean monthly LOS for all spinal fusion surgeries from July 2000 to October 2005. Error bars represent standard deviation.
Figure 3
Adjusted LOS for initial spinal fusion surgeries among idiopathic and neuromuscular patient from July 2000 to October 2005. Adjusted for patient age, surgeon, procedure, and intrathecal morphine use. For slopes: all P values prior to December 2004 were not significant; after December 2004, idiopathic P = .0007, neuromuscular P = .0075.

RESULTS

A total of 759 patients underwent initial spinal fusion surgery between July 1, 2000, and October 1, 2005644 before and 115 after December 2004, when hospitalist involvement started. After December 2004, 12% (14 of 115) of all spinal fusion surgery patients were comanaged by a hospitalist. Most comanaged patients (14 of 15, 93%) had neuromuscular scoliosis, and comanaged patients represented 37% (13 of 35) of all neuromuscular patients (Table 1). Over the course of the study, the number of more invasive and complicated anterior/posterior spinal fusion surgeries declined, whereas the number of posterior spinal fusion surgeries increased significantly because of the introduction of new technology (data not shown).

Patient Characteristics
 LOS Days (95% CI)
All SurgeriesPreintervention (July 2000December 2004)Postintervention (December 2004September 2005)Hospitalist Comanaged (December 2004September 2005)
  • One patient described in hospitalist activities was not included here as it was not a first surgery.

Number of surgeries75964411514*
Age (years), mean (SD)13.6 (3.4)13.7 (3.4)13.1 (3.4)12.6 (4.0)
Diagnosis    
Idiopathic328 (43%)277 (43%)51 (44%)1 (7%)
Neuromuscular247 (32%)212 (33%)35 (30%)13 (93%)
Congenital66 (9%)55 (8%)11 (10%) 
Osteogenic96 (13%)81 (13%)15 (13%) 
Other22 (3%)19 (3%)3 (3%) 
Procedure    
Posterior470 (62%)365 (57%)105 (91%)13 (93%)
Ant/post227 (30%)217 (34%)10 (8%)1 (7%)
Anterior62 (8%)62 (9%)  
Intrathecal morphine use    
Idiopathic 30/50 (60%)45/51 (88%)0 (0%)
Neuromuscular 10/42 (24%)21/35 (62%)5/13 (38%)

The 15 patients seen by the hospitalist received a total of 60 visits by the hospitalist. The hospitalist saw 9 patients preoperatively. Of the 15 patients comanaged in the hospital, 5 (33%) were seen once, 8 (53%) were seen between 2 and 5 times, and 2 (14%) were seen more than 10 times. Patients were seen both in the ICU and on the surgical ward. Among the patients seen preoperatively, the hospitalist recommended nutritional interventions for 5 patients (33%), bowel regimens for 4 patients (27%), and preoperative hospitalization for 1 patient for 5 days to optimize nutritional intake, address reflux, and modify bowel regimen, as well as facilitate multispecialty evaluation. Postoperative involvement generally addressed a variety of issues, but 20% of patients had no changes in their management (Table 2).

Hospitalist Activities
Hospitalist ActivityNumber of Patients (%) (n = 15)
  • Other medical issues included: new labs (6), new medications (5), pulmonary equipment (5), new radiology (4), swallow study (2), sleep study (1).

Care coordination 
Updated family11 (73%)
Coordinated discharge8 (53%)
Updated PCP7 (47%)
Transfer facilitated4 (27%)
Consulted pulmonary3 (20%)
Consulted GI2 (13%)
Type of recommendation 
Home medications reviewed14 (93%)
Nutritional (ie, feed changes)11 (73%)
Pain medications reviewed11 (73%)
Bowel regimen10 (67%)
New medical issues*10 (67%)
Pain medications modified9 (60%)
Foley removed7 (46%)
Unnecessary medication removed6 (40%)
Central line removed4 (27%)
No changes in management3 (20%)
TPN2 (13%)
Harmful medications removed0 (0%)

Initial examination of mean monthly LOS from June 2000 to October 2005 suggested a possible decline in both mean LOS and variability in LOS after hospitalist comanagement was initiated (Fig. 2). Mean LOS for all initial spinal fusion surgeries decreased from 6.5 days (95% CI: 6.26.7) to 4.8 days (95% CI: 4.55.1) after December 2004. The standard deviation in LOS for all initial spinal fusion surgeries decreased from 1.64 to 1.39 days (P < .0001; Table 3). In the 52 months prior to hospitalist comanagement, there was no change in adjusted LOS over time (slope = 0.009 days/month, P = .3997). After December 2004, there was a significant decline in average adjusted LOS (slope = 0.2 days/month; P < .0001).

Mean Length of Stay (LOS) and Standard Deviation in LOS For Spine Fusion Surgery Patients, Before and After Hospitalist Comanagement
 Before Hospitalist 7/0012/04 n=644After Hospitalist After 12/04 n=115p value
LOS Days (95% CI)   
All Initial Spinal Fusion Surgeries6.5 (6.26.7)4.8 (4.55.1) 
Idiopathic5.2 (5.05.4)4.1(3.94.4) 
Neuromuscular8.6 (8.09.2)6.25 (5.56.9) 
Standard Deviation Days   
All Initial Spinal Fusion Surgeries1.641.39<0.0001
Idiopathic1.351.260.03
Neuromuscular1.701.410.002

Mean and adjusted LOS of patients in the 2 main diagnostic categories, idiopathic and neuromuscular scoliosis, decreased. The absolute mean LOS decreased more for neuromuscular patients (8.6 days [95% CI: 8.09.2] to 6.2 days [95% CI: 5.56.9]) than for idiopathic patients (5.2 days [95% CI: 5.05.4] to 4.1 days [95% CI: 3.94.4]). The standard deviation in LOS decreased more for the neuromuscular patients, from 1.70 to 1.41 days (P = .002), as shown in Table 3. In the 52 months prior to hospitalist comanagement, there was no change in adjusted LOS over time (neuromuscular slope = 0.024 to 0.027 days/month, P = .49; idiopathic slope = 0.0005 days/month, P = .96). After December 2004, there was a significant decline in average adjusted LOS (neuromuscular slope = 0.23 to 0.31 days/month, P = .0075; idiopathic slope = 0.10 to 0.12 days/month; P = .0007), as demonstrated in Figure 3. A survey of the orthopedic surgical staff demonstrated no known specific changes in surgical or postoperative management initiated around December 2004 other than intrathecal morphine use. Some surgeons performed fewer surgeries, particularly of idiopathic patients.

DISCUSSION

The introduction of hospitalist comanagement to ongoing multispecialty evaluation for medically complex spinal fusion surgery patients was associated with a decrease in mean LOS among all patients undergoing initial spinal fusion surgery. A greater magnitude of decline in LOS was seen among children with neuromuscular scoliosis, who were often comanaged, than among children with idiopathic scoliosis, who were rarely comanaged. Variability in LOS also decreased following initiation of hospitalist comanagement, particularly in the more complex patients. The decreases in LOS persisted after adjustment for patient age, diagnosis, procedure type, intrathecal morphine use, and surgeon. This study provides support for the hypothesis that selective hospitalist comanagement of pediatric surgical patients in the tertiary‐care setting is associated with decreased LOS and decreased variability in LOS.

Analysis of a nationally representative data set demonstrated that 4504 children with idiopathic scoliosis and 1570 children with neuromuscular scoliosis underwent spinal fusion surgery in the United States in 2000.8 The average LOS for children with neuromuscular scoliosis was 9.2 days versus 6.1 days for those with idiopathic scoliosis. The LOS of both our patient populations, those before hospitalist comanagement and those after hospitalist comanagement, was less than the national estimates. Multidisciplinary management strategies with or without hospitalist comanagement may be associated with decreases in LOS for neuromuscular scoliosis patients undergoing spinal fusion surgery.

The hospitalist performed a variety of activities in comanaging the medically complex pediatric orthopedic patients. Hospitalist comanagement may have been associated with reduction in LOS for several reasons: preoperative prevention of medical problems, early postoperative identification of and intervention on medical complications, improved coordination of care, or simply consistency of postoperative medical care.

These findings are consistent with the pediatric nonsurgical literature, which suggests that hospitalist management of pediatric medical patients in the tertiary‐care setting is associated with decreased LOS.13 Hospitalist comanagement of adult orthopedic patients has been better studied than has been comanagement of pediatric patients. Elderly patients undergoing elective hip or knee arthroplasty were randomized to hospitalist care versus traditional orthopedic care after surgery. Both sets of patients were managed by the same nursing staff according to standardized care pathways. The mean LOS did not differ between the 2 groups, but the adjusted LOS was lower in the group that received hospitalist care.9 When the same center examined outcomes in hip fracture patients before and after implementation of a hospitalist care model, there was a decrease in LOS and no change in readmission or deaths.10 As in these studies, spinal fusion surgery management is highly standardized in our center. Nonetheless, hospitalist comanagement still was associated with a decreased LOS.

This study found a decline in LOS among all patients undergoing spinal fusion surgery, even among children with idiopathic scoliosis, of whom only 1 was comanaged. This finding may suggest hospitalist comanagement had a global, or indirect, effect on the management of all postoperative patients. However, the time‐series design could have been biased by a cointervention implemented at the same time as hospitalist care. Some surgeons performed fewer surgeries on their idiopathic patients over the course of the study; however, we adjusted for that surgeon in our analysis. Intrathecal morphine use is the only known change in postoperative management that may have affected care starting in December 2004; we also adjusted for intrathecal morphine use in our analysis. There may be other changes of which we are unaware. Nonetheless, the decline in LOS seen in the idiopathic population was exceeded by the decline in LOS in the comanaged neuromuscular population.

Unlike earlier reported studies, which examined hospitalist management among pediatric medical patients, this study did not assess complications (such as pneumonia, respiratory failure, urinary tract infection, gastric ulcers, pathologic fractures, poor wound healing, nutritional compromise, and readmission),7, 11, 12 costs, or patient and provider satisfaction with hospitalist comanagement.13 This assessment is critical to defining the value of these services to patients and providers. In addition, because we did not collect information on severity of disability, we were unable to control for disability. These are other covariates and outcomes of interest that should be assessed in future studies. Furthermore, there was potential bias introduced by having the lead author both conducting the study and performing the intervention; this was minimized by having different individuals responsible for primary data collection and having the analyses performed in a blinded fashion. In addition, although this study provided promising initial evidence that selective hospitalist comanagement along with multispecialty evaluation of spinal fusion surgery patients may lead to a significant decrease in LOS, this evidence needs to be replicated in other surgical patient populations and hospital settings. Ideally, the impact of hospitalist comanagement should be more fully evaluated in a randomized controlled trial. Hospitalist comanagement is a promising technique for improving the care of children undergoing spinal fusion surgery, particularly those with complex medical conditions.

Acknowledgements

The authors acknowledge the contributions of Heidi Gullord of University Physician, Incorporated, for assistance with obtaining billing records. We also appreciate the ongoing input of the Children's Hospital Department of Epidemiology, including Lorna Dyk, BSN, MBA; Michael Rannie, RN, MS; Meghan Birkholz, MSPH; and Michael Kahn, MD, PhD. We appreciate the willingness of the Department of Orthopedics to cooperate with this study, including (but not limited to) Mark Erickson, MD; Frank Chang, MD; and Gaia Georgopoulos, MD. In addition, we acknowledge the ongoing efforts of the care providers involved in the High Risk Pathway at the Children's Hospital, including Carol Page, PT; Alice Radic, PTA; Sarah Hack Baltazar, RD; Monte Leidholm, RRT; Cloy Vaneman, RRT; Gail Shattuck, MSW; and Lynn Katz, MSW, as well as the Divisions of Pulmonary Medicine and Intensive Care of the Department of Pediatrics. We also thank Heather Ramey, BS, BA, for her assistance with organizing the multispecialty evaluation of patients undergoing surgery. We also appreciate the efforts of the fellows and faculty of the Primary Care Research Fellowship and appreciate their assistance in crafting this research.

References
  1. Bellet PS,Whitaker RC.Evaluation of a pediatric hospitalist service: impact on length of stay and hospital charges.Pediatrics.2000;105:478484.
  2. Landrigan CP,Srivastava R,Muret‐Wagstaff S, et al.Impact of a health maintenance organization hospitalist system in academic pediatrics.Pediatrics.2002;110:720728.
  3. Dwight P,MacArthur C,Friedman JN,Parkin PC.Evaluation of a staff‐only hospitalist system in a tertiary care, academic children's hospital.Pediatrics.2004;114:15451549.
  4. Percelay JM and theCommittee on Hospital Care.Physicians' roles in coordinating care of hospitalized children.Pediatrics.2003;111:707709.
  5. Conway PH andLandrigan CL.Differences in work environment, responsibilities, and training need between community hospital and academic center hospitalists. E‐PAS2006:59:4128.3.
  6. Benefield E,Erickson M.Development and implementation of a spine fusion high risk pathway. 5th Annual Pre‐Brandon Carrell Pediatric Orthopaedic Symposium for Nursing and Allied Healthcare Professionals, Texas Scottish Rite Hospital for Children, Dallas, Texas, June 26,2003.
  7. Marcantonio ER,Flacker JM,Wright RJ,Resnick NM.Reducing delirium after hip fracture: a randomized trial.J Am Geriatr Soc.2001;49:516522.
  8. Murphy NA,Firth S,Jorgensen T, et al.Spinal fusion in children with idiopathic and neuromuscular scoliosis: what's the difference?J Pediatr Orthop.2006;26(2):216220.
  9. Huddleston JM,Long KH,Naessens JM, et al.Medical and surgical comanagement after elective hip and knee arthroplasty.Ann Intern Med.2004;141:2838.
  10. Phy MP,Vanness DJ,Melton J, et al.Effects of a hospitalist model on elderly patients with hip fracture.Arch Intern Med2005;165:796801.
  11. Lipton GE,Miller F,Dabney KW, et al.Factors predicting post‐operative complications following spinal fusion in children with cerebral palsy.J Spinal Disord.1999;12:297305.
  12. Pruijs JE,van Tol MJ,van Kesteren RG, et al.Neuromuscular scoliosis: clinical evaluation pre‐ and post‐operative.J Pediatr Orthop..2000;9(4):217220.
References
  1. Bellet PS,Whitaker RC.Evaluation of a pediatric hospitalist service: impact on length of stay and hospital charges.Pediatrics.2000;105:478484.
  2. Landrigan CP,Srivastava R,Muret‐Wagstaff S, et al.Impact of a health maintenance organization hospitalist system in academic pediatrics.Pediatrics.2002;110:720728.
  3. Dwight P,MacArthur C,Friedman JN,Parkin PC.Evaluation of a staff‐only hospitalist system in a tertiary care, academic children's hospital.Pediatrics.2004;114:15451549.
  4. Percelay JM and theCommittee on Hospital Care.Physicians' roles in coordinating care of hospitalized children.Pediatrics.2003;111:707709.
  5. Conway PH andLandrigan CL.Differences in work environment, responsibilities, and training need between community hospital and academic center hospitalists. E‐PAS2006:59:4128.3.
  6. Benefield E,Erickson M.Development and implementation of a spine fusion high risk pathway. 5th Annual Pre‐Brandon Carrell Pediatric Orthopaedic Symposium for Nursing and Allied Healthcare Professionals, Texas Scottish Rite Hospital for Children, Dallas, Texas, June 26,2003.
  7. Marcantonio ER,Flacker JM,Wright RJ,Resnick NM.Reducing delirium after hip fracture: a randomized trial.J Am Geriatr Soc.2001;49:516522.
  8. Murphy NA,Firth S,Jorgensen T, et al.Spinal fusion in children with idiopathic and neuromuscular scoliosis: what's the difference?J Pediatr Orthop.2006;26(2):216220.
  9. Huddleston JM,Long KH,Naessens JM, et al.Medical and surgical comanagement after elective hip and knee arthroplasty.Ann Intern Med.2004;141:2838.
  10. Phy MP,Vanness DJ,Melton J, et al.Effects of a hospitalist model on elderly patients with hip fracture.Arch Intern Med2005;165:796801.
  11. Lipton GE,Miller F,Dabney KW, et al.Factors predicting post‐operative complications following spinal fusion in children with cerebral palsy.J Spinal Disord.1999;12:297305.
  12. Pruijs JE,van Tol MJ,van Kesteren RG, et al.Neuromuscular scoliosis: clinical evaluation pre‐ and post‐operative.J Pediatr Orthop..2000;9(4):217220.
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Pediatric hospitalist comanagement of spinal fusion surgery patients
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Discharge Appointments

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In‐room display of day and time patient is anticipated to leave hospital: A “discharge appointment”

Dicharge of a patient from the hospital is a complicated, interprofessional endeavor.1, 2 Several institutions report that discharge is one of the least satisfying elements of the patient's hospital experience.35 Recent evidence suggests that a poorly planned or disorganized discharge may compromise patient safety in the period soon after dismissal.6 Several initiatives have been aimed at improving patient satisfaction and safety related to discharge.710

In 2000 the Mayo Clinic (Rochester, Minnesota) Department of Internal Medicine leadership established a goal to improve patient satisfaction with the hospital dismissal process. Patient focus group data suggested that uncertainty about the anticipated date and time of discharge causes frustration to some patients and families.

We hypothesized that an appointment to leave the hospital might be practicable. We joined an Institute for Healthcare Improvement collaborative (Improving Flow Through Acute Care Settings, 1 of 6 Improvement Action Network [IMPACT] Learning and Innovation Communities) aimed at scheduling discharge appointments (DAs). The collaborating members deemed that, although the ideal DA is set at least a day in advance, a same‐day DA is also desirable for both patient satisfaction and staff task organization in pursuit of a high‐quality discharge.

METHODS

This project was approved by the Mayo Foundation Institutional Review Board. We tested the following hypotheses:

  • It is possible to make and display DAs in various care units.

  • Most DAs can be scheduled a day before dismissal.

  • Most DA patients depart on time.

 

Setting

Mayo Clinic in Rochester, Minnesota, is a tertiary academic medical center with 2 hospitals (Saint Marys and Rochester Methodist) that house a total of 1951 licensed beds in 76 care units.

The preliminary study displaying DAs was carried out in the Innovation and Quality (IQ) Unit of Saint Marys Hospital, a 23‐bed general medical care unit that supports both resident and nonresident services. Traditionally, primary services usually consist of an attending physician and house officer physicians (junior and senior residents). Less commonly, primary services consist of an attending physician and either a nurse‐practitioner or a physician assistant.

The design pilot took place between August 2 and December 24, 2003. The subsequent, larger study of applicability took place across 8 care units (including the IQ Unit) between December 28, 2003, and April 25, 2004.

Preliminary Work: Design Pilot

We designed bedside dry‐erase wall displays and mounted them in the rooms in plain view of patients and their families and caregivers. Pilot testing of DA scheduling was done on a general medical care unit from August 2 to December 24, 2003. To optimize the process for scheduling a DA, our team developed 21 small tests to change the dismissal process through plan, do, study, and act cycles.11

The recommended process was that as soon as an organized discharge could be reasonably envisioned, the primary service provider would discuss with the patient, family, and primary nurse (and a social service worker, if involved) the anticipated discharge day. A member of the primary service was to handwrite (with a marker) the anticipated day on the specially designed bedside dry‐erase board (Fig. 1) in view of the patient. The same primary service prescribers could amend this anticipated day (or time) by repeating the process of consultation and discussion as needed. The time of the DA could be written on the DA board (or amended) by either a member of the primary service or the primary care nurse.

Figure 1
Bedside dry‐erase board for displaying estimated date and time of dismissal.

Each morning, the primary care nurse transmitted the DA board data to the admission, discharge, and transfer log kept at the unit secretary desk (in which the actual discharge time has always been routinely recorded by the unit secretary).

Adoption of DA Scheduling in Other Care Units

Several meetings were held with 7 other patient care unit leaders about adopting the protocol. These units, both medical and surgical, were selected according to 3 criteria: (1) prior participation in unit‐level continuous improvement work, (2) current or recent work in any aspect of the discharge process, and (3) a reputation for having innovative nursing leadership and staff.

Data Acquisition and Analysis

Data were collected daily from each participating unit's admission, discharge, and transfer log: both the actual time of departure and the DA, if one had been scheduled. For each DA patient, the DA time was compared with the actual departure time.

RESULTS

During the 4‐month study of discharges across 8 care units, 1256 of 2046 patients (61%) received a DA; 576 of the DAs (46%) were scheduled at least 1 day in advance (Table 1). Among patients with a DA, 752 were discharged on time (60%), and only 240 (19%) were tardy.

Results of Discharge Appointment (DA) Activity from December 28, 2003, to April 25, 2004
UnitDAsDeparture time of patients compared with DA
No.Type of unitNo. of patientsPatients with DAs, n (%)DAs scheduled ϵ 1 day ahead, n (%)On time, n (%)aEarly, n (%)Late, n (%)
  • IQ, innovation and quality.

  • Actual departure time was within 30 minutes of the scheduled time.

1Neurology/neurosurgery525270 (51)0 (0)175 (65)44 (16)51 (19)
2Surgery (mixed)481325 (68)289 (89)166 (51)101 (31)58 (18)
3General internal medicine (IQ Unit)466243 (52)35 (14)132 (54)50 (21)61 (25)
4Neurology/neurosurgery267189 (71)40 (21)119 (63)41 (22)29 (15)
5Vascular surgery201127 (63)127 (100)90 (71)12 (9)25 (20)
6Psychiatry4642 (91)42 (100)28 (67)9 (21)5 (12)
7Orthopedic surgeryelective3838 (100)22 (58)24 (63)3 (8)11 (29)
8Orthopedic surgerytrauma2222 (100)21 (95)18 (82)4 (18)0 (0)
 Total20461256 (61)576 (46)752 (60)264 (21)240 (19)

DISCUSSION

In response to patient focus group feedback, we designed a tool and a process by which a DA could be made and posted at bedside. Among 2046 patients discharged from 8 care units over 4 months, 61% (1256) had a posted, in‐room DA. Almost half the patients with DAs (46%) had a DA scheduled at least 1 calendar day ahead. Remarkably, among patients with a DA, fewer than 20% were discharged tardily. In‐room posting of DAs across a spectrum of care units appears to be practicable, even in the face of extant diagnostic or therapeutic uncertainty.

This was an initial test‐of‐concept project and an exploratory trial. The limitations are: (1) satisfaction (patient, family, nurse, and physician) was not tested with any validated survey instrument, (2) length of stay was not studied, (3) reasons for variable DA success among care units were not ascertained, and (4) resource use was not measured.

Anecdotal information from a postdischarge phone survey indicated that patients seemed appreciative of a DA. The survey data were not included in this article because the survey tool was not a validated instrument and the interviewer (a coauthor) was not blinded to the hypothesis and was therefore subject to bias. No negative comments were received through informal real‐time feedback from patients and family during the making and posting of DAs, and encouraging comments were common.

Physician participation in posting the DA appeared to be key, and the unavoidable dialogue about the clinical rationale for a chosen date seemed welcome. A telling anecdote came from a patient who did not have a DA board: I didn't get the same treatment as my roommate with the [DA] board. The other doctors talked with [him] more about discharge. I wish my team would have done this more with me.

We cannot be certain of the reasons for the care unit disparity in setting and meeting DAs. We speculate that the level of staff enthusiasm for DAs explains the variation rather than patient population characteristics. Further, we cannot explain why 39% of the patients did not receive a DA. Physician feedback was generally, but not uniformly, positive. Negative comments that might explain DA omissions include: (1) patients already are informed and awarethe tool is superfluous; (2) the day of discharge is unknowable in advance; and (3) patients or family members will hold us to it or be upset if the DA is changed.

We expected that diagnostic uncertainty might pose challenges to providing DAs. When primary service providers were reassured that DAs could be amended, this concern was reduced (but not eliminated). It seemed useful for providers to envision the earliest day of discharge by assuming that the results of a pending key test or consultation would be favorable. Frequency of DA modification was not studied. DAs were amended, however, and patients (to our knowledge) seemed unperturbedperhaps because of an almost unavoidable discussion of the clinical rationale because the act of posting the DA occurred in full view of (and in partnership with) the patient.

A trend toward discharge earlier in the day was observed (data not shown). Theoretically, such a trend offers the potential to improve inpatient flow, in part by discharging patients before morning surgical cases are completed.

Although we had many favorable comments about DAs from patients, family members, and nurses, satisfaction of patients, families, and staff members deserves formal study. Further, it is not known whether unused DA boards might contribute to patient dissatisfaction. Any effect that the display of DAs may have on the length of stay also may be a topic worthy of future study.

CONCLUSIONS

Patients and their families sometimes desire more communication about the anticipated day and time of hospital discharge. We designed a process for making a tentative DA and a tool by which the DA could be posted at the bedside. The results of this study suggest that (1) despite some uncertainty it is possible to schedule and post DAs in‐room in various care units and in various settings, (2) DAs were made at least a day ahead of time in almost half the DA discharges, and (3) most DA discharges were characterized by on‐time departure. In addition, patient, family, and nursing satisfaction (in relation to the DA) warrants further investigation.

Acknowledgements

We acknowledge the valuable insights and collaboration of our colleagues Deborah R. Fischer, Steven L. Bahnemann, Matthew Skelton, MD, Lauri J. Dahl, Pamela O. Johnson, MSN, Debra A. Hernke, MSN, Susan L. Stirn, MSN, Barbara R. Spurrier, Ryan R. Armbruster, Todd J. Bille, and Donna K. Lawson of the Mayo Clinic and Mayo Foundation.

References
  1. Reiley P,Pike A,Phipps M, et al.Learning from patients: a discharge planning improvement project.Jt Comm J Qual Improv.1996;22:31122.
  2. Hickey ML,Kleefield SF,Pearson SD,Hassan SM,Harding M,Haughie P, et al.Payer‐hospital collaboration to improve patient satisfaction with hospital discharge.Jt Comm J Qual Improv.1996;22:336344.
  3. Charles C,Gauld M,Chambers L,O'Brien B,Haynes RB,Labelle R.How was your hospital stay? Patients' reports about their care in Canadian hospitals.CMAJ.1994;150:18131822.
  4. Cleary PD.A hospitalization from hell: a patient's perspective on quality.Ann Intern Med.2003;138:3339.
  5. Bull MJ,Hansen HE,Gross CR.Predictors of elder and family caregiver satisfaction with discharge planning.J Cardiovasc Nurs.2000;14:7687.
  6. Forster AJ,Murff HJ,Peterson JF,Gandhi TK,Bates DW.The incidence and severity of adverse events affecting patients after discharge from the hospital.Ann Intern Med.2003;138:161167.
  7. Gombeski WR,Miller PJ,Hahn JH, et al.Patient callback program: a quality improvement, customer service, and marketing tool.J Health Care Mark.1993;13:6065.
  8. Moher D,Weinberg A,Hanlon R,Runnalls K.Effects of a medical team coordinator on length of hospital stay.CMAJ.1992;146:511515.
  9. Parkes J,Shepperd S.Discharge planning from hospital to home.Cochrane Database Syst Rev.2000;4:CD000313.
  10. van Walraven C,Mamdani M,Fang J,Austin PC.Continuity of care and patient outcomes after hospital discharge.J Gen Intern Med.2004;19:624631.
  11. Institute for Healthcare Improvement. Cambridge, UK: Institute for Healthcare Improvement. Available from: http://www.ihi.org/IHI/Topics/Improvement/ImprovementMethods/HowToImprove/testingchanges.htm. Accessed July 28,2006.
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Dicharge of a patient from the hospital is a complicated, interprofessional endeavor.1, 2 Several institutions report that discharge is one of the least satisfying elements of the patient's hospital experience.35 Recent evidence suggests that a poorly planned or disorganized discharge may compromise patient safety in the period soon after dismissal.6 Several initiatives have been aimed at improving patient satisfaction and safety related to discharge.710

In 2000 the Mayo Clinic (Rochester, Minnesota) Department of Internal Medicine leadership established a goal to improve patient satisfaction with the hospital dismissal process. Patient focus group data suggested that uncertainty about the anticipated date and time of discharge causes frustration to some patients and families.

We hypothesized that an appointment to leave the hospital might be practicable. We joined an Institute for Healthcare Improvement collaborative (Improving Flow Through Acute Care Settings, 1 of 6 Improvement Action Network [IMPACT] Learning and Innovation Communities) aimed at scheduling discharge appointments (DAs). The collaborating members deemed that, although the ideal DA is set at least a day in advance, a same‐day DA is also desirable for both patient satisfaction and staff task organization in pursuit of a high‐quality discharge.

METHODS

This project was approved by the Mayo Foundation Institutional Review Board. We tested the following hypotheses:

  • It is possible to make and display DAs in various care units.

  • Most DAs can be scheduled a day before dismissal.

  • Most DA patients depart on time.

 

Setting

Mayo Clinic in Rochester, Minnesota, is a tertiary academic medical center with 2 hospitals (Saint Marys and Rochester Methodist) that house a total of 1951 licensed beds in 76 care units.

The preliminary study displaying DAs was carried out in the Innovation and Quality (IQ) Unit of Saint Marys Hospital, a 23‐bed general medical care unit that supports both resident and nonresident services. Traditionally, primary services usually consist of an attending physician and house officer physicians (junior and senior residents). Less commonly, primary services consist of an attending physician and either a nurse‐practitioner or a physician assistant.

The design pilot took place between August 2 and December 24, 2003. The subsequent, larger study of applicability took place across 8 care units (including the IQ Unit) between December 28, 2003, and April 25, 2004.

Preliminary Work: Design Pilot

We designed bedside dry‐erase wall displays and mounted them in the rooms in plain view of patients and their families and caregivers. Pilot testing of DA scheduling was done on a general medical care unit from August 2 to December 24, 2003. To optimize the process for scheduling a DA, our team developed 21 small tests to change the dismissal process through plan, do, study, and act cycles.11

The recommended process was that as soon as an organized discharge could be reasonably envisioned, the primary service provider would discuss with the patient, family, and primary nurse (and a social service worker, if involved) the anticipated discharge day. A member of the primary service was to handwrite (with a marker) the anticipated day on the specially designed bedside dry‐erase board (Fig. 1) in view of the patient. The same primary service prescribers could amend this anticipated day (or time) by repeating the process of consultation and discussion as needed. The time of the DA could be written on the DA board (or amended) by either a member of the primary service or the primary care nurse.

Figure 1
Bedside dry‐erase board for displaying estimated date and time of dismissal.

Each morning, the primary care nurse transmitted the DA board data to the admission, discharge, and transfer log kept at the unit secretary desk (in which the actual discharge time has always been routinely recorded by the unit secretary).

Adoption of DA Scheduling in Other Care Units

Several meetings were held with 7 other patient care unit leaders about adopting the protocol. These units, both medical and surgical, were selected according to 3 criteria: (1) prior participation in unit‐level continuous improvement work, (2) current or recent work in any aspect of the discharge process, and (3) a reputation for having innovative nursing leadership and staff.

Data Acquisition and Analysis

Data were collected daily from each participating unit's admission, discharge, and transfer log: both the actual time of departure and the DA, if one had been scheduled. For each DA patient, the DA time was compared with the actual departure time.

RESULTS

During the 4‐month study of discharges across 8 care units, 1256 of 2046 patients (61%) received a DA; 576 of the DAs (46%) were scheduled at least 1 day in advance (Table 1). Among patients with a DA, 752 were discharged on time (60%), and only 240 (19%) were tardy.

Results of Discharge Appointment (DA) Activity from December 28, 2003, to April 25, 2004
UnitDAsDeparture time of patients compared with DA
No.Type of unitNo. of patientsPatients with DAs, n (%)DAs scheduled ϵ 1 day ahead, n (%)On time, n (%)aEarly, n (%)Late, n (%)
  • IQ, innovation and quality.

  • Actual departure time was within 30 minutes of the scheduled time.

1Neurology/neurosurgery525270 (51)0 (0)175 (65)44 (16)51 (19)
2Surgery (mixed)481325 (68)289 (89)166 (51)101 (31)58 (18)
3General internal medicine (IQ Unit)466243 (52)35 (14)132 (54)50 (21)61 (25)
4Neurology/neurosurgery267189 (71)40 (21)119 (63)41 (22)29 (15)
5Vascular surgery201127 (63)127 (100)90 (71)12 (9)25 (20)
6Psychiatry4642 (91)42 (100)28 (67)9 (21)5 (12)
7Orthopedic surgeryelective3838 (100)22 (58)24 (63)3 (8)11 (29)
8Orthopedic surgerytrauma2222 (100)21 (95)18 (82)4 (18)0 (0)
 Total20461256 (61)576 (46)752 (60)264 (21)240 (19)

DISCUSSION

In response to patient focus group feedback, we designed a tool and a process by which a DA could be made and posted at bedside. Among 2046 patients discharged from 8 care units over 4 months, 61% (1256) had a posted, in‐room DA. Almost half the patients with DAs (46%) had a DA scheduled at least 1 calendar day ahead. Remarkably, among patients with a DA, fewer than 20% were discharged tardily. In‐room posting of DAs across a spectrum of care units appears to be practicable, even in the face of extant diagnostic or therapeutic uncertainty.

This was an initial test‐of‐concept project and an exploratory trial. The limitations are: (1) satisfaction (patient, family, nurse, and physician) was not tested with any validated survey instrument, (2) length of stay was not studied, (3) reasons for variable DA success among care units were not ascertained, and (4) resource use was not measured.

Anecdotal information from a postdischarge phone survey indicated that patients seemed appreciative of a DA. The survey data were not included in this article because the survey tool was not a validated instrument and the interviewer (a coauthor) was not blinded to the hypothesis and was therefore subject to bias. No negative comments were received through informal real‐time feedback from patients and family during the making and posting of DAs, and encouraging comments were common.

Physician participation in posting the DA appeared to be key, and the unavoidable dialogue about the clinical rationale for a chosen date seemed welcome. A telling anecdote came from a patient who did not have a DA board: I didn't get the same treatment as my roommate with the [DA] board. The other doctors talked with [him] more about discharge. I wish my team would have done this more with me.

We cannot be certain of the reasons for the care unit disparity in setting and meeting DAs. We speculate that the level of staff enthusiasm for DAs explains the variation rather than patient population characteristics. Further, we cannot explain why 39% of the patients did not receive a DA. Physician feedback was generally, but not uniformly, positive. Negative comments that might explain DA omissions include: (1) patients already are informed and awarethe tool is superfluous; (2) the day of discharge is unknowable in advance; and (3) patients or family members will hold us to it or be upset if the DA is changed.

We expected that diagnostic uncertainty might pose challenges to providing DAs. When primary service providers were reassured that DAs could be amended, this concern was reduced (but not eliminated). It seemed useful for providers to envision the earliest day of discharge by assuming that the results of a pending key test or consultation would be favorable. Frequency of DA modification was not studied. DAs were amended, however, and patients (to our knowledge) seemed unperturbedperhaps because of an almost unavoidable discussion of the clinical rationale because the act of posting the DA occurred in full view of (and in partnership with) the patient.

A trend toward discharge earlier in the day was observed (data not shown). Theoretically, such a trend offers the potential to improve inpatient flow, in part by discharging patients before morning surgical cases are completed.

Although we had many favorable comments about DAs from patients, family members, and nurses, satisfaction of patients, families, and staff members deserves formal study. Further, it is not known whether unused DA boards might contribute to patient dissatisfaction. Any effect that the display of DAs may have on the length of stay also may be a topic worthy of future study.

CONCLUSIONS

Patients and their families sometimes desire more communication about the anticipated day and time of hospital discharge. We designed a process for making a tentative DA and a tool by which the DA could be posted at the bedside. The results of this study suggest that (1) despite some uncertainty it is possible to schedule and post DAs in‐room in various care units and in various settings, (2) DAs were made at least a day ahead of time in almost half the DA discharges, and (3) most DA discharges were characterized by on‐time departure. In addition, patient, family, and nursing satisfaction (in relation to the DA) warrants further investigation.

Acknowledgements

We acknowledge the valuable insights and collaboration of our colleagues Deborah R. Fischer, Steven L. Bahnemann, Matthew Skelton, MD, Lauri J. Dahl, Pamela O. Johnson, MSN, Debra A. Hernke, MSN, Susan L. Stirn, MSN, Barbara R. Spurrier, Ryan R. Armbruster, Todd J. Bille, and Donna K. Lawson of the Mayo Clinic and Mayo Foundation.

Dicharge of a patient from the hospital is a complicated, interprofessional endeavor.1, 2 Several institutions report that discharge is one of the least satisfying elements of the patient's hospital experience.35 Recent evidence suggests that a poorly planned or disorganized discharge may compromise patient safety in the period soon after dismissal.6 Several initiatives have been aimed at improving patient satisfaction and safety related to discharge.710

In 2000 the Mayo Clinic (Rochester, Minnesota) Department of Internal Medicine leadership established a goal to improve patient satisfaction with the hospital dismissal process. Patient focus group data suggested that uncertainty about the anticipated date and time of discharge causes frustration to some patients and families.

We hypothesized that an appointment to leave the hospital might be practicable. We joined an Institute for Healthcare Improvement collaborative (Improving Flow Through Acute Care Settings, 1 of 6 Improvement Action Network [IMPACT] Learning and Innovation Communities) aimed at scheduling discharge appointments (DAs). The collaborating members deemed that, although the ideal DA is set at least a day in advance, a same‐day DA is also desirable for both patient satisfaction and staff task organization in pursuit of a high‐quality discharge.

METHODS

This project was approved by the Mayo Foundation Institutional Review Board. We tested the following hypotheses:

  • It is possible to make and display DAs in various care units.

  • Most DAs can be scheduled a day before dismissal.

  • Most DA patients depart on time.

 

Setting

Mayo Clinic in Rochester, Minnesota, is a tertiary academic medical center with 2 hospitals (Saint Marys and Rochester Methodist) that house a total of 1951 licensed beds in 76 care units.

The preliminary study displaying DAs was carried out in the Innovation and Quality (IQ) Unit of Saint Marys Hospital, a 23‐bed general medical care unit that supports both resident and nonresident services. Traditionally, primary services usually consist of an attending physician and house officer physicians (junior and senior residents). Less commonly, primary services consist of an attending physician and either a nurse‐practitioner or a physician assistant.

The design pilot took place between August 2 and December 24, 2003. The subsequent, larger study of applicability took place across 8 care units (including the IQ Unit) between December 28, 2003, and April 25, 2004.

Preliminary Work: Design Pilot

We designed bedside dry‐erase wall displays and mounted them in the rooms in plain view of patients and their families and caregivers. Pilot testing of DA scheduling was done on a general medical care unit from August 2 to December 24, 2003. To optimize the process for scheduling a DA, our team developed 21 small tests to change the dismissal process through plan, do, study, and act cycles.11

The recommended process was that as soon as an organized discharge could be reasonably envisioned, the primary service provider would discuss with the patient, family, and primary nurse (and a social service worker, if involved) the anticipated discharge day. A member of the primary service was to handwrite (with a marker) the anticipated day on the specially designed bedside dry‐erase board (Fig. 1) in view of the patient. The same primary service prescribers could amend this anticipated day (or time) by repeating the process of consultation and discussion as needed. The time of the DA could be written on the DA board (or amended) by either a member of the primary service or the primary care nurse.

Figure 1
Bedside dry‐erase board for displaying estimated date and time of dismissal.

Each morning, the primary care nurse transmitted the DA board data to the admission, discharge, and transfer log kept at the unit secretary desk (in which the actual discharge time has always been routinely recorded by the unit secretary).

Adoption of DA Scheduling in Other Care Units

Several meetings were held with 7 other patient care unit leaders about adopting the protocol. These units, both medical and surgical, were selected according to 3 criteria: (1) prior participation in unit‐level continuous improvement work, (2) current or recent work in any aspect of the discharge process, and (3) a reputation for having innovative nursing leadership and staff.

Data Acquisition and Analysis

Data were collected daily from each participating unit's admission, discharge, and transfer log: both the actual time of departure and the DA, if one had been scheduled. For each DA patient, the DA time was compared with the actual departure time.

RESULTS

During the 4‐month study of discharges across 8 care units, 1256 of 2046 patients (61%) received a DA; 576 of the DAs (46%) were scheduled at least 1 day in advance (Table 1). Among patients with a DA, 752 were discharged on time (60%), and only 240 (19%) were tardy.

Results of Discharge Appointment (DA) Activity from December 28, 2003, to April 25, 2004
UnitDAsDeparture time of patients compared with DA
No.Type of unitNo. of patientsPatients with DAs, n (%)DAs scheduled ϵ 1 day ahead, n (%)On time, n (%)aEarly, n (%)Late, n (%)
  • IQ, innovation and quality.

  • Actual departure time was within 30 minutes of the scheduled time.

1Neurology/neurosurgery525270 (51)0 (0)175 (65)44 (16)51 (19)
2Surgery (mixed)481325 (68)289 (89)166 (51)101 (31)58 (18)
3General internal medicine (IQ Unit)466243 (52)35 (14)132 (54)50 (21)61 (25)
4Neurology/neurosurgery267189 (71)40 (21)119 (63)41 (22)29 (15)
5Vascular surgery201127 (63)127 (100)90 (71)12 (9)25 (20)
6Psychiatry4642 (91)42 (100)28 (67)9 (21)5 (12)
7Orthopedic surgeryelective3838 (100)22 (58)24 (63)3 (8)11 (29)
8Orthopedic surgerytrauma2222 (100)21 (95)18 (82)4 (18)0 (0)
 Total20461256 (61)576 (46)752 (60)264 (21)240 (19)

DISCUSSION

In response to patient focus group feedback, we designed a tool and a process by which a DA could be made and posted at bedside. Among 2046 patients discharged from 8 care units over 4 months, 61% (1256) had a posted, in‐room DA. Almost half the patients with DAs (46%) had a DA scheduled at least 1 calendar day ahead. Remarkably, among patients with a DA, fewer than 20% were discharged tardily. In‐room posting of DAs across a spectrum of care units appears to be practicable, even in the face of extant diagnostic or therapeutic uncertainty.

This was an initial test‐of‐concept project and an exploratory trial. The limitations are: (1) satisfaction (patient, family, nurse, and physician) was not tested with any validated survey instrument, (2) length of stay was not studied, (3) reasons for variable DA success among care units were not ascertained, and (4) resource use was not measured.

Anecdotal information from a postdischarge phone survey indicated that patients seemed appreciative of a DA. The survey data were not included in this article because the survey tool was not a validated instrument and the interviewer (a coauthor) was not blinded to the hypothesis and was therefore subject to bias. No negative comments were received through informal real‐time feedback from patients and family during the making and posting of DAs, and encouraging comments were common.

Physician participation in posting the DA appeared to be key, and the unavoidable dialogue about the clinical rationale for a chosen date seemed welcome. A telling anecdote came from a patient who did not have a DA board: I didn't get the same treatment as my roommate with the [DA] board. The other doctors talked with [him] more about discharge. I wish my team would have done this more with me.

We cannot be certain of the reasons for the care unit disparity in setting and meeting DAs. We speculate that the level of staff enthusiasm for DAs explains the variation rather than patient population characteristics. Further, we cannot explain why 39% of the patients did not receive a DA. Physician feedback was generally, but not uniformly, positive. Negative comments that might explain DA omissions include: (1) patients already are informed and awarethe tool is superfluous; (2) the day of discharge is unknowable in advance; and (3) patients or family members will hold us to it or be upset if the DA is changed.

We expected that diagnostic uncertainty might pose challenges to providing DAs. When primary service providers were reassured that DAs could be amended, this concern was reduced (but not eliminated). It seemed useful for providers to envision the earliest day of discharge by assuming that the results of a pending key test or consultation would be favorable. Frequency of DA modification was not studied. DAs were amended, however, and patients (to our knowledge) seemed unperturbedperhaps because of an almost unavoidable discussion of the clinical rationale because the act of posting the DA occurred in full view of (and in partnership with) the patient.

A trend toward discharge earlier in the day was observed (data not shown). Theoretically, such a trend offers the potential to improve inpatient flow, in part by discharging patients before morning surgical cases are completed.

Although we had many favorable comments about DAs from patients, family members, and nurses, satisfaction of patients, families, and staff members deserves formal study. Further, it is not known whether unused DA boards might contribute to patient dissatisfaction. Any effect that the display of DAs may have on the length of stay also may be a topic worthy of future study.

CONCLUSIONS

Patients and their families sometimes desire more communication about the anticipated day and time of hospital discharge. We designed a process for making a tentative DA and a tool by which the DA could be posted at the bedside. The results of this study suggest that (1) despite some uncertainty it is possible to schedule and post DAs in‐room in various care units and in various settings, (2) DAs were made at least a day ahead of time in almost half the DA discharges, and (3) most DA discharges were characterized by on‐time departure. In addition, patient, family, and nursing satisfaction (in relation to the DA) warrants further investigation.

Acknowledgements

We acknowledge the valuable insights and collaboration of our colleagues Deborah R. Fischer, Steven L. Bahnemann, Matthew Skelton, MD, Lauri J. Dahl, Pamela O. Johnson, MSN, Debra A. Hernke, MSN, Susan L. Stirn, MSN, Barbara R. Spurrier, Ryan R. Armbruster, Todd J. Bille, and Donna K. Lawson of the Mayo Clinic and Mayo Foundation.

References
  1. Reiley P,Pike A,Phipps M, et al.Learning from patients: a discharge planning improvement project.Jt Comm J Qual Improv.1996;22:31122.
  2. Hickey ML,Kleefield SF,Pearson SD,Hassan SM,Harding M,Haughie P, et al.Payer‐hospital collaboration to improve patient satisfaction with hospital discharge.Jt Comm J Qual Improv.1996;22:336344.
  3. Charles C,Gauld M,Chambers L,O'Brien B,Haynes RB,Labelle R.How was your hospital stay? Patients' reports about their care in Canadian hospitals.CMAJ.1994;150:18131822.
  4. Cleary PD.A hospitalization from hell: a patient's perspective on quality.Ann Intern Med.2003;138:3339.
  5. Bull MJ,Hansen HE,Gross CR.Predictors of elder and family caregiver satisfaction with discharge planning.J Cardiovasc Nurs.2000;14:7687.
  6. Forster AJ,Murff HJ,Peterson JF,Gandhi TK,Bates DW.The incidence and severity of adverse events affecting patients after discharge from the hospital.Ann Intern Med.2003;138:161167.
  7. Gombeski WR,Miller PJ,Hahn JH, et al.Patient callback program: a quality improvement, customer service, and marketing tool.J Health Care Mark.1993;13:6065.
  8. Moher D,Weinberg A,Hanlon R,Runnalls K.Effects of a medical team coordinator on length of hospital stay.CMAJ.1992;146:511515.
  9. Parkes J,Shepperd S.Discharge planning from hospital to home.Cochrane Database Syst Rev.2000;4:CD000313.
  10. van Walraven C,Mamdani M,Fang J,Austin PC.Continuity of care and patient outcomes after hospital discharge.J Gen Intern Med.2004;19:624631.
  11. Institute for Healthcare Improvement. Cambridge, UK: Institute for Healthcare Improvement. Available from: http://www.ihi.org/IHI/Topics/Improvement/ImprovementMethods/HowToImprove/testingchanges.htm. Accessed July 28,2006.
References
  1. Reiley P,Pike A,Phipps M, et al.Learning from patients: a discharge planning improvement project.Jt Comm J Qual Improv.1996;22:31122.
  2. Hickey ML,Kleefield SF,Pearson SD,Hassan SM,Harding M,Haughie P, et al.Payer‐hospital collaboration to improve patient satisfaction with hospital discharge.Jt Comm J Qual Improv.1996;22:336344.
  3. Charles C,Gauld M,Chambers L,O'Brien B,Haynes RB,Labelle R.How was your hospital stay? Patients' reports about their care in Canadian hospitals.CMAJ.1994;150:18131822.
  4. Cleary PD.A hospitalization from hell: a patient's perspective on quality.Ann Intern Med.2003;138:3339.
  5. Bull MJ,Hansen HE,Gross CR.Predictors of elder and family caregiver satisfaction with discharge planning.J Cardiovasc Nurs.2000;14:7687.
  6. Forster AJ,Murff HJ,Peterson JF,Gandhi TK,Bates DW.The incidence and severity of adverse events affecting patients after discharge from the hospital.Ann Intern Med.2003;138:161167.
  7. Gombeski WR,Miller PJ,Hahn JH, et al.Patient callback program: a quality improvement, customer service, and marketing tool.J Health Care Mark.1993;13:6065.
  8. Moher D,Weinberg A,Hanlon R,Runnalls K.Effects of a medical team coordinator on length of hospital stay.CMAJ.1992;146:511515.
  9. Parkes J,Shepperd S.Discharge planning from hospital to home.Cochrane Database Syst Rev.2000;4:CD000313.
  10. van Walraven C,Mamdani M,Fang J,Austin PC.Continuity of care and patient outcomes after hospital discharge.J Gen Intern Med.2004;19:624631.
  11. Institute for Healthcare Improvement. Cambridge, UK: Institute for Healthcare Improvement. Available from: http://www.ihi.org/IHI/Topics/Improvement/ImprovementMethods/HowToImprove/testingchanges.htm. Accessed July 28,2006.
Issue
Journal of Hospital Medicine - 2(1)
Issue
Journal of Hospital Medicine - 2(1)
Page Number
13-16
Page Number
13-16
Article Type
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In‐room display of day and time patient is anticipated to leave hospital: A “discharge appointment”
Display Headline
In‐room display of day and time patient is anticipated to leave hospital: A “discharge appointment”
Legacy Keywords
discharge, discharge planning, dismissal, hospitalization, patient satisfaction
Legacy Keywords
discharge, discharge planning, dismissal, hospitalization, patient satisfaction
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Copyright © 2007 Society of Hospital Medicine

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Division of General Internal Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905
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Medical Student Evaluation of Hospitalist and Nonhospitalist Faculty

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Third‐year medical students' evaluation of hospitalist and nonhospitalist faculty during the inpatient portion of their pediatrics clerkships

In 1996 Wachter and Goldman anticipated the emergence of hospitalists,1 physicians who are responsible for the care of hospitalized patients in place of their primary care physicians. The number of physicians who identify themselves as hospitalists has grown rapidly since 1996 and is currently estimated to be 10,00012,000, with the potential to reach as high as 30,000 in the next decade.2 This growth includes academic medical centers. In surveys of chairs of internal medicine and pediatric departments, 50% have hospitalists employed at their institutions.3, 4

Hospitalists in academic institutions are playing an increasingly prominent role in the medical education of both residents and medical students. The implications of adopting a hospitalist model on medical education has been discussed.57 Despite such concerns as fragmented continuity of care; decreased exposure to primary care physicians, subspecialists and physician‐scientists; reduced autonomy; and fewer educational opportunities to observe the natural histories of illnesses because of improved efficiency,57 the overall impact of hospitalists on medical and resident education has generally been favorable.818 Internal medicine residents have rated the teaching skills of hospitalists comparable to traditional academic physicians,8, 9 and believe the addition of hospitalists has contributed to an improved educational experience.10, 11, 14 In addition, a survey of third‐year medical students at a single academic teaching hospital concluded that hospitalists were able to provide at least as positive an educational experience during their inpatient medicine rotations as highly rated nonhospitalist teaching faculty.13

The role of hospitalists as educators in pediatrics has been studied much less. Pediatric resident satisfaction has improved in institutions that have used a hospitalist model.1618 In another study, hospitalists were rated by pediatric residents as more effective teachers than nonhospitalists.15 Because we are unaware of any study that has evaluated hospitalists in the education of medical students during their inpatient pediatric rotation, the purpose of our study was to compare hospitalist and nonhospitalist faculty on the educational experience of third‐year medical students during the inpatient portion of their pediatric clerkships at a single university children's hospital.

METHODS

Study Design

We conducted a retrospective study using evaluations of third‐year medical students comparing hospitalist and nonhospitalist faculty during the inpatient portions of their pediatrics clerkships at a single academic children's hospital over a 15‐month period (July 1999September 2000).

Setting and Sample

We conducted our study at Penn State Children's Hospital (PSCH), a 120‐bed tertiary‐care facility within the 504‐bed Hershey Medical Center, the main teaching hospital affiliated with the Penn State College of Medicine, Hershey, Pennsylvania. The pediatric hospitalist program commenced on July 1, 1999, and during the 15‐month study period the hospitalist staff consisted of 2 physicians who attended a total of 8 months, whereas the nonhospitalist staff consisted of 4 academic general pediatricians and 4 academic pediatric subspecialists who attended the remaining 7 months.

The inpatient clinical responsibilities of both groups of physicians during each month were similar. Both groups of physicians conducted daily rounds with a team that included a senior resident (postgraduate year 3), 2 to 4 interns (postgraduate year 1), 1 acting intern (fourth‐year medical student), and 2 to 4 third‐year medical students. This team was responsible for all admissions to the general pediatrics service, which averages 100 admissions per month. Both the hospitalists and nonhospitalists had outpatient responsibilities during the time they served as inpatient attendings.

During the 15‐month study period, 131 students completed their third‐year pediatrics clerkships. Students at the Penn State College of Medicine may complete their pediatrics clerkship at PSCH or at one of several alternative sites. Because of variability in the structure of the rotation from site to site, it was considered valid only to analyze evaluations completed by students who rotated at PSCH. Sixty‐seven students rotated at PSCH during the study period. Students spent 3 weeks of the 6‐week rotation on the inpatient general pediatrics service. The remaining 3 weeks occurred in multiple outpatient pediatric practice settings and in the newborn nursery. During the 3 weeks the students spent on the inpatient service they did not have outpatient clinic responsibilities, so they did not interact with either the hospitalists or nonhospitalists in the outpatient setting. At the end of the rotation, students were asked to rate the effectiveness of the faculty as teachers, pediatricians, and student advocates and overall on a 4‐point scale (1 = inadequate; 2 = adequate; 3 = very good; 4 = excellent). Students were also asked to evaluate 7 components of the clerkship on the same 4‐point scale (Table 1). Finally, students were asked to provide additional written comments in an unstructured format.

Results of Third‐Year Medical Student Survey at Penn State University Children's Hospital
Evaluation itemHospitalist mean score (32 evaluations)Nonhospitalist mean score (35 evaluations)P valueNo. of evaluations rated adequate or inadequate (%)b
HospitalistNonhospitalist
  • Student responses based on a 4‐point scale (1 = inadequate, 2 = adequate, 3 = very good, 4 = excellent)

  • Statistically significant response (P < .05)

  • Adequate and inadequate responses were not calculated in the remaining evaluation items, as hospitalists and nonhospitalists did not have specific responsibilities in these areas.

  • Students were to consider the following skills in rating this category: knowledge, effectiveness of instruction, and intellectual stimulation.

  • Students were to consider the following skills in rating this category: pediatric knowledge, patient management, and role model.

  • Students were to consider the following skills in rating this category: availability to students, supervision of students, interest in students, and guidance of students.

Effectiveness as teacherc3.872.91< .001a1 (2.9)13 (40.6)
Effectiveness as pediatriciand3.943.25< .001a0 (0.0)5 (15.6)
Effectiveness as student advocatee3.762.97< .001a2 (5.7)13 (40.6)
Overall evaluation3.933.06< .001a0 (0.0)10 (31.3)
Ward rounds3.152.58< .006a5 (15.6)12 (37.5)
Morning report3.163.140.923  
Sick newborn2.792.600.518  
Well newborn2.893.130.211  
Outpatient department clinics2.963.060.425  
Private physician's office2.973.010.794  
Noon conference3.033.130.512  

After reviewing the literature concerning faculty evaluation forms and their components, an evaluation form was created for students to indicate their reactions to clerkship components. All the medical students' faculty evaluations were anonymous, and the faculty was not able to review student evaluations prior to assigning grades. Students were required to turn in an evaluation at the end of their rotations. The study was limited to 15 months, as the format of the evaluation form was changed after September 2000 and the general pediatrics service was in the process of transitioning to an exclusively hospitalist‐run service, thereby limiting the number of nonhospitalists available as a comparison group. Demographic characteristics of the hospitalist and nonhospitalist faculty were collected from a faculty database. The study was approved by the Penn State Milton S. Hershey Medical Center's Institutional Review Board.

Statistics and Analysis

For all questions, a Wilcoxon rank sum test was used to evaluate whether the responses for nonhospitalists were different than those for hospitalists. Differences in response by group whose 2‐tailed P values were less than .05 were considered statistically significant. All analyses were performed using the SAS statistical software, version 8.2 (SAS Institute Inc., Cary, NC).

RESULTS

All 67 of the students who completed a pediatrics clerkship at PSCH returned evaluation forms, which were the data for further analysis. Thirty‐five students rotated with the hospitalist faculty, and 32 students rotated with the nonhospitalist faculty. There were no significant demographic differences between the hospitalist and nonhospitalist faculty in age, sex, academic rank, specialty, and years since completing training (Table 2). All the hospitalist faculty fulfilled the definition of a hospitalist,2 whereas none of the physicians in the nonhospitalist group did.

Demographic Characteristics of Hospitalist and Nonhospitalist Faculty
CharacteristicHospitalists (n = 2)Nonhospitalists (n = 8)P value
Age, mean (range)36.0 (3141)46.5 (3063)0.30
Male/Female1/16/20.95
Academic rank   
Instructor01 
Assistant professor23 
Associate professor000.56
Professor04 
Specialty   
General pediatrics14 
Nephrology11 
Genetics010.95
Infectious ciseases01 
Rheumatology01 
Years since training, mean (range)4.0 (08)13.8 (030)0.43

The hospitalists were rated significantly higher than the nonhospitalist faculty in all 4 of the attending characteristics measured (Table 1): teaching effectiveness (3.87 vs. 2.91; P < .0001), effectiveness as a pediatrician (3.94 vs. 3.25; P < .001), student advocacy effectiveness (3.76 vs. 2.97; P < .0001), and overall evaluation (3.93 vs. 3.06; P < .001).

Analysis of specific aspects of the rotation showed the only feature that hospitalists were rated significantly higher on was quality of ward rounds (3.15 vs. 2.58, P < .006). There was no significant difference between the hospitalists and nonhospitalists on features that were not specifically part of the inpatient rotation, including various conferences, outpatient clinics, and newborn care (Table 1).

DISCUSSION

Our study demonstrates that pediatric hospitalists had a positive impact on the overall educational experience of third‐year medical students during the inpatient portions of their pediatrics clerkships. Hospitalists were rated more favorably than nonhospitalists as teachers, as pediatricians, as student advocates, and overall. Medical students also rated the value of ward rounds more favorably when hospitalists conducted them. In addition, higher percentages of nonhospitalists than hospitalists were rated as adequate or inadequate for the above items. When other aspects of the clerkship were analyzed, there were no statistically significant differences between the students who rotated with hospitalists and the students who rotated with nonhospitalists. This suggests that the higher scores for hospitalists were specifically related to their interactions with students, rather than with an overall more positive view of the rotation.

It has been suggested that forces promoting the use of hospitalists in adult medicine are even more persuasive in the pediatric population, as the difference in severity of illness between the inpatient and outpatient setting is greater, and the average pediatrician has less experience than the average internist in managing hospitalized patients.19 In a recent systematic review of the literature, Landrigan et al.20 reported that 6 of 7 studies demonstrated hospitalist systems had decreased hospital length of stay compared to systems in which a primary pediatrician served as the physician of record. This improved efficiency, if combined with the pressure to see more patients while trying to balance teaching and research demands, may have a negative impact on the quality of medical education.

Several factors may have contributed to the students' satisfaction with hospitalists. Studies have demonstrated that students rate clinical teachers more favorably with whom they have greater involvement.21 Hospitalists may be more likely to spend time on the inpatient wards given that is the primary site of their clinical activity. This increased presence may have contributed to more favorable evaluations for the hospitalist faculty, whereas the additional outpatient workload for nonhospitalist faculty may have reduced inpatient teaching opportunities, accounting for their lower teaching score. Included in the pediatrician category was the attribute of being a role model. In a study by Wright et al.,22 spending more than 25% of the time or 25 or more hours per week teaching and conducting rounds was independently associated with being considered an excellent role model. Again, the increased availability of the hospitalists on the inpatient wards may have led to more teaching opportunities, contributing to their higher score.

Our study had several limitations. First, it was a retrospective study conducted at a single institution with only 2 hospitalists. Although there were not statistical significant demographic differences between the 2 groups, this may simply reflect the small size of the sample in our study; therefore, the results may not be applicable to other academic institutions. Second, we retrospectively analyzed an evaluation form that had not been validated or specifically designed to compare 2 physician groups. Third, there were multiple statements in each category that students were asked to consider before scoring each attending on the parameters measured. Although hospitalists were rated higher in each category, there may have been individual characteristics within each category for which the nonhospitalist faculty performed better. Fourth, although hospitalists received higher average ratings than nonhospitalist faculty from third‐year medical students, it is important to emphasize this study measured students' attitudes and beliefs not specific educational outcomes. However, even though we cannot rule out the possibility that potentially confounding factors such as the personality of an attending physician influenced the results, prior studies have demonstrated that medical students make sophisticated judgments about teaching in the clinical setting.23, 24 It is unlikely that hospitalists at our institution were specifically selected to attend more months on a new inpatient service because they had a history of having more favorable teaching qualities because 1 of the 2 hospitalists had just finished residency training, and there were no significant demographic differences between the 2 groups. In a study examining trainee satisfaction in an internal medicine rotation 4 years after adoption of a hospitalist model, where nonhospitalist faculty attended based on their own interest and inpatient skill rather than as a requirement, Hauer et al.14 reported that trainees experienced more effective teaching and a more satisfying inpatient rotation when supervised by hospitalists. This suggests that hospitalists may possess or develop a specific inpatient knowledge base and teaching acumen over time that distinguishes them from nonhospitalists. There is evidence of accumulated experience leading to improved outcomes in the clinical setting for HIV infection,25 various surgical procedures,26 and hospitalist systems.27

In conclusion, this is the first study to evaluate the performance of hospitalists in the setting of a third‐year medical student pediatrics clerkship. Although third‐year medical students rate hospitalists at least as highly as nonhospitalist faculty, further studies are needed to reproduce this finding. In addition to the increased time spent on the wards with students and increased experience in caring for hospitalized patients, further studies should also examine the role that communication plays in clinical teaching. Also, the recent development of core competencies in hospital medicine28 may lead to the development of educational outcomes that can be objectively measured.

Acknowledgements

The authors thank David Mauger, PhD, from the Department of Health Evaluation Sciences at the Penn State College of Medicine for providing statistical analysis of the survey results.

References
  1. Wachter RM,Goldman L.The emerging role of “hospitalists” in the American health care system.N Engl J Med.1996;335:514517.
  2. Society of Hospital Medicine. Frequently asked questions. Available at: http://www.hospitalmedicine.org/AM/Template.cfm?Section=FAQs75:S346.
  3. Srivastava R,Landrigan C,Gidwani P,Harary OH,Muret‐Wagstaff S,Homer CJ.Pediatric hospitalists in Canada and the United States: a survey of pediatric academic department chairs.Ambul Pediatr.2001;1:338339.
  4. Goldman L.The impact of hospitalists on medical education and the academic health system.Ann Intern Med.1999;130:364367.
  5. Whitcomb WF,Nelson JR.The role of hospitalists in medical education.Am J Med.1999;107:305309.
  6. Hauer KE,Wachter RM.Implications of the hospitalist model for medical students' education.Acad. Med.2001;76:324330.
  7. Wachter RM,Katz P,Showstack J,Bindman AB,Goldman L.Reorganizing an academic medical service: impact on cost, quality, patient satisfaction, and education.JAMA.1998;279:15601565.
  8. Kripalani S,Pope AC,Rask K, et al.Hospitalists as teachers: how do they compare to subspecialty and general medicine faculty.J Gen Intern Med.2004;19:815.
  9. Brown MD,Halpert A,McKean S,Sussman A,Dzau VJ.Assessing the value of hospitalists to academic health centers: Brigham and Women's Hospital and Harvard Medical School.Am J Med.1999;106:134137.
  10. Chung P,Morrison J,Jin L,Levinson W,Humphrey H,Meltzer D.Resident satisfaction on an academic hospitalist service: time to teach.Am J Med.2002;112:597601.
  11. Kulaga ME,Charney P,O'Mahony SP,Cleary JP,McClung TM,Schildkamp DE,Mazur EM.The positive impact of initiation of hospitalist clinician educators: resource utilization and medical resident education.J Gen Intern Med.2004;19:293301.
  12. Hunter AJ,Desai SS,Harrison RA,Chan BKS.Medical student evaluation of the quality of hospitalist and nonhospitalist teaching faculty on inpatient medicine rotations.Acad Med.2004;79:7882.
  13. Hauer KE,Wachter RM,McCulloch CE,Woo GA,Auerbach AA.Effects of hospitalist attending physicians on trainee satisfaction with teaching and with internal medicine rotations.Arch Intern Med.2004;164:18661871.
  14. Landrigan CP,Muret‐Wagstaff S,Chiang VW,Nigrin DJ,Goldmann DA,Finkelstein JA.Effect of a pediatric hospitalist system on housestaff education and experience.Arch Pediatr Adolesc Med.2002;156:877883.
  15. Wilson SD.Employing hospitalists to improve residents' inpatient learning.Acad Med.2001;76:556.
  16. Ponitz K,Mortimer J,Berman B.Establishing a pediatric hospitalist program at an academic medical center.Clin Pediatr.2000;39:221227.
  17. Ogershok PR,Li X,Palmer HC,Moore RS,Weisse ME,Ferrari ND.Restructuring an academic pediatric inpatient service using concepts developed by hospitalists.Clin Pediatr.2001;40:653660.
  18. Bellet PS,Wachter RM.The hospitalist movement and its implications for the care of hospitalized children.Pediatrics.1999;103:473477.
  19. Landrigan CP,Conway PH,Edwards S,Srivastava R.Pediatric hospitalists: a systematic review of the literature.Pediatrics.2006;117:17361744.
  20. Irby DM,Gillmore GM,Ramsey PG.Factors affecting ratings of clinical teachers by medical students and residents.J Med Educ.1987;62:17.
  21. Wright SM,Kern DE,Kolodner K,Howard DM,Brancati FL.Attributes of excellent attending‐physician role models.N Engl J Med.1998;339:19861993.
  22. Donnelly MB,Woolliscroft JO.Evaluation of clinical instructors by third‐year medical students.Acad Med.1989;64:159164.
  23. McLeod PJ,James CA,Abrahamowicz M.Clinical tutor evaluation: a 5‐year study by students on an in‐patient service and residents in an ambulatory care clinic.Med Educ.1993;27:4853.
  24. Kitahata MM,Koepsell TD,Deyo RA,Maxwell CL,Dodge WT,Wagner EH.Physicians' experience with the acquired immunodeficiency syndrome as a factor in patients' survival.N Engl J Med.1996;334:701706.
  25. Luft HS,Garnick DW,Mark DH,McPhee SJ.Hospital Volume, Physician Volume, and Patient Outcomes: Assessing the Evidence. Ann Arbor, MI: Health Administration Perspectives;1990.
  26. Meltzer D,Manning WG,Morrison J, et al.Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists.Ann Intern Med.2002;137:866874.
  27. Pistoria MJ,Amin AN,Dressler DD,McKean SCW,Budnitz TL, eds.The core competencies in hospital medicine: a framework for curriculum development by the Society of Hospital Medicine.J Hosp Med.2006;1(S1):167.
Article PDF
Issue
Journal of Hospital Medicine - 2(1)
Page Number
17-22
Legacy Keywords
hospitalists, medical students, medical education, pediatrics
Sections
Article PDF
Article PDF

In 1996 Wachter and Goldman anticipated the emergence of hospitalists,1 physicians who are responsible for the care of hospitalized patients in place of their primary care physicians. The number of physicians who identify themselves as hospitalists has grown rapidly since 1996 and is currently estimated to be 10,00012,000, with the potential to reach as high as 30,000 in the next decade.2 This growth includes academic medical centers. In surveys of chairs of internal medicine and pediatric departments, 50% have hospitalists employed at their institutions.3, 4

Hospitalists in academic institutions are playing an increasingly prominent role in the medical education of both residents and medical students. The implications of adopting a hospitalist model on medical education has been discussed.57 Despite such concerns as fragmented continuity of care; decreased exposure to primary care physicians, subspecialists and physician‐scientists; reduced autonomy; and fewer educational opportunities to observe the natural histories of illnesses because of improved efficiency,57 the overall impact of hospitalists on medical and resident education has generally been favorable.818 Internal medicine residents have rated the teaching skills of hospitalists comparable to traditional academic physicians,8, 9 and believe the addition of hospitalists has contributed to an improved educational experience.10, 11, 14 In addition, a survey of third‐year medical students at a single academic teaching hospital concluded that hospitalists were able to provide at least as positive an educational experience during their inpatient medicine rotations as highly rated nonhospitalist teaching faculty.13

The role of hospitalists as educators in pediatrics has been studied much less. Pediatric resident satisfaction has improved in institutions that have used a hospitalist model.1618 In another study, hospitalists were rated by pediatric residents as more effective teachers than nonhospitalists.15 Because we are unaware of any study that has evaluated hospitalists in the education of medical students during their inpatient pediatric rotation, the purpose of our study was to compare hospitalist and nonhospitalist faculty on the educational experience of third‐year medical students during the inpatient portion of their pediatric clerkships at a single university children's hospital.

METHODS

Study Design

We conducted a retrospective study using evaluations of third‐year medical students comparing hospitalist and nonhospitalist faculty during the inpatient portions of their pediatrics clerkships at a single academic children's hospital over a 15‐month period (July 1999September 2000).

Setting and Sample

We conducted our study at Penn State Children's Hospital (PSCH), a 120‐bed tertiary‐care facility within the 504‐bed Hershey Medical Center, the main teaching hospital affiliated with the Penn State College of Medicine, Hershey, Pennsylvania. The pediatric hospitalist program commenced on July 1, 1999, and during the 15‐month study period the hospitalist staff consisted of 2 physicians who attended a total of 8 months, whereas the nonhospitalist staff consisted of 4 academic general pediatricians and 4 academic pediatric subspecialists who attended the remaining 7 months.

The inpatient clinical responsibilities of both groups of physicians during each month were similar. Both groups of physicians conducted daily rounds with a team that included a senior resident (postgraduate year 3), 2 to 4 interns (postgraduate year 1), 1 acting intern (fourth‐year medical student), and 2 to 4 third‐year medical students. This team was responsible for all admissions to the general pediatrics service, which averages 100 admissions per month. Both the hospitalists and nonhospitalists had outpatient responsibilities during the time they served as inpatient attendings.

During the 15‐month study period, 131 students completed their third‐year pediatrics clerkships. Students at the Penn State College of Medicine may complete their pediatrics clerkship at PSCH or at one of several alternative sites. Because of variability in the structure of the rotation from site to site, it was considered valid only to analyze evaluations completed by students who rotated at PSCH. Sixty‐seven students rotated at PSCH during the study period. Students spent 3 weeks of the 6‐week rotation on the inpatient general pediatrics service. The remaining 3 weeks occurred in multiple outpatient pediatric practice settings and in the newborn nursery. During the 3 weeks the students spent on the inpatient service they did not have outpatient clinic responsibilities, so they did not interact with either the hospitalists or nonhospitalists in the outpatient setting. At the end of the rotation, students were asked to rate the effectiveness of the faculty as teachers, pediatricians, and student advocates and overall on a 4‐point scale (1 = inadequate; 2 = adequate; 3 = very good; 4 = excellent). Students were also asked to evaluate 7 components of the clerkship on the same 4‐point scale (Table 1). Finally, students were asked to provide additional written comments in an unstructured format.

Results of Third‐Year Medical Student Survey at Penn State University Children's Hospital
Evaluation itemHospitalist mean score (32 evaluations)Nonhospitalist mean score (35 evaluations)P valueNo. of evaluations rated adequate or inadequate (%)b
HospitalistNonhospitalist
  • Student responses based on a 4‐point scale (1 = inadequate, 2 = adequate, 3 = very good, 4 = excellent)

  • Statistically significant response (P < .05)

  • Adequate and inadequate responses were not calculated in the remaining evaluation items, as hospitalists and nonhospitalists did not have specific responsibilities in these areas.

  • Students were to consider the following skills in rating this category: knowledge, effectiveness of instruction, and intellectual stimulation.

  • Students were to consider the following skills in rating this category: pediatric knowledge, patient management, and role model.

  • Students were to consider the following skills in rating this category: availability to students, supervision of students, interest in students, and guidance of students.

Effectiveness as teacherc3.872.91< .001a1 (2.9)13 (40.6)
Effectiveness as pediatriciand3.943.25< .001a0 (0.0)5 (15.6)
Effectiveness as student advocatee3.762.97< .001a2 (5.7)13 (40.6)
Overall evaluation3.933.06< .001a0 (0.0)10 (31.3)
Ward rounds3.152.58< .006a5 (15.6)12 (37.5)
Morning report3.163.140.923  
Sick newborn2.792.600.518  
Well newborn2.893.130.211  
Outpatient department clinics2.963.060.425  
Private physician's office2.973.010.794  
Noon conference3.033.130.512  

After reviewing the literature concerning faculty evaluation forms and their components, an evaluation form was created for students to indicate their reactions to clerkship components. All the medical students' faculty evaluations were anonymous, and the faculty was not able to review student evaluations prior to assigning grades. Students were required to turn in an evaluation at the end of their rotations. The study was limited to 15 months, as the format of the evaluation form was changed after September 2000 and the general pediatrics service was in the process of transitioning to an exclusively hospitalist‐run service, thereby limiting the number of nonhospitalists available as a comparison group. Demographic characteristics of the hospitalist and nonhospitalist faculty were collected from a faculty database. The study was approved by the Penn State Milton S. Hershey Medical Center's Institutional Review Board.

Statistics and Analysis

For all questions, a Wilcoxon rank sum test was used to evaluate whether the responses for nonhospitalists were different than those for hospitalists. Differences in response by group whose 2‐tailed P values were less than .05 were considered statistically significant. All analyses were performed using the SAS statistical software, version 8.2 (SAS Institute Inc., Cary, NC).

RESULTS

All 67 of the students who completed a pediatrics clerkship at PSCH returned evaluation forms, which were the data for further analysis. Thirty‐five students rotated with the hospitalist faculty, and 32 students rotated with the nonhospitalist faculty. There were no significant demographic differences between the hospitalist and nonhospitalist faculty in age, sex, academic rank, specialty, and years since completing training (Table 2). All the hospitalist faculty fulfilled the definition of a hospitalist,2 whereas none of the physicians in the nonhospitalist group did.

Demographic Characteristics of Hospitalist and Nonhospitalist Faculty
CharacteristicHospitalists (n = 2)Nonhospitalists (n = 8)P value
Age, mean (range)36.0 (3141)46.5 (3063)0.30
Male/Female1/16/20.95
Academic rank   
Instructor01 
Assistant professor23 
Associate professor000.56
Professor04 
Specialty   
General pediatrics14 
Nephrology11 
Genetics010.95
Infectious ciseases01 
Rheumatology01 
Years since training, mean (range)4.0 (08)13.8 (030)0.43

The hospitalists were rated significantly higher than the nonhospitalist faculty in all 4 of the attending characteristics measured (Table 1): teaching effectiveness (3.87 vs. 2.91; P < .0001), effectiveness as a pediatrician (3.94 vs. 3.25; P < .001), student advocacy effectiveness (3.76 vs. 2.97; P < .0001), and overall evaluation (3.93 vs. 3.06; P < .001).

Analysis of specific aspects of the rotation showed the only feature that hospitalists were rated significantly higher on was quality of ward rounds (3.15 vs. 2.58, P < .006). There was no significant difference between the hospitalists and nonhospitalists on features that were not specifically part of the inpatient rotation, including various conferences, outpatient clinics, and newborn care (Table 1).

DISCUSSION

Our study demonstrates that pediatric hospitalists had a positive impact on the overall educational experience of third‐year medical students during the inpatient portions of their pediatrics clerkships. Hospitalists were rated more favorably than nonhospitalists as teachers, as pediatricians, as student advocates, and overall. Medical students also rated the value of ward rounds more favorably when hospitalists conducted them. In addition, higher percentages of nonhospitalists than hospitalists were rated as adequate or inadequate for the above items. When other aspects of the clerkship were analyzed, there were no statistically significant differences between the students who rotated with hospitalists and the students who rotated with nonhospitalists. This suggests that the higher scores for hospitalists were specifically related to their interactions with students, rather than with an overall more positive view of the rotation.

It has been suggested that forces promoting the use of hospitalists in adult medicine are even more persuasive in the pediatric population, as the difference in severity of illness between the inpatient and outpatient setting is greater, and the average pediatrician has less experience than the average internist in managing hospitalized patients.19 In a recent systematic review of the literature, Landrigan et al.20 reported that 6 of 7 studies demonstrated hospitalist systems had decreased hospital length of stay compared to systems in which a primary pediatrician served as the physician of record. This improved efficiency, if combined with the pressure to see more patients while trying to balance teaching and research demands, may have a negative impact on the quality of medical education.

Several factors may have contributed to the students' satisfaction with hospitalists. Studies have demonstrated that students rate clinical teachers more favorably with whom they have greater involvement.21 Hospitalists may be more likely to spend time on the inpatient wards given that is the primary site of their clinical activity. This increased presence may have contributed to more favorable evaluations for the hospitalist faculty, whereas the additional outpatient workload for nonhospitalist faculty may have reduced inpatient teaching opportunities, accounting for their lower teaching score. Included in the pediatrician category was the attribute of being a role model. In a study by Wright et al.,22 spending more than 25% of the time or 25 or more hours per week teaching and conducting rounds was independently associated with being considered an excellent role model. Again, the increased availability of the hospitalists on the inpatient wards may have led to more teaching opportunities, contributing to their higher score.

Our study had several limitations. First, it was a retrospective study conducted at a single institution with only 2 hospitalists. Although there were not statistical significant demographic differences between the 2 groups, this may simply reflect the small size of the sample in our study; therefore, the results may not be applicable to other academic institutions. Second, we retrospectively analyzed an evaluation form that had not been validated or specifically designed to compare 2 physician groups. Third, there were multiple statements in each category that students were asked to consider before scoring each attending on the parameters measured. Although hospitalists were rated higher in each category, there may have been individual characteristics within each category for which the nonhospitalist faculty performed better. Fourth, although hospitalists received higher average ratings than nonhospitalist faculty from third‐year medical students, it is important to emphasize this study measured students' attitudes and beliefs not specific educational outcomes. However, even though we cannot rule out the possibility that potentially confounding factors such as the personality of an attending physician influenced the results, prior studies have demonstrated that medical students make sophisticated judgments about teaching in the clinical setting.23, 24 It is unlikely that hospitalists at our institution were specifically selected to attend more months on a new inpatient service because they had a history of having more favorable teaching qualities because 1 of the 2 hospitalists had just finished residency training, and there were no significant demographic differences between the 2 groups. In a study examining trainee satisfaction in an internal medicine rotation 4 years after adoption of a hospitalist model, where nonhospitalist faculty attended based on their own interest and inpatient skill rather than as a requirement, Hauer et al.14 reported that trainees experienced more effective teaching and a more satisfying inpatient rotation when supervised by hospitalists. This suggests that hospitalists may possess or develop a specific inpatient knowledge base and teaching acumen over time that distinguishes them from nonhospitalists. There is evidence of accumulated experience leading to improved outcomes in the clinical setting for HIV infection,25 various surgical procedures,26 and hospitalist systems.27

In conclusion, this is the first study to evaluate the performance of hospitalists in the setting of a third‐year medical student pediatrics clerkship. Although third‐year medical students rate hospitalists at least as highly as nonhospitalist faculty, further studies are needed to reproduce this finding. In addition to the increased time spent on the wards with students and increased experience in caring for hospitalized patients, further studies should also examine the role that communication plays in clinical teaching. Also, the recent development of core competencies in hospital medicine28 may lead to the development of educational outcomes that can be objectively measured.

Acknowledgements

The authors thank David Mauger, PhD, from the Department of Health Evaluation Sciences at the Penn State College of Medicine for providing statistical analysis of the survey results.

In 1996 Wachter and Goldman anticipated the emergence of hospitalists,1 physicians who are responsible for the care of hospitalized patients in place of their primary care physicians. The number of physicians who identify themselves as hospitalists has grown rapidly since 1996 and is currently estimated to be 10,00012,000, with the potential to reach as high as 30,000 in the next decade.2 This growth includes academic medical centers. In surveys of chairs of internal medicine and pediatric departments, 50% have hospitalists employed at their institutions.3, 4

Hospitalists in academic institutions are playing an increasingly prominent role in the medical education of both residents and medical students. The implications of adopting a hospitalist model on medical education has been discussed.57 Despite such concerns as fragmented continuity of care; decreased exposure to primary care physicians, subspecialists and physician‐scientists; reduced autonomy; and fewer educational opportunities to observe the natural histories of illnesses because of improved efficiency,57 the overall impact of hospitalists on medical and resident education has generally been favorable.818 Internal medicine residents have rated the teaching skills of hospitalists comparable to traditional academic physicians,8, 9 and believe the addition of hospitalists has contributed to an improved educational experience.10, 11, 14 In addition, a survey of third‐year medical students at a single academic teaching hospital concluded that hospitalists were able to provide at least as positive an educational experience during their inpatient medicine rotations as highly rated nonhospitalist teaching faculty.13

The role of hospitalists as educators in pediatrics has been studied much less. Pediatric resident satisfaction has improved in institutions that have used a hospitalist model.1618 In another study, hospitalists were rated by pediatric residents as more effective teachers than nonhospitalists.15 Because we are unaware of any study that has evaluated hospitalists in the education of medical students during their inpatient pediatric rotation, the purpose of our study was to compare hospitalist and nonhospitalist faculty on the educational experience of third‐year medical students during the inpatient portion of their pediatric clerkships at a single university children's hospital.

METHODS

Study Design

We conducted a retrospective study using evaluations of third‐year medical students comparing hospitalist and nonhospitalist faculty during the inpatient portions of their pediatrics clerkships at a single academic children's hospital over a 15‐month period (July 1999September 2000).

Setting and Sample

We conducted our study at Penn State Children's Hospital (PSCH), a 120‐bed tertiary‐care facility within the 504‐bed Hershey Medical Center, the main teaching hospital affiliated with the Penn State College of Medicine, Hershey, Pennsylvania. The pediatric hospitalist program commenced on July 1, 1999, and during the 15‐month study period the hospitalist staff consisted of 2 physicians who attended a total of 8 months, whereas the nonhospitalist staff consisted of 4 academic general pediatricians and 4 academic pediatric subspecialists who attended the remaining 7 months.

The inpatient clinical responsibilities of both groups of physicians during each month were similar. Both groups of physicians conducted daily rounds with a team that included a senior resident (postgraduate year 3), 2 to 4 interns (postgraduate year 1), 1 acting intern (fourth‐year medical student), and 2 to 4 third‐year medical students. This team was responsible for all admissions to the general pediatrics service, which averages 100 admissions per month. Both the hospitalists and nonhospitalists had outpatient responsibilities during the time they served as inpatient attendings.

During the 15‐month study period, 131 students completed their third‐year pediatrics clerkships. Students at the Penn State College of Medicine may complete their pediatrics clerkship at PSCH or at one of several alternative sites. Because of variability in the structure of the rotation from site to site, it was considered valid only to analyze evaluations completed by students who rotated at PSCH. Sixty‐seven students rotated at PSCH during the study period. Students spent 3 weeks of the 6‐week rotation on the inpatient general pediatrics service. The remaining 3 weeks occurred in multiple outpatient pediatric practice settings and in the newborn nursery. During the 3 weeks the students spent on the inpatient service they did not have outpatient clinic responsibilities, so they did not interact with either the hospitalists or nonhospitalists in the outpatient setting. At the end of the rotation, students were asked to rate the effectiveness of the faculty as teachers, pediatricians, and student advocates and overall on a 4‐point scale (1 = inadequate; 2 = adequate; 3 = very good; 4 = excellent). Students were also asked to evaluate 7 components of the clerkship on the same 4‐point scale (Table 1). Finally, students were asked to provide additional written comments in an unstructured format.

Results of Third‐Year Medical Student Survey at Penn State University Children's Hospital
Evaluation itemHospitalist mean score (32 evaluations)Nonhospitalist mean score (35 evaluations)P valueNo. of evaluations rated adequate or inadequate (%)b
HospitalistNonhospitalist
  • Student responses based on a 4‐point scale (1 = inadequate, 2 = adequate, 3 = very good, 4 = excellent)

  • Statistically significant response (P < .05)

  • Adequate and inadequate responses were not calculated in the remaining evaluation items, as hospitalists and nonhospitalists did not have specific responsibilities in these areas.

  • Students were to consider the following skills in rating this category: knowledge, effectiveness of instruction, and intellectual stimulation.

  • Students were to consider the following skills in rating this category: pediatric knowledge, patient management, and role model.

  • Students were to consider the following skills in rating this category: availability to students, supervision of students, interest in students, and guidance of students.

Effectiveness as teacherc3.872.91< .001a1 (2.9)13 (40.6)
Effectiveness as pediatriciand3.943.25< .001a0 (0.0)5 (15.6)
Effectiveness as student advocatee3.762.97< .001a2 (5.7)13 (40.6)
Overall evaluation3.933.06< .001a0 (0.0)10 (31.3)
Ward rounds3.152.58< .006a5 (15.6)12 (37.5)
Morning report3.163.140.923  
Sick newborn2.792.600.518  
Well newborn2.893.130.211  
Outpatient department clinics2.963.060.425  
Private physician's office2.973.010.794  
Noon conference3.033.130.512  

After reviewing the literature concerning faculty evaluation forms and their components, an evaluation form was created for students to indicate their reactions to clerkship components. All the medical students' faculty evaluations were anonymous, and the faculty was not able to review student evaluations prior to assigning grades. Students were required to turn in an evaluation at the end of their rotations. The study was limited to 15 months, as the format of the evaluation form was changed after September 2000 and the general pediatrics service was in the process of transitioning to an exclusively hospitalist‐run service, thereby limiting the number of nonhospitalists available as a comparison group. Demographic characteristics of the hospitalist and nonhospitalist faculty were collected from a faculty database. The study was approved by the Penn State Milton S. Hershey Medical Center's Institutional Review Board.

Statistics and Analysis

For all questions, a Wilcoxon rank sum test was used to evaluate whether the responses for nonhospitalists were different than those for hospitalists. Differences in response by group whose 2‐tailed P values were less than .05 were considered statistically significant. All analyses were performed using the SAS statistical software, version 8.2 (SAS Institute Inc., Cary, NC).

RESULTS

All 67 of the students who completed a pediatrics clerkship at PSCH returned evaluation forms, which were the data for further analysis. Thirty‐five students rotated with the hospitalist faculty, and 32 students rotated with the nonhospitalist faculty. There were no significant demographic differences between the hospitalist and nonhospitalist faculty in age, sex, academic rank, specialty, and years since completing training (Table 2). All the hospitalist faculty fulfilled the definition of a hospitalist,2 whereas none of the physicians in the nonhospitalist group did.

Demographic Characteristics of Hospitalist and Nonhospitalist Faculty
CharacteristicHospitalists (n = 2)Nonhospitalists (n = 8)P value
Age, mean (range)36.0 (3141)46.5 (3063)0.30
Male/Female1/16/20.95
Academic rank   
Instructor01 
Assistant professor23 
Associate professor000.56
Professor04 
Specialty   
General pediatrics14 
Nephrology11 
Genetics010.95
Infectious ciseases01 
Rheumatology01 
Years since training, mean (range)4.0 (08)13.8 (030)0.43

The hospitalists were rated significantly higher than the nonhospitalist faculty in all 4 of the attending characteristics measured (Table 1): teaching effectiveness (3.87 vs. 2.91; P < .0001), effectiveness as a pediatrician (3.94 vs. 3.25; P < .001), student advocacy effectiveness (3.76 vs. 2.97; P < .0001), and overall evaluation (3.93 vs. 3.06; P < .001).

Analysis of specific aspects of the rotation showed the only feature that hospitalists were rated significantly higher on was quality of ward rounds (3.15 vs. 2.58, P < .006). There was no significant difference between the hospitalists and nonhospitalists on features that were not specifically part of the inpatient rotation, including various conferences, outpatient clinics, and newborn care (Table 1).

DISCUSSION

Our study demonstrates that pediatric hospitalists had a positive impact on the overall educational experience of third‐year medical students during the inpatient portions of their pediatrics clerkships. Hospitalists were rated more favorably than nonhospitalists as teachers, as pediatricians, as student advocates, and overall. Medical students also rated the value of ward rounds more favorably when hospitalists conducted them. In addition, higher percentages of nonhospitalists than hospitalists were rated as adequate or inadequate for the above items. When other aspects of the clerkship were analyzed, there were no statistically significant differences between the students who rotated with hospitalists and the students who rotated with nonhospitalists. This suggests that the higher scores for hospitalists were specifically related to their interactions with students, rather than with an overall more positive view of the rotation.

It has been suggested that forces promoting the use of hospitalists in adult medicine are even more persuasive in the pediatric population, as the difference in severity of illness between the inpatient and outpatient setting is greater, and the average pediatrician has less experience than the average internist in managing hospitalized patients.19 In a recent systematic review of the literature, Landrigan et al.20 reported that 6 of 7 studies demonstrated hospitalist systems had decreased hospital length of stay compared to systems in which a primary pediatrician served as the physician of record. This improved efficiency, if combined with the pressure to see more patients while trying to balance teaching and research demands, may have a negative impact on the quality of medical education.

Several factors may have contributed to the students' satisfaction with hospitalists. Studies have demonstrated that students rate clinical teachers more favorably with whom they have greater involvement.21 Hospitalists may be more likely to spend time on the inpatient wards given that is the primary site of their clinical activity. This increased presence may have contributed to more favorable evaluations for the hospitalist faculty, whereas the additional outpatient workload for nonhospitalist faculty may have reduced inpatient teaching opportunities, accounting for their lower teaching score. Included in the pediatrician category was the attribute of being a role model. In a study by Wright et al.,22 spending more than 25% of the time or 25 or more hours per week teaching and conducting rounds was independently associated with being considered an excellent role model. Again, the increased availability of the hospitalists on the inpatient wards may have led to more teaching opportunities, contributing to their higher score.

Our study had several limitations. First, it was a retrospective study conducted at a single institution with only 2 hospitalists. Although there were not statistical significant demographic differences between the 2 groups, this may simply reflect the small size of the sample in our study; therefore, the results may not be applicable to other academic institutions. Second, we retrospectively analyzed an evaluation form that had not been validated or specifically designed to compare 2 physician groups. Third, there were multiple statements in each category that students were asked to consider before scoring each attending on the parameters measured. Although hospitalists were rated higher in each category, there may have been individual characteristics within each category for which the nonhospitalist faculty performed better. Fourth, although hospitalists received higher average ratings than nonhospitalist faculty from third‐year medical students, it is important to emphasize this study measured students' attitudes and beliefs not specific educational outcomes. However, even though we cannot rule out the possibility that potentially confounding factors such as the personality of an attending physician influenced the results, prior studies have demonstrated that medical students make sophisticated judgments about teaching in the clinical setting.23, 24 It is unlikely that hospitalists at our institution were specifically selected to attend more months on a new inpatient service because they had a history of having more favorable teaching qualities because 1 of the 2 hospitalists had just finished residency training, and there were no significant demographic differences between the 2 groups. In a study examining trainee satisfaction in an internal medicine rotation 4 years after adoption of a hospitalist model, where nonhospitalist faculty attended based on their own interest and inpatient skill rather than as a requirement, Hauer et al.14 reported that trainees experienced more effective teaching and a more satisfying inpatient rotation when supervised by hospitalists. This suggests that hospitalists may possess or develop a specific inpatient knowledge base and teaching acumen over time that distinguishes them from nonhospitalists. There is evidence of accumulated experience leading to improved outcomes in the clinical setting for HIV infection,25 various surgical procedures,26 and hospitalist systems.27

In conclusion, this is the first study to evaluate the performance of hospitalists in the setting of a third‐year medical student pediatrics clerkship. Although third‐year medical students rate hospitalists at least as highly as nonhospitalist faculty, further studies are needed to reproduce this finding. In addition to the increased time spent on the wards with students and increased experience in caring for hospitalized patients, further studies should also examine the role that communication plays in clinical teaching. Also, the recent development of core competencies in hospital medicine28 may lead to the development of educational outcomes that can be objectively measured.

Acknowledgements

The authors thank David Mauger, PhD, from the Department of Health Evaluation Sciences at the Penn State College of Medicine for providing statistical analysis of the survey results.

References
  1. Wachter RM,Goldman L.The emerging role of “hospitalists” in the American health care system.N Engl J Med.1996;335:514517.
  2. Society of Hospital Medicine. Frequently asked questions. Available at: http://www.hospitalmedicine.org/AM/Template.cfm?Section=FAQs75:S346.
  3. Srivastava R,Landrigan C,Gidwani P,Harary OH,Muret‐Wagstaff S,Homer CJ.Pediatric hospitalists in Canada and the United States: a survey of pediatric academic department chairs.Ambul Pediatr.2001;1:338339.
  4. Goldman L.The impact of hospitalists on medical education and the academic health system.Ann Intern Med.1999;130:364367.
  5. Whitcomb WF,Nelson JR.The role of hospitalists in medical education.Am J Med.1999;107:305309.
  6. Hauer KE,Wachter RM.Implications of the hospitalist model for medical students' education.Acad. Med.2001;76:324330.
  7. Wachter RM,Katz P,Showstack J,Bindman AB,Goldman L.Reorganizing an academic medical service: impact on cost, quality, patient satisfaction, and education.JAMA.1998;279:15601565.
  8. Kripalani S,Pope AC,Rask K, et al.Hospitalists as teachers: how do they compare to subspecialty and general medicine faculty.J Gen Intern Med.2004;19:815.
  9. Brown MD,Halpert A,McKean S,Sussman A,Dzau VJ.Assessing the value of hospitalists to academic health centers: Brigham and Women's Hospital and Harvard Medical School.Am J Med.1999;106:134137.
  10. Chung P,Morrison J,Jin L,Levinson W,Humphrey H,Meltzer D.Resident satisfaction on an academic hospitalist service: time to teach.Am J Med.2002;112:597601.
  11. Kulaga ME,Charney P,O'Mahony SP,Cleary JP,McClung TM,Schildkamp DE,Mazur EM.The positive impact of initiation of hospitalist clinician educators: resource utilization and medical resident education.J Gen Intern Med.2004;19:293301.
  12. Hunter AJ,Desai SS,Harrison RA,Chan BKS.Medical student evaluation of the quality of hospitalist and nonhospitalist teaching faculty on inpatient medicine rotations.Acad Med.2004;79:7882.
  13. Hauer KE,Wachter RM,McCulloch CE,Woo GA,Auerbach AA.Effects of hospitalist attending physicians on trainee satisfaction with teaching and with internal medicine rotations.Arch Intern Med.2004;164:18661871.
  14. Landrigan CP,Muret‐Wagstaff S,Chiang VW,Nigrin DJ,Goldmann DA,Finkelstein JA.Effect of a pediatric hospitalist system on housestaff education and experience.Arch Pediatr Adolesc Med.2002;156:877883.
  15. Wilson SD.Employing hospitalists to improve residents' inpatient learning.Acad Med.2001;76:556.
  16. Ponitz K,Mortimer J,Berman B.Establishing a pediatric hospitalist program at an academic medical center.Clin Pediatr.2000;39:221227.
  17. Ogershok PR,Li X,Palmer HC,Moore RS,Weisse ME,Ferrari ND.Restructuring an academic pediatric inpatient service using concepts developed by hospitalists.Clin Pediatr.2001;40:653660.
  18. Bellet PS,Wachter RM.The hospitalist movement and its implications for the care of hospitalized children.Pediatrics.1999;103:473477.
  19. Landrigan CP,Conway PH,Edwards S,Srivastava R.Pediatric hospitalists: a systematic review of the literature.Pediatrics.2006;117:17361744.
  20. Irby DM,Gillmore GM,Ramsey PG.Factors affecting ratings of clinical teachers by medical students and residents.J Med Educ.1987;62:17.
  21. Wright SM,Kern DE,Kolodner K,Howard DM,Brancati FL.Attributes of excellent attending‐physician role models.N Engl J Med.1998;339:19861993.
  22. Donnelly MB,Woolliscroft JO.Evaluation of clinical instructors by third‐year medical students.Acad Med.1989;64:159164.
  23. McLeod PJ,James CA,Abrahamowicz M.Clinical tutor evaluation: a 5‐year study by students on an in‐patient service and residents in an ambulatory care clinic.Med Educ.1993;27:4853.
  24. Kitahata MM,Koepsell TD,Deyo RA,Maxwell CL,Dodge WT,Wagner EH.Physicians' experience with the acquired immunodeficiency syndrome as a factor in patients' survival.N Engl J Med.1996;334:701706.
  25. Luft HS,Garnick DW,Mark DH,McPhee SJ.Hospital Volume, Physician Volume, and Patient Outcomes: Assessing the Evidence. Ann Arbor, MI: Health Administration Perspectives;1990.
  26. Meltzer D,Manning WG,Morrison J, et al.Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists.Ann Intern Med.2002;137:866874.
  27. Pistoria MJ,Amin AN,Dressler DD,McKean SCW,Budnitz TL, eds.The core competencies in hospital medicine: a framework for curriculum development by the Society of Hospital Medicine.J Hosp Med.2006;1(S1):167.
References
  1. Wachter RM,Goldman L.The emerging role of “hospitalists” in the American health care system.N Engl J Med.1996;335:514517.
  2. Society of Hospital Medicine. Frequently asked questions. Available at: http://www.hospitalmedicine.org/AM/Template.cfm?Section=FAQs75:S346.
  3. Srivastava R,Landrigan C,Gidwani P,Harary OH,Muret‐Wagstaff S,Homer CJ.Pediatric hospitalists in Canada and the United States: a survey of pediatric academic department chairs.Ambul Pediatr.2001;1:338339.
  4. Goldman L.The impact of hospitalists on medical education and the academic health system.Ann Intern Med.1999;130:364367.
  5. Whitcomb WF,Nelson JR.The role of hospitalists in medical education.Am J Med.1999;107:305309.
  6. Hauer KE,Wachter RM.Implications of the hospitalist model for medical students' education.Acad. Med.2001;76:324330.
  7. Wachter RM,Katz P,Showstack J,Bindman AB,Goldman L.Reorganizing an academic medical service: impact on cost, quality, patient satisfaction, and education.JAMA.1998;279:15601565.
  8. Kripalani S,Pope AC,Rask K, et al.Hospitalists as teachers: how do they compare to subspecialty and general medicine faculty.J Gen Intern Med.2004;19:815.
  9. Brown MD,Halpert A,McKean S,Sussman A,Dzau VJ.Assessing the value of hospitalists to academic health centers: Brigham and Women's Hospital and Harvard Medical School.Am J Med.1999;106:134137.
  10. Chung P,Morrison J,Jin L,Levinson W,Humphrey H,Meltzer D.Resident satisfaction on an academic hospitalist service: time to teach.Am J Med.2002;112:597601.
  11. Kulaga ME,Charney P,O'Mahony SP,Cleary JP,McClung TM,Schildkamp DE,Mazur EM.The positive impact of initiation of hospitalist clinician educators: resource utilization and medical resident education.J Gen Intern Med.2004;19:293301.
  12. Hunter AJ,Desai SS,Harrison RA,Chan BKS.Medical student evaluation of the quality of hospitalist and nonhospitalist teaching faculty on inpatient medicine rotations.Acad Med.2004;79:7882.
  13. Hauer KE,Wachter RM,McCulloch CE,Woo GA,Auerbach AA.Effects of hospitalist attending physicians on trainee satisfaction with teaching and with internal medicine rotations.Arch Intern Med.2004;164:18661871.
  14. Landrigan CP,Muret‐Wagstaff S,Chiang VW,Nigrin DJ,Goldmann DA,Finkelstein JA.Effect of a pediatric hospitalist system on housestaff education and experience.Arch Pediatr Adolesc Med.2002;156:877883.
  15. Wilson SD.Employing hospitalists to improve residents' inpatient learning.Acad Med.2001;76:556.
  16. Ponitz K,Mortimer J,Berman B.Establishing a pediatric hospitalist program at an academic medical center.Clin Pediatr.2000;39:221227.
  17. Ogershok PR,Li X,Palmer HC,Moore RS,Weisse ME,Ferrari ND.Restructuring an academic pediatric inpatient service using concepts developed by hospitalists.Clin Pediatr.2001;40:653660.
  18. Bellet PS,Wachter RM.The hospitalist movement and its implications for the care of hospitalized children.Pediatrics.1999;103:473477.
  19. Landrigan CP,Conway PH,Edwards S,Srivastava R.Pediatric hospitalists: a systematic review of the literature.Pediatrics.2006;117:17361744.
  20. Irby DM,Gillmore GM,Ramsey PG.Factors affecting ratings of clinical teachers by medical students and residents.J Med Educ.1987;62:17.
  21. Wright SM,Kern DE,Kolodner K,Howard DM,Brancati FL.Attributes of excellent attending‐physician role models.N Engl J Med.1998;339:19861993.
  22. Donnelly MB,Woolliscroft JO.Evaluation of clinical instructors by third‐year medical students.Acad Med.1989;64:159164.
  23. McLeod PJ,James CA,Abrahamowicz M.Clinical tutor evaluation: a 5‐year study by students on an in‐patient service and residents in an ambulatory care clinic.Med Educ.1993;27:4853.
  24. Kitahata MM,Koepsell TD,Deyo RA,Maxwell CL,Dodge WT,Wagner EH.Physicians' experience with the acquired immunodeficiency syndrome as a factor in patients' survival.N Engl J Med.1996;334:701706.
  25. Luft HS,Garnick DW,Mark DH,McPhee SJ.Hospital Volume, Physician Volume, and Patient Outcomes: Assessing the Evidence. Ann Arbor, MI: Health Administration Perspectives;1990.
  26. Meltzer D,Manning WG,Morrison J, et al.Effects of physician experience on costs and outcomes on an academic general medicine service: results of a trial of hospitalists.Ann Intern Med.2002;137:866874.
  27. Pistoria MJ,Amin AN,Dressler DD,McKean SCW,Budnitz TL, eds.The core competencies in hospital medicine: a framework for curriculum development by the Society of Hospital Medicine.J Hosp Med.2006;1(S1):167.
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Third‐year medical students' evaluation of hospitalist and nonhospitalist faculty during the inpatient portion of their pediatrics clerkships
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A new home awaits the hospitalist

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A new home awaits the hospitalist

In this issue of the Journal of Hospital Medicine, Simon et al.1 provide the first report of pediatric hospitalist comanagement of patients undergoing spinal fusion surgery. In this retrospective cohort study, 14 of 115 patients were comanaged by a pediatric hospitalist. The primary outcomes of the study were length of stay and variability in length of stay. Prior to the initiation of hospitalist comanagement service, all patients were managed preoperatively by a spine surgery nurse and aided by medical subspecialists and other allied health professionals (nutritionists, respiratory therapists, physical therapists, social workers). After the intervention, patients with the most complex medical disease were assigned to comanagement by a pediatric hospitalist. When compared to a historical control of patients with similar medical complexity but not comanaged by hospitalists, the length of stay was reduced by 2.4 days (8.6 vs. 6.2 days). The variability in mean length of stay was also reduced.

This study follows on the heels of 3 important studies addressing the utility of hospitalists in the comanagement of surgical patients. The HOT (Hospitalist Orthopedic Team) trial was a randomized controlled trial assessing the effect of hospitalists on the management of patients undergoing elective hip and knee arthroplasty.2 There was no effect on length of stay or patient outcomes, though the comanagement model did decrease minor postoperative medical complications and improve physician and nurse satisfaction. Macpherson et al. conducted a retrospective trial where an internist joined a cardiothoracic surgery service at a tertiary‐care center.3 They found a decrease in overall mortality and resource utilization such as labs testing and consultations. There was significant reduction in the length of stay and number of x‐rays performed. The third study, by Jaffer et al.,4 showed that an outpatient, preoperative evaluation clinic staffed by hospitalists at a large tertiary‐care center provides a practical model for managing preoperative patients and may be associated with a low rate of postoperative pulmonary complications.

The study by Simon et al. in this issue of the journal has limitations. It is a retrospective cohort trial, and like all such study designs, the validity of the results is subject to confounding. Severity of patient medical disease, intraoperative complications, and advances in surgical technique are examples. While the authors did everything possible to minimize the effect of confounding, it remains a limitation of the study. The study also enrolled only 14 patients in the comanagement group, and this limited any stratification or subgroup analysis to offset known confounders. Patients assigned to the hospitalist comanagement service were by design more medically complex than other spinal fusion patients, and generalizing the results of this trial to all spinal fusion patients may not be possible.

From the study's limitations, however, comes great insight into the role of the hospitalist in surgical comanagement. It is clear from the aforementioned studies that there is a role for the hospitalist in comanagement of surgical patients. While the evidence is conflicting, there are scenarios in which comanagement improves efficiency and quality of care. Yet it is also possible that hospitalist comanagement is not ideal for all surgical patients. The HOT trial did not show benefits in length‐of‐stay reduction or patient mortality because the patients were homogenous in their complexity and pre‐ and postoperative care was protocol driven. Length of stay was limited by accessibility of rehabilitation facilities after discharge and not the efficiency of medical care in the hospital. The study in this issue of the Journal of Hospital Medicine selectively included patients with the highest complexity of medical disease, and there was a reduction in length of stay. Both trials suggest that the greatest potential benefit for augmenting efficiency and outcomes with hospitalist comanagement may be predicated on the complexity of the patients involved and the surgical system through which they will receive care.

The next step in assessing hospitalist comanagement should not be a hunt‐and‐peck exercise to stumble on the surgical procedures that show benefit from comanagement. Rather, the prudent next step is to follow the lead of Simon et al. and others3, 4 in trying to identify those surgical patients who represent the greatest medical complexity or have the most variability in their preoperative and postoperative medical care. These are the patients for whom the hospitalist can effect the greatest benefit and the services for which the hospitalist can best augment efficiency. High‐risk procedures, patients with multiple comorbitities or elevated preoperative risks, and surgical procedures without defined pre‐ and postoperative protocols would appear to be the ideal candidates for hospitalist comanagement.

As the discussion of hospital comanagement progresses, it is important to recognize comanagement as a paradigm shift. Surgical comanagement is not merely medical consultation. To be successful, the role of the hospitalist in comanaging surgical patients must be clearly defined as advancing postoperative care as much as it is in assessing preoperative risk. As a comanager, a hospitalist must actively manage preexisting and newly developed medical issues rather than just make recommendations for the surgical team.

The hospitalist must also be more than a discharge coordinator postoperatively; investing in hospitalists merely for discharge planning is a poor use of resources both from a financial and an opportunity‐cost perspective. The paradigm of comanagement is not foreign, however, and hospitalists are likely to prosper by learning from the experience of our nephrology and hepatology colleagues, who have successfully found collaborative roles in improving patient care on renal and liver transplant services. The success of these services is due to the precisely defined roles for the internist and the surgeon and because the complexity of the patient being managed warrants continuity of expert consultation.

There is great potential for the hospitalist in surgical comanagement. In less than a decade, the focus of hospital‐based medical care has shifted from staffing a shift to improving the quality of the system through which patients traverse the hospital. The lessons hospitalists have learned in quality improvement and in augmenting systems of care are perfectly suited for application to surgical services. Hospitalist comanagement is right not only because it may offer improvement in a surgical patient's medical care, but also for the augmentation of quality improvement in surgical services that have yet to reap the benefits that have defined the excellence of hospitalist medicine. The next step is to embark on the road of prudent prospective research: identifying the patients, and the procedures, that have the greatest opportunity for improvement by hospitalist comanagement. And at the end of that road will be a new home for the hospitalist, assuming the role of the quality‐advocate for all aspects of hospital care: pediatric, medical, and surgical patients.

References
  1. Simon TD,Eilert R,Dickinson LM,Kempe A,Benefield E.Pediatric co‐management of spine fusion surgery patients.J Hosp Med.2007;2:2330.
  2. Huddleston JM,Long KH,Naessens JM, et al.;Hospitalist‐Orthopedic Team Trial Investigators.Medical and surgical comanagement after elective hip and knee arthroplasty: a randomized, controlled trial.Ann Intern Med.2004;141(1):2838.
  3. Macpherson DS,Parenti C,Nee J,Petzel RA,Ward H.An internist joins the surgery service: does comanagement make a difference?J Gen Intern Med.1994;9:440444.
  4. Jaffer AK,Brotman DJ,Sridharan ST, et al.Postoperative pulmonary complications: experience with an outpatient pre‐operative assessment program.J Clin Outcomes Manag.2005;12:505510.
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In this issue of the Journal of Hospital Medicine, Simon et al.1 provide the first report of pediatric hospitalist comanagement of patients undergoing spinal fusion surgery. In this retrospective cohort study, 14 of 115 patients were comanaged by a pediatric hospitalist. The primary outcomes of the study were length of stay and variability in length of stay. Prior to the initiation of hospitalist comanagement service, all patients were managed preoperatively by a spine surgery nurse and aided by medical subspecialists and other allied health professionals (nutritionists, respiratory therapists, physical therapists, social workers). After the intervention, patients with the most complex medical disease were assigned to comanagement by a pediatric hospitalist. When compared to a historical control of patients with similar medical complexity but not comanaged by hospitalists, the length of stay was reduced by 2.4 days (8.6 vs. 6.2 days). The variability in mean length of stay was also reduced.

This study follows on the heels of 3 important studies addressing the utility of hospitalists in the comanagement of surgical patients. The HOT (Hospitalist Orthopedic Team) trial was a randomized controlled trial assessing the effect of hospitalists on the management of patients undergoing elective hip and knee arthroplasty.2 There was no effect on length of stay or patient outcomes, though the comanagement model did decrease minor postoperative medical complications and improve physician and nurse satisfaction. Macpherson et al. conducted a retrospective trial where an internist joined a cardiothoracic surgery service at a tertiary‐care center.3 They found a decrease in overall mortality and resource utilization such as labs testing and consultations. There was significant reduction in the length of stay and number of x‐rays performed. The third study, by Jaffer et al.,4 showed that an outpatient, preoperative evaluation clinic staffed by hospitalists at a large tertiary‐care center provides a practical model for managing preoperative patients and may be associated with a low rate of postoperative pulmonary complications.

The study by Simon et al. in this issue of the journal has limitations. It is a retrospective cohort trial, and like all such study designs, the validity of the results is subject to confounding. Severity of patient medical disease, intraoperative complications, and advances in surgical technique are examples. While the authors did everything possible to minimize the effect of confounding, it remains a limitation of the study. The study also enrolled only 14 patients in the comanagement group, and this limited any stratification or subgroup analysis to offset known confounders. Patients assigned to the hospitalist comanagement service were by design more medically complex than other spinal fusion patients, and generalizing the results of this trial to all spinal fusion patients may not be possible.

From the study's limitations, however, comes great insight into the role of the hospitalist in surgical comanagement. It is clear from the aforementioned studies that there is a role for the hospitalist in comanagement of surgical patients. While the evidence is conflicting, there are scenarios in which comanagement improves efficiency and quality of care. Yet it is also possible that hospitalist comanagement is not ideal for all surgical patients. The HOT trial did not show benefits in length‐of‐stay reduction or patient mortality because the patients were homogenous in their complexity and pre‐ and postoperative care was protocol driven. Length of stay was limited by accessibility of rehabilitation facilities after discharge and not the efficiency of medical care in the hospital. The study in this issue of the Journal of Hospital Medicine selectively included patients with the highest complexity of medical disease, and there was a reduction in length of stay. Both trials suggest that the greatest potential benefit for augmenting efficiency and outcomes with hospitalist comanagement may be predicated on the complexity of the patients involved and the surgical system through which they will receive care.

The next step in assessing hospitalist comanagement should not be a hunt‐and‐peck exercise to stumble on the surgical procedures that show benefit from comanagement. Rather, the prudent next step is to follow the lead of Simon et al. and others3, 4 in trying to identify those surgical patients who represent the greatest medical complexity or have the most variability in their preoperative and postoperative medical care. These are the patients for whom the hospitalist can effect the greatest benefit and the services for which the hospitalist can best augment efficiency. High‐risk procedures, patients with multiple comorbitities or elevated preoperative risks, and surgical procedures without defined pre‐ and postoperative protocols would appear to be the ideal candidates for hospitalist comanagement.

As the discussion of hospital comanagement progresses, it is important to recognize comanagement as a paradigm shift. Surgical comanagement is not merely medical consultation. To be successful, the role of the hospitalist in comanaging surgical patients must be clearly defined as advancing postoperative care as much as it is in assessing preoperative risk. As a comanager, a hospitalist must actively manage preexisting and newly developed medical issues rather than just make recommendations for the surgical team.

The hospitalist must also be more than a discharge coordinator postoperatively; investing in hospitalists merely for discharge planning is a poor use of resources both from a financial and an opportunity‐cost perspective. The paradigm of comanagement is not foreign, however, and hospitalists are likely to prosper by learning from the experience of our nephrology and hepatology colleagues, who have successfully found collaborative roles in improving patient care on renal and liver transplant services. The success of these services is due to the precisely defined roles for the internist and the surgeon and because the complexity of the patient being managed warrants continuity of expert consultation.

There is great potential for the hospitalist in surgical comanagement. In less than a decade, the focus of hospital‐based medical care has shifted from staffing a shift to improving the quality of the system through which patients traverse the hospital. The lessons hospitalists have learned in quality improvement and in augmenting systems of care are perfectly suited for application to surgical services. Hospitalist comanagement is right not only because it may offer improvement in a surgical patient's medical care, but also for the augmentation of quality improvement in surgical services that have yet to reap the benefits that have defined the excellence of hospitalist medicine. The next step is to embark on the road of prudent prospective research: identifying the patients, and the procedures, that have the greatest opportunity for improvement by hospitalist comanagement. And at the end of that road will be a new home for the hospitalist, assuming the role of the quality‐advocate for all aspects of hospital care: pediatric, medical, and surgical patients.

In this issue of the Journal of Hospital Medicine, Simon et al.1 provide the first report of pediatric hospitalist comanagement of patients undergoing spinal fusion surgery. In this retrospective cohort study, 14 of 115 patients were comanaged by a pediatric hospitalist. The primary outcomes of the study were length of stay and variability in length of stay. Prior to the initiation of hospitalist comanagement service, all patients were managed preoperatively by a spine surgery nurse and aided by medical subspecialists and other allied health professionals (nutritionists, respiratory therapists, physical therapists, social workers). After the intervention, patients with the most complex medical disease were assigned to comanagement by a pediatric hospitalist. When compared to a historical control of patients with similar medical complexity but not comanaged by hospitalists, the length of stay was reduced by 2.4 days (8.6 vs. 6.2 days). The variability in mean length of stay was also reduced.

This study follows on the heels of 3 important studies addressing the utility of hospitalists in the comanagement of surgical patients. The HOT (Hospitalist Orthopedic Team) trial was a randomized controlled trial assessing the effect of hospitalists on the management of patients undergoing elective hip and knee arthroplasty.2 There was no effect on length of stay or patient outcomes, though the comanagement model did decrease minor postoperative medical complications and improve physician and nurse satisfaction. Macpherson et al. conducted a retrospective trial where an internist joined a cardiothoracic surgery service at a tertiary‐care center.3 They found a decrease in overall mortality and resource utilization such as labs testing and consultations. There was significant reduction in the length of stay and number of x‐rays performed. The third study, by Jaffer et al.,4 showed that an outpatient, preoperative evaluation clinic staffed by hospitalists at a large tertiary‐care center provides a practical model for managing preoperative patients and may be associated with a low rate of postoperative pulmonary complications.

The study by Simon et al. in this issue of the journal has limitations. It is a retrospective cohort trial, and like all such study designs, the validity of the results is subject to confounding. Severity of patient medical disease, intraoperative complications, and advances in surgical technique are examples. While the authors did everything possible to minimize the effect of confounding, it remains a limitation of the study. The study also enrolled only 14 patients in the comanagement group, and this limited any stratification or subgroup analysis to offset known confounders. Patients assigned to the hospitalist comanagement service were by design more medically complex than other spinal fusion patients, and generalizing the results of this trial to all spinal fusion patients may not be possible.

From the study's limitations, however, comes great insight into the role of the hospitalist in surgical comanagement. It is clear from the aforementioned studies that there is a role for the hospitalist in comanagement of surgical patients. While the evidence is conflicting, there are scenarios in which comanagement improves efficiency and quality of care. Yet it is also possible that hospitalist comanagement is not ideal for all surgical patients. The HOT trial did not show benefits in length‐of‐stay reduction or patient mortality because the patients were homogenous in their complexity and pre‐ and postoperative care was protocol driven. Length of stay was limited by accessibility of rehabilitation facilities after discharge and not the efficiency of medical care in the hospital. The study in this issue of the Journal of Hospital Medicine selectively included patients with the highest complexity of medical disease, and there was a reduction in length of stay. Both trials suggest that the greatest potential benefit for augmenting efficiency and outcomes with hospitalist comanagement may be predicated on the complexity of the patients involved and the surgical system through which they will receive care.

The next step in assessing hospitalist comanagement should not be a hunt‐and‐peck exercise to stumble on the surgical procedures that show benefit from comanagement. Rather, the prudent next step is to follow the lead of Simon et al. and others3, 4 in trying to identify those surgical patients who represent the greatest medical complexity or have the most variability in their preoperative and postoperative medical care. These are the patients for whom the hospitalist can effect the greatest benefit and the services for which the hospitalist can best augment efficiency. High‐risk procedures, patients with multiple comorbitities or elevated preoperative risks, and surgical procedures without defined pre‐ and postoperative protocols would appear to be the ideal candidates for hospitalist comanagement.

As the discussion of hospital comanagement progresses, it is important to recognize comanagement as a paradigm shift. Surgical comanagement is not merely medical consultation. To be successful, the role of the hospitalist in comanaging surgical patients must be clearly defined as advancing postoperative care as much as it is in assessing preoperative risk. As a comanager, a hospitalist must actively manage preexisting and newly developed medical issues rather than just make recommendations for the surgical team.

The hospitalist must also be more than a discharge coordinator postoperatively; investing in hospitalists merely for discharge planning is a poor use of resources both from a financial and an opportunity‐cost perspective. The paradigm of comanagement is not foreign, however, and hospitalists are likely to prosper by learning from the experience of our nephrology and hepatology colleagues, who have successfully found collaborative roles in improving patient care on renal and liver transplant services. The success of these services is due to the precisely defined roles for the internist and the surgeon and because the complexity of the patient being managed warrants continuity of expert consultation.

There is great potential for the hospitalist in surgical comanagement. In less than a decade, the focus of hospital‐based medical care has shifted from staffing a shift to improving the quality of the system through which patients traverse the hospital. The lessons hospitalists have learned in quality improvement and in augmenting systems of care are perfectly suited for application to surgical services. Hospitalist comanagement is right not only because it may offer improvement in a surgical patient's medical care, but also for the augmentation of quality improvement in surgical services that have yet to reap the benefits that have defined the excellence of hospitalist medicine. The next step is to embark on the road of prudent prospective research: identifying the patients, and the procedures, that have the greatest opportunity for improvement by hospitalist comanagement. And at the end of that road will be a new home for the hospitalist, assuming the role of the quality‐advocate for all aspects of hospital care: pediatric, medical, and surgical patients.

References
  1. Simon TD,Eilert R,Dickinson LM,Kempe A,Benefield E.Pediatric co‐management of spine fusion surgery patients.J Hosp Med.2007;2:2330.
  2. Huddleston JM,Long KH,Naessens JM, et al.;Hospitalist‐Orthopedic Team Trial Investigators.Medical and surgical comanagement after elective hip and knee arthroplasty: a randomized, controlled trial.Ann Intern Med.2004;141(1):2838.
  3. Macpherson DS,Parenti C,Nee J,Petzel RA,Ward H.An internist joins the surgery service: does comanagement make a difference?J Gen Intern Med.1994;9:440444.
  4. Jaffer AK,Brotman DJ,Sridharan ST, et al.Postoperative pulmonary complications: experience with an outpatient pre‐operative assessment program.J Clin Outcomes Manag.2005;12:505510.
References
  1. Simon TD,Eilert R,Dickinson LM,Kempe A,Benefield E.Pediatric co‐management of spine fusion surgery patients.J Hosp Med.2007;2:2330.
  2. Huddleston JM,Long KH,Naessens JM, et al.;Hospitalist‐Orthopedic Team Trial Investigators.Medical and surgical comanagement after elective hip and knee arthroplasty: a randomized, controlled trial.Ann Intern Med.2004;141(1):2838.
  3. Macpherson DS,Parenti C,Nee J,Petzel RA,Ward H.An internist joins the surgery service: does comanagement make a difference?J Gen Intern Med.1994;9:440444.
  4. Jaffer AK,Brotman DJ,Sridharan ST, et al.Postoperative pulmonary complications: experience with an outpatient pre‐operative assessment program.J Clin Outcomes Manag.2005;12:505510.
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Editorial

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One year done & moving onward

One year of the Journal of Hospital Medicine is done, and we now embark on our second with this first issue of volume 2. Before moving on, I heartily thank all the authors who contributed their manuscripts to the Journal of Hospital Medicine (JHM), bravely investing in this new academic periodical. A remarkable 284 manuscripts have been submitted since we first opened the JHM Web site, 197 of them during 2006. This clearly reflects the robust demand by hospitalists and their colleagues for original research and relevant clinical reviews about our evolving specialty of hospital medicine. I probably should not be surprised that this demand exists among the 15,000‐plus hospitalists in America and the 6000‐plus members of the Society of Hospital Medicine. Regardless, I am ineffably humbled by the enthusiasm and energy of all the contributors.

Understandably, this volume of submissions, exceeding our projections by nearly 50%, required yeoman's work by our associate editors and reviewers. On page 55 we list the 203 reviewers who donated their time and acumen to assure the quality of our publication. Many reviewed more than 4 articles during the year. Our associate editors deserve particular appreciation and gratitude for their willingness to donate extraordinary amounts of time and effort to ensure the success of JHMVincent Chang from Boston Children's Hospital, Scott Flanders from the University of Michigan, Karen Hauer from the University of California, San Francisco, Jean Kutner from the University of Colorado, James Pile from Cleveland MetroHealth, and Kaveh Shojania from the University of Ottawa. Additionally, the energetic assistant editors have supported them and me with frequent reviews, article submissions, and creative ideas for improving the journal. Finally, our auspicious editorial board has proffered sage guidance, and many of its members have also submitted manuscripts and participated in reviewing articles.

Moving forward we expect continued growth, as both the submitted articles and demand for the journal are being recognized. At 7:29 a.m. on November 30, 2006, Vickie Thaw (Vice President and Publisher, John Wiley & Sons, Inc.) called me to report that the National Library of Medicine validated all our efforts. The Journal of Hospital Medicine had been selected for indexing and inclusion in the National Library of Medicine's MEDLINE (Medical Literature Analysis and Retrieval System Online). The primary component of PubMed, MEDLINE is a bibliographic database containing approximately 13 million references to journal articles on medicine, nursing, dentistry, veterinary medicine, health care systems, and preclinical sciences dating to the mid‐1960s. With this approval, hospital medicine has achieved another milestone in its evolution into a new specialty.

We now hope to respond to the robust interest in clinical materials as well as to continue publication of original research. To achieve our aim of increasing the amount of clinically relevant content for practicing hospitalists, authors are encouraged to submit to JHM case reports, clinical updates, and clinical images that convey novel or underappreciated teaching points. Teaching points may be purely clinical and may focus on clinical pearls or unusual presentations of well‐known diseases, although submission of straightforward presentations of rare diseases is discouraged. Alternatively, manuscripts may involve succinct case‐based descriptions of innovations, quality improvementrelated issues, or medical errors. Submitted case reports should be less than 800 words and should contain a maximum of 5 references and no more than 1 table or figure. Case reports should not include an abstract. Submission of the case report and review type should be avoided. Instead, we seek formal clinical updates of no more than 2000 words that present important aspects of a case along with new research findings and citations from the literature that change what has historically been the standard of delivery of care. Finally, we continue to seek cases most appropriate for the Hospital Images Dx section, edited by Paul Aronowitz. They should be submitted with that designation and have fewer than 150 words. These 3 categories are identified on our Manuscript Central website (http://mc.manuscriptcentral.com/jhm).

Again, thanks to all of you for making the launch of the Journal of Hospital Medicine an unqualified success. We look forward to your continued participation as we grow as the premier journal for the specialty of hospital medicine.

P.S. Sadly, one of our superstar associate editors, Kaveh Shojania, is stepping aside, and we sincerely express thanks for his terrific contributions. We welcome suggestions for an alternative to fulfill his responsibilities.

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Journal of Hospital Medicine - 2(1)
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One year of the Journal of Hospital Medicine is done, and we now embark on our second with this first issue of volume 2. Before moving on, I heartily thank all the authors who contributed their manuscripts to the Journal of Hospital Medicine (JHM), bravely investing in this new academic periodical. A remarkable 284 manuscripts have been submitted since we first opened the JHM Web site, 197 of them during 2006. This clearly reflects the robust demand by hospitalists and their colleagues for original research and relevant clinical reviews about our evolving specialty of hospital medicine. I probably should not be surprised that this demand exists among the 15,000‐plus hospitalists in America and the 6000‐plus members of the Society of Hospital Medicine. Regardless, I am ineffably humbled by the enthusiasm and energy of all the contributors.

Understandably, this volume of submissions, exceeding our projections by nearly 50%, required yeoman's work by our associate editors and reviewers. On page 55 we list the 203 reviewers who donated their time and acumen to assure the quality of our publication. Many reviewed more than 4 articles during the year. Our associate editors deserve particular appreciation and gratitude for their willingness to donate extraordinary amounts of time and effort to ensure the success of JHMVincent Chang from Boston Children's Hospital, Scott Flanders from the University of Michigan, Karen Hauer from the University of California, San Francisco, Jean Kutner from the University of Colorado, James Pile from Cleveland MetroHealth, and Kaveh Shojania from the University of Ottawa. Additionally, the energetic assistant editors have supported them and me with frequent reviews, article submissions, and creative ideas for improving the journal. Finally, our auspicious editorial board has proffered sage guidance, and many of its members have also submitted manuscripts and participated in reviewing articles.

Moving forward we expect continued growth, as both the submitted articles and demand for the journal are being recognized. At 7:29 a.m. on November 30, 2006, Vickie Thaw (Vice President and Publisher, John Wiley & Sons, Inc.) called me to report that the National Library of Medicine validated all our efforts. The Journal of Hospital Medicine had been selected for indexing and inclusion in the National Library of Medicine's MEDLINE (Medical Literature Analysis and Retrieval System Online). The primary component of PubMed, MEDLINE is a bibliographic database containing approximately 13 million references to journal articles on medicine, nursing, dentistry, veterinary medicine, health care systems, and preclinical sciences dating to the mid‐1960s. With this approval, hospital medicine has achieved another milestone in its evolution into a new specialty.

We now hope to respond to the robust interest in clinical materials as well as to continue publication of original research. To achieve our aim of increasing the amount of clinically relevant content for practicing hospitalists, authors are encouraged to submit to JHM case reports, clinical updates, and clinical images that convey novel or underappreciated teaching points. Teaching points may be purely clinical and may focus on clinical pearls or unusual presentations of well‐known diseases, although submission of straightforward presentations of rare diseases is discouraged. Alternatively, manuscripts may involve succinct case‐based descriptions of innovations, quality improvementrelated issues, or medical errors. Submitted case reports should be less than 800 words and should contain a maximum of 5 references and no more than 1 table or figure. Case reports should not include an abstract. Submission of the case report and review type should be avoided. Instead, we seek formal clinical updates of no more than 2000 words that present important aspects of a case along with new research findings and citations from the literature that change what has historically been the standard of delivery of care. Finally, we continue to seek cases most appropriate for the Hospital Images Dx section, edited by Paul Aronowitz. They should be submitted with that designation and have fewer than 150 words. These 3 categories are identified on our Manuscript Central website (http://mc.manuscriptcentral.com/jhm).

Again, thanks to all of you for making the launch of the Journal of Hospital Medicine an unqualified success. We look forward to your continued participation as we grow as the premier journal for the specialty of hospital medicine.

P.S. Sadly, one of our superstar associate editors, Kaveh Shojania, is stepping aside, and we sincerely express thanks for his terrific contributions. We welcome suggestions for an alternative to fulfill his responsibilities.

One year of the Journal of Hospital Medicine is done, and we now embark on our second with this first issue of volume 2. Before moving on, I heartily thank all the authors who contributed their manuscripts to the Journal of Hospital Medicine (JHM), bravely investing in this new academic periodical. A remarkable 284 manuscripts have been submitted since we first opened the JHM Web site, 197 of them during 2006. This clearly reflects the robust demand by hospitalists and their colleagues for original research and relevant clinical reviews about our evolving specialty of hospital medicine. I probably should not be surprised that this demand exists among the 15,000‐plus hospitalists in America and the 6000‐plus members of the Society of Hospital Medicine. Regardless, I am ineffably humbled by the enthusiasm and energy of all the contributors.

Understandably, this volume of submissions, exceeding our projections by nearly 50%, required yeoman's work by our associate editors and reviewers. On page 55 we list the 203 reviewers who donated their time and acumen to assure the quality of our publication. Many reviewed more than 4 articles during the year. Our associate editors deserve particular appreciation and gratitude for their willingness to donate extraordinary amounts of time and effort to ensure the success of JHMVincent Chang from Boston Children's Hospital, Scott Flanders from the University of Michigan, Karen Hauer from the University of California, San Francisco, Jean Kutner from the University of Colorado, James Pile from Cleveland MetroHealth, and Kaveh Shojania from the University of Ottawa. Additionally, the energetic assistant editors have supported them and me with frequent reviews, article submissions, and creative ideas for improving the journal. Finally, our auspicious editorial board has proffered sage guidance, and many of its members have also submitted manuscripts and participated in reviewing articles.

Moving forward we expect continued growth, as both the submitted articles and demand for the journal are being recognized. At 7:29 a.m. on November 30, 2006, Vickie Thaw (Vice President and Publisher, John Wiley & Sons, Inc.) called me to report that the National Library of Medicine validated all our efforts. The Journal of Hospital Medicine had been selected for indexing and inclusion in the National Library of Medicine's MEDLINE (Medical Literature Analysis and Retrieval System Online). The primary component of PubMed, MEDLINE is a bibliographic database containing approximately 13 million references to journal articles on medicine, nursing, dentistry, veterinary medicine, health care systems, and preclinical sciences dating to the mid‐1960s. With this approval, hospital medicine has achieved another milestone in its evolution into a new specialty.

We now hope to respond to the robust interest in clinical materials as well as to continue publication of original research. To achieve our aim of increasing the amount of clinically relevant content for practicing hospitalists, authors are encouraged to submit to JHM case reports, clinical updates, and clinical images that convey novel or underappreciated teaching points. Teaching points may be purely clinical and may focus on clinical pearls or unusual presentations of well‐known diseases, although submission of straightforward presentations of rare diseases is discouraged. Alternatively, manuscripts may involve succinct case‐based descriptions of innovations, quality improvementrelated issues, or medical errors. Submitted case reports should be less than 800 words and should contain a maximum of 5 references and no more than 1 table or figure. Case reports should not include an abstract. Submission of the case report and review type should be avoided. Instead, we seek formal clinical updates of no more than 2000 words that present important aspects of a case along with new research findings and citations from the literature that change what has historically been the standard of delivery of care. Finally, we continue to seek cases most appropriate for the Hospital Images Dx section, edited by Paul Aronowitz. They should be submitted with that designation and have fewer than 150 words. These 3 categories are identified on our Manuscript Central website (http://mc.manuscriptcentral.com/jhm).

Again, thanks to all of you for making the launch of the Journal of Hospital Medicine an unqualified success. We look forward to your continued participation as we grow as the premier journal for the specialty of hospital medicine.

P.S. Sadly, one of our superstar associate editors, Kaveh Shojania, is stepping aside, and we sincerely express thanks for his terrific contributions. We welcome suggestions for an alternative to fulfill his responsibilities.

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Mortality Predictors from the CBC

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Which observations from the complete blood cell count predict mortality for hospitalized patients?

The complete blood count (CBC) bundles the automated hemogram, an automated differential count of 5 types of cells, and a reflex manual differential count (when required by protocol) and is one of the most frequently ordered laboratory tests on admission to the hospital. In practice, it is a routine ingredient of all hospital admission ordersphysicians order a hemogram either alone or as part of a complete blood count for 98% of our medical/surgical admissions, and the same is true at most institutions.1 We know that the white blood cell count and hematocrit from the automated hemogram predict disease severity and mortality risk.25 For example, elevated WBC counts predict a worse prognosis in patients with cancer or coronary artery disease,6, 7 and anemia predicts increased risk of death of patients with heart failure.8, 9 Further, these two tests provide direct management guidance in common circumstances, for example, bleeding and infection.

The CBC describes the number and morphology of more than 40 cell types, from acanthocytosis to vacuolated white blood cells. Disagreement exists about the clinical significance of many of these observations.1013 And only a few components of the manual differential, for example, nucleated red blood cells (NRBCs) and lymphocytosis, have been quantitatively evaluated to determine their prognostic significance.1417 But these two observations have not been examined to determine their independent contributions to predictions of mortality when taken in conjunction with their accompanying CBC observations. Which of the numerous cell types and cell counts in the commonly ordered CBC, indicate that a patient is at high risk of death? In this article we report an inpatient study that used univariate and multivariate analyses of admission CBCs to predict 30‐day mortality in order to answer that question.

METHODS

Patients and Protocol

The institutional review board of Indiana University, Purdue University, Indianapolis, approved this study. We included in the study all adult patients (those at least 18 years old) admitted to Wishard Hospital between January 1, 1993, and December 31, 2002, except for prisoners (for IRB reasons) and obstetric patients (because their 30‐day mortality is very close to zero0.07% at our institution). Wishard Hospital is a large urban hospital that serves a diverse but predominantly inner‐city population in Indianapolis. If a patient was admitted more than once during the 10 years of observation, we included only the first admission in the analysis in order to assure statistical independence of the observations. We extracted data from the Regenstrief Medical Record System (RMRS), a comprehensive medical records system that has demographic data, vital signs, diagnoses, results of clinical tests, and pharmacy information on all inpatient, emergency department, and outpatient encounter sites.18

We obtained the admission and discharge ICD9 and DRG codes to assess the disease patterns associated with individual CBC abnormalities. We obtained these codes from routine hospital case abstractions performed by Wishard Hospital's medical records department using NCoder+ and Quadramed. Patients assigned DRG codes 370‐384 were identified as obstetric and therefore excluded. Using the ICD9 and CPT codes according to the Charlson algorithm, we calculated a Charlson Comorbidity Index value19 for each patient as a marker of coexisting conditions.

Outcomes

The primary outcome was 30‐day mortality counted from the date of admission. We used information from the hospital record (inpatient deaths) and the Indiana state death tapes to determine the dates of death of all patients. Patients were matched to the Indiana death tapes by an algorithm using name, social security number, date of birth, and sex.20

Hemogram and Differential Count Test Methods

The hemogram, differential counts, and blood smear exam results included in this study all came from Wishard Hospital's laboratory. During this study, the hospital used only 2 cell counters, the Coulter STK‐S and the Gen‐S automated blood analyzer (Beckman Coulter, Brea, California), to produce hemogram and automated blood differential counts. Both instruments provided automated differential counts of 5 cell types: neutrophils, lymphocytes, monocytes, basophils, and eosinophils. The latter machine also produced platelet counts and reticulocyte counts, but during the study period these counts were not routinely reported to physicians unless ordered specifically, so we did not include them in the analyses. The laboratory reflexively performed 100‐cell manual differential counts and blood smear exams when abnormalities as defined by College of American Pathologists (CAP) criteria were observed in the automated measures. Both automated blood analyzers used the same automated CAP criteria to decide when to add a manual differential count and blood smear analysis, and these criteria were constant throughout the study. This protocol predicts manual differential abnormalities with high sensitivity, missing less than 1% of important findings in a manual differential.21 When the CAP criteria did not require a manual differential count and blood smear exam, we assumed that those counts unique to a manual count, for example, blast cell count, were zero and that there were no abnormalities in blood smear morphology.

Laboratories may report white blood cells as absolute counts (eg, number of cells/mm3) and/or as percentages. We converted all counts reported as percentages to absolute numbers (eg, WBC count 1000 cell type percent/100). For absolute counts that have both high and low ranges, such as white blood cell (WBC) count, we constructed two binary variables. WBC‐low was 1 when the WBC was below the lower limit of normal; otherwise it was 0. WBC‐high was 1 if the WBC was above the upper limit of normal; otherwise it was 0. For continuous variables such as NRBCs or blasts where any presence on the manual differential count is abnormal, we constructed binary variables with 0 indicating absence of the cell type and 1 indicating a cell count was at least 1.

Measurements of many cell types in the manual differential count and smear assessment (eg, burr cells) are reported in qualitative terms such as occasional, few, increased, or present, if observed, or none seen, unremarkable, or no mention, if not observed. We dichotomized all such results as present or absent for analysis purposes.

Statistical Analysis

For all the original variables, we plotted cell counts against 30‐day mortality to graphically show this univariate association. To screen the effects of these 45 binary CBC variables univariately, we used each as the sole independent variable in a logistic regression model with 30‐day mortality as the dependent variable.

The simultaneous effects of the 45 CBC measures on mortality were investigated using multiple logistic regression models, always controlling for patient age (in years, as a continuous variable) and sex (as a dichotomous variable). Two approaches were taken to handle the large number of predictors in the model. First, we formed subgroups of predictors based on clinical judgment (eg, the subgroup of bands, Dohle bodies, and toxic granules associated with infections) and ran logistic regressions of each subgroup to choose the significant predictors of these subgroups to fit them into an overall prediction model of 30‐day mortality. The results were verified using a second approach that did not depend on subjective judgment. Both backward and forward stepwise variable selection procedures were used to choose the subset of significant predictors (P < .005) of 30‐day mortality in logistic regression, again controlling for age and sex. To be sure that the predictive power of the models was not decreased by converting continuous variables into categorical variables, we also ran models that included the continuous variables as potential predictors. We used the c statistic as a measure of the goodness‐of‐fit of the models. We included the Charlson Index and the 10 most common admission diagnoses in our model to control for comorbidities and prime reason for admission, respectively.

We performed the analysis using SAS software, version 8.02 (SAS Institute, Inc., Cary, NC).

Chart Review

For each independent predictor of 30‐day mortality that was both statistically significant and had a very high relative risk (>2.5), one author (A.K.) took a random sample of 100200 patients with positive values for this predictor and reviewed the dictated discharge summaries in order to asses the clinical correlates of these findings.

RESULTS

During the 10 years from January 1993 through December 2002, physicians admitted 46,522 unique eligible patients to Wishard Memorial Hospital. Each patient averaged 2 admissions during the study period, for a total of 94,582 admissions. The overall 30‐day mortality of these admissions was 3.4%. Automated hemograms (white blood cell count, hemoglobin, red cell count, and red blood cell indices) were performed on blood samples from 45,709 of these patients (98%) within one day of admission. Seventy‐seven percent (35,692) had a complete blood count that included an automated differential count plus a reflex manual count and smear when required by the CAP protocol, as well as an automated hemogram. The patients with an admission CBC with differential count had a 30‐day mortality rate of 4%, slightly higher than that of patients who had only a hemogram. The patients' mean Charlson score for the CBC with differential count was 0.83, which was lower than the national average, which is closer to 1.22 Table 1 shows the demographics of this study population.

Characteristics of 35,692 Unique Patients with a CBC and Automated Differential Count
CharacteristicValue
Average age (years)46.2 17.7
Average LOS (days)6.5 8.1
Male (%)55.4
Race
White (%)52.9
Black (%)43.4
Other (%)3.7
Charlson Index (mean)0.83 1.5
Most common admission diagnoses (ICD9)Chest pain
 Pneumonia, organism unspecified
 Other symptoms involving abdomen or pelvis
 Unspecified heart failure
 Intermediate coronary syndrome
 Unspecified hemorrhage of GI tract
 Acute but ill‐defined cerebrovascular disease
 Diseases of pancreas
 Cellulitis and abscess of leg except foot
 Convulsions

Predictors of 30‐Day Mortality

We examined the univariate effect of age, sex, and the 45 CBC variables (Table 2) on 30‐day mortality. Most of these variables showed a significant (P < .0001) effect on mortality. Only a few abnormalities, for example, a low WBC (< 5000/L), basophilia (>200/L), and eosinophilia (>450/L), were unrelated to 30‐day mortality. Increasing age and male sex were associated with increased mortality. Of the 45 CBC variables, 29 were strong (P < .0001) univariate predictors of mortality and had odds ratios (ORs) greater than 2.5. Eight variables had univariate ORs greater than 4: toxic granules, Dohle bodies, smudge cells, promyelocytes, myelocytes, metamyelocytes, NRBCs, and burr cells. All but 2 of these are white blood cell observations.

Univariate Risk of 30‐Day Mortality in Patients with an Admission CBC and Automated Differential Count
  Number (%)Odds ratioP value
HemogramAge ( 18 years)35,688 (100)1.039< .0001
Sex (male)19,788 (55.4)1.420<.0001
 WBC > 12,00011,124 (31.2)2.049<.0001
 WBC < 50002176 (6.1)0.938.5765
 Hematocrit (>54)212 (0.6)2.633<.0001
 Hematocrit (<37)8687 (24.3)2.359<.0001
 MCV (>94)6552 (18.4)1.584<.0001
 MCV (<80)2815 (7.9)1.258.0121
 High RDW (>14.5)9478 (26.6)2.647<.0001
 High MCH (>32)5308 (14.9)1.367<.0001
 Low MCH (<26)2064 (5.8)1.392.0011
 High MCHC (>36)28 (0.1)3.964.0109
 Low MCHC (<32)738 (2.1)2.190<.0001
 Automated differential countNeutrophilia (>7700)10,578 (37.8)1.601<.0001
Neutropenia (<1500)469 (1.3)2.831<.0001
 Basophilia (>200)1137 (3.2)1.362.0215
 Eosinophilia (>450)1529 (4.3)1.074.5788
 Monocytosis (>800)10,066 (28.2)1.262<.0001
 Lymphocytosis (>4000)3046 (8.5)2.495<.0001
Manual differential countBlast cells (Y/N)31 (0.1)1.638.5001
Myelocytes (Y/N)215 (0.6)8.231< .0001
 Promyelocytes (Y/N)25 (0.1)13.429< .0001
 Metamyeloctyes (Y/N)905 (2.5)5.798< .0001
 Atypical lymphocytes (Y/N)1303 (3.7)1.881< .0001
 Hypersegmented neutrophils (Y/N)141 (0.4)3.061< .0001
 Microcytes (Y/N)3452 (9.7)2.578< .0001
 Macrocytes (Y/N)3475 (9.7)3.282< .0001
 Hypochromic RBCs (Y/N)2252 (6.3)2.290< .0001
 Basophilic stippling (Y/N)273 (0.8)3.553< .0001
 Target cells (Y/N)1140 (3.2)2.866< .0001
 Polychromasia (Y/N)1675 (4.7)3.622< .0001
 Toxic granules (Y/N)1063 (3.0)4.021< .0001
 Dohle bodies (Y/N)524 (1.5)4.821< .0001
 Ovalocytes (Y/N)1555 (4.4)2.558< .0001
 Spherocytes (Y/N)465 (1.3)3.132< .0001
 Schistocytes (Y/N)1484 (4.2)3.150< .0001
 Sickle Cells (Y/N)62 (0.2)0.389.3490
 Howell‐Jolly bodies (Y/N)71 (0.2)3.025.0033
 Pappenheimer bodies (Y/N)67 (0.2)2.344.0468
 Burr cells (Y/N)253 (0.7)9.297<.0001
 Teardrop cells (Y/N)538 (1.5)2.150< .0001
 Vacuolated cells (Y/N)897 (2.5)3.667< .0001
 Giant platelets (Y/N)781 (2.2)3.102< .0001
 Smudge cells (Y/N)50 (0.1)5.237< .0001
 Cleaved cells (Y/N)8 (0.0)3.393.2533
 Band forms (Y/N)7594 (21.3)2.964< .0001
 NRBCs (Y/N)467 (1.3)8.756< .0001

All the statistical approaches produced essentially the same model for predicting mortality. Table 3 shows that age, sex, and 13 of the CBC variables were retained in the final model of dichotomous variables using backward and forward selection. Lymphocytosis, burr cells, and NRBCs were the greatest independent predictors of mortality, with odds ratios greater than 2.5. Only 1 variable, sickle cells, predicted reduced mortality (with an odds ratio well below 1).

Multivariate Model of Statistically Significant (P < .005) Predictors of 30‐Day Mortality from the CBC and Automated Differential Count Pared Stepwise Backward Selection
ParameterOdds ratioConfidence intervalP value
Age (years)1.0401.0371.043< .0001
Sex (male)1.9651.7462.213< .0001
WBC > 12,0001.7011.5081.919< .0001
Hematocrit (>54)2.3311.4383.780< .0006
Hematocrit (<37)1.7141.5141.941< .0001
MCV (>94)1.3521.1861.543< .0001
High RDW (>14.5)1.4631.2911.658< .0001
Lymphocytosis (>4000)2.8482.4353.332< .0001
Metamyeloctye (Y/N)2.0741.6662.581< .0001
Macrocytes (Y/N)1.3171.1271.539< .0005
Toxic granules (Y/N)1.4941.2001.859.0003
Sickle cells (Y/N)0.0390.0050.292.0016
Burr cells (Y/N)3.2542.3474.513< .0001
Band forms (Y/N)1.5861.3861.814< .0001
NRBCs (Y/N)2.9062.2403.770< .0001

The c statistic (the ratio of the area under the ROC curve to the whole area, which reflects the overall predictive power of the final model), was about 0.80 by any approach, which compared favorably with previous prediction models.3, 4 Using continuous measures of CBC in the model did not increase the predictive power. Inclusion of the Charlson Index and the top 10 admission diagnoses did not significantly change the prediction model, although 2 admission diagnoses, chest pain and acute but ill‐defined cerebrovascular disease, emerged as independent predictors of 30‐day mortality, with odds ratios of 0.314 and 2.033, respectively, at P < .0001.

Chart Review

Of the 200 cases with NRBCs, the leading probable causes for this finding were severe hypoxia (average A‐a gradient = 326 mm Hg), acute anemia (average hgb = 6.1 gm/dL), and sickle‐cell anemia. Other diseases associated with NRBCs were infection/sepsis, HIV, solid tumors (breast/lung/colon/prostate), and leukemia or multiple myeloma. Having even a single NRBC at admission correlated with a 25.5% mortality rate. Of note, 30%40% of patients with sickle‐cell disease had NRBCs and moderate anemia (hgb = 8.7 gm/dL) on admission to the hospital, but there was no excess risk of mortality. Indeed, the 49 patients with sickle‐cell disease who had NRBCs at admission had a 30‐day mortality of 0%.

Most of the patients with NRBCs reviewed exhibited overt signs of severe disease, for example, shock, respiratory failure, or severe trauma, in addition to having NRBCs. However, in 2 patients the NRBCs were the only strong signal of disease severity. Both had NRBCs on the day of discharge and were readmitted within 3 days in extremis and died. One was readmitted in fulminant septic shock, likely from a bacterial peritonitis or urinary tract infection, and the other was readmitted in shock, likely from decompensated heart failure.

In univariate analysis, burr cells at admission correlated with a mortality rate of 27.3%. A review of 100 randomly chosen patients with burr cells revealed a pattern of associated diseases, that is, acute renal failure, liver failure, and congestive heart failure, different from that of patients with NRBCs. There was little overlap in the presence of burr cells and NRBCs, but the 12% who had burr cells and NRBCs had a high mortality rate (57%).

Absolute lymphocytosis was associated with a mortality rate of 8.6%. Although univariate analysis showed that the risk with lymphocytosis was not as high as that for patients with NRBCs or burr cells at admission, lymphocytosis was much more common (8.5%), and within the logistic model its presence explained more of the chi‐square statistic than any other variable except age. Indeed, lymphocytosis was a stronger predictor of 30‐day mortality than was high WBCs or anemia. Chart review of 200 patients with lymphocytosis showed a preponderance of them had large physiologic stressors, for example, traumatic tissue injury (surgery) or cerebrovascular injury. In one subset, half the patients (50.9% of 53 patients) who underwent craniotomy for trauma and had absolute lymphocytosis at admission died, compared with 20.8% of 101 patients admitted for the same diagnosis without absolute lymphocytosis.

DISCUSSION

Some investigators have incorporated selected CBC measures, for example, white blood cell count and hemoglobin/hematocrit, into multivariable models that predict mortality or rehospitalizations.6, 7, 9, 23 However, CBC reports can include a spectrum of more than 40 distinct counts and morphologic findings. Our study was the first to take into account all the different variables in the complete blood count and differential to determine elements that independently predict a high risk of mortality.

In addition to age and sex, our multivariable analysis of the 45 CBC variables found 13 independent predictors of mortality. Five were observations about white blood cells: absolute leukocytosis, high band form cell count, the presence of metamyelocytes, the presence of toxic granules, and absolute lymphocytosis. Eight were observations about red blood cells: high hematocrit, low hematocrit, high MCV and the presence of macrocytes, high red cell distribution width, the presence of NRBCs, the presence of burr cells, and the presence of sickle cells. Because controlling for severity of illness by Charlson comorbidity scores did not significantly change the model, the CBC abnormalities among the predictors of mortality did not simply reflect how sick the patients were. Including the 10 most common admission diagnoses did not significantly attenuate our reported odds ratios, suggesting the CBC predictors did not merely reflect the primary reason for admission. Interestingly, however, admission for chest pain did correlate with a greatly reduced risk of 30‐day mortality, which may reflect the low threshold that physicians have for admitting patients with this complaint. Admission for acute but ill‐defined cerebrovascular disease independently predicted a 2‐fold increased risk of 30‐day mortality.

What is the message to physicians from this analysis? Physicians commonly order CBCs and may rely on quick heuristics to sift through the myriad findings in CBC reports. Our analysis focuses physician attention on high‐impact findings in the CBC. We assume that physicians already consider low hematocrit, high hematocrit (a sign of fluid loss and/or chronic hypoxia), high WBC count, high band cell count, and the presence of metamyeloctes (left shift) as important prognostic indicators. These abnormal findings are routinely mentioned at morning report and in a physician's notes.

Physicians, however, may not appreciate the importance of other CBC findings that our analysis found are predictive of mortality. Macrocytosis and a high RDW count (indicating an abnormally wide distribution of red blood cell size) have not previously been reported as predictors of mortality. And although other studies have suggested that bands are not predictors of mortality,11 our study found they were an important prognostic indicator, with an OR =1.59, approaching those of leukocytosis and anemia.

The most impressive predictors of mortality were burr cells, NRBCs, and absolute lymphocytosis. The multivariate ORs of these 3, ranging from 2.8 to 3.2, were the highest of any CBC finding. In univariate analysis, the first 2 were associated with mortality rates 8 to 10 times higher than that of the average admitted patient. There are anecdotal reports in the literature of burr cells being associated with ominous prognoses2426 and more robust statistical analyses showing NRBCs to be associated with increased mortality.14 Lymphocytosis has also been reported as a mortality risk in patients with trauma and emergency medical conditions.15, 16 Our analysis has shown that, indeed, all 3 of these findings are strong independent predictors of mortality.

The presence of sickle cells was also a strong predictor, but of decreased mortality. Patients with sickle cells in their smear had a risk of death one third that of patients without sickle cells. This does not indicate a protective effect. Rather, patients with sickle‐cell disease typically are young and admitted for pain control and other non‐life‐threatening conditions. The presence of NRBCs in patients with sickle‐cell disease appears to be intrinsic to the disease itself and did not have the same implications for mortality as it did for other patients in our study.

The overall logistic model including age, sex, and admission CBC variables had a respectable c statistic for predicting 30‐day mortality of 0.80. This compares well with findings in other multivariable models. For example, the APACHE II score used to predict the mortality of hospitalized critical care patients has a c statistic that ranges from 0.78 to 0.86.3, 27, 28 The APACHE score uses the worst value from the first 2 days after admission for some of its predictors so it cannot provide as early a warning as the admission CBC, and it requires collection of significantly more data. The inclusion of more CBC findings in the APACHE model might increase its predictive accuracy.

Our multivariate analysis was based on a very large number of patient samples using data collected through routine clinical care. However, our study has a number of limitations. The analysis was done at only a single institution, and the exact logistic regression model may not apply to other institutions that have different case mixes and laboratory procedures. Our institution's reported 30‐day mortality rate of 3.4% was lower than the 4.6%11.9% reported in studies of patients admitted to general ward services,2931 but this may be accounted for by the lower‐than‐average Charlson comorbidity scores in our study population. Our risk adjustment by Charlson comorbidity scores may not be as precise as a risk adjustment tailored for our particular institution.32 Our 30‐day mortality rate was calculated using state death tapes, which means we would have missed patients who died outside the state, although we believe this rarely happens. We developed predictive equations on the basis of 30‐day mortality, so we cannot comment on whether the CBC elements predict mortality beyond 30 days. We analyzed most variables as either high or low or as present or absent. Increasing degrees of abnormality may further increase the predictive power of some variables. Finally, the CBC is only one of many tests and clinical findings; it may be that some of these other variables would displace some CBC variables and/or improve the overall predictive power at the time the admission laboratory tests were performed. In this initial study, we have described the prognostic implication of the CBC across a wide range of diagnoses. Future work will focus on the predictive power of commonly gathered variables in more specific conditions (eg, low white blood cell count in sepsis).

Physicians generally have an intuitive ability to identify patients who are seriously ill and at high risk of dying33 and adjust their diagnostic and therapeutic efforts accordingly. Our analysis highlights the value that certain observations in the CBC, notably burr cells, NRBCs, and absolute lymphocytosis, add to physicians' assessments of mortality risk. Even after adjustment for age, sex, comorbidities, common admission diagnoses, and other variables in the CBC, the presence of these findings predicted a 3‐fold increase in 30‐day mortality. Identifying the red flags within this ubiquitously performed test can make the difference in premature discharge or inappropriate triage of patients. Busy physicians can choose from a wide selection of ever‐improving diagnostic tests, yet the workhorse CBC can serve as a simple and early identifier of patients with a poor prognosis.

References
  1. Shapiro MF,Greenfield SG.The complete blood count and leukocyte differential count.Ann Intern Med.1987;106:6574.
  2. Chang R,Wong GY.Prognostic significance of marked leukocytosis in hospitalized patients.J Gen Intern Med.1991;6:199203.
  3. Knaus WA,Wagner DP,Draper EA, et al.The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults.Chest.1991;100:16191636.
  4. Knaus WA,Draper EA,Wagner DP,Zimmerman JE.APACHE II: a severity of disease classification system.Crit Care Med.1985;13:818829.
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  6. Grimm R,Neaton J,Ludwig W.Prognostic importance of the white blood cell count for coronary, cancer, and all‐cause mortality.JAMA.1985;254:19321937.
  7. Labry LD,Campion E,Glynn R,Vokonas P.White blood cell count as a predictor of mortality: results over 18 years from the Normative Aging Study.J Clin Epidemiol.1990;43:153157.
  8. Frumin AM,Mendell TH,Mintz SS,Novack P,Faulk AT.Nucleated red blood cells in congestive heart failure.Circulation.1959;20:367370.
  9. Mozaffarian D,Nye R,Levy WC.Anemia predicts mortality in severe heart failure: the prospective randomized amlodipine survival evaluation (PRAISE).J Am Coll Cardiol.2003;41:19331939.
  10. Ardron MJ,Westengard JC,Dutcher TF.Band neutrophil counts are unnecessary for the diagnosis of infection in patients with normal total leukocyte counts.Am J Clin Pathol.1994;102:646649.
  11. Brigden M,Page N.The lack of clinical utility of white blood cell differential counts and blood morphology in elderly individuals with normal hematology profiles.Arch Pathol Lab Med.1990;114:394398.
  12. Wenz B,Gennis P,Canova C,Burns ER.The clinical utility of the leucocyte differential in emergency medicine.Am J Clin Pathol.1986;86:298303.
  13. Wile MJ,Homer LD,Gaehler S,Phillips S,Millan J.Manual differential cell counts help predict bacterial infection.Am J Clin Pathol.2001;115:644649.
  14. Schwartz SO,Stansbury F.Significance of nucleated red blood cells in peripheral blood; analysis of 1,496 cases.JAMA.1954;154:13391340.
  15. Stachon A,Sondermann N,Imohl M,Krieg M.Nucleated red blood cells indicate high risk of in‐hospital mortality.J Lab Clin Med.2002;140:407412.
  16. Teggatz JR,Parkin J,Peterson L.Transient atypical lymphocytosis in patients with emergency medical conditions.Arch Pathol Lab Med.1987;111:712714.
  17. Pinkerton PH,McLellan BA,Quantz MC,Robinson JB.Acute lymphocytosis after trauma—early recognition of the high‐risk patient?J Trauma.1989;29:749751.
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Journal of Hospital Medicine - 2(1)
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5-12
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diagnostic decision making, laboratory testing, electronic medical record
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The complete blood count (CBC) bundles the automated hemogram, an automated differential count of 5 types of cells, and a reflex manual differential count (when required by protocol) and is one of the most frequently ordered laboratory tests on admission to the hospital. In practice, it is a routine ingredient of all hospital admission ordersphysicians order a hemogram either alone or as part of a complete blood count for 98% of our medical/surgical admissions, and the same is true at most institutions.1 We know that the white blood cell count and hematocrit from the automated hemogram predict disease severity and mortality risk.25 For example, elevated WBC counts predict a worse prognosis in patients with cancer or coronary artery disease,6, 7 and anemia predicts increased risk of death of patients with heart failure.8, 9 Further, these two tests provide direct management guidance in common circumstances, for example, bleeding and infection.

The CBC describes the number and morphology of more than 40 cell types, from acanthocytosis to vacuolated white blood cells. Disagreement exists about the clinical significance of many of these observations.1013 And only a few components of the manual differential, for example, nucleated red blood cells (NRBCs) and lymphocytosis, have been quantitatively evaluated to determine their prognostic significance.1417 But these two observations have not been examined to determine their independent contributions to predictions of mortality when taken in conjunction with their accompanying CBC observations. Which of the numerous cell types and cell counts in the commonly ordered CBC, indicate that a patient is at high risk of death? In this article we report an inpatient study that used univariate and multivariate analyses of admission CBCs to predict 30‐day mortality in order to answer that question.

METHODS

Patients and Protocol

The institutional review board of Indiana University, Purdue University, Indianapolis, approved this study. We included in the study all adult patients (those at least 18 years old) admitted to Wishard Hospital between January 1, 1993, and December 31, 2002, except for prisoners (for IRB reasons) and obstetric patients (because their 30‐day mortality is very close to zero0.07% at our institution). Wishard Hospital is a large urban hospital that serves a diverse but predominantly inner‐city population in Indianapolis. If a patient was admitted more than once during the 10 years of observation, we included only the first admission in the analysis in order to assure statistical independence of the observations. We extracted data from the Regenstrief Medical Record System (RMRS), a comprehensive medical records system that has demographic data, vital signs, diagnoses, results of clinical tests, and pharmacy information on all inpatient, emergency department, and outpatient encounter sites.18

We obtained the admission and discharge ICD9 and DRG codes to assess the disease patterns associated with individual CBC abnormalities. We obtained these codes from routine hospital case abstractions performed by Wishard Hospital's medical records department using NCoder+ and Quadramed. Patients assigned DRG codes 370‐384 were identified as obstetric and therefore excluded. Using the ICD9 and CPT codes according to the Charlson algorithm, we calculated a Charlson Comorbidity Index value19 for each patient as a marker of coexisting conditions.

Outcomes

The primary outcome was 30‐day mortality counted from the date of admission. We used information from the hospital record (inpatient deaths) and the Indiana state death tapes to determine the dates of death of all patients. Patients were matched to the Indiana death tapes by an algorithm using name, social security number, date of birth, and sex.20

Hemogram and Differential Count Test Methods

The hemogram, differential counts, and blood smear exam results included in this study all came from Wishard Hospital's laboratory. During this study, the hospital used only 2 cell counters, the Coulter STK‐S and the Gen‐S automated blood analyzer (Beckman Coulter, Brea, California), to produce hemogram and automated blood differential counts. Both instruments provided automated differential counts of 5 cell types: neutrophils, lymphocytes, monocytes, basophils, and eosinophils. The latter machine also produced platelet counts and reticulocyte counts, but during the study period these counts were not routinely reported to physicians unless ordered specifically, so we did not include them in the analyses. The laboratory reflexively performed 100‐cell manual differential counts and blood smear exams when abnormalities as defined by College of American Pathologists (CAP) criteria were observed in the automated measures. Both automated blood analyzers used the same automated CAP criteria to decide when to add a manual differential count and blood smear analysis, and these criteria were constant throughout the study. This protocol predicts manual differential abnormalities with high sensitivity, missing less than 1% of important findings in a manual differential.21 When the CAP criteria did not require a manual differential count and blood smear exam, we assumed that those counts unique to a manual count, for example, blast cell count, were zero and that there were no abnormalities in blood smear morphology.

Laboratories may report white blood cells as absolute counts (eg, number of cells/mm3) and/or as percentages. We converted all counts reported as percentages to absolute numbers (eg, WBC count 1000 cell type percent/100). For absolute counts that have both high and low ranges, such as white blood cell (WBC) count, we constructed two binary variables. WBC‐low was 1 when the WBC was below the lower limit of normal; otherwise it was 0. WBC‐high was 1 if the WBC was above the upper limit of normal; otherwise it was 0. For continuous variables such as NRBCs or blasts where any presence on the manual differential count is abnormal, we constructed binary variables with 0 indicating absence of the cell type and 1 indicating a cell count was at least 1.

Measurements of many cell types in the manual differential count and smear assessment (eg, burr cells) are reported in qualitative terms such as occasional, few, increased, or present, if observed, or none seen, unremarkable, or no mention, if not observed. We dichotomized all such results as present or absent for analysis purposes.

Statistical Analysis

For all the original variables, we plotted cell counts against 30‐day mortality to graphically show this univariate association. To screen the effects of these 45 binary CBC variables univariately, we used each as the sole independent variable in a logistic regression model with 30‐day mortality as the dependent variable.

The simultaneous effects of the 45 CBC measures on mortality were investigated using multiple logistic regression models, always controlling for patient age (in years, as a continuous variable) and sex (as a dichotomous variable). Two approaches were taken to handle the large number of predictors in the model. First, we formed subgroups of predictors based on clinical judgment (eg, the subgroup of bands, Dohle bodies, and toxic granules associated with infections) and ran logistic regressions of each subgroup to choose the significant predictors of these subgroups to fit them into an overall prediction model of 30‐day mortality. The results were verified using a second approach that did not depend on subjective judgment. Both backward and forward stepwise variable selection procedures were used to choose the subset of significant predictors (P < .005) of 30‐day mortality in logistic regression, again controlling for age and sex. To be sure that the predictive power of the models was not decreased by converting continuous variables into categorical variables, we also ran models that included the continuous variables as potential predictors. We used the c statistic as a measure of the goodness‐of‐fit of the models. We included the Charlson Index and the 10 most common admission diagnoses in our model to control for comorbidities and prime reason for admission, respectively.

We performed the analysis using SAS software, version 8.02 (SAS Institute, Inc., Cary, NC).

Chart Review

For each independent predictor of 30‐day mortality that was both statistically significant and had a very high relative risk (>2.5), one author (A.K.) took a random sample of 100200 patients with positive values for this predictor and reviewed the dictated discharge summaries in order to asses the clinical correlates of these findings.

RESULTS

During the 10 years from January 1993 through December 2002, physicians admitted 46,522 unique eligible patients to Wishard Memorial Hospital. Each patient averaged 2 admissions during the study period, for a total of 94,582 admissions. The overall 30‐day mortality of these admissions was 3.4%. Automated hemograms (white blood cell count, hemoglobin, red cell count, and red blood cell indices) were performed on blood samples from 45,709 of these patients (98%) within one day of admission. Seventy‐seven percent (35,692) had a complete blood count that included an automated differential count plus a reflex manual count and smear when required by the CAP protocol, as well as an automated hemogram. The patients with an admission CBC with differential count had a 30‐day mortality rate of 4%, slightly higher than that of patients who had only a hemogram. The patients' mean Charlson score for the CBC with differential count was 0.83, which was lower than the national average, which is closer to 1.22 Table 1 shows the demographics of this study population.

Characteristics of 35,692 Unique Patients with a CBC and Automated Differential Count
CharacteristicValue
Average age (years)46.2 17.7
Average LOS (days)6.5 8.1
Male (%)55.4
Race
White (%)52.9
Black (%)43.4
Other (%)3.7
Charlson Index (mean)0.83 1.5
Most common admission diagnoses (ICD9)Chest pain
 Pneumonia, organism unspecified
 Other symptoms involving abdomen or pelvis
 Unspecified heart failure
 Intermediate coronary syndrome
 Unspecified hemorrhage of GI tract
 Acute but ill‐defined cerebrovascular disease
 Diseases of pancreas
 Cellulitis and abscess of leg except foot
 Convulsions

Predictors of 30‐Day Mortality

We examined the univariate effect of age, sex, and the 45 CBC variables (Table 2) on 30‐day mortality. Most of these variables showed a significant (P < .0001) effect on mortality. Only a few abnormalities, for example, a low WBC (< 5000/L), basophilia (>200/L), and eosinophilia (>450/L), were unrelated to 30‐day mortality. Increasing age and male sex were associated with increased mortality. Of the 45 CBC variables, 29 were strong (P < .0001) univariate predictors of mortality and had odds ratios (ORs) greater than 2.5. Eight variables had univariate ORs greater than 4: toxic granules, Dohle bodies, smudge cells, promyelocytes, myelocytes, metamyelocytes, NRBCs, and burr cells. All but 2 of these are white blood cell observations.

Univariate Risk of 30‐Day Mortality in Patients with an Admission CBC and Automated Differential Count
  Number (%)Odds ratioP value
HemogramAge ( 18 years)35,688 (100)1.039< .0001
Sex (male)19,788 (55.4)1.420<.0001
 WBC > 12,00011,124 (31.2)2.049<.0001
 WBC < 50002176 (6.1)0.938.5765
 Hematocrit (>54)212 (0.6)2.633<.0001
 Hematocrit (<37)8687 (24.3)2.359<.0001
 MCV (>94)6552 (18.4)1.584<.0001
 MCV (<80)2815 (7.9)1.258.0121
 High RDW (>14.5)9478 (26.6)2.647<.0001
 High MCH (>32)5308 (14.9)1.367<.0001
 Low MCH (<26)2064 (5.8)1.392.0011
 High MCHC (>36)28 (0.1)3.964.0109
 Low MCHC (<32)738 (2.1)2.190<.0001
 Automated differential countNeutrophilia (>7700)10,578 (37.8)1.601<.0001
Neutropenia (<1500)469 (1.3)2.831<.0001
 Basophilia (>200)1137 (3.2)1.362.0215
 Eosinophilia (>450)1529 (4.3)1.074.5788
 Monocytosis (>800)10,066 (28.2)1.262<.0001
 Lymphocytosis (>4000)3046 (8.5)2.495<.0001
Manual differential countBlast cells (Y/N)31 (0.1)1.638.5001
Myelocytes (Y/N)215 (0.6)8.231< .0001
 Promyelocytes (Y/N)25 (0.1)13.429< .0001
 Metamyeloctyes (Y/N)905 (2.5)5.798< .0001
 Atypical lymphocytes (Y/N)1303 (3.7)1.881< .0001
 Hypersegmented neutrophils (Y/N)141 (0.4)3.061< .0001
 Microcytes (Y/N)3452 (9.7)2.578< .0001
 Macrocytes (Y/N)3475 (9.7)3.282< .0001
 Hypochromic RBCs (Y/N)2252 (6.3)2.290< .0001
 Basophilic stippling (Y/N)273 (0.8)3.553< .0001
 Target cells (Y/N)1140 (3.2)2.866< .0001
 Polychromasia (Y/N)1675 (4.7)3.622< .0001
 Toxic granules (Y/N)1063 (3.0)4.021< .0001
 Dohle bodies (Y/N)524 (1.5)4.821< .0001
 Ovalocytes (Y/N)1555 (4.4)2.558< .0001
 Spherocytes (Y/N)465 (1.3)3.132< .0001
 Schistocytes (Y/N)1484 (4.2)3.150< .0001
 Sickle Cells (Y/N)62 (0.2)0.389.3490
 Howell‐Jolly bodies (Y/N)71 (0.2)3.025.0033
 Pappenheimer bodies (Y/N)67 (0.2)2.344.0468
 Burr cells (Y/N)253 (0.7)9.297<.0001
 Teardrop cells (Y/N)538 (1.5)2.150< .0001
 Vacuolated cells (Y/N)897 (2.5)3.667< .0001
 Giant platelets (Y/N)781 (2.2)3.102< .0001
 Smudge cells (Y/N)50 (0.1)5.237< .0001
 Cleaved cells (Y/N)8 (0.0)3.393.2533
 Band forms (Y/N)7594 (21.3)2.964< .0001
 NRBCs (Y/N)467 (1.3)8.756< .0001

All the statistical approaches produced essentially the same model for predicting mortality. Table 3 shows that age, sex, and 13 of the CBC variables were retained in the final model of dichotomous variables using backward and forward selection. Lymphocytosis, burr cells, and NRBCs were the greatest independent predictors of mortality, with odds ratios greater than 2.5. Only 1 variable, sickle cells, predicted reduced mortality (with an odds ratio well below 1).

Multivariate Model of Statistically Significant (P < .005) Predictors of 30‐Day Mortality from the CBC and Automated Differential Count Pared Stepwise Backward Selection
ParameterOdds ratioConfidence intervalP value
Age (years)1.0401.0371.043< .0001
Sex (male)1.9651.7462.213< .0001
WBC > 12,0001.7011.5081.919< .0001
Hematocrit (>54)2.3311.4383.780< .0006
Hematocrit (<37)1.7141.5141.941< .0001
MCV (>94)1.3521.1861.543< .0001
High RDW (>14.5)1.4631.2911.658< .0001
Lymphocytosis (>4000)2.8482.4353.332< .0001
Metamyeloctye (Y/N)2.0741.6662.581< .0001
Macrocytes (Y/N)1.3171.1271.539< .0005
Toxic granules (Y/N)1.4941.2001.859.0003
Sickle cells (Y/N)0.0390.0050.292.0016
Burr cells (Y/N)3.2542.3474.513< .0001
Band forms (Y/N)1.5861.3861.814< .0001
NRBCs (Y/N)2.9062.2403.770< .0001

The c statistic (the ratio of the area under the ROC curve to the whole area, which reflects the overall predictive power of the final model), was about 0.80 by any approach, which compared favorably with previous prediction models.3, 4 Using continuous measures of CBC in the model did not increase the predictive power. Inclusion of the Charlson Index and the top 10 admission diagnoses did not significantly change the prediction model, although 2 admission diagnoses, chest pain and acute but ill‐defined cerebrovascular disease, emerged as independent predictors of 30‐day mortality, with odds ratios of 0.314 and 2.033, respectively, at P < .0001.

Chart Review

Of the 200 cases with NRBCs, the leading probable causes for this finding were severe hypoxia (average A‐a gradient = 326 mm Hg), acute anemia (average hgb = 6.1 gm/dL), and sickle‐cell anemia. Other diseases associated with NRBCs were infection/sepsis, HIV, solid tumors (breast/lung/colon/prostate), and leukemia or multiple myeloma. Having even a single NRBC at admission correlated with a 25.5% mortality rate. Of note, 30%40% of patients with sickle‐cell disease had NRBCs and moderate anemia (hgb = 8.7 gm/dL) on admission to the hospital, but there was no excess risk of mortality. Indeed, the 49 patients with sickle‐cell disease who had NRBCs at admission had a 30‐day mortality of 0%.

Most of the patients with NRBCs reviewed exhibited overt signs of severe disease, for example, shock, respiratory failure, or severe trauma, in addition to having NRBCs. However, in 2 patients the NRBCs were the only strong signal of disease severity. Both had NRBCs on the day of discharge and were readmitted within 3 days in extremis and died. One was readmitted in fulminant septic shock, likely from a bacterial peritonitis or urinary tract infection, and the other was readmitted in shock, likely from decompensated heart failure.

In univariate analysis, burr cells at admission correlated with a mortality rate of 27.3%. A review of 100 randomly chosen patients with burr cells revealed a pattern of associated diseases, that is, acute renal failure, liver failure, and congestive heart failure, different from that of patients with NRBCs. There was little overlap in the presence of burr cells and NRBCs, but the 12% who had burr cells and NRBCs had a high mortality rate (57%).

Absolute lymphocytosis was associated with a mortality rate of 8.6%. Although univariate analysis showed that the risk with lymphocytosis was not as high as that for patients with NRBCs or burr cells at admission, lymphocytosis was much more common (8.5%), and within the logistic model its presence explained more of the chi‐square statistic than any other variable except age. Indeed, lymphocytosis was a stronger predictor of 30‐day mortality than was high WBCs or anemia. Chart review of 200 patients with lymphocytosis showed a preponderance of them had large physiologic stressors, for example, traumatic tissue injury (surgery) or cerebrovascular injury. In one subset, half the patients (50.9% of 53 patients) who underwent craniotomy for trauma and had absolute lymphocytosis at admission died, compared with 20.8% of 101 patients admitted for the same diagnosis without absolute lymphocytosis.

DISCUSSION

Some investigators have incorporated selected CBC measures, for example, white blood cell count and hemoglobin/hematocrit, into multivariable models that predict mortality or rehospitalizations.6, 7, 9, 23 However, CBC reports can include a spectrum of more than 40 distinct counts and morphologic findings. Our study was the first to take into account all the different variables in the complete blood count and differential to determine elements that independently predict a high risk of mortality.

In addition to age and sex, our multivariable analysis of the 45 CBC variables found 13 independent predictors of mortality. Five were observations about white blood cells: absolute leukocytosis, high band form cell count, the presence of metamyelocytes, the presence of toxic granules, and absolute lymphocytosis. Eight were observations about red blood cells: high hematocrit, low hematocrit, high MCV and the presence of macrocytes, high red cell distribution width, the presence of NRBCs, the presence of burr cells, and the presence of sickle cells. Because controlling for severity of illness by Charlson comorbidity scores did not significantly change the model, the CBC abnormalities among the predictors of mortality did not simply reflect how sick the patients were. Including the 10 most common admission diagnoses did not significantly attenuate our reported odds ratios, suggesting the CBC predictors did not merely reflect the primary reason for admission. Interestingly, however, admission for chest pain did correlate with a greatly reduced risk of 30‐day mortality, which may reflect the low threshold that physicians have for admitting patients with this complaint. Admission for acute but ill‐defined cerebrovascular disease independently predicted a 2‐fold increased risk of 30‐day mortality.

What is the message to physicians from this analysis? Physicians commonly order CBCs and may rely on quick heuristics to sift through the myriad findings in CBC reports. Our analysis focuses physician attention on high‐impact findings in the CBC. We assume that physicians already consider low hematocrit, high hematocrit (a sign of fluid loss and/or chronic hypoxia), high WBC count, high band cell count, and the presence of metamyeloctes (left shift) as important prognostic indicators. These abnormal findings are routinely mentioned at morning report and in a physician's notes.

Physicians, however, may not appreciate the importance of other CBC findings that our analysis found are predictive of mortality. Macrocytosis and a high RDW count (indicating an abnormally wide distribution of red blood cell size) have not previously been reported as predictors of mortality. And although other studies have suggested that bands are not predictors of mortality,11 our study found they were an important prognostic indicator, with an OR =1.59, approaching those of leukocytosis and anemia.

The most impressive predictors of mortality were burr cells, NRBCs, and absolute lymphocytosis. The multivariate ORs of these 3, ranging from 2.8 to 3.2, were the highest of any CBC finding. In univariate analysis, the first 2 were associated with mortality rates 8 to 10 times higher than that of the average admitted patient. There are anecdotal reports in the literature of burr cells being associated with ominous prognoses2426 and more robust statistical analyses showing NRBCs to be associated with increased mortality.14 Lymphocytosis has also been reported as a mortality risk in patients with trauma and emergency medical conditions.15, 16 Our analysis has shown that, indeed, all 3 of these findings are strong independent predictors of mortality.

The presence of sickle cells was also a strong predictor, but of decreased mortality. Patients with sickle cells in their smear had a risk of death one third that of patients without sickle cells. This does not indicate a protective effect. Rather, patients with sickle‐cell disease typically are young and admitted for pain control and other non‐life‐threatening conditions. The presence of NRBCs in patients with sickle‐cell disease appears to be intrinsic to the disease itself and did not have the same implications for mortality as it did for other patients in our study.

The overall logistic model including age, sex, and admission CBC variables had a respectable c statistic for predicting 30‐day mortality of 0.80. This compares well with findings in other multivariable models. For example, the APACHE II score used to predict the mortality of hospitalized critical care patients has a c statistic that ranges from 0.78 to 0.86.3, 27, 28 The APACHE score uses the worst value from the first 2 days after admission for some of its predictors so it cannot provide as early a warning as the admission CBC, and it requires collection of significantly more data. The inclusion of more CBC findings in the APACHE model might increase its predictive accuracy.

Our multivariate analysis was based on a very large number of patient samples using data collected through routine clinical care. However, our study has a number of limitations. The analysis was done at only a single institution, and the exact logistic regression model may not apply to other institutions that have different case mixes and laboratory procedures. Our institution's reported 30‐day mortality rate of 3.4% was lower than the 4.6%11.9% reported in studies of patients admitted to general ward services,2931 but this may be accounted for by the lower‐than‐average Charlson comorbidity scores in our study population. Our risk adjustment by Charlson comorbidity scores may not be as precise as a risk adjustment tailored for our particular institution.32 Our 30‐day mortality rate was calculated using state death tapes, which means we would have missed patients who died outside the state, although we believe this rarely happens. We developed predictive equations on the basis of 30‐day mortality, so we cannot comment on whether the CBC elements predict mortality beyond 30 days. We analyzed most variables as either high or low or as present or absent. Increasing degrees of abnormality may further increase the predictive power of some variables. Finally, the CBC is only one of many tests and clinical findings; it may be that some of these other variables would displace some CBC variables and/or improve the overall predictive power at the time the admission laboratory tests were performed. In this initial study, we have described the prognostic implication of the CBC across a wide range of diagnoses. Future work will focus on the predictive power of commonly gathered variables in more specific conditions (eg, low white blood cell count in sepsis).

Physicians generally have an intuitive ability to identify patients who are seriously ill and at high risk of dying33 and adjust their diagnostic and therapeutic efforts accordingly. Our analysis highlights the value that certain observations in the CBC, notably burr cells, NRBCs, and absolute lymphocytosis, add to physicians' assessments of mortality risk. Even after adjustment for age, sex, comorbidities, common admission diagnoses, and other variables in the CBC, the presence of these findings predicted a 3‐fold increase in 30‐day mortality. Identifying the red flags within this ubiquitously performed test can make the difference in premature discharge or inappropriate triage of patients. Busy physicians can choose from a wide selection of ever‐improving diagnostic tests, yet the workhorse CBC can serve as a simple and early identifier of patients with a poor prognosis.

The complete blood count (CBC) bundles the automated hemogram, an automated differential count of 5 types of cells, and a reflex manual differential count (when required by protocol) and is one of the most frequently ordered laboratory tests on admission to the hospital. In practice, it is a routine ingredient of all hospital admission ordersphysicians order a hemogram either alone or as part of a complete blood count for 98% of our medical/surgical admissions, and the same is true at most institutions.1 We know that the white blood cell count and hematocrit from the automated hemogram predict disease severity and mortality risk.25 For example, elevated WBC counts predict a worse prognosis in patients with cancer or coronary artery disease,6, 7 and anemia predicts increased risk of death of patients with heart failure.8, 9 Further, these two tests provide direct management guidance in common circumstances, for example, bleeding and infection.

The CBC describes the number and morphology of more than 40 cell types, from acanthocytosis to vacuolated white blood cells. Disagreement exists about the clinical significance of many of these observations.1013 And only a few components of the manual differential, for example, nucleated red blood cells (NRBCs) and lymphocytosis, have been quantitatively evaluated to determine their prognostic significance.1417 But these two observations have not been examined to determine their independent contributions to predictions of mortality when taken in conjunction with their accompanying CBC observations. Which of the numerous cell types and cell counts in the commonly ordered CBC, indicate that a patient is at high risk of death? In this article we report an inpatient study that used univariate and multivariate analyses of admission CBCs to predict 30‐day mortality in order to answer that question.

METHODS

Patients and Protocol

The institutional review board of Indiana University, Purdue University, Indianapolis, approved this study. We included in the study all adult patients (those at least 18 years old) admitted to Wishard Hospital between January 1, 1993, and December 31, 2002, except for prisoners (for IRB reasons) and obstetric patients (because their 30‐day mortality is very close to zero0.07% at our institution). Wishard Hospital is a large urban hospital that serves a diverse but predominantly inner‐city population in Indianapolis. If a patient was admitted more than once during the 10 years of observation, we included only the first admission in the analysis in order to assure statistical independence of the observations. We extracted data from the Regenstrief Medical Record System (RMRS), a comprehensive medical records system that has demographic data, vital signs, diagnoses, results of clinical tests, and pharmacy information on all inpatient, emergency department, and outpatient encounter sites.18

We obtained the admission and discharge ICD9 and DRG codes to assess the disease patterns associated with individual CBC abnormalities. We obtained these codes from routine hospital case abstractions performed by Wishard Hospital's medical records department using NCoder+ and Quadramed. Patients assigned DRG codes 370‐384 were identified as obstetric and therefore excluded. Using the ICD9 and CPT codes according to the Charlson algorithm, we calculated a Charlson Comorbidity Index value19 for each patient as a marker of coexisting conditions.

Outcomes

The primary outcome was 30‐day mortality counted from the date of admission. We used information from the hospital record (inpatient deaths) and the Indiana state death tapes to determine the dates of death of all patients. Patients were matched to the Indiana death tapes by an algorithm using name, social security number, date of birth, and sex.20

Hemogram and Differential Count Test Methods

The hemogram, differential counts, and blood smear exam results included in this study all came from Wishard Hospital's laboratory. During this study, the hospital used only 2 cell counters, the Coulter STK‐S and the Gen‐S automated blood analyzer (Beckman Coulter, Brea, California), to produce hemogram and automated blood differential counts. Both instruments provided automated differential counts of 5 cell types: neutrophils, lymphocytes, monocytes, basophils, and eosinophils. The latter machine also produced platelet counts and reticulocyte counts, but during the study period these counts were not routinely reported to physicians unless ordered specifically, so we did not include them in the analyses. The laboratory reflexively performed 100‐cell manual differential counts and blood smear exams when abnormalities as defined by College of American Pathologists (CAP) criteria were observed in the automated measures. Both automated blood analyzers used the same automated CAP criteria to decide when to add a manual differential count and blood smear analysis, and these criteria were constant throughout the study. This protocol predicts manual differential abnormalities with high sensitivity, missing less than 1% of important findings in a manual differential.21 When the CAP criteria did not require a manual differential count and blood smear exam, we assumed that those counts unique to a manual count, for example, blast cell count, were zero and that there were no abnormalities in blood smear morphology.

Laboratories may report white blood cells as absolute counts (eg, number of cells/mm3) and/or as percentages. We converted all counts reported as percentages to absolute numbers (eg, WBC count 1000 cell type percent/100). For absolute counts that have both high and low ranges, such as white blood cell (WBC) count, we constructed two binary variables. WBC‐low was 1 when the WBC was below the lower limit of normal; otherwise it was 0. WBC‐high was 1 if the WBC was above the upper limit of normal; otherwise it was 0. For continuous variables such as NRBCs or blasts where any presence on the manual differential count is abnormal, we constructed binary variables with 0 indicating absence of the cell type and 1 indicating a cell count was at least 1.

Measurements of many cell types in the manual differential count and smear assessment (eg, burr cells) are reported in qualitative terms such as occasional, few, increased, or present, if observed, or none seen, unremarkable, or no mention, if not observed. We dichotomized all such results as present or absent for analysis purposes.

Statistical Analysis

For all the original variables, we plotted cell counts against 30‐day mortality to graphically show this univariate association. To screen the effects of these 45 binary CBC variables univariately, we used each as the sole independent variable in a logistic regression model with 30‐day mortality as the dependent variable.

The simultaneous effects of the 45 CBC measures on mortality were investigated using multiple logistic regression models, always controlling for patient age (in years, as a continuous variable) and sex (as a dichotomous variable). Two approaches were taken to handle the large number of predictors in the model. First, we formed subgroups of predictors based on clinical judgment (eg, the subgroup of bands, Dohle bodies, and toxic granules associated with infections) and ran logistic regressions of each subgroup to choose the significant predictors of these subgroups to fit them into an overall prediction model of 30‐day mortality. The results were verified using a second approach that did not depend on subjective judgment. Both backward and forward stepwise variable selection procedures were used to choose the subset of significant predictors (P < .005) of 30‐day mortality in logistic regression, again controlling for age and sex. To be sure that the predictive power of the models was not decreased by converting continuous variables into categorical variables, we also ran models that included the continuous variables as potential predictors. We used the c statistic as a measure of the goodness‐of‐fit of the models. We included the Charlson Index and the 10 most common admission diagnoses in our model to control for comorbidities and prime reason for admission, respectively.

We performed the analysis using SAS software, version 8.02 (SAS Institute, Inc., Cary, NC).

Chart Review

For each independent predictor of 30‐day mortality that was both statistically significant and had a very high relative risk (>2.5), one author (A.K.) took a random sample of 100200 patients with positive values for this predictor and reviewed the dictated discharge summaries in order to asses the clinical correlates of these findings.

RESULTS

During the 10 years from January 1993 through December 2002, physicians admitted 46,522 unique eligible patients to Wishard Memorial Hospital. Each patient averaged 2 admissions during the study period, for a total of 94,582 admissions. The overall 30‐day mortality of these admissions was 3.4%. Automated hemograms (white blood cell count, hemoglobin, red cell count, and red blood cell indices) were performed on blood samples from 45,709 of these patients (98%) within one day of admission. Seventy‐seven percent (35,692) had a complete blood count that included an automated differential count plus a reflex manual count and smear when required by the CAP protocol, as well as an automated hemogram. The patients with an admission CBC with differential count had a 30‐day mortality rate of 4%, slightly higher than that of patients who had only a hemogram. The patients' mean Charlson score for the CBC with differential count was 0.83, which was lower than the national average, which is closer to 1.22 Table 1 shows the demographics of this study population.

Characteristics of 35,692 Unique Patients with a CBC and Automated Differential Count
CharacteristicValue
Average age (years)46.2 17.7
Average LOS (days)6.5 8.1
Male (%)55.4
Race
White (%)52.9
Black (%)43.4
Other (%)3.7
Charlson Index (mean)0.83 1.5
Most common admission diagnoses (ICD9)Chest pain
 Pneumonia, organism unspecified
 Other symptoms involving abdomen or pelvis
 Unspecified heart failure
 Intermediate coronary syndrome
 Unspecified hemorrhage of GI tract
 Acute but ill‐defined cerebrovascular disease
 Diseases of pancreas
 Cellulitis and abscess of leg except foot
 Convulsions

Predictors of 30‐Day Mortality

We examined the univariate effect of age, sex, and the 45 CBC variables (Table 2) on 30‐day mortality. Most of these variables showed a significant (P < .0001) effect on mortality. Only a few abnormalities, for example, a low WBC (< 5000/L), basophilia (>200/L), and eosinophilia (>450/L), were unrelated to 30‐day mortality. Increasing age and male sex were associated with increased mortality. Of the 45 CBC variables, 29 were strong (P < .0001) univariate predictors of mortality and had odds ratios (ORs) greater than 2.5. Eight variables had univariate ORs greater than 4: toxic granules, Dohle bodies, smudge cells, promyelocytes, myelocytes, metamyelocytes, NRBCs, and burr cells. All but 2 of these are white blood cell observations.

Univariate Risk of 30‐Day Mortality in Patients with an Admission CBC and Automated Differential Count
  Number (%)Odds ratioP value
HemogramAge ( 18 years)35,688 (100)1.039< .0001
Sex (male)19,788 (55.4)1.420<.0001
 WBC > 12,00011,124 (31.2)2.049<.0001
 WBC < 50002176 (6.1)0.938.5765
 Hematocrit (>54)212 (0.6)2.633<.0001
 Hematocrit (<37)8687 (24.3)2.359<.0001
 MCV (>94)6552 (18.4)1.584<.0001
 MCV (<80)2815 (7.9)1.258.0121
 High RDW (>14.5)9478 (26.6)2.647<.0001
 High MCH (>32)5308 (14.9)1.367<.0001
 Low MCH (<26)2064 (5.8)1.392.0011
 High MCHC (>36)28 (0.1)3.964.0109
 Low MCHC (<32)738 (2.1)2.190<.0001
 Automated differential countNeutrophilia (>7700)10,578 (37.8)1.601<.0001
Neutropenia (<1500)469 (1.3)2.831<.0001
 Basophilia (>200)1137 (3.2)1.362.0215
 Eosinophilia (>450)1529 (4.3)1.074.5788
 Monocytosis (>800)10,066 (28.2)1.262<.0001
 Lymphocytosis (>4000)3046 (8.5)2.495<.0001
Manual differential countBlast cells (Y/N)31 (0.1)1.638.5001
Myelocytes (Y/N)215 (0.6)8.231< .0001
 Promyelocytes (Y/N)25 (0.1)13.429< .0001
 Metamyeloctyes (Y/N)905 (2.5)5.798< .0001
 Atypical lymphocytes (Y/N)1303 (3.7)1.881< .0001
 Hypersegmented neutrophils (Y/N)141 (0.4)3.061< .0001
 Microcytes (Y/N)3452 (9.7)2.578< .0001
 Macrocytes (Y/N)3475 (9.7)3.282< .0001
 Hypochromic RBCs (Y/N)2252 (6.3)2.290< .0001
 Basophilic stippling (Y/N)273 (0.8)3.553< .0001
 Target cells (Y/N)1140 (3.2)2.866< .0001
 Polychromasia (Y/N)1675 (4.7)3.622< .0001
 Toxic granules (Y/N)1063 (3.0)4.021< .0001
 Dohle bodies (Y/N)524 (1.5)4.821< .0001
 Ovalocytes (Y/N)1555 (4.4)2.558< .0001
 Spherocytes (Y/N)465 (1.3)3.132< .0001
 Schistocytes (Y/N)1484 (4.2)3.150< .0001
 Sickle Cells (Y/N)62 (0.2)0.389.3490
 Howell‐Jolly bodies (Y/N)71 (0.2)3.025.0033
 Pappenheimer bodies (Y/N)67 (0.2)2.344.0468
 Burr cells (Y/N)253 (0.7)9.297<.0001
 Teardrop cells (Y/N)538 (1.5)2.150< .0001
 Vacuolated cells (Y/N)897 (2.5)3.667< .0001
 Giant platelets (Y/N)781 (2.2)3.102< .0001
 Smudge cells (Y/N)50 (0.1)5.237< .0001
 Cleaved cells (Y/N)8 (0.0)3.393.2533
 Band forms (Y/N)7594 (21.3)2.964< .0001
 NRBCs (Y/N)467 (1.3)8.756< .0001

All the statistical approaches produced essentially the same model for predicting mortality. Table 3 shows that age, sex, and 13 of the CBC variables were retained in the final model of dichotomous variables using backward and forward selection. Lymphocytosis, burr cells, and NRBCs were the greatest independent predictors of mortality, with odds ratios greater than 2.5. Only 1 variable, sickle cells, predicted reduced mortality (with an odds ratio well below 1).

Multivariate Model of Statistically Significant (P < .005) Predictors of 30‐Day Mortality from the CBC and Automated Differential Count Pared Stepwise Backward Selection
ParameterOdds ratioConfidence intervalP value
Age (years)1.0401.0371.043< .0001
Sex (male)1.9651.7462.213< .0001
WBC > 12,0001.7011.5081.919< .0001
Hematocrit (>54)2.3311.4383.780< .0006
Hematocrit (<37)1.7141.5141.941< .0001
MCV (>94)1.3521.1861.543< .0001
High RDW (>14.5)1.4631.2911.658< .0001
Lymphocytosis (>4000)2.8482.4353.332< .0001
Metamyeloctye (Y/N)2.0741.6662.581< .0001
Macrocytes (Y/N)1.3171.1271.539< .0005
Toxic granules (Y/N)1.4941.2001.859.0003
Sickle cells (Y/N)0.0390.0050.292.0016
Burr cells (Y/N)3.2542.3474.513< .0001
Band forms (Y/N)1.5861.3861.814< .0001
NRBCs (Y/N)2.9062.2403.770< .0001

The c statistic (the ratio of the area under the ROC curve to the whole area, which reflects the overall predictive power of the final model), was about 0.80 by any approach, which compared favorably with previous prediction models.3, 4 Using continuous measures of CBC in the model did not increase the predictive power. Inclusion of the Charlson Index and the top 10 admission diagnoses did not significantly change the prediction model, although 2 admission diagnoses, chest pain and acute but ill‐defined cerebrovascular disease, emerged as independent predictors of 30‐day mortality, with odds ratios of 0.314 and 2.033, respectively, at P < .0001.

Chart Review

Of the 200 cases with NRBCs, the leading probable causes for this finding were severe hypoxia (average A‐a gradient = 326 mm Hg), acute anemia (average hgb = 6.1 gm/dL), and sickle‐cell anemia. Other diseases associated with NRBCs were infection/sepsis, HIV, solid tumors (breast/lung/colon/prostate), and leukemia or multiple myeloma. Having even a single NRBC at admission correlated with a 25.5% mortality rate. Of note, 30%40% of patients with sickle‐cell disease had NRBCs and moderate anemia (hgb = 8.7 gm/dL) on admission to the hospital, but there was no excess risk of mortality. Indeed, the 49 patients with sickle‐cell disease who had NRBCs at admission had a 30‐day mortality of 0%.

Most of the patients with NRBCs reviewed exhibited overt signs of severe disease, for example, shock, respiratory failure, or severe trauma, in addition to having NRBCs. However, in 2 patients the NRBCs were the only strong signal of disease severity. Both had NRBCs on the day of discharge and were readmitted within 3 days in extremis and died. One was readmitted in fulminant septic shock, likely from a bacterial peritonitis or urinary tract infection, and the other was readmitted in shock, likely from decompensated heart failure.

In univariate analysis, burr cells at admission correlated with a mortality rate of 27.3%. A review of 100 randomly chosen patients with burr cells revealed a pattern of associated diseases, that is, acute renal failure, liver failure, and congestive heart failure, different from that of patients with NRBCs. There was little overlap in the presence of burr cells and NRBCs, but the 12% who had burr cells and NRBCs had a high mortality rate (57%).

Absolute lymphocytosis was associated with a mortality rate of 8.6%. Although univariate analysis showed that the risk with lymphocytosis was not as high as that for patients with NRBCs or burr cells at admission, lymphocytosis was much more common (8.5%), and within the logistic model its presence explained more of the chi‐square statistic than any other variable except age. Indeed, lymphocytosis was a stronger predictor of 30‐day mortality than was high WBCs or anemia. Chart review of 200 patients with lymphocytosis showed a preponderance of them had large physiologic stressors, for example, traumatic tissue injury (surgery) or cerebrovascular injury. In one subset, half the patients (50.9% of 53 patients) who underwent craniotomy for trauma and had absolute lymphocytosis at admission died, compared with 20.8% of 101 patients admitted for the same diagnosis without absolute lymphocytosis.

DISCUSSION

Some investigators have incorporated selected CBC measures, for example, white blood cell count and hemoglobin/hematocrit, into multivariable models that predict mortality or rehospitalizations.6, 7, 9, 23 However, CBC reports can include a spectrum of more than 40 distinct counts and morphologic findings. Our study was the first to take into account all the different variables in the complete blood count and differential to determine elements that independently predict a high risk of mortality.

In addition to age and sex, our multivariable analysis of the 45 CBC variables found 13 independent predictors of mortality. Five were observations about white blood cells: absolute leukocytosis, high band form cell count, the presence of metamyelocytes, the presence of toxic granules, and absolute lymphocytosis. Eight were observations about red blood cells: high hematocrit, low hematocrit, high MCV and the presence of macrocytes, high red cell distribution width, the presence of NRBCs, the presence of burr cells, and the presence of sickle cells. Because controlling for severity of illness by Charlson comorbidity scores did not significantly change the model, the CBC abnormalities among the predictors of mortality did not simply reflect how sick the patients were. Including the 10 most common admission diagnoses did not significantly attenuate our reported odds ratios, suggesting the CBC predictors did not merely reflect the primary reason for admission. Interestingly, however, admission for chest pain did correlate with a greatly reduced risk of 30‐day mortality, which may reflect the low threshold that physicians have for admitting patients with this complaint. Admission for acute but ill‐defined cerebrovascular disease independently predicted a 2‐fold increased risk of 30‐day mortality.

What is the message to physicians from this analysis? Physicians commonly order CBCs and may rely on quick heuristics to sift through the myriad findings in CBC reports. Our analysis focuses physician attention on high‐impact findings in the CBC. We assume that physicians already consider low hematocrit, high hematocrit (a sign of fluid loss and/or chronic hypoxia), high WBC count, high band cell count, and the presence of metamyeloctes (left shift) as important prognostic indicators. These abnormal findings are routinely mentioned at morning report and in a physician's notes.

Physicians, however, may not appreciate the importance of other CBC findings that our analysis found are predictive of mortality. Macrocytosis and a high RDW count (indicating an abnormally wide distribution of red blood cell size) have not previously been reported as predictors of mortality. And although other studies have suggested that bands are not predictors of mortality,11 our study found they were an important prognostic indicator, with an OR =1.59, approaching those of leukocytosis and anemia.

The most impressive predictors of mortality were burr cells, NRBCs, and absolute lymphocytosis. The multivariate ORs of these 3, ranging from 2.8 to 3.2, were the highest of any CBC finding. In univariate analysis, the first 2 were associated with mortality rates 8 to 10 times higher than that of the average admitted patient. There are anecdotal reports in the literature of burr cells being associated with ominous prognoses2426 and more robust statistical analyses showing NRBCs to be associated with increased mortality.14 Lymphocytosis has also been reported as a mortality risk in patients with trauma and emergency medical conditions.15, 16 Our analysis has shown that, indeed, all 3 of these findings are strong independent predictors of mortality.

The presence of sickle cells was also a strong predictor, but of decreased mortality. Patients with sickle cells in their smear had a risk of death one third that of patients without sickle cells. This does not indicate a protective effect. Rather, patients with sickle‐cell disease typically are young and admitted for pain control and other non‐life‐threatening conditions. The presence of NRBCs in patients with sickle‐cell disease appears to be intrinsic to the disease itself and did not have the same implications for mortality as it did for other patients in our study.

The overall logistic model including age, sex, and admission CBC variables had a respectable c statistic for predicting 30‐day mortality of 0.80. This compares well with findings in other multivariable models. For example, the APACHE II score used to predict the mortality of hospitalized critical care patients has a c statistic that ranges from 0.78 to 0.86.3, 27, 28 The APACHE score uses the worst value from the first 2 days after admission for some of its predictors so it cannot provide as early a warning as the admission CBC, and it requires collection of significantly more data. The inclusion of more CBC findings in the APACHE model might increase its predictive accuracy.

Our multivariate analysis was based on a very large number of patient samples using data collected through routine clinical care. However, our study has a number of limitations. The analysis was done at only a single institution, and the exact logistic regression model may not apply to other institutions that have different case mixes and laboratory procedures. Our institution's reported 30‐day mortality rate of 3.4% was lower than the 4.6%11.9% reported in studies of patients admitted to general ward services,2931 but this may be accounted for by the lower‐than‐average Charlson comorbidity scores in our study population. Our risk adjustment by Charlson comorbidity scores may not be as precise as a risk adjustment tailored for our particular institution.32 Our 30‐day mortality rate was calculated using state death tapes, which means we would have missed patients who died outside the state, although we believe this rarely happens. We developed predictive equations on the basis of 30‐day mortality, so we cannot comment on whether the CBC elements predict mortality beyond 30 days. We analyzed most variables as either high or low or as present or absent. Increasing degrees of abnormality may further increase the predictive power of some variables. Finally, the CBC is only one of many tests and clinical findings; it may be that some of these other variables would displace some CBC variables and/or improve the overall predictive power at the time the admission laboratory tests were performed. In this initial study, we have described the prognostic implication of the CBC across a wide range of diagnoses. Future work will focus on the predictive power of commonly gathered variables in more specific conditions (eg, low white blood cell count in sepsis).

Physicians generally have an intuitive ability to identify patients who are seriously ill and at high risk of dying33 and adjust their diagnostic and therapeutic efforts accordingly. Our analysis highlights the value that certain observations in the CBC, notably burr cells, NRBCs, and absolute lymphocytosis, add to physicians' assessments of mortality risk. Even after adjustment for age, sex, comorbidities, common admission diagnoses, and other variables in the CBC, the presence of these findings predicted a 3‐fold increase in 30‐day mortality. Identifying the red flags within this ubiquitously performed test can make the difference in premature discharge or inappropriate triage of patients. Busy physicians can choose from a wide selection of ever‐improving diagnostic tests, yet the workhorse CBC can serve as a simple and early identifier of patients with a poor prognosis.

References
  1. Shapiro MF,Greenfield SG.The complete blood count and leukocyte differential count.Ann Intern Med.1987;106:6574.
  2. Chang R,Wong GY.Prognostic significance of marked leukocytosis in hospitalized patients.J Gen Intern Med.1991;6:199203.
  3. Knaus WA,Wagner DP,Draper EA, et al.The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults.Chest.1991;100:16191636.
  4. Knaus WA,Draper EA,Wagner DP,Zimmerman JE.APACHE II: a severity of disease classification system.Crit Care Med.1985;13:818829.
  5. Fine MJ,Auble TE,Yealy DM, et al.A prediction rule to identify low‐risk patients with community‐acquired pneumonia.N Engl J Med.1997;336:243250.
  6. Grimm R,Neaton J,Ludwig W.Prognostic importance of the white blood cell count for coronary, cancer, and all‐cause mortality.JAMA.1985;254:19321937.
  7. Labry LD,Campion E,Glynn R,Vokonas P.White blood cell count as a predictor of mortality: results over 18 years from the Normative Aging Study.J Clin Epidemiol.1990;43:153157.
  8. Frumin AM,Mendell TH,Mintz SS,Novack P,Faulk AT.Nucleated red blood cells in congestive heart failure.Circulation.1959;20:367370.
  9. Mozaffarian D,Nye R,Levy WC.Anemia predicts mortality in severe heart failure: the prospective randomized amlodipine survival evaluation (PRAISE).J Am Coll Cardiol.2003;41:19331939.
  10. Ardron MJ,Westengard JC,Dutcher TF.Band neutrophil counts are unnecessary for the diagnosis of infection in patients with normal total leukocyte counts.Am J Clin Pathol.1994;102:646649.
  11. Brigden M,Page N.The lack of clinical utility of white blood cell differential counts and blood morphology in elderly individuals with normal hematology profiles.Arch Pathol Lab Med.1990;114:394398.
  12. Wenz B,Gennis P,Canova C,Burns ER.The clinical utility of the leucocyte differential in emergency medicine.Am J Clin Pathol.1986;86:298303.
  13. Wile MJ,Homer LD,Gaehler S,Phillips S,Millan J.Manual differential cell counts help predict bacterial infection.Am J Clin Pathol.2001;115:644649.
  14. Schwartz SO,Stansbury F.Significance of nucleated red blood cells in peripheral blood; analysis of 1,496 cases.JAMA.1954;154:13391340.
  15. Stachon A,Sondermann N,Imohl M,Krieg M.Nucleated red blood cells indicate high risk of in‐hospital mortality.J Lab Clin Med.2002;140:407412.
  16. Teggatz JR,Parkin J,Peterson L.Transient atypical lymphocytosis in patients with emergency medical conditions.Arch Pathol Lab Med.1987;111:712714.
  17. Pinkerton PH,McLellan BA,Quantz MC,Robinson JB.Acute lymphocytosis after trauma—early recognition of the high‐risk patient?J Trauma.1989;29:749751.
  18. McDonald CJ,Overhage JM,Tierney WM, et al.The Regenstrief Medical Record System: a quarter century experience.Int J Med Inf.1999;54:225253.
  19. Charlson M,Szatrowski TP,Peterson J,Gold J.Validation of a combined comorbidity index.J Clin Epidemiol.1994;47:12451251.
  20. Grannis S,Overhage JM,McDonald CJ.Real world performance of approximate string comparators for use in patient matching.Medinfo.2004;11(Pt1):4347.
  21. Picard F,Gicquel C,Marnet L,Guesnu M,Levy JP.Preliminary evaluation of the new hematology analyzer COULTER GEN‐S in a university hospital.Clin Chem Lab Med.1999;37:681686.
  22. Rosenthal GE,Kaboli PJ,Barnett MJ.Differences in length of stay in veterans health administration and other united states hospitals: is the gap closing?Med Care.2003;41:882894.
  23. Kosiborod M,Smith G,Radford M,Foody J,Krumholz H.The Prognostic importance of anemia in patients with heart failure.Am J Med.2003;114:112119.
  24. Schwartz SO,Motto SA.The diagnostic significance of “burr” red blood cells.Am J Med Sci.1949;218:563.
  25. Aherne WA.The “burr” red cell and azotemia.J Clin Pathol.1957;10:252257.
  26. Bell RE.The origin of ‘burr’ erythrocytes.Br J Haematol.1963;9:552555.
  27. de Keizer NF,Bonsel GJ,Goldfad C,Rowan KM.The added value that increasing levels of diagnostic information provide in prognostic models to estimate hospital mortality for adult intensive care patients.Intern Care Med.2000;26:577584.
  28. Harrell F,Califf R,Pryor D,Lee K,Rosati R.Evaluating the yield of medical tests.JAMA.1982;247:25432546.
  29. Meltzer D,Manning WG,Morrison J, et al.Effects of physician experience on costs and outcomes on an academic general medical service: results of a trial of hospitalists.Ann Intern Med.2002;137:866874.
  30. Kearns PJ,Wang CC,Morris WJ, et al.Hospital care by hospital‐based and clinic‐based faculty. a prospective, controlled trial.Arch Intern Med.2001;161:235241.
  31. Auerbach AD,Wachter RM,Katz P,Showstack J,Baron RB,Goldman L.Implementation of a voluntary hospitalist service at a community teaching hospital: improved clinical efficiency and patient outcomes.Ann Intern Med.2002;137:859865.
  32. Rosenthal GE,Harper DL,Quinn LM,Cooper GS.Severity‐adjusted mortality and length of stay in teaching and nonteaching hospitals: results of a regional study.JAMA.1997;278:485490.
  33. McClish DK,Powell SH.How well can physicians estimate mortality in a medical intensive care unit?Med Decis Mak.1989;9:125132.
References
  1. Shapiro MF,Greenfield SG.The complete blood count and leukocyte differential count.Ann Intern Med.1987;106:6574.
  2. Chang R,Wong GY.Prognostic significance of marked leukocytosis in hospitalized patients.J Gen Intern Med.1991;6:199203.
  3. Knaus WA,Wagner DP,Draper EA, et al.The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults.Chest.1991;100:16191636.
  4. Knaus WA,Draper EA,Wagner DP,Zimmerman JE.APACHE II: a severity of disease classification system.Crit Care Med.1985;13:818829.
  5. Fine MJ,Auble TE,Yealy DM, et al.A prediction rule to identify low‐risk patients with community‐acquired pneumonia.N Engl J Med.1997;336:243250.
  6. Grimm R,Neaton J,Ludwig W.Prognostic importance of the white blood cell count for coronary, cancer, and all‐cause mortality.JAMA.1985;254:19321937.
  7. Labry LD,Campion E,Glynn R,Vokonas P.White blood cell count as a predictor of mortality: results over 18 years from the Normative Aging Study.J Clin Epidemiol.1990;43:153157.
  8. Frumin AM,Mendell TH,Mintz SS,Novack P,Faulk AT.Nucleated red blood cells in congestive heart failure.Circulation.1959;20:367370.
  9. Mozaffarian D,Nye R,Levy WC.Anemia predicts mortality in severe heart failure: the prospective randomized amlodipine survival evaluation (PRAISE).J Am Coll Cardiol.2003;41:19331939.
  10. Ardron MJ,Westengard JC,Dutcher TF.Band neutrophil counts are unnecessary for the diagnosis of infection in patients with normal total leukocyte counts.Am J Clin Pathol.1994;102:646649.
  11. Brigden M,Page N.The lack of clinical utility of white blood cell differential counts and blood morphology in elderly individuals with normal hematology profiles.Arch Pathol Lab Med.1990;114:394398.
  12. Wenz B,Gennis P,Canova C,Burns ER.The clinical utility of the leucocyte differential in emergency medicine.Am J Clin Pathol.1986;86:298303.
  13. Wile MJ,Homer LD,Gaehler S,Phillips S,Millan J.Manual differential cell counts help predict bacterial infection.Am J Clin Pathol.2001;115:644649.
  14. Schwartz SO,Stansbury F.Significance of nucleated red blood cells in peripheral blood; analysis of 1,496 cases.JAMA.1954;154:13391340.
  15. Stachon A,Sondermann N,Imohl M,Krieg M.Nucleated red blood cells indicate high risk of in‐hospital mortality.J Lab Clin Med.2002;140:407412.
  16. Teggatz JR,Parkin J,Peterson L.Transient atypical lymphocytosis in patients with emergency medical conditions.Arch Pathol Lab Med.1987;111:712714.
  17. Pinkerton PH,McLellan BA,Quantz MC,Robinson JB.Acute lymphocytosis after trauma—early recognition of the high‐risk patient?J Trauma.1989;29:749751.
  18. McDonald CJ,Overhage JM,Tierney WM, et al.The Regenstrief Medical Record System: a quarter century experience.Int J Med Inf.1999;54:225253.
  19. Charlson M,Szatrowski TP,Peterson J,Gold J.Validation of a combined comorbidity index.J Clin Epidemiol.1994;47:12451251.
  20. Grannis S,Overhage JM,McDonald CJ.Real world performance of approximate string comparators for use in patient matching.Medinfo.2004;11(Pt1):4347.
  21. Picard F,Gicquel C,Marnet L,Guesnu M,Levy JP.Preliminary evaluation of the new hematology analyzer COULTER GEN‐S in a university hospital.Clin Chem Lab Med.1999;37:681686.
  22. Rosenthal GE,Kaboli PJ,Barnett MJ.Differences in length of stay in veterans health administration and other united states hospitals: is the gap closing?Med Care.2003;41:882894.
  23. Kosiborod M,Smith G,Radford M,Foody J,Krumholz H.The Prognostic importance of anemia in patients with heart failure.Am J Med.2003;114:112119.
  24. Schwartz SO,Motto SA.The diagnostic significance of “burr” red blood cells.Am J Med Sci.1949;218:563.
  25. Aherne WA.The “burr” red cell and azotemia.J Clin Pathol.1957;10:252257.
  26. Bell RE.The origin of ‘burr’ erythrocytes.Br J Haematol.1963;9:552555.
  27. de Keizer NF,Bonsel GJ,Goldfad C,Rowan KM.The added value that increasing levels of diagnostic information provide in prognostic models to estimate hospital mortality for adult intensive care patients.Intern Care Med.2000;26:577584.
  28. Harrell F,Califf R,Pryor D,Lee K,Rosati R.Evaluating the yield of medical tests.JAMA.1982;247:25432546.
  29. Meltzer D,Manning WG,Morrison J, et al.Effects of physician experience on costs and outcomes on an academic general medical service: results of a trial of hospitalists.Ann Intern Med.2002;137:866874.
  30. Kearns PJ,Wang CC,Morris WJ, et al.Hospital care by hospital‐based and clinic‐based faculty. a prospective, controlled trial.Arch Intern Med.2001;161:235241.
  31. Auerbach AD,Wachter RM,Katz P,Showstack J,Baron RB,Goldman L.Implementation of a voluntary hospitalist service at a community teaching hospital: improved clinical efficiency and patient outcomes.Ann Intern Med.2002;137:859865.
  32. Rosenthal GE,Harper DL,Quinn LM,Cooper GS.Severity‐adjusted mortality and length of stay in teaching and nonteaching hospitals: results of a regional study.JAMA.1997;278:485490.
  33. McClish DK,Powell SH.How well can physicians estimate mortality in a medical intensive care unit?Med Decis Mak.1989;9:125132.
Issue
Journal of Hospital Medicine - 2(1)
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Journal of Hospital Medicine - 2(1)
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5-12
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5-12
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Which observations from the complete blood cell count predict mortality for hospitalized patients?
Display Headline
Which observations from the complete blood cell count predict mortality for hospitalized patients?
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diagnostic decision making, laboratory testing, electronic medical record
Legacy Keywords
diagnostic decision making, laboratory testing, electronic medical record
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