User login
Study may explain how CSCs survive treatment
Credit: Andre Karwath
Experiments conducted in fruit flies showed that when researchers eliminated a type of stem cell, a group of non-stem cells stepped in to replace them.
The team said this discovery sheds new light on stem cell niches and may help explain how cancer stem cells (CSCs) replenish themselves after exposure to radiation and chemotherapy.
Erika Matunis, PhD, of the Johns Hopkins University School of Medicine in Baltimore, Maryland, and her colleagues detailed these findings in Cell Reports.
The researchers used the fruit fly as a model to examine stem cells in their natural state, studying stem cell niches in Drosophila testes.
In these niches are 3 kinds of cells: germ-line stem cells, which divide to produce sperm; somatic cyst stem cells, which make cyst cells; and hub cells, which produce signals that keep these 2 cell types going.
The hub cells have settled on their final form and are incapable of dividing further or changing their function—or so everyone thought.
In a bid to determine what happens when the somatic cyst stem cells are killed off, the researchers tried to figure out how to best do away with them. They thought the task would be straightforward, but it took many combinations of different genes working together to kill the somatic cyst cells.
“When we finally figured out a way to kill all of the somatic stem cells, we thought that the rest of the tissue would probably just empty out,” Dr Matunis said.
In 35% of testes, that’s just what happened. But in the rest, the somatic stem cells grew back.
This was a surprise, Dr Matunis said, and it raised the question of where these new stem cells originated.
The answer was another surprise: the hub cells. When the somatic stem cells were destroyed, the hub cells ramped up their machinery for cell division.
The team did several experiments to confirm the hub cells were involved, including one in which they genetically marked the hub cells and saw the mark appear in the newly formed somatic stem cells—a clear sign that hub cells had divided to make new stem cells.
Dr Matunis noted, however, that the new stem cells created by the hub cells weren’t exactly the same as the old ones. Sometimes, the new cells made molecules that only hub cells normally make.
As the researchers looked closer, they realized the damaged and recovered testes were making new niches. Instead of just one pocket of stem cells, a damaged testis might have 2 or 3.
The researchers have not determined how the new niches are formed, but they speculate that the original niche gets bigger as the new cells divide, then splits. The group is now conducting more experiments aimed at explaining the basics of how niches work, according to Dr Matunis.
She said this research may be useful for understanding CSCs. Knowing how tumor niches support the continued growth and division of CSCs might one day offer new targets for controlling such growth. 
Credit: Andre Karwath
Experiments conducted in fruit flies showed that when researchers eliminated a type of stem cell, a group of non-stem cells stepped in to replace them.
The team said this discovery sheds new light on stem cell niches and may help explain how cancer stem cells (CSCs) replenish themselves after exposure to radiation and chemotherapy.
Erika Matunis, PhD, of the Johns Hopkins University School of Medicine in Baltimore, Maryland, and her colleagues detailed these findings in Cell Reports.
The researchers used the fruit fly as a model to examine stem cells in their natural state, studying stem cell niches in Drosophila testes.
In these niches are 3 kinds of cells: germ-line stem cells, which divide to produce sperm; somatic cyst stem cells, which make cyst cells; and hub cells, which produce signals that keep these 2 cell types going.
The hub cells have settled on their final form and are incapable of dividing further or changing their function—or so everyone thought.
In a bid to determine what happens when the somatic cyst stem cells are killed off, the researchers tried to figure out how to best do away with them. They thought the task would be straightforward, but it took many combinations of different genes working together to kill the somatic cyst cells.
“When we finally figured out a way to kill all of the somatic stem cells, we thought that the rest of the tissue would probably just empty out,” Dr Matunis said.
In 35% of testes, that’s just what happened. But in the rest, the somatic stem cells grew back.
This was a surprise, Dr Matunis said, and it raised the question of where these new stem cells originated.
The answer was another surprise: the hub cells. When the somatic stem cells were destroyed, the hub cells ramped up their machinery for cell division.
The team did several experiments to confirm the hub cells were involved, including one in which they genetically marked the hub cells and saw the mark appear in the newly formed somatic stem cells—a clear sign that hub cells had divided to make new stem cells.
Dr Matunis noted, however, that the new stem cells created by the hub cells weren’t exactly the same as the old ones. Sometimes, the new cells made molecules that only hub cells normally make.
As the researchers looked closer, they realized the damaged and recovered testes were making new niches. Instead of just one pocket of stem cells, a damaged testis might have 2 or 3.
The researchers have not determined how the new niches are formed, but they speculate that the original niche gets bigger as the new cells divide, then splits. The group is now conducting more experiments aimed at explaining the basics of how niches work, according to Dr Matunis.
She said this research may be useful for understanding CSCs. Knowing how tumor niches support the continued growth and division of CSCs might one day offer new targets for controlling such growth.
Credit: Andre Karwath
Experiments conducted in fruit flies showed that when researchers eliminated a type of stem cell, a group of non-stem cells stepped in to replace them.
The team said this discovery sheds new light on stem cell niches and may help explain how cancer stem cells (CSCs) replenish themselves after exposure to radiation and chemotherapy.
Erika Matunis, PhD, of the Johns Hopkins University School of Medicine in Baltimore, Maryland, and her colleagues detailed these findings in Cell Reports.
The researchers used the fruit fly as a model to examine stem cells in their natural state, studying stem cell niches in Drosophila testes.
In these niches are 3 kinds of cells: germ-line stem cells, which divide to produce sperm; somatic cyst stem cells, which make cyst cells; and hub cells, which produce signals that keep these 2 cell types going.
The hub cells have settled on their final form and are incapable of dividing further or changing their function—or so everyone thought.
In a bid to determine what happens when the somatic cyst stem cells are killed off, the researchers tried to figure out how to best do away with them. They thought the task would be straightforward, but it took many combinations of different genes working together to kill the somatic cyst cells.
“When we finally figured out a way to kill all of the somatic stem cells, we thought that the rest of the tissue would probably just empty out,” Dr Matunis said.
In 35% of testes, that’s just what happened. But in the rest, the somatic stem cells grew back.
This was a surprise, Dr Matunis said, and it raised the question of where these new stem cells originated.
The answer was another surprise: the hub cells. When the somatic stem cells were destroyed, the hub cells ramped up their machinery for cell division.
The team did several experiments to confirm the hub cells were involved, including one in which they genetically marked the hub cells and saw the mark appear in the newly formed somatic stem cells—a clear sign that hub cells had divided to make new stem cells.
Dr Matunis noted, however, that the new stem cells created by the hub cells weren’t exactly the same as the old ones. Sometimes, the new cells made molecules that only hub cells normally make.
As the researchers looked closer, they realized the damaged and recovered testes were making new niches. Instead of just one pocket of stem cells, a damaged testis might have 2 or 3.
The researchers have not determined how the new niches are formed, but they speculate that the original niche gets bigger as the new cells divide, then splits. The group is now conducting more experiments aimed at explaining the basics of how niches work, according to Dr Matunis.
She said this research may be useful for understanding CSCs. Knowing how tumor niches support the continued growth and division of CSCs might one day offer new targets for controlling such growth.
Even mild preop sepsis boosts postop thrombosis risk
WASHINGTON – Preoperative sepsis proved to be an important independent risk factor for both arterial and venous thrombosis during or after surgery in an analysis of nearly 1.75 million U.S. surgical procedures.
The take-home message here is that the risk-benefit assessment of surgical procedures should take into account the presence of sepsis. And if the surgery can’t be delayed, prophylaxis against arterial as well as venous thrombosis should be employed, Dr. Jacques Donze said at the annual meeting of the American College of Cardiology.
Another key finding in this study was that the risk of postoperative thrombosis varied according to the severity of preoperative sepsis. Even the early form of sepsis known as systemic inflammatory response syndrome, or SIRS, was associated with a 2.5-fold increased risk.
"Include even early signs of sepsis as a risk factor," urged Dr. Donze of Brigham and Women’s Hospital, Boston.
Also, preoperative sepsis was a risk factor for postoperative thrombosis in connection with outpatient elective surgery as well as inpatient operations, he added.
Dr. Donze presented an analysis of 1,744,808 surgical procedures performed during 2005-2011 at 314 U.S. hospitals participating in the American College of Surgeons National Surgical Quality Improvement Program. This large, prospective, observational registry is known for its high-quality data.
Within 48 hours prior to surgery, 7.8% of patients – totaling more than 136,000 – had SIRS, sepsis, or septic shock. Their postoperative thrombosis rate was 4.2%, compared with a 1.2% rate in patients without sepsis. In a multivariate regression analysis adjusted for potential confounding factors, the postoperative thrombosis risk climbed with increasing severity of preoperative sepsis.
SIRS was defined on the basis of temperature, heart rate, respiratory rate, WBC count, and/or the presence of anion gap acidosis. "Sepsis" was defined as SIRS plus infection. And septic shock required the presence of sepsis plus documented organ dysfunction, such as hypotension.
The importance of recognizing this newly spotlighted sepsis/postoperative thrombosis connection is that most of the other known risk factors for thrombosis in surgical patients, including age, cancer, renal failure, and immobilization, are nonmodifiable, Dr. Donze observed.
Among the factors known to contribute to thrombosis are a hypercoagulable state, a proinflammatory state, hypoxemia, hypotension, and endothelial dysfunction. "All of these factors can be triggered by sepsis," Dr. Donze noted.
He reported having no financial conflicts regarding this study.
WASHINGTON – Preoperative sepsis proved to be an important independent risk factor for both arterial and venous thrombosis during or after surgery in an analysis of nearly 1.75 million U.S. surgical procedures.
The take-home message here is that the risk-benefit assessment of surgical procedures should take into account the presence of sepsis. And if the surgery can’t be delayed, prophylaxis against arterial as well as venous thrombosis should be employed, Dr. Jacques Donze said at the annual meeting of the American College of Cardiology.
Another key finding in this study was that the risk of postoperative thrombosis varied according to the severity of preoperative sepsis. Even the early form of sepsis known as systemic inflammatory response syndrome, or SIRS, was associated with a 2.5-fold increased risk.
"Include even early signs of sepsis as a risk factor," urged Dr. Donze of Brigham and Women’s Hospital, Boston.
Also, preoperative sepsis was a risk factor for postoperative thrombosis in connection with outpatient elective surgery as well as inpatient operations, he added.
Dr. Donze presented an analysis of 1,744,808 surgical procedures performed during 2005-2011 at 314 U.S. hospitals participating in the American College of Surgeons National Surgical Quality Improvement Program. This large, prospective, observational registry is known for its high-quality data.
Within 48 hours prior to surgery, 7.8% of patients – totaling more than 136,000 – had SIRS, sepsis, or septic shock. Their postoperative thrombosis rate was 4.2%, compared with a 1.2% rate in patients without sepsis. In a multivariate regression analysis adjusted for potential confounding factors, the postoperative thrombosis risk climbed with increasing severity of preoperative sepsis.
SIRS was defined on the basis of temperature, heart rate, respiratory rate, WBC count, and/or the presence of anion gap acidosis. "Sepsis" was defined as SIRS plus infection. And septic shock required the presence of sepsis plus documented organ dysfunction, such as hypotension.
The importance of recognizing this newly spotlighted sepsis/postoperative thrombosis connection is that most of the other known risk factors for thrombosis in surgical patients, including age, cancer, renal failure, and immobilization, are nonmodifiable, Dr. Donze observed.
Among the factors known to contribute to thrombosis are a hypercoagulable state, a proinflammatory state, hypoxemia, hypotension, and endothelial dysfunction. "All of these factors can be triggered by sepsis," Dr. Donze noted.
He reported having no financial conflicts regarding this study.
WASHINGTON – Preoperative sepsis proved to be an important independent risk factor for both arterial and venous thrombosis during or after surgery in an analysis of nearly 1.75 million U.S. surgical procedures.
The take-home message here is that the risk-benefit assessment of surgical procedures should take into account the presence of sepsis. And if the surgery can’t be delayed, prophylaxis against arterial as well as venous thrombosis should be employed, Dr. Jacques Donze said at the annual meeting of the American College of Cardiology.
Another key finding in this study was that the risk of postoperative thrombosis varied according to the severity of preoperative sepsis. Even the early form of sepsis known as systemic inflammatory response syndrome, or SIRS, was associated with a 2.5-fold increased risk.
"Include even early signs of sepsis as a risk factor," urged Dr. Donze of Brigham and Women’s Hospital, Boston.
Also, preoperative sepsis was a risk factor for postoperative thrombosis in connection with outpatient elective surgery as well as inpatient operations, he added.
Dr. Donze presented an analysis of 1,744,808 surgical procedures performed during 2005-2011 at 314 U.S. hospitals participating in the American College of Surgeons National Surgical Quality Improvement Program. This large, prospective, observational registry is known for its high-quality data.
Within 48 hours prior to surgery, 7.8% of patients – totaling more than 136,000 – had SIRS, sepsis, or septic shock. Their postoperative thrombosis rate was 4.2%, compared with a 1.2% rate in patients without sepsis. In a multivariate regression analysis adjusted for potential confounding factors, the postoperative thrombosis risk climbed with increasing severity of preoperative sepsis.
SIRS was defined on the basis of temperature, heart rate, respiratory rate, WBC count, and/or the presence of anion gap acidosis. "Sepsis" was defined as SIRS plus infection. And septic shock required the presence of sepsis plus documented organ dysfunction, such as hypotension.
The importance of recognizing this newly spotlighted sepsis/postoperative thrombosis connection is that most of the other known risk factors for thrombosis in surgical patients, including age, cancer, renal failure, and immobilization, are nonmodifiable, Dr. Donze observed.
Among the factors known to contribute to thrombosis are a hypercoagulable state, a proinflammatory state, hypoxemia, hypotension, and endothelial dysfunction. "All of these factors can be triggered by sepsis," Dr. Donze noted.
He reported having no financial conflicts regarding this study.
AT ACC 14
Major finding: Preoperative sepsis is a strong independent risk factor for postoperative arterial and venous thrombosis; the more severe the sepsis, the greater the thrombosis risk.
Data source: This was an analysis of nearly 1.75 million surgical procedures at 314 U.S. hospitals detailed in the American College of Surgeons National Quality Improvement Program registry.
Disclosures: The presenter reported having no financial conflicts.
Report suggests reforms for mentally ill in prison
The Treatment Advocacy Center has released an update of a national survey of prison and jail involuntary treatment policies in its 116-page report, "The Treatment of Persons with Mental Illness in Prisons and Jails: A State Survey."
The survey was a replication of a previous study done in 2008. The purpose of the study was to compare treatment bed capacity and the numbers of seriously mentally ill patients housed within a state’s correctional system versus its public mental health system, and to promote the use of involuntary treatment procedures within correctional facilities.
To prepare the report, the center gathered data from each state prison system, as well as from non–randomly selected jails, regarding total bed capacity and the percentage of seriously mentally ill prisoners housed in the correctional system. Information about nonemergency involuntary medication procedures was gathered from prison websites or through Freedom of Information requests. For jails, some policies were obtained or clarified from administrative personnel or mental health professionals within the facility. Information about available psychiatric state hospital beds was gathered from a previous TAC report on state per-capita treatment capacity.
The new report found that the ratio of seriously mentally ill patients housed in correctional facilities versus state hospitals has increased substantially since 2008. Then, the ratio was 3:1. Currently, the ratio is 10 patients held in jail or prison for every single patient in a state hospital. This is clearly a significant change, which TAC attributes to closure of state hospital beds and failure to implement outpatient commitment laws.
As I’ve said in previous columns, I’m reluctant to attribute the incarceration of mentally ill people solely to mental illness. I’m uncomfortable with a reductionist hypothesis that overlooks the whole person. All of my prison patients have challenges common to many non–mentally ill prisoners: substance abuse, lack of social supports, illiteracy, poor vocational skills, and poverty. Psychiatric patients also suffer the baser instincts common to all humanity: fear, greed, and jealous rage. Changes in laws governing sentencing also will affect all offenders, regardless of psychiatric status. Psychiatric medication, voluntary or involuntary, is not the sole answer to the problem of criminality and will do nothing to address these other issues.
Nevertheless, I agree with the majority of the TAC report recommendations, and I applaud the emphasis placed upon expanded use of mental health courts and crisis intervention teams (CIT) to avert incarceration. The recommendation to screen prisoners for mental illness is already mandated by the National Commission on Correctional Health Care (NCCHC) for any accredited facility. According to a 1994 national survey by Dr. Jeffrey L. Metzner and associates, all prisons systems provided either reception or prompt intake mental health screening to all newly admitted intakes (Bull. Am. Acad. Psychiatry Law 1994;22:451-7). Twenty-six percent of the prison systems exceeded screening standards recommended by the American Psychiatric Association. This is good news.
The TAC report also recommended mandatory release planning. Systematic release planning is a challenge to implement for most correctional systems for several reasons. In jail, release may be contingent upon the outcome of a trial and is therefore unpredictable. If the trial is postponed, a valuable community treatment slot is tied up for a patient who will never arrive. Conversely, failure to plan prior to a court date might leave a prisoner on the street directly from court with no aftercare. Nevertheless, states are beginning to realize the cost and public safety benefit of release plans that integrate medical, mental health, and substance abuse services.
One recent outcome study showed that more than half of released prisoners stayed in treatment in the community when an in-reach program provided integrated release planning services, and that annual criminal charges dropped by more than 50% in the year following engagement. More good news.
Finally, a minor quibble. My own state, Maryland, was cited in the report as one of the few states in which the involuntary treatment of inmates is most difficult because of the requirement to transfer the inmate to a hospital first. What the TAC failed to mention was that, in Maryland, the involuntary medication process was substantially undermined by case law. In 2006, the Maryland Court of Appeals decided in Department of Health and Mental Hygiene v. Anthony Kelly that involuntary medication only can be administered if the patient demonstrates dangerousness within the institution. Given this restriction, involuntary medication could not be administered on a nonemergency basis even in a correctional facility.
Dr. Hanson is a forensic psychiatrist and coauthor of "Shrink Rap: Three Psychiatrists Explain Their Work" (Baltimore: The Johns Hopkins University Press, 2011). The opinions expressed are those of the author only, and do not represent those of any of Dr. Hanson’s employers or consultees, including the Maryland Department of Health and Mental Hygiene or the Maryland Division of Correction.
The Treatment Advocacy Center has released an update of a national survey of prison and jail involuntary treatment policies in its 116-page report, "The Treatment of Persons with Mental Illness in Prisons and Jails: A State Survey."
The survey was a replication of a previous study done in 2008. The purpose of the study was to compare treatment bed capacity and the numbers of seriously mentally ill patients housed within a state’s correctional system versus its public mental health system, and to promote the use of involuntary treatment procedures within correctional facilities.
To prepare the report, the center gathered data from each state prison system, as well as from non–randomly selected jails, regarding total bed capacity and the percentage of seriously mentally ill prisoners housed in the correctional system. Information about nonemergency involuntary medication procedures was gathered from prison websites or through Freedom of Information requests. For jails, some policies were obtained or clarified from administrative personnel or mental health professionals within the facility. Information about available psychiatric state hospital beds was gathered from a previous TAC report on state per-capita treatment capacity.
The new report found that the ratio of seriously mentally ill patients housed in correctional facilities versus state hospitals has increased substantially since 2008. Then, the ratio was 3:1. Currently, the ratio is 10 patients held in jail or prison for every single patient in a state hospital. This is clearly a significant change, which TAC attributes to closure of state hospital beds and failure to implement outpatient commitment laws.
As I’ve said in previous columns, I’m reluctant to attribute the incarceration of mentally ill people solely to mental illness. I’m uncomfortable with a reductionist hypothesis that overlooks the whole person. All of my prison patients have challenges common to many non–mentally ill prisoners: substance abuse, lack of social supports, illiteracy, poor vocational skills, and poverty. Psychiatric patients also suffer the baser instincts common to all humanity: fear, greed, and jealous rage. Changes in laws governing sentencing also will affect all offenders, regardless of psychiatric status. Psychiatric medication, voluntary or involuntary, is not the sole answer to the problem of criminality and will do nothing to address these other issues.
Nevertheless, I agree with the majority of the TAC report recommendations, and I applaud the emphasis placed upon expanded use of mental health courts and crisis intervention teams (CIT) to avert incarceration. The recommendation to screen prisoners for mental illness is already mandated by the National Commission on Correctional Health Care (NCCHC) for any accredited facility. According to a 1994 national survey by Dr. Jeffrey L. Metzner and associates, all prisons systems provided either reception or prompt intake mental health screening to all newly admitted intakes (Bull. Am. Acad. Psychiatry Law 1994;22:451-7). Twenty-six percent of the prison systems exceeded screening standards recommended by the American Psychiatric Association. This is good news.
The TAC report also recommended mandatory release planning. Systematic release planning is a challenge to implement for most correctional systems for several reasons. In jail, release may be contingent upon the outcome of a trial and is therefore unpredictable. If the trial is postponed, a valuable community treatment slot is tied up for a patient who will never arrive. Conversely, failure to plan prior to a court date might leave a prisoner on the street directly from court with no aftercare. Nevertheless, states are beginning to realize the cost and public safety benefit of release plans that integrate medical, mental health, and substance abuse services.
One recent outcome study showed that more than half of released prisoners stayed in treatment in the community when an in-reach program provided integrated release planning services, and that annual criminal charges dropped by more than 50% in the year following engagement. More good news.
Finally, a minor quibble. My own state, Maryland, was cited in the report as one of the few states in which the involuntary treatment of inmates is most difficult because of the requirement to transfer the inmate to a hospital first. What the TAC failed to mention was that, in Maryland, the involuntary medication process was substantially undermined by case law. In 2006, the Maryland Court of Appeals decided in Department of Health and Mental Hygiene v. Anthony Kelly that involuntary medication only can be administered if the patient demonstrates dangerousness within the institution. Given this restriction, involuntary medication could not be administered on a nonemergency basis even in a correctional facility.
Dr. Hanson is a forensic psychiatrist and coauthor of "Shrink Rap: Three Psychiatrists Explain Their Work" (Baltimore: The Johns Hopkins University Press, 2011). The opinions expressed are those of the author only, and do not represent those of any of Dr. Hanson’s employers or consultees, including the Maryland Department of Health and Mental Hygiene or the Maryland Division of Correction.
The Treatment Advocacy Center has released an update of a national survey of prison and jail involuntary treatment policies in its 116-page report, "The Treatment of Persons with Mental Illness in Prisons and Jails: A State Survey."
The survey was a replication of a previous study done in 2008. The purpose of the study was to compare treatment bed capacity and the numbers of seriously mentally ill patients housed within a state’s correctional system versus its public mental health system, and to promote the use of involuntary treatment procedures within correctional facilities.
To prepare the report, the center gathered data from each state prison system, as well as from non–randomly selected jails, regarding total bed capacity and the percentage of seriously mentally ill prisoners housed in the correctional system. Information about nonemergency involuntary medication procedures was gathered from prison websites or through Freedom of Information requests. For jails, some policies were obtained or clarified from administrative personnel or mental health professionals within the facility. Information about available psychiatric state hospital beds was gathered from a previous TAC report on state per-capita treatment capacity.
The new report found that the ratio of seriously mentally ill patients housed in correctional facilities versus state hospitals has increased substantially since 2008. Then, the ratio was 3:1. Currently, the ratio is 10 patients held in jail or prison for every single patient in a state hospital. This is clearly a significant change, which TAC attributes to closure of state hospital beds and failure to implement outpatient commitment laws.
As I’ve said in previous columns, I’m reluctant to attribute the incarceration of mentally ill people solely to mental illness. I’m uncomfortable with a reductionist hypothesis that overlooks the whole person. All of my prison patients have challenges common to many non–mentally ill prisoners: substance abuse, lack of social supports, illiteracy, poor vocational skills, and poverty. Psychiatric patients also suffer the baser instincts common to all humanity: fear, greed, and jealous rage. Changes in laws governing sentencing also will affect all offenders, regardless of psychiatric status. Psychiatric medication, voluntary or involuntary, is not the sole answer to the problem of criminality and will do nothing to address these other issues.
Nevertheless, I agree with the majority of the TAC report recommendations, and I applaud the emphasis placed upon expanded use of mental health courts and crisis intervention teams (CIT) to avert incarceration. The recommendation to screen prisoners for mental illness is already mandated by the National Commission on Correctional Health Care (NCCHC) for any accredited facility. According to a 1994 national survey by Dr. Jeffrey L. Metzner and associates, all prisons systems provided either reception or prompt intake mental health screening to all newly admitted intakes (Bull. Am. Acad. Psychiatry Law 1994;22:451-7). Twenty-six percent of the prison systems exceeded screening standards recommended by the American Psychiatric Association. This is good news.
The TAC report also recommended mandatory release planning. Systematic release planning is a challenge to implement for most correctional systems for several reasons. In jail, release may be contingent upon the outcome of a trial and is therefore unpredictable. If the trial is postponed, a valuable community treatment slot is tied up for a patient who will never arrive. Conversely, failure to plan prior to a court date might leave a prisoner on the street directly from court with no aftercare. Nevertheless, states are beginning to realize the cost and public safety benefit of release plans that integrate medical, mental health, and substance abuse services.
One recent outcome study showed that more than half of released prisoners stayed in treatment in the community when an in-reach program provided integrated release planning services, and that annual criminal charges dropped by more than 50% in the year following engagement. More good news.
Finally, a minor quibble. My own state, Maryland, was cited in the report as one of the few states in which the involuntary treatment of inmates is most difficult because of the requirement to transfer the inmate to a hospital first. What the TAC failed to mention was that, in Maryland, the involuntary medication process was substantially undermined by case law. In 2006, the Maryland Court of Appeals decided in Department of Health and Mental Hygiene v. Anthony Kelly that involuntary medication only can be administered if the patient demonstrates dangerousness within the institution. Given this restriction, involuntary medication could not be administered on a nonemergency basis even in a correctional facility.
Dr. Hanson is a forensic psychiatrist and coauthor of "Shrink Rap: Three Psychiatrists Explain Their Work" (Baltimore: The Johns Hopkins University Press, 2011). The opinions expressed are those of the author only, and do not represent those of any of Dr. Hanson’s employers or consultees, including the Maryland Department of Health and Mental Hygiene or the Maryland Division of Correction.
Compound targets mutated DLBCL, WM cells
SAN DIEGO—A Toll-like receptor (TLR) antagonist can target B-cell lymphoma cells harboring the MYD88 L265P mutation, preclinical research suggests.
The compound, IMO-8400, decreased the viability of mutated diffuse large B-cell lymphoma (DLBCL) cells and Waldenström’s macroglobulinemia (WM) cells in vitro.
IMO-8400 also decreased tumor growth and prolonged survival in mice with MYD88 L265P-positive DLBCL.
Lakshmi Bhagat, PhD, and colleagues from the Cambridge, Massachusetts-based Idera Pharmaceuticals, Inc.—the company developing IMO-8400—presented these results at the AACR Annual Meeting 2014 (abstract 2570).
The researchers said their data provide additional evidence that the MYD88 L265P mutation results in over-activation of TLR7- and TLR9-mediated signaling, and blocking these TLRs leads to tumor cell death. IMO-8400 is an oligonucleotide-based antagonist of TLRs 7, 8, and 9.
In experiments with OCI‐Ly10 cells (DLBCL cells harboring the MYD88 L265P mutation), IMO-8400 prompted cell death and decreased proliferative cell signaling. But the compound did not produce these effects in SU-DHL-6 cells (DLBCL cells without the MYD88 L265P mutation).
In OCI‐Ly10 cells, IMO-8400 inhibited the IRAK-1, IRAK-4, BTK, STAT-3, Ik-Ba, and NF-κB pathways. The compound did not affect signaling pathways in SU-DHL-6 cells.
IMO-8400 also inhibited tumor growth in a mouse model of MYD88 L265P-positive, activated B-cell-like DLBCL. This inhibition was linked to the suppression of tumor-associated cytokines, including human IL-10, IL-2R, IP-10, and MIG.
Treated mice had significantly longer survival than controls, and the effect was dose-dependent. When IMO-8400 was given at 12.5 mg/kg, the P value was 0.0002. At 25 mg/kg, the P value was less than 0.0002. And at 50 mg/kg, the P value was less than 0.0001.
The researchers also found that IMO‐8400 inhibited cell viability, cytokine production, and signaling pathways in MYD88 L265P-positive WM cells. They observed these effects in the MWCL‐1 cell line and in cells from WM patients.
The team said these results provide a “strong foundation” for accelerating the clinical development of IMO-8400 in patients with B-cell lymphomas harboring the MYD88 L265P mutation.
To that end, Idera has opened enrollment in a phase 1/2 trial of IMO-8400 in WM patients who are refractory to prior therapies. The company has also submitted a protocol to the US Food and Drug Administration to conduct a phase 1/2 trial in patients with MYD88 L265P-positive DLBCL.
SAN DIEGO—A Toll-like receptor (TLR) antagonist can target B-cell lymphoma cells harboring the MYD88 L265P mutation, preclinical research suggests.
The compound, IMO-8400, decreased the viability of mutated diffuse large B-cell lymphoma (DLBCL) cells and Waldenström’s macroglobulinemia (WM) cells in vitro.
IMO-8400 also decreased tumor growth and prolonged survival in mice with MYD88 L265P-positive DLBCL.
Lakshmi Bhagat, PhD, and colleagues from the Cambridge, Massachusetts-based Idera Pharmaceuticals, Inc.—the company developing IMO-8400—presented these results at the AACR Annual Meeting 2014 (abstract 2570).
The researchers said their data provide additional evidence that the MYD88 L265P mutation results in over-activation of TLR7- and TLR9-mediated signaling, and blocking these TLRs leads to tumor cell death. IMO-8400 is an oligonucleotide-based antagonist of TLRs 7, 8, and 9.
In experiments with OCI‐Ly10 cells (DLBCL cells harboring the MYD88 L265P mutation), IMO-8400 prompted cell death and decreased proliferative cell signaling. But the compound did not produce these effects in SU-DHL-6 cells (DLBCL cells without the MYD88 L265P mutation).
In OCI‐Ly10 cells, IMO-8400 inhibited the IRAK-1, IRAK-4, BTK, STAT-3, Ik-Ba, and NF-κB pathways. The compound did not affect signaling pathways in SU-DHL-6 cells.
IMO-8400 also inhibited tumor growth in a mouse model of MYD88 L265P-positive, activated B-cell-like DLBCL. This inhibition was linked to the suppression of tumor-associated cytokines, including human IL-10, IL-2R, IP-10, and MIG.
Treated mice had significantly longer survival than controls, and the effect was dose-dependent. When IMO-8400 was given at 12.5 mg/kg, the P value was 0.0002. At 25 mg/kg, the P value was less than 0.0002. And at 50 mg/kg, the P value was less than 0.0001.
The researchers also found that IMO‐8400 inhibited cell viability, cytokine production, and signaling pathways in MYD88 L265P-positive WM cells. They observed these effects in the MWCL‐1 cell line and in cells from WM patients.
The team said these results provide a “strong foundation” for accelerating the clinical development of IMO-8400 in patients with B-cell lymphomas harboring the MYD88 L265P mutation.
To that end, Idera has opened enrollment in a phase 1/2 trial of IMO-8400 in WM patients who are refractory to prior therapies. The company has also submitted a protocol to the US Food and Drug Administration to conduct a phase 1/2 trial in patients with MYD88 L265P-positive DLBCL.
SAN DIEGO—A Toll-like receptor (TLR) antagonist can target B-cell lymphoma cells harboring the MYD88 L265P mutation, preclinical research suggests.
The compound, IMO-8400, decreased the viability of mutated diffuse large B-cell lymphoma (DLBCL) cells and Waldenström’s macroglobulinemia (WM) cells in vitro.
IMO-8400 also decreased tumor growth and prolonged survival in mice with MYD88 L265P-positive DLBCL.
Lakshmi Bhagat, PhD, and colleagues from the Cambridge, Massachusetts-based Idera Pharmaceuticals, Inc.—the company developing IMO-8400—presented these results at the AACR Annual Meeting 2014 (abstract 2570).
The researchers said their data provide additional evidence that the MYD88 L265P mutation results in over-activation of TLR7- and TLR9-mediated signaling, and blocking these TLRs leads to tumor cell death. IMO-8400 is an oligonucleotide-based antagonist of TLRs 7, 8, and 9.
In experiments with OCI‐Ly10 cells (DLBCL cells harboring the MYD88 L265P mutation), IMO-8400 prompted cell death and decreased proliferative cell signaling. But the compound did not produce these effects in SU-DHL-6 cells (DLBCL cells without the MYD88 L265P mutation).
In OCI‐Ly10 cells, IMO-8400 inhibited the IRAK-1, IRAK-4, BTK, STAT-3, Ik-Ba, and NF-κB pathways. The compound did not affect signaling pathways in SU-DHL-6 cells.
IMO-8400 also inhibited tumor growth in a mouse model of MYD88 L265P-positive, activated B-cell-like DLBCL. This inhibition was linked to the suppression of tumor-associated cytokines, including human IL-10, IL-2R, IP-10, and MIG.
Treated mice had significantly longer survival than controls, and the effect was dose-dependent. When IMO-8400 was given at 12.5 mg/kg, the P value was 0.0002. At 25 mg/kg, the P value was less than 0.0002. And at 50 mg/kg, the P value was less than 0.0001.
The researchers also found that IMO‐8400 inhibited cell viability, cytokine production, and signaling pathways in MYD88 L265P-positive WM cells. They observed these effects in the MWCL‐1 cell line and in cells from WM patients.
The team said these results provide a “strong foundation” for accelerating the clinical development of IMO-8400 in patients with B-cell lymphomas harboring the MYD88 L265P mutation.
To that end, Idera has opened enrollment in a phase 1/2 trial of IMO-8400 in WM patients who are refractory to prior therapies. The company has also submitted a protocol to the US Food and Drug Administration to conduct a phase 1/2 trial in patients with MYD88 L265P-positive DLBCL.
FDA approves ofatumumab in combination for CLL
Credit: Linda Bartlett
The US Food and Drug Administration (FDA) has approved ofatumumab (Arzerra) in combination with chlorambucil for previously untreated patients with chronic lymphocytic leukemia (CLL) who should not receive fludarabine-based therapy.
Ofatumumab, a CD20-directed monoclonal antibody, is already FDA-approved as monotherapy for CLL patients who are refractory to fludarabine and alemtuzumab.
The latest approval was based on results of the phase 3 COMPLEMENT 1 trial.
In this randomized trial, researchers compared single-agent chlorambucil to chlorambucil plus ofatumumab. They enrolled 447 patients for whom fludarabine-based therapy was considered inappropriate (due to factors such as advanced age or comorbidities).
In the overall trial population, the median age was 69 years (range, 35 to 92). Seventy-two percent of patients had 2 or more comorbidities, and 48% of patients had a creatinine clearance of less than 70 mL/min.
The researchers randomized 221 patients to receive chlorambucil plus ofatumumab and 226 patients to receive chlorambucil alone.
Patients in the ofatumumab arm received the drug as an intravenous infusion according to the following schedule: 300 mg in cycle 1 on day 1, 1000 mg in cycle 1 on day 8, and 1000 mg administered on day 1 of all subsequent 28-day cycles.
In both arms, patients received chlorambucil at a dose of 10 mg/m2 orally on days 1 to 7, every 28 days.
Prior to each infusion of ofatumumab, patients received acetaminophen, an antihistamine, and a glucocorticoid.
The primary endpoint of the trial was progression-free survival, as assessed by a blinded independent review committee using the 2008 International Workshop on Chronic Lymphocytic Leukemia update of the National Cancer Institute Working Group guidelines.
The median progression-free survival was 22.4 months for patients receiving ofatumumab and chlorambucil, compared to 13.1 months for patients receiving chlorambucil alone. The hazard ratio was 0.57 (P<0.001).
The most common adverse reactions observed in patients receiving ofatumumab and chlorambucil (at least 2% more than in the control arm) were infusion reactions, neutropenia, asthenia, headache, leukopenia, herpes simplex, lower respiratory tract infection, arthralgia, and upper abdominal pain.
Overall, 67% of patients who received ofatumumab experienced 1 or more symptoms of infusion reaction. Ten percent of patients experienced a grade 3 or greater infusion reaction.
The drug’s label carries a boxed warning detailing the risk of hepatitis B virus reactivation—which can result in fulminant hepatitis, hepatic failure, and death—as well as the risk of progressive multifocal leukoencephalopathy—which can result in death.
The recommended dose and schedule for ofatumumab in previously untreated CLL is 300 mg on day 1, followed 1 week later by 1000 mg on day 8 (cycle 1), followed by 1000 mg on day 1 of subsequent 28-day cycles, for a minimum of 3 cycles until best response or a maximum of 12 cycles.
Ofatumumab is under development by GlaxoSmithKline and GenMab. For more details on the drug, see the full prescribing information.
Credit: Linda Bartlett
The US Food and Drug Administration (FDA) has approved ofatumumab (Arzerra) in combination with chlorambucil for previously untreated patients with chronic lymphocytic leukemia (CLL) who should not receive fludarabine-based therapy.
Ofatumumab, a CD20-directed monoclonal antibody, is already FDA-approved as monotherapy for CLL patients who are refractory to fludarabine and alemtuzumab.
The latest approval was based on results of the phase 3 COMPLEMENT 1 trial.
In this randomized trial, researchers compared single-agent chlorambucil to chlorambucil plus ofatumumab. They enrolled 447 patients for whom fludarabine-based therapy was considered inappropriate (due to factors such as advanced age or comorbidities).
In the overall trial population, the median age was 69 years (range, 35 to 92). Seventy-two percent of patients had 2 or more comorbidities, and 48% of patients had a creatinine clearance of less than 70 mL/min.
The researchers randomized 221 patients to receive chlorambucil plus ofatumumab and 226 patients to receive chlorambucil alone.
Patients in the ofatumumab arm received the drug as an intravenous infusion according to the following schedule: 300 mg in cycle 1 on day 1, 1000 mg in cycle 1 on day 8, and 1000 mg administered on day 1 of all subsequent 28-day cycles.
In both arms, patients received chlorambucil at a dose of 10 mg/m2 orally on days 1 to 7, every 28 days.
Prior to each infusion of ofatumumab, patients received acetaminophen, an antihistamine, and a glucocorticoid.
The primary endpoint of the trial was progression-free survival, as assessed by a blinded independent review committee using the 2008 International Workshop on Chronic Lymphocytic Leukemia update of the National Cancer Institute Working Group guidelines.
The median progression-free survival was 22.4 months for patients receiving ofatumumab and chlorambucil, compared to 13.1 months for patients receiving chlorambucil alone. The hazard ratio was 0.57 (P<0.001).
The most common adverse reactions observed in patients receiving ofatumumab and chlorambucil (at least 2% more than in the control arm) were infusion reactions, neutropenia, asthenia, headache, leukopenia, herpes simplex, lower respiratory tract infection, arthralgia, and upper abdominal pain.
Overall, 67% of patients who received ofatumumab experienced 1 or more symptoms of infusion reaction. Ten percent of patients experienced a grade 3 or greater infusion reaction.
The drug’s label carries a boxed warning detailing the risk of hepatitis B virus reactivation—which can result in fulminant hepatitis, hepatic failure, and death—as well as the risk of progressive multifocal leukoencephalopathy—which can result in death.
The recommended dose and schedule for ofatumumab in previously untreated CLL is 300 mg on day 1, followed 1 week later by 1000 mg on day 8 (cycle 1), followed by 1000 mg on day 1 of subsequent 28-day cycles, for a minimum of 3 cycles until best response or a maximum of 12 cycles.
Ofatumumab is under development by GlaxoSmithKline and GenMab. For more details on the drug, see the full prescribing information.
Credit: Linda Bartlett
The US Food and Drug Administration (FDA) has approved ofatumumab (Arzerra) in combination with chlorambucil for previously untreated patients with chronic lymphocytic leukemia (CLL) who should not receive fludarabine-based therapy.
Ofatumumab, a CD20-directed monoclonal antibody, is already FDA-approved as monotherapy for CLL patients who are refractory to fludarabine and alemtuzumab.
The latest approval was based on results of the phase 3 COMPLEMENT 1 trial.
In this randomized trial, researchers compared single-agent chlorambucil to chlorambucil plus ofatumumab. They enrolled 447 patients for whom fludarabine-based therapy was considered inappropriate (due to factors such as advanced age or comorbidities).
In the overall trial population, the median age was 69 years (range, 35 to 92). Seventy-two percent of patients had 2 or more comorbidities, and 48% of patients had a creatinine clearance of less than 70 mL/min.
The researchers randomized 221 patients to receive chlorambucil plus ofatumumab and 226 patients to receive chlorambucil alone.
Patients in the ofatumumab arm received the drug as an intravenous infusion according to the following schedule: 300 mg in cycle 1 on day 1, 1000 mg in cycle 1 on day 8, and 1000 mg administered on day 1 of all subsequent 28-day cycles.
In both arms, patients received chlorambucil at a dose of 10 mg/m2 orally on days 1 to 7, every 28 days.
Prior to each infusion of ofatumumab, patients received acetaminophen, an antihistamine, and a glucocorticoid.
The primary endpoint of the trial was progression-free survival, as assessed by a blinded independent review committee using the 2008 International Workshop on Chronic Lymphocytic Leukemia update of the National Cancer Institute Working Group guidelines.
The median progression-free survival was 22.4 months for patients receiving ofatumumab and chlorambucil, compared to 13.1 months for patients receiving chlorambucil alone. The hazard ratio was 0.57 (P<0.001).
The most common adverse reactions observed in patients receiving ofatumumab and chlorambucil (at least 2% more than in the control arm) were infusion reactions, neutropenia, asthenia, headache, leukopenia, herpes simplex, lower respiratory tract infection, arthralgia, and upper abdominal pain.
Overall, 67% of patients who received ofatumumab experienced 1 or more symptoms of infusion reaction. Ten percent of patients experienced a grade 3 or greater infusion reaction.
The drug’s label carries a boxed warning detailing the risk of hepatitis B virus reactivation—which can result in fulminant hepatitis, hepatic failure, and death—as well as the risk of progressive multifocal leukoencephalopathy—which can result in death.
The recommended dose and schedule for ofatumumab in previously untreated CLL is 300 mg on day 1, followed 1 week later by 1000 mg on day 8 (cycle 1), followed by 1000 mg on day 1 of subsequent 28-day cycles, for a minimum of 3 cycles until best response or a maximum of 12 cycles.
Ofatumumab is under development by GlaxoSmithKline and GenMab. For more details on the drug, see the full prescribing information.
Groups investigate malaria complications in children
Credit: Peter H. Seeberger
Two studies published in PLOS Pathogens provide new insight into the malaria-related complications that can occur in children.
One study revealed how the immune system manages to prevent malaria fever in children infected with Plasmodium falciparum.
And with the other study, researchers identified proteins that can help them distinguish children with complicated malaria syndromes from those with uncomplicated malaria.
Analyzing immune response
In the first study, Peter Crompton, MD, of the US National Institute of Allergy and Infectious Diseases in Rockville, Maryland, and his colleagues analyzed immune cells from healthy children before the malaria season and from the same children after their first bout of malaria fever during the ensuing malaria season.
The researchers exposed both sets of immune cells to parasite-infected red blood cells and found that their responses were different.
When confronted with parasites before the malaria season, the children’s immune cells produced large amounts of molecules that promote inflammation—such as IL-1b, IL-6, and IL-8—which results in fever and other malaria symptoms.
But after a malaria fever episode, the immune cells responded by producing more anti-inflammatory molecules—such as IL-10 and TGF-b—and showed evidence of an enhanced ability to recognize and destroy parasites.
The ability of the immune cells to mount this response—somewhat effective in controlling the parasites but avoiding systemic inflammation and fever—seems to depend on the continued exposure to parasites through bites of infected mosquitoes.
When the researchers took blood again from the same children after the subsequent dry season (when there are few or no new infections) and exposed the immune cells to parasite-infected red blood cells, the anti-inflammatory response had returned to baseline, leaving children susceptible again to malaria-induced inflammation and fever.
The researchers said these findings shed new light on the notion of premunition, an immune response that protects against illness and high numbers of parasites in the blood without completely eliminating the infection.
They suggested that it evolved as an appropriate immune response to at least partially protect young children from potentially life-threatening inflammation and unchecked parasite replication before they acquire antibodies that protect against the onset of malaria symptoms.
Proteins provide answers
In the second study, Peter Nilsson, PhD, of SciLifeLab in Stockholm, Sweden, and his colleagues used a systematic proteomics approach to distinguish children who develop malaria-related complications from those who do not.
The researchers compared proteins in the blood of uninfected children with proteins in malaria-infected children. And they compared proteins in children with severe malaria syndromes to proteins in uncomplicated cases.
The team analyzed 1015 proteins in blood samples from more than 719 children. They divided the samples into “discovery” and “verification” sets, and only associations found in both sets were reported.
The researchers identified 41 proteins that distinguished malaria patients from uninfected children from the same community. Most of these were components of the inflammatory response.
Thirteen proteins helped the team distinguish uncomplicated malaria from severe malaria syndromes. They identified proteins specific to the 2 most deadly complicated malaria syndromes in children—severe malarial anemia and cerebral malaria.
Markers of oxidative stress were related to severe malarial anemia. And markers of endothelial activation, platelet adhesion, and muscular damage were identified in children with cerebral malaria.
The researchers said their study could aid the discovery of distinct mechanisms in the human response to malaria infection between the 2 most fatal syndromes of childhood malaria.
Credit: Peter H. Seeberger
Two studies published in PLOS Pathogens provide new insight into the malaria-related complications that can occur in children.
One study revealed how the immune system manages to prevent malaria fever in children infected with Plasmodium falciparum.
And with the other study, researchers identified proteins that can help them distinguish children with complicated malaria syndromes from those with uncomplicated malaria.
Analyzing immune response
In the first study, Peter Crompton, MD, of the US National Institute of Allergy and Infectious Diseases in Rockville, Maryland, and his colleagues analyzed immune cells from healthy children before the malaria season and from the same children after their first bout of malaria fever during the ensuing malaria season.
The researchers exposed both sets of immune cells to parasite-infected red blood cells and found that their responses were different.
When confronted with parasites before the malaria season, the children’s immune cells produced large amounts of molecules that promote inflammation—such as IL-1b, IL-6, and IL-8—which results in fever and other malaria symptoms.
But after a malaria fever episode, the immune cells responded by producing more anti-inflammatory molecules—such as IL-10 and TGF-b—and showed evidence of an enhanced ability to recognize and destroy parasites.
The ability of the immune cells to mount this response—somewhat effective in controlling the parasites but avoiding systemic inflammation and fever—seems to depend on the continued exposure to parasites through bites of infected mosquitoes.
When the researchers took blood again from the same children after the subsequent dry season (when there are few or no new infections) and exposed the immune cells to parasite-infected red blood cells, the anti-inflammatory response had returned to baseline, leaving children susceptible again to malaria-induced inflammation and fever.
The researchers said these findings shed new light on the notion of premunition, an immune response that protects against illness and high numbers of parasites in the blood without completely eliminating the infection.
They suggested that it evolved as an appropriate immune response to at least partially protect young children from potentially life-threatening inflammation and unchecked parasite replication before they acquire antibodies that protect against the onset of malaria symptoms.
Proteins provide answers
In the second study, Peter Nilsson, PhD, of SciLifeLab in Stockholm, Sweden, and his colleagues used a systematic proteomics approach to distinguish children who develop malaria-related complications from those who do not.
The researchers compared proteins in the blood of uninfected children with proteins in malaria-infected children. And they compared proteins in children with severe malaria syndromes to proteins in uncomplicated cases.
The team analyzed 1015 proteins in blood samples from more than 719 children. They divided the samples into “discovery” and “verification” sets, and only associations found in both sets were reported.
The researchers identified 41 proteins that distinguished malaria patients from uninfected children from the same community. Most of these were components of the inflammatory response.
Thirteen proteins helped the team distinguish uncomplicated malaria from severe malaria syndromes. They identified proteins specific to the 2 most deadly complicated malaria syndromes in children—severe malarial anemia and cerebral malaria.
Markers of oxidative stress were related to severe malarial anemia. And markers of endothelial activation, platelet adhesion, and muscular damage were identified in children with cerebral malaria.
The researchers said their study could aid the discovery of distinct mechanisms in the human response to malaria infection between the 2 most fatal syndromes of childhood malaria.
Credit: Peter H. Seeberger
Two studies published in PLOS Pathogens provide new insight into the malaria-related complications that can occur in children.
One study revealed how the immune system manages to prevent malaria fever in children infected with Plasmodium falciparum.
And with the other study, researchers identified proteins that can help them distinguish children with complicated malaria syndromes from those with uncomplicated malaria.
Analyzing immune response
In the first study, Peter Crompton, MD, of the US National Institute of Allergy and Infectious Diseases in Rockville, Maryland, and his colleagues analyzed immune cells from healthy children before the malaria season and from the same children after their first bout of malaria fever during the ensuing malaria season.
The researchers exposed both sets of immune cells to parasite-infected red blood cells and found that their responses were different.
When confronted with parasites before the malaria season, the children’s immune cells produced large amounts of molecules that promote inflammation—such as IL-1b, IL-6, and IL-8—which results in fever and other malaria symptoms.
But after a malaria fever episode, the immune cells responded by producing more anti-inflammatory molecules—such as IL-10 and TGF-b—and showed evidence of an enhanced ability to recognize and destroy parasites.
The ability of the immune cells to mount this response—somewhat effective in controlling the parasites but avoiding systemic inflammation and fever—seems to depend on the continued exposure to parasites through bites of infected mosquitoes.
When the researchers took blood again from the same children after the subsequent dry season (when there are few or no new infections) and exposed the immune cells to parasite-infected red blood cells, the anti-inflammatory response had returned to baseline, leaving children susceptible again to malaria-induced inflammation and fever.
The researchers said these findings shed new light on the notion of premunition, an immune response that protects against illness and high numbers of parasites in the blood without completely eliminating the infection.
They suggested that it evolved as an appropriate immune response to at least partially protect young children from potentially life-threatening inflammation and unchecked parasite replication before they acquire antibodies that protect against the onset of malaria symptoms.
Proteins provide answers
In the second study, Peter Nilsson, PhD, of SciLifeLab in Stockholm, Sweden, and his colleagues used a systematic proteomics approach to distinguish children who develop malaria-related complications from those who do not.
The researchers compared proteins in the blood of uninfected children with proteins in malaria-infected children. And they compared proteins in children with severe malaria syndromes to proteins in uncomplicated cases.
The team analyzed 1015 proteins in blood samples from more than 719 children. They divided the samples into “discovery” and “verification” sets, and only associations found in both sets were reported.
The researchers identified 41 proteins that distinguished malaria patients from uninfected children from the same community. Most of these were components of the inflammatory response.
Thirteen proteins helped the team distinguish uncomplicated malaria from severe malaria syndromes. They identified proteins specific to the 2 most deadly complicated malaria syndromes in children—severe malarial anemia and cerebral malaria.
Markers of oxidative stress were related to severe malarial anemia. And markers of endothelial activation, platelet adhesion, and muscular damage were identified in children with cerebral malaria.
The researchers said their study could aid the discovery of distinct mechanisms in the human response to malaria infection between the 2 most fatal syndromes of childhood malaria.
Agent can reduce ESR in SCD, study suggests
Credit: Graham Colm
MIAMI—Results of a small study suggest an experimental agent can decrease the erythrocyte sedimentation rate (ESR) in patients with sickle cell disease (SCD).
Previous research has shown the ESR is elevated in SCD patients during vaso-occlusive crisis.
In the current study, the experimental agent MST-188 decreased elevated ESRs by 50% in blood from SCD patients.
According to researchers, this reflects reduced red blood cell (RBC) aggregation and suggests improved microvascular blood flow.
“The data from this study are consistent with observations in prior studies that MST-188 decreases blood viscosity and RBC aggregation and improves microvascular blood flow, and supportive of the potential for MST-188 to shorten the duration of sickle cell crisis,” said Martin Emanuele, PhD, of Mast Therapeutics, the company developing MST-188.
Dr Emanuele presented the study data at the recent 8th Annual Sickle Cell Disease Research & Educational Symposium.
MST-188 is a non-ionic, linear block copolymer composed of a central chain of hydrophobic polyoxypropylene and 2 flanking chains of hydrophylic polyoxyethylene. In previous studies, the agent has shown hemorheologic properties that result in improved microvascular blood flow.
For the current study, the researchers compared MST-188 to dextrans, evaluating their effects on the ESR in blood collected from SCD patients and healthy controls. Dextrans are branched polysaccharides of 10-70 kDa that have been used as antithrombotic agents and plasma expanders.
The researchers analyzed EDTA-anticoagulated whole blood collected from 8 healthy individuals and 11 SCD patients. The team treated samples with MST-188; dextran 10K, 18K , 40K, and 70K at various concentrations; or saline control.
At baseline, ESRs for SCD patients were significantly higher than for the healthy subjects. The mean ESRs were 26.4 ± 7.1 mm/hr and 14.6 ± 2.1 mm/hr, respectively.
However, adding MST-188 to the SCD patient samples decreased the mean ESR to 14.1 ± 4.6 mm/hr (Δ47%). On the other hand, comparable concentrations of dextrans showed little or no effect on the ESR in SCD samples.
The researchers said MST-188 may reduce the ESR by inhibiting acute-phase-reactant-induced RBC aggregates, and this may result from the effect of MST-188 on RBC membranes or cell-protein interactions.
Regardless of the exact mechanism, the team said lowering the ESR reflects reduced RBC aggregation and suggests improved microvascular blood flow, which indicates that MST-188 may be able to shorten the duration of vaso-occlusive crisis.
“It is widely understood that multiple biological processes contribute to vaso-occlusion and that an effective solution requires a broad, multi-modal approach rather than a single targeted therapy,” Dr Emanuele said.
“In addition to the effects on RBC aggregation, our data suggest that MST-188 addresses cell adhesion and platelet activation, reduces hemolysis, lowers blood viscosity, and limits reperfusion injury following restoration of blood flow.”
He and his colleagues at Mast Therapeutics are planning additional studies of MST-188 in SCD. The agent is currently under investigation in a phase 3 trial.
Credit: Graham Colm
MIAMI—Results of a small study suggest an experimental agent can decrease the erythrocyte sedimentation rate (ESR) in patients with sickle cell disease (SCD).
Previous research has shown the ESR is elevated in SCD patients during vaso-occlusive crisis.
In the current study, the experimental agent MST-188 decreased elevated ESRs by 50% in blood from SCD patients.
According to researchers, this reflects reduced red blood cell (RBC) aggregation and suggests improved microvascular blood flow.
“The data from this study are consistent with observations in prior studies that MST-188 decreases blood viscosity and RBC aggregation and improves microvascular blood flow, and supportive of the potential for MST-188 to shorten the duration of sickle cell crisis,” said Martin Emanuele, PhD, of Mast Therapeutics, the company developing MST-188.
Dr Emanuele presented the study data at the recent 8th Annual Sickle Cell Disease Research & Educational Symposium.
MST-188 is a non-ionic, linear block copolymer composed of a central chain of hydrophobic polyoxypropylene and 2 flanking chains of hydrophylic polyoxyethylene. In previous studies, the agent has shown hemorheologic properties that result in improved microvascular blood flow.
For the current study, the researchers compared MST-188 to dextrans, evaluating their effects on the ESR in blood collected from SCD patients and healthy controls. Dextrans are branched polysaccharides of 10-70 kDa that have been used as antithrombotic agents and plasma expanders.
The researchers analyzed EDTA-anticoagulated whole blood collected from 8 healthy individuals and 11 SCD patients. The team treated samples with MST-188; dextran 10K, 18K , 40K, and 70K at various concentrations; or saline control.
At baseline, ESRs for SCD patients were significantly higher than for the healthy subjects. The mean ESRs were 26.4 ± 7.1 mm/hr and 14.6 ± 2.1 mm/hr, respectively.
However, adding MST-188 to the SCD patient samples decreased the mean ESR to 14.1 ± 4.6 mm/hr (Δ47%). On the other hand, comparable concentrations of dextrans showed little or no effect on the ESR in SCD samples.
The researchers said MST-188 may reduce the ESR by inhibiting acute-phase-reactant-induced RBC aggregates, and this may result from the effect of MST-188 on RBC membranes or cell-protein interactions.
Regardless of the exact mechanism, the team said lowering the ESR reflects reduced RBC aggregation and suggests improved microvascular blood flow, which indicates that MST-188 may be able to shorten the duration of vaso-occlusive crisis.
“It is widely understood that multiple biological processes contribute to vaso-occlusion and that an effective solution requires a broad, multi-modal approach rather than a single targeted therapy,” Dr Emanuele said.
“In addition to the effects on RBC aggregation, our data suggest that MST-188 addresses cell adhesion and platelet activation, reduces hemolysis, lowers blood viscosity, and limits reperfusion injury following restoration of blood flow.”
He and his colleagues at Mast Therapeutics are planning additional studies of MST-188 in SCD. The agent is currently under investigation in a phase 3 trial.
Credit: Graham Colm
MIAMI—Results of a small study suggest an experimental agent can decrease the erythrocyte sedimentation rate (ESR) in patients with sickle cell disease (SCD).
Previous research has shown the ESR is elevated in SCD patients during vaso-occlusive crisis.
In the current study, the experimental agent MST-188 decreased elevated ESRs by 50% in blood from SCD patients.
According to researchers, this reflects reduced red blood cell (RBC) aggregation and suggests improved microvascular blood flow.
“The data from this study are consistent with observations in prior studies that MST-188 decreases blood viscosity and RBC aggregation and improves microvascular blood flow, and supportive of the potential for MST-188 to shorten the duration of sickle cell crisis,” said Martin Emanuele, PhD, of Mast Therapeutics, the company developing MST-188.
Dr Emanuele presented the study data at the recent 8th Annual Sickle Cell Disease Research & Educational Symposium.
MST-188 is a non-ionic, linear block copolymer composed of a central chain of hydrophobic polyoxypropylene and 2 flanking chains of hydrophylic polyoxyethylene. In previous studies, the agent has shown hemorheologic properties that result in improved microvascular blood flow.
For the current study, the researchers compared MST-188 to dextrans, evaluating their effects on the ESR in blood collected from SCD patients and healthy controls. Dextrans are branched polysaccharides of 10-70 kDa that have been used as antithrombotic agents and plasma expanders.
The researchers analyzed EDTA-anticoagulated whole blood collected from 8 healthy individuals and 11 SCD patients. The team treated samples with MST-188; dextran 10K, 18K , 40K, and 70K at various concentrations; or saline control.
At baseline, ESRs for SCD patients were significantly higher than for the healthy subjects. The mean ESRs were 26.4 ± 7.1 mm/hr and 14.6 ± 2.1 mm/hr, respectively.
However, adding MST-188 to the SCD patient samples decreased the mean ESR to 14.1 ± 4.6 mm/hr (Δ47%). On the other hand, comparable concentrations of dextrans showed little or no effect on the ESR in SCD samples.
The researchers said MST-188 may reduce the ESR by inhibiting acute-phase-reactant-induced RBC aggregates, and this may result from the effect of MST-188 on RBC membranes or cell-protein interactions.
Regardless of the exact mechanism, the team said lowering the ESR reflects reduced RBC aggregation and suggests improved microvascular blood flow, which indicates that MST-188 may be able to shorten the duration of vaso-occlusive crisis.
“It is widely understood that multiple biological processes contribute to vaso-occlusion and that an effective solution requires a broad, multi-modal approach rather than a single targeted therapy,” Dr Emanuele said.
“In addition to the effects on RBC aggregation, our data suggest that MST-188 addresses cell adhesion and platelet activation, reduces hemolysis, lowers blood viscosity, and limits reperfusion injury following restoration of blood flow.”
He and his colleagues at Mast Therapeutics are planning additional studies of MST-188 in SCD. The agent is currently under investigation in a phase 3 trial.
Patient Flow Composite Measurement
Patient flow refers to the management and movement of patients in a healthcare facility. Healthcare institutions utilize patient flow analyses to evaluate and improve aspects of the patient experience including safety, effectiveness, efficiency, timeliness, patient centeredness, and equity.[1, 2, 3, 4, 5, 6, 7, 8] Hospitals can evaluate patient flow using specific metrics, such as time in emergency department (ED) or percent of discharges completed by a certain time of day. However, no single metric can represent the full spectrum of processes inherent to patient flow. For example, ED length of stay (LOS) is dependent on inpatient occupancy, which is dependent on discharge timeliness. Each of these activities depends on various smaller activities, such as cleaning rooms or identifying available beds.
Evaluating the quality that healthcare organizations deliver is growing in importance.[9] Composite scores are being used increasingly to assess clinical processes and outcomes for professionals and institutions.[10, 11] Where various aspects of performance coexist, composite measures can incorporate multiple metrics into a comprehensive summary.[12, 13, 14, 15, 16] They also allow organizations to track a range of metrics for more holistic, comprehensive evaluations.[9, 13]
This article describes a balanced scorecard with composite scoring used at a large urban children's hospital to evaluate patient flow and direct improvement resources where they are needed most.
METHODS
The Children's Hospital of Philadelphia identified patient flow improvement as an operating plan initiative. Previously, performance was measured with a series of independent measures including time from ED arrival to transfer to the inpatient floor, and time from discharge order to room vacancy. These metrics were dismissed as sole measures of flow because they did not reflect the complexity and interdependence of processes or improvement efforts. There were also concerns that efforts to improve a measure caused unintended consequences for others, which at best lead to little overall improvement, and at worst reduced performance elsewhere in the value chain. For example, to meet a goal time for entering discharge orders, physicians could enter orders earlier. But, if patients were not actually ready to leave, their beds were not made available any earlier. Similarly, bed management staff could rush to meet a goal for speed of unit assignment, but this could cause an increase in patients admitted to the wrong specialty floor.
To address these concerns, a group of physicians, nurses, quality improvement specialists, and researchers designed a patient flow scorecard with composite measurement. Five domains of patient flow were identified: (1) ED and ED‐to‐inpatient transition, (2) bed management, (3) discharge process, (4) room turnover and environmental services department (ESD) activities, and (5) scheduling and utilization. Component measures for each domain were selected for 1 of 3 purposes: (1) to correspond to processes of importance to flow and improvement work, (2) to act as adjusters for factors that affect performance, or (3) to act as balancing measures so that progress in a measure would not result in the degradation of another. Each domain was assigned 20 points, which were distributed across the domain's components based on a consensus of the component's relative importance to overall domain performance (Figure 1). Data from the previous year were used as guidelines for setting performance percentile goals. For example, a goal of 80% in 60 minutes for arrival to physician evaluation meant that 80% of patients should see a physician within 1 hour of arriving at the ED.
Scores were also categorized to correspond to commonly used color descriptors.[17] For each component measure, performance meeting or exceeding the goal fell into the green category. Performances <10 percentage points below the goal fell into the yellow category, and performances below that level fell into the red category. Domain‐level scores and overall composite scores were also assigned colors. Performance at or above 80% (16 on the 20‐point domain scale, or 80 on the 100‐point overall scale) were designated green, scores between 70% and 79% were yellow, and scores below 70% were red.
DOMAINS OF THE PATIENT FLOW COMPOSITE SCORE
ED and ED‐to‐Inpatient Transition
Patient progression from the ED to an inpatient unit was separated into 4 steps (Figure 1A): (1) arrival to physician evaluation, (2) ED physician evaluation to decision to admit, (3) decision to admit to medical doctor (MD) report complete, and (4) registered nurse (RN) report to patient to floor. Four additional metrics included: (5) ED LOS for nonadmitted patients, (6) leaving without being seen (LWBS) rate, (7) ED admission rate, and (8) ED volume.
Arrival to physician evaluation measures time between patient arrival in the ED and self‐assignment by the first doctor or nurse practitioner in the electronic record, with a goal of 80% of patients seen within 60 minutes. The component score is calculated as percent of patients meeting this goal (ie, seen within 60 minutes) component weight. ED physician evaluation to decision to admit measures time from the start of the physician evaluation to the decision to admit, using bed request as a proxy; the goal was 80% within 4 hours. Decision to admit to MD report complete measures time from bed request to patient sign‐out to the inpatient floor, with a goal of 80% within 2 hours. RN report to patient to floor measures time from sign‐out to the patient leaving the ED, with a goal of 80% within 1 hour. ED LOS for nonadmitted patients measures time in the ED for patients who are not admitted, and the goal was 80% in <5 hours. The domain also tracks the LWBS rate, with a goal of keeping it below 3%. Its component score is calculated as percent patients seen component weight. ED admission rate is an adjusting factor for the severity of patients visiting the ED. Its component score is calculated as (percent of patients visiting the ED who are admitted to the hospital 5) component weight. Because the average admission rate is around 20%, the percent admitted is multiplied by 5 to more effectively adjust for high‐severity patients. ED volume is an adjusting factor that accounts for high volume. Its component score is calculated as percent of days in a month with more than 250 visits (a threshold chosen by the ED team) component weight. If these days exceed 50%, that percent would be added to the component score as an additional adjustment for excessive volume.
Bed Management
The bed management domain measures how efficiently and effectively patients are assigned to units and beds using 4 metrics (Figure 1B): (1) bed request to unit assignment, (2) unit assignment to bed assignment, (3) percentage of patients placed on right unit for service, and (4) percent of days with peak occupancy >95%.
Bed request to unit assignment measures time from the ED request for a bed in the electronic system to patient being assigned to a unit, with a goal of 80% of assignments made within 20 minutes. Unit assignment to bed assignment measures time from unit assignment to bed assignment, with a goal of 75% within 25 minutes. Because this goal was set to 75% rather than 80%, this component score was multiplied by 80/75 so that all component scores could be compared on the same scale. Percentage of patients placed on right unit for service is a balancing measure for speed of assignment. Because the goal was set to 90% rather than 80%, this component score was also multiplied by an adjusting factor (80/90) so that all components could be compared on the same scale. Percent of days with peak occupancy >95% is an adjusting measure that reflects that locating an appropriate bed takes longer when the hospital is approaching full occupancy. Its component score is calculated as (percent of days with peak occupancy >95% + 1) component weight. The was added to more effectively adjust for high occupancy. If more than 20% of days had peak occupancy greater than 95%, that percent would be added to the component score as an additional adjustment for excessive capacity.
Discharge Process
The discharge process domain measures the efficiency of patient discharge using 2 metrics (Figure 1C): (1) decision to discharge and (2) homeward bound time.
Decision to discharge tracks when clinicians enter electronic discharge orders. The goal was 50% by 1:30 pm for medical services and 10:30 am for surgical services. This encourages physicians to enter discharge orders early to enable downstream discharge work to begin. The component score is calculated as percent entered by goal time component weight (80/50) to adjust the 50% goal up to 80% so all component scores could be compared on the same scale. Homeward bound time measures the time between the discharge order and room vacancy as entered by the unit clerk, with a goal of 80% of patients leaving within 110 minutes for medical services and 240 minutes for surgical services. This balancing measure captures the fact that entering discharge orders early does not facilitate flow if the patients do not actually leave the hospital.
Room Turnover and Environmental Services Department
The room turnover and ESD domain measures the quality of the room turnover processes using 4 metrics (Figure 1D): (1) discharge to in progress time, (2) in progress to complete time, (3) total discharge to clean time, and (4) room cleanliness.
Discharge to in progress time measures time from patient vacancy until ESD staff enters the room, with a goal of 75% within 35 minutes. Because the goal was set to 75% rather than 80%, this component score was multiplied by 80/75 so all component scores could be compared on the same scale. In progress to complete time measures time as entered in the electronic health record from ESD staff entering the room to the room being clean, with a goal of 75% within 55 minutes. The component score is calculated identically to the previous metric. Total discharge to clean time measures the length of the total process, with a goal of 75% within 90 minutes. This component score was also multiplied by 80/75 so that all component scores could be compared on the same scale. Although this repeats the first 2 measures, given workflow and interface issues with our electronic health record (Epic, Epic Systems Corporation, Verona Wisconsin), it is necessary to include a total end‐to‐end measure in addition to the subparts. Patient and family ratings of room cleanliness serve as balancing measures, with the component score calculated as percent satisfaction component weight (80/85) to adjust the 85% satisfaction goal to 80% so all component scores could be compared on the same scale.
Scheduling and Utilization
The scheduling and utilization domain measures hospital operations and variations in bed utilization using 7 metrics including (Figure 1E): (1) coefficient of variation (CV): scheduled admissions, (2) CV: scheduled admissions for weekdays only, (3) CV: emergent admissions, (4) CV: scheduled occupancy, (5) CV: emergent occupancy, (6) percent emergent admissions with LOS >1 day, and (7) percent of days with peak occupancy <95%.
The CV, standard deviation divided by the mean of a distribution, is a measure of dispersion. Because it is a normalized value reported as a percentage, CV can be used to compare variability when sample sizes differ. CV: scheduled admissions captures the variability in admissions coded as an elective across all days in a month. The raw CV score is the standard deviation of the elective admissions for each day divided by the mean. The component score is (1 CV) component weight. A higher CV indicates greater variability, and yields a lower component score. CV on scheduled and emergent occupancy is derived from peak daily occupancy. Percent emergent admissions with LOS >1 day captures the efficiency of bed use, because high volumes of short‐stay patients increases turnover work. Its component score is calculated as the percent of emergent admissions in a month with LOS >1 day component weight. Percent of days with peak occupancy <95% incentivizes the hospital to avoid full occupancy, because effective flow requires that some beds remain open.[18, 19] Its component score is calculated as the percent of days in the month with peak occupancy <95% component weight. Although a similar measure, percent of days with peak occupancy >95%, was an adjusting factor in the bed management domain, it is included again here, because this factor has a unique effect on both domains.
RESULTS
The balanced scorecard with composite measures provided improvement teams and administrators with a picture of patient flow (Figure 2). The overall score provided a global perspective on patient flow over time and captured trends in performance during various states of hospital occupancy. One trend that it captured was an association between high volume and poor composite scores (Figure 3). Notably, the H1N1 influenza pandemic in the fall of 2009 and the turnover of computer systems in January 2011 can be linked to dips in performance. The changes between fiscal years reflect a shift in baseline metrics.
In addition to the overall composite score, the domain level and individual component scores allowed for more specific evaluation of variables affecting quality of care and enabled targeted improvement activities (Figure 4). For example, in December 2010 and January 2011, room turnover and ESD domain scores dropped, especially in the total discharge to clean time component. In response, the ESD made staffing adjustments, and starting in February 2011, component scores and the domain score improved. Feedback from the scheduling and utilization domain scores also initiated positive change. In August 2010, the CV: scheduled occupancy component score started to drop. In response, certain elective admissions were shifted to weekends to distribute hospital occupancy more evenly throughout the week. By February 2011, the component returned to its goal level. This continual evaluation of performance motivates continual improvement.
DISCUSSION
The use of a patient flow balanced scorecard with composite measurement overcomes pitfalls associated with a single or unaggregated measure. Aggregate scores alone mask important differences and relationships among components.[13] For example, 2 domains may be inversely related, or a provider with an overall average score might score above average in 1 domain but below in another. The composite scorecard, however, shows individual component and domain scores in addition to an aggregate score. The individual component and domain level scores highlight specific areas that need improvement and allow attention to be directed to those areas.
Additionally, a composite score is more likely to engage the range of staff involved in patient flow. Scaling out of 100 points and the red‐yellow‐green model are familiar for operations performance and can be easily understood.[17] Moreover, a composite score allows for dynamic performance goals while maintaining a stable measurement structure. For example, standardized LOS ratios, readmission rates, and denied hospital days can be added to the scorecard to provide more information and balancing measures.
Although balanced scorecards with composites can make holistic performance visible across multiple operational domains, they have some disadvantages. First, because there is a degree of complexity associated with a measure that incorporates multiple aspects of flow, certain elements, such as the relationship between a metric and its balancing measure, may not be readily apparent. Second, composite measures may not provide actionable information if the measure is not clearly related to a process that can be improved.[13, 14] Third, individual metrics may not be replicable between locations, so composites may need to be individualized to each setting.[10, 20]
Improving patient flow is a goal at many hospitals. Although measurement is crucial to identifying and mitigating variations, measuring the multidimensional aspects of flow and their impact on quality is difficult. Our scorecard, with composite measurement, addresses the need for an improved method to assess patient flow and improve quality by tracking care processes simultaneously.
Acknowledgements
The authors thank Bhuvaneswari Jayaraman for her contributions to the original calculations for the first version of the composite score.
Disclosures: Internal funds from The Children's Hospital of Philadelphia supported the conduct of this work. The authors report no conflicts of interest.
- AHA Solutions. Patient Flow Challenges Assessment 2009. Chicago, IL: American Hospital Association; 2009.
- , , , et al. The impact of emergency department crowding measures on time to antibiotics for patients with community‐acquired pneumonia. Ann Emerg Med. 2007;50(5):510–516.
- . Practice variation: implications for our health care system. Manag Care. 2004;13(9 suppl):3–7.
- . Managing variability in patient flow is the key to improving access to care, nursing staffing, quality of care, and reducing its cost. Paper presented at: Institute of Medicine; June 24, 2004; Washington, DC.
- , , . Developing models for patient flow and daily surge capacity research. Acad Emerg Med. 2006;13(11):1109–1113.
- , , , , . Patient flow variability and unplanned readmissions to an intensive care unit. Crit Care Med. 2009;37(11):2882–2887.
- , , , , , . Scheduled admissions and high occupancy at a children's hospital. J Hosp Med. 2011;6(2):81–87.
- , , . Frequent overcrowding in US emergency departments. Acad Emerg Med. 2001;8(2):151–155.
- Institute of Medicine. Performance measurement: accelerating improvement. Available at: http://www.iom.edu/Reports/2005/Performance‐Measurement‐Accelerating‐Improvement.aspx. Published December 1, 2005. Accessed December 5, 2012.
- , , , . Emergency department performance measures and benchmarking summit. Acad Emerg Med. 2006;13(10):1074–1080.
- . The Surgical Infection Prevention and Surgical Care Improvement Projects: promises and pitfalls. Am Surg. 2006;72(11):1010–1016; discussion 1021–1030, 1133–1048.
- , , . Patient safety quality indicators. Composite measures workgroup. Final report. Rockville, MD; Agency for Healthcare Research and Quality; 2008.
- , , , et al. ACCF/AHA 2010 position statement on composite measures for healthcare performance assessment: a report of the American College of Cardiology Foundation/American Heart Association Task Force on performance measures (Writing Committee to develop a position statement on composite measures). Circulation. 2010;121(15):1780–1791.
- , . A five‐point checklist to help performance reports incentivize improvement and effectively guide patients. Health Aff (Millwood). 2012;31(3):612–618.
- , , , , . Composite measures for profiling hospitals on surgical morbidity. Ann Surg. 2013;257(1):67–72.
- , . All‐or‐none measurement raises the bar on performance. JAMA. 2006;295(10):1168–1170.
- , , , et al. Quality improvement. Red light‐green light: from kids' game to discharge tool. Healthc Q. 2011;14:77–81.
- , , , . Myths of ideal hospital occupancy. Med J Aust. 2010;192(1):42–43.
- , . Emergency department overcrowding in the United States: an emerging threat to patient safety and public health. Emerg Med J. 2003;20(5):402–405.
- , , , . Emergency department crowding: consensus development of potential measures. Ann Emerg Med. 2003;42(6):824–834.
Patient flow refers to the management and movement of patients in a healthcare facility. Healthcare institutions utilize patient flow analyses to evaluate and improve aspects of the patient experience including safety, effectiveness, efficiency, timeliness, patient centeredness, and equity.[1, 2, 3, 4, 5, 6, 7, 8] Hospitals can evaluate patient flow using specific metrics, such as time in emergency department (ED) or percent of discharges completed by a certain time of day. However, no single metric can represent the full spectrum of processes inherent to patient flow. For example, ED length of stay (LOS) is dependent on inpatient occupancy, which is dependent on discharge timeliness. Each of these activities depends on various smaller activities, such as cleaning rooms or identifying available beds.
Evaluating the quality that healthcare organizations deliver is growing in importance.[9] Composite scores are being used increasingly to assess clinical processes and outcomes for professionals and institutions.[10, 11] Where various aspects of performance coexist, composite measures can incorporate multiple metrics into a comprehensive summary.[12, 13, 14, 15, 16] They also allow organizations to track a range of metrics for more holistic, comprehensive evaluations.[9, 13]
This article describes a balanced scorecard with composite scoring used at a large urban children's hospital to evaluate patient flow and direct improvement resources where they are needed most.
METHODS
The Children's Hospital of Philadelphia identified patient flow improvement as an operating plan initiative. Previously, performance was measured with a series of independent measures including time from ED arrival to transfer to the inpatient floor, and time from discharge order to room vacancy. These metrics were dismissed as sole measures of flow because they did not reflect the complexity and interdependence of processes or improvement efforts. There were also concerns that efforts to improve a measure caused unintended consequences for others, which at best lead to little overall improvement, and at worst reduced performance elsewhere in the value chain. For example, to meet a goal time for entering discharge orders, physicians could enter orders earlier. But, if patients were not actually ready to leave, their beds were not made available any earlier. Similarly, bed management staff could rush to meet a goal for speed of unit assignment, but this could cause an increase in patients admitted to the wrong specialty floor.
To address these concerns, a group of physicians, nurses, quality improvement specialists, and researchers designed a patient flow scorecard with composite measurement. Five domains of patient flow were identified: (1) ED and ED‐to‐inpatient transition, (2) bed management, (3) discharge process, (4) room turnover and environmental services department (ESD) activities, and (5) scheduling and utilization. Component measures for each domain were selected for 1 of 3 purposes: (1) to correspond to processes of importance to flow and improvement work, (2) to act as adjusters for factors that affect performance, or (3) to act as balancing measures so that progress in a measure would not result in the degradation of another. Each domain was assigned 20 points, which were distributed across the domain's components based on a consensus of the component's relative importance to overall domain performance (Figure 1). Data from the previous year were used as guidelines for setting performance percentile goals. For example, a goal of 80% in 60 minutes for arrival to physician evaluation meant that 80% of patients should see a physician within 1 hour of arriving at the ED.
Scores were also categorized to correspond to commonly used color descriptors.[17] For each component measure, performance meeting or exceeding the goal fell into the green category. Performances <10 percentage points below the goal fell into the yellow category, and performances below that level fell into the red category. Domain‐level scores and overall composite scores were also assigned colors. Performance at or above 80% (16 on the 20‐point domain scale, or 80 on the 100‐point overall scale) were designated green, scores between 70% and 79% were yellow, and scores below 70% were red.
DOMAINS OF THE PATIENT FLOW COMPOSITE SCORE
ED and ED‐to‐Inpatient Transition
Patient progression from the ED to an inpatient unit was separated into 4 steps (Figure 1A): (1) arrival to physician evaluation, (2) ED physician evaluation to decision to admit, (3) decision to admit to medical doctor (MD) report complete, and (4) registered nurse (RN) report to patient to floor. Four additional metrics included: (5) ED LOS for nonadmitted patients, (6) leaving without being seen (LWBS) rate, (7) ED admission rate, and (8) ED volume.
Arrival to physician evaluation measures time between patient arrival in the ED and self‐assignment by the first doctor or nurse practitioner in the electronic record, with a goal of 80% of patients seen within 60 minutes. The component score is calculated as percent of patients meeting this goal (ie, seen within 60 minutes) component weight. ED physician evaluation to decision to admit measures time from the start of the physician evaluation to the decision to admit, using bed request as a proxy; the goal was 80% within 4 hours. Decision to admit to MD report complete measures time from bed request to patient sign‐out to the inpatient floor, with a goal of 80% within 2 hours. RN report to patient to floor measures time from sign‐out to the patient leaving the ED, with a goal of 80% within 1 hour. ED LOS for nonadmitted patients measures time in the ED for patients who are not admitted, and the goal was 80% in <5 hours. The domain also tracks the LWBS rate, with a goal of keeping it below 3%. Its component score is calculated as percent patients seen component weight. ED admission rate is an adjusting factor for the severity of patients visiting the ED. Its component score is calculated as (percent of patients visiting the ED who are admitted to the hospital 5) component weight. Because the average admission rate is around 20%, the percent admitted is multiplied by 5 to more effectively adjust for high‐severity patients. ED volume is an adjusting factor that accounts for high volume. Its component score is calculated as percent of days in a month with more than 250 visits (a threshold chosen by the ED team) component weight. If these days exceed 50%, that percent would be added to the component score as an additional adjustment for excessive volume.
Bed Management
The bed management domain measures how efficiently and effectively patients are assigned to units and beds using 4 metrics (Figure 1B): (1) bed request to unit assignment, (2) unit assignment to bed assignment, (3) percentage of patients placed on right unit for service, and (4) percent of days with peak occupancy >95%.
Bed request to unit assignment measures time from the ED request for a bed in the electronic system to patient being assigned to a unit, with a goal of 80% of assignments made within 20 minutes. Unit assignment to bed assignment measures time from unit assignment to bed assignment, with a goal of 75% within 25 minutes. Because this goal was set to 75% rather than 80%, this component score was multiplied by 80/75 so that all component scores could be compared on the same scale. Percentage of patients placed on right unit for service is a balancing measure for speed of assignment. Because the goal was set to 90% rather than 80%, this component score was also multiplied by an adjusting factor (80/90) so that all components could be compared on the same scale. Percent of days with peak occupancy >95% is an adjusting measure that reflects that locating an appropriate bed takes longer when the hospital is approaching full occupancy. Its component score is calculated as (percent of days with peak occupancy >95% + 1) component weight. The was added to more effectively adjust for high occupancy. If more than 20% of days had peak occupancy greater than 95%, that percent would be added to the component score as an additional adjustment for excessive capacity.
Discharge Process
The discharge process domain measures the efficiency of patient discharge using 2 metrics (Figure 1C): (1) decision to discharge and (2) homeward bound time.
Decision to discharge tracks when clinicians enter electronic discharge orders. The goal was 50% by 1:30 pm for medical services and 10:30 am for surgical services. This encourages physicians to enter discharge orders early to enable downstream discharge work to begin. The component score is calculated as percent entered by goal time component weight (80/50) to adjust the 50% goal up to 80% so all component scores could be compared on the same scale. Homeward bound time measures the time between the discharge order and room vacancy as entered by the unit clerk, with a goal of 80% of patients leaving within 110 minutes for medical services and 240 minutes for surgical services. This balancing measure captures the fact that entering discharge orders early does not facilitate flow if the patients do not actually leave the hospital.
Room Turnover and Environmental Services Department
The room turnover and ESD domain measures the quality of the room turnover processes using 4 metrics (Figure 1D): (1) discharge to in progress time, (2) in progress to complete time, (3) total discharge to clean time, and (4) room cleanliness.
Discharge to in progress time measures time from patient vacancy until ESD staff enters the room, with a goal of 75% within 35 minutes. Because the goal was set to 75% rather than 80%, this component score was multiplied by 80/75 so all component scores could be compared on the same scale. In progress to complete time measures time as entered in the electronic health record from ESD staff entering the room to the room being clean, with a goal of 75% within 55 minutes. The component score is calculated identically to the previous metric. Total discharge to clean time measures the length of the total process, with a goal of 75% within 90 minutes. This component score was also multiplied by 80/75 so that all component scores could be compared on the same scale. Although this repeats the first 2 measures, given workflow and interface issues with our electronic health record (Epic, Epic Systems Corporation, Verona Wisconsin), it is necessary to include a total end‐to‐end measure in addition to the subparts. Patient and family ratings of room cleanliness serve as balancing measures, with the component score calculated as percent satisfaction component weight (80/85) to adjust the 85% satisfaction goal to 80% so all component scores could be compared on the same scale.
Scheduling and Utilization
The scheduling and utilization domain measures hospital operations and variations in bed utilization using 7 metrics including (Figure 1E): (1) coefficient of variation (CV): scheduled admissions, (2) CV: scheduled admissions for weekdays only, (3) CV: emergent admissions, (4) CV: scheduled occupancy, (5) CV: emergent occupancy, (6) percent emergent admissions with LOS >1 day, and (7) percent of days with peak occupancy <95%.
The CV, standard deviation divided by the mean of a distribution, is a measure of dispersion. Because it is a normalized value reported as a percentage, CV can be used to compare variability when sample sizes differ. CV: scheduled admissions captures the variability in admissions coded as an elective across all days in a month. The raw CV score is the standard deviation of the elective admissions for each day divided by the mean. The component score is (1 CV) component weight. A higher CV indicates greater variability, and yields a lower component score. CV on scheduled and emergent occupancy is derived from peak daily occupancy. Percent emergent admissions with LOS >1 day captures the efficiency of bed use, because high volumes of short‐stay patients increases turnover work. Its component score is calculated as the percent of emergent admissions in a month with LOS >1 day component weight. Percent of days with peak occupancy <95% incentivizes the hospital to avoid full occupancy, because effective flow requires that some beds remain open.[18, 19] Its component score is calculated as the percent of days in the month with peak occupancy <95% component weight. Although a similar measure, percent of days with peak occupancy >95%, was an adjusting factor in the bed management domain, it is included again here, because this factor has a unique effect on both domains.
RESULTS
The balanced scorecard with composite measures provided improvement teams and administrators with a picture of patient flow (Figure 2). The overall score provided a global perspective on patient flow over time and captured trends in performance during various states of hospital occupancy. One trend that it captured was an association between high volume and poor composite scores (Figure 3). Notably, the H1N1 influenza pandemic in the fall of 2009 and the turnover of computer systems in January 2011 can be linked to dips in performance. The changes between fiscal years reflect a shift in baseline metrics.
In addition to the overall composite score, the domain level and individual component scores allowed for more specific evaluation of variables affecting quality of care and enabled targeted improvement activities (Figure 4). For example, in December 2010 and January 2011, room turnover and ESD domain scores dropped, especially in the total discharge to clean time component. In response, the ESD made staffing adjustments, and starting in February 2011, component scores and the domain score improved. Feedback from the scheduling and utilization domain scores also initiated positive change. In August 2010, the CV: scheduled occupancy component score started to drop. In response, certain elective admissions were shifted to weekends to distribute hospital occupancy more evenly throughout the week. By February 2011, the component returned to its goal level. This continual evaluation of performance motivates continual improvement.
DISCUSSION
The use of a patient flow balanced scorecard with composite measurement overcomes pitfalls associated with a single or unaggregated measure. Aggregate scores alone mask important differences and relationships among components.[13] For example, 2 domains may be inversely related, or a provider with an overall average score might score above average in 1 domain but below in another. The composite scorecard, however, shows individual component and domain scores in addition to an aggregate score. The individual component and domain level scores highlight specific areas that need improvement and allow attention to be directed to those areas.
Additionally, a composite score is more likely to engage the range of staff involved in patient flow. Scaling out of 100 points and the red‐yellow‐green model are familiar for operations performance and can be easily understood.[17] Moreover, a composite score allows for dynamic performance goals while maintaining a stable measurement structure. For example, standardized LOS ratios, readmission rates, and denied hospital days can be added to the scorecard to provide more information and balancing measures.
Although balanced scorecards with composites can make holistic performance visible across multiple operational domains, they have some disadvantages. First, because there is a degree of complexity associated with a measure that incorporates multiple aspects of flow, certain elements, such as the relationship between a metric and its balancing measure, may not be readily apparent. Second, composite measures may not provide actionable information if the measure is not clearly related to a process that can be improved.[13, 14] Third, individual metrics may not be replicable between locations, so composites may need to be individualized to each setting.[10, 20]
Improving patient flow is a goal at many hospitals. Although measurement is crucial to identifying and mitigating variations, measuring the multidimensional aspects of flow and their impact on quality is difficult. Our scorecard, with composite measurement, addresses the need for an improved method to assess patient flow and improve quality by tracking care processes simultaneously.
Acknowledgements
The authors thank Bhuvaneswari Jayaraman for her contributions to the original calculations for the first version of the composite score.
Disclosures: Internal funds from The Children's Hospital of Philadelphia supported the conduct of this work. The authors report no conflicts of interest.
Patient flow refers to the management and movement of patients in a healthcare facility. Healthcare institutions utilize patient flow analyses to evaluate and improve aspects of the patient experience including safety, effectiveness, efficiency, timeliness, patient centeredness, and equity.[1, 2, 3, 4, 5, 6, 7, 8] Hospitals can evaluate patient flow using specific metrics, such as time in emergency department (ED) or percent of discharges completed by a certain time of day. However, no single metric can represent the full spectrum of processes inherent to patient flow. For example, ED length of stay (LOS) is dependent on inpatient occupancy, which is dependent on discharge timeliness. Each of these activities depends on various smaller activities, such as cleaning rooms or identifying available beds.
Evaluating the quality that healthcare organizations deliver is growing in importance.[9] Composite scores are being used increasingly to assess clinical processes and outcomes for professionals and institutions.[10, 11] Where various aspects of performance coexist, composite measures can incorporate multiple metrics into a comprehensive summary.[12, 13, 14, 15, 16] They also allow organizations to track a range of metrics for more holistic, comprehensive evaluations.[9, 13]
This article describes a balanced scorecard with composite scoring used at a large urban children's hospital to evaluate patient flow and direct improvement resources where they are needed most.
METHODS
The Children's Hospital of Philadelphia identified patient flow improvement as an operating plan initiative. Previously, performance was measured with a series of independent measures including time from ED arrival to transfer to the inpatient floor, and time from discharge order to room vacancy. These metrics were dismissed as sole measures of flow because they did not reflect the complexity and interdependence of processes or improvement efforts. There were also concerns that efforts to improve a measure caused unintended consequences for others, which at best lead to little overall improvement, and at worst reduced performance elsewhere in the value chain. For example, to meet a goal time for entering discharge orders, physicians could enter orders earlier. But, if patients were not actually ready to leave, their beds were not made available any earlier. Similarly, bed management staff could rush to meet a goal for speed of unit assignment, but this could cause an increase in patients admitted to the wrong specialty floor.
To address these concerns, a group of physicians, nurses, quality improvement specialists, and researchers designed a patient flow scorecard with composite measurement. Five domains of patient flow were identified: (1) ED and ED‐to‐inpatient transition, (2) bed management, (3) discharge process, (4) room turnover and environmental services department (ESD) activities, and (5) scheduling and utilization. Component measures for each domain were selected for 1 of 3 purposes: (1) to correspond to processes of importance to flow and improvement work, (2) to act as adjusters for factors that affect performance, or (3) to act as balancing measures so that progress in a measure would not result in the degradation of another. Each domain was assigned 20 points, which were distributed across the domain's components based on a consensus of the component's relative importance to overall domain performance (Figure 1). Data from the previous year were used as guidelines for setting performance percentile goals. For example, a goal of 80% in 60 minutes for arrival to physician evaluation meant that 80% of patients should see a physician within 1 hour of arriving at the ED.
Scores were also categorized to correspond to commonly used color descriptors.[17] For each component measure, performance meeting or exceeding the goal fell into the green category. Performances <10 percentage points below the goal fell into the yellow category, and performances below that level fell into the red category. Domain‐level scores and overall composite scores were also assigned colors. Performance at or above 80% (16 on the 20‐point domain scale, or 80 on the 100‐point overall scale) were designated green, scores between 70% and 79% were yellow, and scores below 70% were red.
DOMAINS OF THE PATIENT FLOW COMPOSITE SCORE
ED and ED‐to‐Inpatient Transition
Patient progression from the ED to an inpatient unit was separated into 4 steps (Figure 1A): (1) arrival to physician evaluation, (2) ED physician evaluation to decision to admit, (3) decision to admit to medical doctor (MD) report complete, and (4) registered nurse (RN) report to patient to floor. Four additional metrics included: (5) ED LOS for nonadmitted patients, (6) leaving without being seen (LWBS) rate, (7) ED admission rate, and (8) ED volume.
Arrival to physician evaluation measures time between patient arrival in the ED and self‐assignment by the first doctor or nurse practitioner in the electronic record, with a goal of 80% of patients seen within 60 minutes. The component score is calculated as percent of patients meeting this goal (ie, seen within 60 minutes) component weight. ED physician evaluation to decision to admit measures time from the start of the physician evaluation to the decision to admit, using bed request as a proxy; the goal was 80% within 4 hours. Decision to admit to MD report complete measures time from bed request to patient sign‐out to the inpatient floor, with a goal of 80% within 2 hours. RN report to patient to floor measures time from sign‐out to the patient leaving the ED, with a goal of 80% within 1 hour. ED LOS for nonadmitted patients measures time in the ED for patients who are not admitted, and the goal was 80% in <5 hours. The domain also tracks the LWBS rate, with a goal of keeping it below 3%. Its component score is calculated as percent patients seen component weight. ED admission rate is an adjusting factor for the severity of patients visiting the ED. Its component score is calculated as (percent of patients visiting the ED who are admitted to the hospital 5) component weight. Because the average admission rate is around 20%, the percent admitted is multiplied by 5 to more effectively adjust for high‐severity patients. ED volume is an adjusting factor that accounts for high volume. Its component score is calculated as percent of days in a month with more than 250 visits (a threshold chosen by the ED team) component weight. If these days exceed 50%, that percent would be added to the component score as an additional adjustment for excessive volume.
Bed Management
The bed management domain measures how efficiently and effectively patients are assigned to units and beds using 4 metrics (Figure 1B): (1) bed request to unit assignment, (2) unit assignment to bed assignment, (3) percentage of patients placed on right unit for service, and (4) percent of days with peak occupancy >95%.
Bed request to unit assignment measures time from the ED request for a bed in the electronic system to patient being assigned to a unit, with a goal of 80% of assignments made within 20 minutes. Unit assignment to bed assignment measures time from unit assignment to bed assignment, with a goal of 75% within 25 minutes. Because this goal was set to 75% rather than 80%, this component score was multiplied by 80/75 so that all component scores could be compared on the same scale. Percentage of patients placed on right unit for service is a balancing measure for speed of assignment. Because the goal was set to 90% rather than 80%, this component score was also multiplied by an adjusting factor (80/90) so that all components could be compared on the same scale. Percent of days with peak occupancy >95% is an adjusting measure that reflects that locating an appropriate bed takes longer when the hospital is approaching full occupancy. Its component score is calculated as (percent of days with peak occupancy >95% + 1) component weight. The was added to more effectively adjust for high occupancy. If more than 20% of days had peak occupancy greater than 95%, that percent would be added to the component score as an additional adjustment for excessive capacity.
Discharge Process
The discharge process domain measures the efficiency of patient discharge using 2 metrics (Figure 1C): (1) decision to discharge and (2) homeward bound time.
Decision to discharge tracks when clinicians enter electronic discharge orders. The goal was 50% by 1:30 pm for medical services and 10:30 am for surgical services. This encourages physicians to enter discharge orders early to enable downstream discharge work to begin. The component score is calculated as percent entered by goal time component weight (80/50) to adjust the 50% goal up to 80% so all component scores could be compared on the same scale. Homeward bound time measures the time between the discharge order and room vacancy as entered by the unit clerk, with a goal of 80% of patients leaving within 110 minutes for medical services and 240 minutes for surgical services. This balancing measure captures the fact that entering discharge orders early does not facilitate flow if the patients do not actually leave the hospital.
Room Turnover and Environmental Services Department
The room turnover and ESD domain measures the quality of the room turnover processes using 4 metrics (Figure 1D): (1) discharge to in progress time, (2) in progress to complete time, (3) total discharge to clean time, and (4) room cleanliness.
Discharge to in progress time measures time from patient vacancy until ESD staff enters the room, with a goal of 75% within 35 minutes. Because the goal was set to 75% rather than 80%, this component score was multiplied by 80/75 so all component scores could be compared on the same scale. In progress to complete time measures time as entered in the electronic health record from ESD staff entering the room to the room being clean, with a goal of 75% within 55 minutes. The component score is calculated identically to the previous metric. Total discharge to clean time measures the length of the total process, with a goal of 75% within 90 minutes. This component score was also multiplied by 80/75 so that all component scores could be compared on the same scale. Although this repeats the first 2 measures, given workflow and interface issues with our electronic health record (Epic, Epic Systems Corporation, Verona Wisconsin), it is necessary to include a total end‐to‐end measure in addition to the subparts. Patient and family ratings of room cleanliness serve as balancing measures, with the component score calculated as percent satisfaction component weight (80/85) to adjust the 85% satisfaction goal to 80% so all component scores could be compared on the same scale.
Scheduling and Utilization
The scheduling and utilization domain measures hospital operations and variations in bed utilization using 7 metrics including (Figure 1E): (1) coefficient of variation (CV): scheduled admissions, (2) CV: scheduled admissions for weekdays only, (3) CV: emergent admissions, (4) CV: scheduled occupancy, (5) CV: emergent occupancy, (6) percent emergent admissions with LOS >1 day, and (7) percent of days with peak occupancy <95%.
The CV, standard deviation divided by the mean of a distribution, is a measure of dispersion. Because it is a normalized value reported as a percentage, CV can be used to compare variability when sample sizes differ. CV: scheduled admissions captures the variability in admissions coded as an elective across all days in a month. The raw CV score is the standard deviation of the elective admissions for each day divided by the mean. The component score is (1 CV) component weight. A higher CV indicates greater variability, and yields a lower component score. CV on scheduled and emergent occupancy is derived from peak daily occupancy. Percent emergent admissions with LOS >1 day captures the efficiency of bed use, because high volumes of short‐stay patients increases turnover work. Its component score is calculated as the percent of emergent admissions in a month with LOS >1 day component weight. Percent of days with peak occupancy <95% incentivizes the hospital to avoid full occupancy, because effective flow requires that some beds remain open.[18, 19] Its component score is calculated as the percent of days in the month with peak occupancy <95% component weight. Although a similar measure, percent of days with peak occupancy >95%, was an adjusting factor in the bed management domain, it is included again here, because this factor has a unique effect on both domains.
RESULTS
The balanced scorecard with composite measures provided improvement teams and administrators with a picture of patient flow (Figure 2). The overall score provided a global perspective on patient flow over time and captured trends in performance during various states of hospital occupancy. One trend that it captured was an association between high volume and poor composite scores (Figure 3). Notably, the H1N1 influenza pandemic in the fall of 2009 and the turnover of computer systems in January 2011 can be linked to dips in performance. The changes between fiscal years reflect a shift in baseline metrics.
In addition to the overall composite score, the domain level and individual component scores allowed for more specific evaluation of variables affecting quality of care and enabled targeted improvement activities (Figure 4). For example, in December 2010 and January 2011, room turnover and ESD domain scores dropped, especially in the total discharge to clean time component. In response, the ESD made staffing adjustments, and starting in February 2011, component scores and the domain score improved. Feedback from the scheduling and utilization domain scores also initiated positive change. In August 2010, the CV: scheduled occupancy component score started to drop. In response, certain elective admissions were shifted to weekends to distribute hospital occupancy more evenly throughout the week. By February 2011, the component returned to its goal level. This continual evaluation of performance motivates continual improvement.
DISCUSSION
The use of a patient flow balanced scorecard with composite measurement overcomes pitfalls associated with a single or unaggregated measure. Aggregate scores alone mask important differences and relationships among components.[13] For example, 2 domains may be inversely related, or a provider with an overall average score might score above average in 1 domain but below in another. The composite scorecard, however, shows individual component and domain scores in addition to an aggregate score. The individual component and domain level scores highlight specific areas that need improvement and allow attention to be directed to those areas.
Additionally, a composite score is more likely to engage the range of staff involved in patient flow. Scaling out of 100 points and the red‐yellow‐green model are familiar for operations performance and can be easily understood.[17] Moreover, a composite score allows for dynamic performance goals while maintaining a stable measurement structure. For example, standardized LOS ratios, readmission rates, and denied hospital days can be added to the scorecard to provide more information and balancing measures.
Although balanced scorecards with composites can make holistic performance visible across multiple operational domains, they have some disadvantages. First, because there is a degree of complexity associated with a measure that incorporates multiple aspects of flow, certain elements, such as the relationship between a metric and its balancing measure, may not be readily apparent. Second, composite measures may not provide actionable information if the measure is not clearly related to a process that can be improved.[13, 14] Third, individual metrics may not be replicable between locations, so composites may need to be individualized to each setting.[10, 20]
Improving patient flow is a goal at many hospitals. Although measurement is crucial to identifying and mitigating variations, measuring the multidimensional aspects of flow and their impact on quality is difficult. Our scorecard, with composite measurement, addresses the need for an improved method to assess patient flow and improve quality by tracking care processes simultaneously.
Acknowledgements
The authors thank Bhuvaneswari Jayaraman for her contributions to the original calculations for the first version of the composite score.
Disclosures: Internal funds from The Children's Hospital of Philadelphia supported the conduct of this work. The authors report no conflicts of interest.
- AHA Solutions. Patient Flow Challenges Assessment 2009. Chicago, IL: American Hospital Association; 2009.
- , , , et al. The impact of emergency department crowding measures on time to antibiotics for patients with community‐acquired pneumonia. Ann Emerg Med. 2007;50(5):510–516.
- . Practice variation: implications for our health care system. Manag Care. 2004;13(9 suppl):3–7.
- . Managing variability in patient flow is the key to improving access to care, nursing staffing, quality of care, and reducing its cost. Paper presented at: Institute of Medicine; June 24, 2004; Washington, DC.
- , , . Developing models for patient flow and daily surge capacity research. Acad Emerg Med. 2006;13(11):1109–1113.
- , , , , . Patient flow variability and unplanned readmissions to an intensive care unit. Crit Care Med. 2009;37(11):2882–2887.
- , , , , , . Scheduled admissions and high occupancy at a children's hospital. J Hosp Med. 2011;6(2):81–87.
- , , . Frequent overcrowding in US emergency departments. Acad Emerg Med. 2001;8(2):151–155.
- Institute of Medicine. Performance measurement: accelerating improvement. Available at: http://www.iom.edu/Reports/2005/Performance‐Measurement‐Accelerating‐Improvement.aspx. Published December 1, 2005. Accessed December 5, 2012.
- , , , . Emergency department performance measures and benchmarking summit. Acad Emerg Med. 2006;13(10):1074–1080.
- . The Surgical Infection Prevention and Surgical Care Improvement Projects: promises and pitfalls. Am Surg. 2006;72(11):1010–1016; discussion 1021–1030, 1133–1048.
- , , . Patient safety quality indicators. Composite measures workgroup. Final report. Rockville, MD; Agency for Healthcare Research and Quality; 2008.
- , , , et al. ACCF/AHA 2010 position statement on composite measures for healthcare performance assessment: a report of the American College of Cardiology Foundation/American Heart Association Task Force on performance measures (Writing Committee to develop a position statement on composite measures). Circulation. 2010;121(15):1780–1791.
- , . A five‐point checklist to help performance reports incentivize improvement and effectively guide patients. Health Aff (Millwood). 2012;31(3):612–618.
- , , , , . Composite measures for profiling hospitals on surgical morbidity. Ann Surg. 2013;257(1):67–72.
- , . All‐or‐none measurement raises the bar on performance. JAMA. 2006;295(10):1168–1170.
- , , , et al. Quality improvement. Red light‐green light: from kids' game to discharge tool. Healthc Q. 2011;14:77–81.
- , , , . Myths of ideal hospital occupancy. Med J Aust. 2010;192(1):42–43.
- , . Emergency department overcrowding in the United States: an emerging threat to patient safety and public health. Emerg Med J. 2003;20(5):402–405.
- , , , . Emergency department crowding: consensus development of potential measures. Ann Emerg Med. 2003;42(6):824–834.
- AHA Solutions. Patient Flow Challenges Assessment 2009. Chicago, IL: American Hospital Association; 2009.
- , , , et al. The impact of emergency department crowding measures on time to antibiotics for patients with community‐acquired pneumonia. Ann Emerg Med. 2007;50(5):510–516.
- . Practice variation: implications for our health care system. Manag Care. 2004;13(9 suppl):3–7.
- . Managing variability in patient flow is the key to improving access to care, nursing staffing, quality of care, and reducing its cost. Paper presented at: Institute of Medicine; June 24, 2004; Washington, DC.
- , , . Developing models for patient flow and daily surge capacity research. Acad Emerg Med. 2006;13(11):1109–1113.
- , , , , . Patient flow variability and unplanned readmissions to an intensive care unit. Crit Care Med. 2009;37(11):2882–2887.
- , , , , , . Scheduled admissions and high occupancy at a children's hospital. J Hosp Med. 2011;6(2):81–87.
- , , . Frequent overcrowding in US emergency departments. Acad Emerg Med. 2001;8(2):151–155.
- Institute of Medicine. Performance measurement: accelerating improvement. Available at: http://www.iom.edu/Reports/2005/Performance‐Measurement‐Accelerating‐Improvement.aspx. Published December 1, 2005. Accessed December 5, 2012.
- , , , . Emergency department performance measures and benchmarking summit. Acad Emerg Med. 2006;13(10):1074–1080.
- . The Surgical Infection Prevention and Surgical Care Improvement Projects: promises and pitfalls. Am Surg. 2006;72(11):1010–1016; discussion 1021–1030, 1133–1048.
- , , . Patient safety quality indicators. Composite measures workgroup. Final report. Rockville, MD; Agency for Healthcare Research and Quality; 2008.
- , , , et al. ACCF/AHA 2010 position statement on composite measures for healthcare performance assessment: a report of the American College of Cardiology Foundation/American Heart Association Task Force on performance measures (Writing Committee to develop a position statement on composite measures). Circulation. 2010;121(15):1780–1791.
- , . A five‐point checklist to help performance reports incentivize improvement and effectively guide patients. Health Aff (Millwood). 2012;31(3):612–618.
- , , , , . Composite measures for profiling hospitals on surgical morbidity. Ann Surg. 2013;257(1):67–72.
- , . All‐or‐none measurement raises the bar on performance. JAMA. 2006;295(10):1168–1170.
- , , , et al. Quality improvement. Red light‐green light: from kids' game to discharge tool. Healthc Q. 2011;14:77–81.
- , , , . Myths of ideal hospital occupancy. Med J Aust. 2010;192(1):42–43.
- , . Emergency department overcrowding in the United States: an emerging threat to patient safety and public health. Emerg Med J. 2003;20(5):402–405.
- , , , . Emergency department crowding: consensus development of potential measures. Ann Emerg Med. 2003;42(6):824–834.
FDA discourages use of laparoscopic power morcellation during hysterectomy and myomectomy
On April 17, 2014, the US Food and Drug Administration (FDA) issued a Safety Communication discouraging the use of laparoscopic power morcellation in hysterectomy and myomectomy for uterine fibroids.
Based on an FDA analysis of current data, “… it is estimated that 1 in 350 women undergoing hysterectomy or myomectomy for the treatment of fibroids is found to have an unsuspected uterine sarcoma, a type of uterine cancer that includes leiomyosarcoma. If laparoscopic power morcellation is performed in women with unsuspected uterine sarcoma, there is a risk that the procedure will spread the cancerous tissue within the abdomen and pelvis, significantly worsening the patient’s likelihood of long-term survival.”1
FDA recommendations
The FDA posted the following recommendations for health-care providers1:
- Laparoscopic uterine power morcellation should not be used in women with suspected or known uterine cancer
- All available treatment options should be considered for women with symptomatic uterine fibroids
- The benefits and risks of all treatments should be discussed thoroughly with each patient
- If, after a careful benefit-risk evaluation, laparoscopic power morcellation is considered the best therapeutic option for an individual patient, then:
- Inform the patient that her fibroid(s) may contain unexpected cancerous tissue and that laparoscopic power morcellation may spread the cancer, significantly worsening her prognosis
- Some clinicians and medical institutions now advocate using a specimen “bag” during morcellation in an attempt to contain the uterine tissue and minimize the risk of spread in the abdomen and pelvis.
Although many women choose laparoscopic hysterectomy or myomectomy because of the associated benefits, there are other treatments available, including vaginal or abdominal hysterectomy and myomectomy; laparoscopic hysterectomy or myomectomy without morcellation; minilaparotomy; uterine artery embolization; high-intensity focused ultrasound; and drug therapy.
FDA actions
To reduce the risk of inadvertent spread of unsuspected cancer to the abdomen and pelvis, the FDA has instructed manufacturers of power morcellators used during laparoscopic hysterectomy and myomectomy to immediately review labeling for accurate risk information.
A to-be-convened public meeting of the FDA’s Obstetrics and Gynecological Medical Device Advisory Committee will discuss1:
- the clinical role of laparoscopic power morcellation in the treatment of uterine fibroids
- whether surgical techniques and/or use of accessories, such as morcellation and/or specimen bags, can enhance the safe and effective use of these devices
- if a boxed warning relating the risk of cancer spread should be required for laparoscopic power morcellators.
The FDA will continue to review adverse event reports and peer-reviewed literature, as well as patient information and evidence from health-care providers, gynecologic and surgical professional societies, and medical device manufacturers.
Adverse events should promptly be reported to the FDA by filing a voluntary report through MedWatch, the FDA Safety Information and Adverse Event Reporting program.
Institution reaction
Brigham and Women's Hospital had banned the use of open power morcellation on March 31, 2014, allowing for the use of power morcellators within a containment system. In light of the FDA notice that discourages the use of power morcellators during hysterectomy or myomectomy for the treatment of uterine fibroids, however, Robert L. Barbieri, MD, chair of obstetrics and gynecology at Brigham and Women’s advised surgical staff to "immediately suspend use of power morcellators in all cases until further notice."
Massachusetts General, which also had placed restrictions on the use of power morcellators prior to the FDA communication, suspended the use of power morcellation.
“I have asked our doctors to stop the procedure immediately until more information is available,’’ Dr. Isaac Schiff, Massachusetts General’s chief of obstetrics and gynecology, told the Boston Globe.
Reference
- US Food and Drug Administration. Laparoscopic uterine power morcellation in Hysterectomy and Myomectomy: FDA Safety Communication. http://www.fda.gov/MedicalDevices/Safety/AlertsandNotices/ucm393576.htm. Published April 17, 2014. Accessed April 17, 2014.
On April 17, 2014, the US Food and Drug Administration (FDA) issued a Safety Communication discouraging the use of laparoscopic power morcellation in hysterectomy and myomectomy for uterine fibroids.
Based on an FDA analysis of current data, “… it is estimated that 1 in 350 women undergoing hysterectomy or myomectomy for the treatment of fibroids is found to have an unsuspected uterine sarcoma, a type of uterine cancer that includes leiomyosarcoma. If laparoscopic power morcellation is performed in women with unsuspected uterine sarcoma, there is a risk that the procedure will spread the cancerous tissue within the abdomen and pelvis, significantly worsening the patient’s likelihood of long-term survival.”1
FDA recommendations
The FDA posted the following recommendations for health-care providers1:
- Laparoscopic uterine power morcellation should not be used in women with suspected or known uterine cancer
- All available treatment options should be considered for women with symptomatic uterine fibroids
- The benefits and risks of all treatments should be discussed thoroughly with each patient
- If, after a careful benefit-risk evaluation, laparoscopic power morcellation is considered the best therapeutic option for an individual patient, then:
- Inform the patient that her fibroid(s) may contain unexpected cancerous tissue and that laparoscopic power morcellation may spread the cancer, significantly worsening her prognosis
- Some clinicians and medical institutions now advocate using a specimen “bag” during morcellation in an attempt to contain the uterine tissue and minimize the risk of spread in the abdomen and pelvis.
Although many women choose laparoscopic hysterectomy or myomectomy because of the associated benefits, there are other treatments available, including vaginal or abdominal hysterectomy and myomectomy; laparoscopic hysterectomy or myomectomy without morcellation; minilaparotomy; uterine artery embolization; high-intensity focused ultrasound; and drug therapy.
FDA actions
To reduce the risk of inadvertent spread of unsuspected cancer to the abdomen and pelvis, the FDA has instructed manufacturers of power morcellators used during laparoscopic hysterectomy and myomectomy to immediately review labeling for accurate risk information.
A to-be-convened public meeting of the FDA’s Obstetrics and Gynecological Medical Device Advisory Committee will discuss1:
- the clinical role of laparoscopic power morcellation in the treatment of uterine fibroids
- whether surgical techniques and/or use of accessories, such as morcellation and/or specimen bags, can enhance the safe and effective use of these devices
- if a boxed warning relating the risk of cancer spread should be required for laparoscopic power morcellators.
The FDA will continue to review adverse event reports and peer-reviewed literature, as well as patient information and evidence from health-care providers, gynecologic and surgical professional societies, and medical device manufacturers.
Adverse events should promptly be reported to the FDA by filing a voluntary report through MedWatch, the FDA Safety Information and Adverse Event Reporting program.
Institution reaction
Brigham and Women's Hospital had banned the use of open power morcellation on March 31, 2014, allowing for the use of power morcellators within a containment system. In light of the FDA notice that discourages the use of power morcellators during hysterectomy or myomectomy for the treatment of uterine fibroids, however, Robert L. Barbieri, MD, chair of obstetrics and gynecology at Brigham and Women’s advised surgical staff to "immediately suspend use of power morcellators in all cases until further notice."
Massachusetts General, which also had placed restrictions on the use of power morcellators prior to the FDA communication, suspended the use of power morcellation.
“I have asked our doctors to stop the procedure immediately until more information is available,’’ Dr. Isaac Schiff, Massachusetts General’s chief of obstetrics and gynecology, told the Boston Globe.
On April 17, 2014, the US Food and Drug Administration (FDA) issued a Safety Communication discouraging the use of laparoscopic power morcellation in hysterectomy and myomectomy for uterine fibroids.
Based on an FDA analysis of current data, “… it is estimated that 1 in 350 women undergoing hysterectomy or myomectomy for the treatment of fibroids is found to have an unsuspected uterine sarcoma, a type of uterine cancer that includes leiomyosarcoma. If laparoscopic power morcellation is performed in women with unsuspected uterine sarcoma, there is a risk that the procedure will spread the cancerous tissue within the abdomen and pelvis, significantly worsening the patient’s likelihood of long-term survival.”1
FDA recommendations
The FDA posted the following recommendations for health-care providers1:
- Laparoscopic uterine power morcellation should not be used in women with suspected or known uterine cancer
- All available treatment options should be considered for women with symptomatic uterine fibroids
- The benefits and risks of all treatments should be discussed thoroughly with each patient
- If, after a careful benefit-risk evaluation, laparoscopic power morcellation is considered the best therapeutic option for an individual patient, then:
- Inform the patient that her fibroid(s) may contain unexpected cancerous tissue and that laparoscopic power morcellation may spread the cancer, significantly worsening her prognosis
- Some clinicians and medical institutions now advocate using a specimen “bag” during morcellation in an attempt to contain the uterine tissue and minimize the risk of spread in the abdomen and pelvis.
Although many women choose laparoscopic hysterectomy or myomectomy because of the associated benefits, there are other treatments available, including vaginal or abdominal hysterectomy and myomectomy; laparoscopic hysterectomy or myomectomy without morcellation; minilaparotomy; uterine artery embolization; high-intensity focused ultrasound; and drug therapy.
FDA actions
To reduce the risk of inadvertent spread of unsuspected cancer to the abdomen and pelvis, the FDA has instructed manufacturers of power morcellators used during laparoscopic hysterectomy and myomectomy to immediately review labeling for accurate risk information.
A to-be-convened public meeting of the FDA’s Obstetrics and Gynecological Medical Device Advisory Committee will discuss1:
- the clinical role of laparoscopic power morcellation in the treatment of uterine fibroids
- whether surgical techniques and/or use of accessories, such as morcellation and/or specimen bags, can enhance the safe and effective use of these devices
- if a boxed warning relating the risk of cancer spread should be required for laparoscopic power morcellators.
The FDA will continue to review adverse event reports and peer-reviewed literature, as well as patient information and evidence from health-care providers, gynecologic and surgical professional societies, and medical device manufacturers.
Adverse events should promptly be reported to the FDA by filing a voluntary report through MedWatch, the FDA Safety Information and Adverse Event Reporting program.
Institution reaction
Brigham and Women's Hospital had banned the use of open power morcellation on March 31, 2014, allowing for the use of power morcellators within a containment system. In light of the FDA notice that discourages the use of power morcellators during hysterectomy or myomectomy for the treatment of uterine fibroids, however, Robert L. Barbieri, MD, chair of obstetrics and gynecology at Brigham and Women’s advised surgical staff to "immediately suspend use of power morcellators in all cases until further notice."
Massachusetts General, which also had placed restrictions on the use of power morcellators prior to the FDA communication, suspended the use of power morcellation.
“I have asked our doctors to stop the procedure immediately until more information is available,’’ Dr. Isaac Schiff, Massachusetts General’s chief of obstetrics and gynecology, told the Boston Globe.
Reference
- US Food and Drug Administration. Laparoscopic uterine power morcellation in Hysterectomy and Myomectomy: FDA Safety Communication. http://www.fda.gov/MedicalDevices/Safety/AlertsandNotices/ucm393576.htm. Published April 17, 2014. Accessed April 17, 2014.
Reference
- US Food and Drug Administration. Laparoscopic uterine power morcellation in Hysterectomy and Myomectomy: FDA Safety Communication. http://www.fda.gov/MedicalDevices/Safety/AlertsandNotices/ucm393576.htm. Published April 17, 2014. Accessed April 17, 2014.
Click here to access articles, videos, and audiocasts in the Morcellation Topic Collection
The Use of Secure Messaging in Medical Specialty Care
Secure messaging (SM) is an encrypted, web-based mode of communication within the My HealtheVet (MHV) website. It was developed for the nonurgent, nonemergency communication of test results and other health information as well as for scheduling appointments and renewing medication prescriptions. Secure messaging is asynchronous, which means that communication between parties is not done at the same time. It was designed to address the need for a secure means of communication between patient and provider.1 Messages can be triaged across teams and saved to the Computerized Patient Record System (CPRS).
The VA patients who use MHV can upgrade their account through an in-person authentication process (IPA), which takes about 10 minutes. Any health care provider (HCP) team or administrator can use SM if set up in the system. Health care providers can only receive messages from patients who have been associated with their triage care group. Patients may only message an HCP with which they are associated. In general, this group would comprise their HCP and 1 or more specialty clinics where they have already been seen. Patients can choose an HCP from a limited drop-down menu.
Patients using SM choose a subject, such as appointments, medications, tests, or general. Patients are then able to type a message, and they are also able to see the threads of previous messages. They may access test results or attachments sent to them by the HCPs. Patients are notified of messages through their previously registered e-mail account, which displays a message asking them to log on to MHV.
Health care providers may access MHV either through the CPRS on the tools menu or as a link in an e-mail. Once HCPs log on, they will see their inbox and messages listed by sender and type of inquiry (ie, prescription refill, test question, and so forth). The HCPs can view escalated messages (those that have not been answered within 3 days), drafts, and sent and completed messages. Health care providers can also create special folders to store their messages.
The health care team can personalize how and to whom messages appear. There are 2 main models used by Specialty Care. The first involves a staff member designated to triage messages for the team. This staff member will see all incoming messages and forward them appropriately. For example, in one clinic model, the program assistant reviews all messages and then forwards them to the appropriate provider. The team pharmacist receives prescription requests, the HCP receives general or test inquiries from patients, and the program assistant retains and answers all communication related to appointments and cancellations. Another model involves employing a staff person or administrator as a co-user with each HCP. The HCP can then forward messages that may need administrative action.
The HCPs receive an e-mail notification with a link when a message has been received. Clicking on the link takes them directly to SM within MHV, where they can sign in to see all their messages. Users can also add a signature block, which will appear on all correspondence. They may also designate a surrogate to answer messages when they are unavailable, such as during administrative or personal leave. The HCPs also have the ability to create a SM even if the patient has not yet messaged them. Users can also send copies of messages to other staff members. Providers and staff have the ability to attach a file, which can be a test result, letter, records, etc. Messages can then be saved in the CPRS if desired.
Patients, however, cannot send attachments to their HCP. Only those HCPs who have seen the patient will be available for communication. This system eliminates the possibility of patients self-referring to a specialist and asking questions of HCPs who have never seen them. The HCPs and staff may also forward messages to the appropriate person.
Secure messaging can provide unique opportunities for communication and improvement in outcome measures in certain specialties. For example, in endocrinology patients may be asked to send home blood sugar or blood pressure (BP) readings in between visits, to allow for more rapid medication titration and achievement of treatment goals. A study by Harris and colleagues showed that the frequent use of electronic SM was associated with improved glycemic control.2
At the Atlanta VAMC, SM was implemented in the Primary Care Service Line prior to the Medicine Specialty Care Service Line. The implementation was a natural fit for the organized Primary Care teams. Implementation within the specialties brought forth a new set of issues. Many specialties were not formally organized with a team leader. There were often multiple HCPs in a division, some full time, some part time, in addition to subspecialty pharmacists, physician assistants, and nurse practitioners. Because the Atlanta VAMC is also a training hospital for the Emory University School of Medicine, new residents and fellows are included in the teams each month. It was, therefore, necessary for each specialty to design a message flow that would best fit its needs. Initially, there was concern that SM would add yet another layer of responsibilities to the already stretched HCPs.
The reality has been the opposite. Secure messaging was found to be an additional type of communication, which could be completed more rapidly than a phone call or generating a results letter. The HCPs were also concerned that patients would attempt to use them as primary care providers (PCPs). However, as patients were able to view both their PCP and their specialty care provider in the drop-down menu, they were generally able to direct their questions appropriately.
At the Atlanta VAMC, 60% of the messages were completed by the provider, 29% by a clinical team member, and 11% by the triage staff from 2013 to 2014 (Figure 1). Some HCPs were concerned that once SM was in place, they would be inundated with messages. The reality seems to be that most patients use SM judiciously, and although they are comfortable in the knowledge that they can communicate directly with their HCP, the need is infrequent. The number of messages has slowly increased over the past year as more patients join MHV and SM (Table). Surprisingly, as the number of inbound messages increased, the percentage of escalated messages (messages not answered within 3 days) declined, indicating a learning curve as HCPs begin using SM.
There are 3 steps to patient enrollment in SM. The first is enrollment in MHV, which can be done either online or at the VAMC. The second step requires the patient to go to the VAMC and present identification to complete the IPA. Finally, the enrolled patients must opt-in to the program. Enrollment in MHV has steadily increased through advertising campaigns on the VAMC website, within the VAMC, and through HCPs and staff (Figure 2).
However, barriers still exist. Some patients do not have an Internet connection and are not computer savvy. Other patients express interest but put it off to another visit. Some patients have been confused about the additional step of IPA that is required for SM and stop at enrollment in MHV only.
Therefore the key challenges for implementing SM are facilitating MHV enrollment, IPA, and completion of the opt-in feature. To encourage participation, VISN 7 mailed postcards to all 33,000 patients who had undergone IPA but had not yet opted-in. The number of patients who opted-in quadrupled, demonstrating that this type of promotion is an effective recruitment tool.
Another ongoing challenge is developing a method to easily generate workload credit for the HCPs’ time spent using SM for patient care. This will be an important parameter to track, as the time spent on SM per provider is expected to increase. It has also been suggested that there be an out-of-office response for nonemergent messages and the assignment of a surrogate to handle incoming messages for HCPs who are on leave. An unforeseen example of a nonemergent message occurs when a patient replies “Thank you” to a message from an HCP. That message is then counted as a new message and must be viewed and completed like any other message. It can also become an escalated message, even though there is no important information being transmitted.
Conclusions
Secure messaging provides a simple means of rapid communication and feedback between HCPs and their patients. An e-mail notification is generated, HCPs access SM through the link, the reply is sent, and a CPRS note is automatically generated. That same communication would require a far more time-consuming and complicated process without SM: The patient must contact the service, usually the program assistant, and leave a message; that message would be passed on via voicemail or e-mail to the appropriate HCP; the provider would need to access the CPRS, phone the patient, discuss the issue if the patient is available, and then document the contact with a note in the CPRS. If the patient was unavailable, this process would require multiple phone calls.
With respect to patients, the benefits of SM are significant and include easy access to prescription refills and a quick response to questions about medications, dosages, or tests. Patients are able to change or cancel appointments, thereby avoiding no-shows. Frustration concerning the inability to reach the correct party or to speak with staff directly is reduced with SM, and overall communication between HCP and patient is streamlined.
Author disclosures
The authors report no actual or potential conflicts of interest with regard to this article.
Disclaimer
The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the U.S. Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.
1. Delbanco T, Sands DZ. Electrons in flight—e-mail between doctors and patients. New Engl J Med. 2004;350(17):1705-1707.
2. Harris LT, Haneuse SJ, Martin DP, Ralston JD. Diabetes quality of care and outpatient utilization associated with electronic patient-provider messaging: A cross-sectional analysis. Diabetes Care. 2009;32(7):1182-1187.
Secure messaging (SM) is an encrypted, web-based mode of communication within the My HealtheVet (MHV) website. It was developed for the nonurgent, nonemergency communication of test results and other health information as well as for scheduling appointments and renewing medication prescriptions. Secure messaging is asynchronous, which means that communication between parties is not done at the same time. It was designed to address the need for a secure means of communication between patient and provider.1 Messages can be triaged across teams and saved to the Computerized Patient Record System (CPRS).
The VA patients who use MHV can upgrade their account through an in-person authentication process (IPA), which takes about 10 minutes. Any health care provider (HCP) team or administrator can use SM if set up in the system. Health care providers can only receive messages from patients who have been associated with their triage care group. Patients may only message an HCP with which they are associated. In general, this group would comprise their HCP and 1 or more specialty clinics where they have already been seen. Patients can choose an HCP from a limited drop-down menu.
Patients using SM choose a subject, such as appointments, medications, tests, or general. Patients are then able to type a message, and they are also able to see the threads of previous messages. They may access test results or attachments sent to them by the HCPs. Patients are notified of messages through their previously registered e-mail account, which displays a message asking them to log on to MHV.
Health care providers may access MHV either through the CPRS on the tools menu or as a link in an e-mail. Once HCPs log on, they will see their inbox and messages listed by sender and type of inquiry (ie, prescription refill, test question, and so forth). The HCPs can view escalated messages (those that have not been answered within 3 days), drafts, and sent and completed messages. Health care providers can also create special folders to store their messages.
The health care team can personalize how and to whom messages appear. There are 2 main models used by Specialty Care. The first involves a staff member designated to triage messages for the team. This staff member will see all incoming messages and forward them appropriately. For example, in one clinic model, the program assistant reviews all messages and then forwards them to the appropriate provider. The team pharmacist receives prescription requests, the HCP receives general or test inquiries from patients, and the program assistant retains and answers all communication related to appointments and cancellations. Another model involves employing a staff person or administrator as a co-user with each HCP. The HCP can then forward messages that may need administrative action.
The HCPs receive an e-mail notification with a link when a message has been received. Clicking on the link takes them directly to SM within MHV, where they can sign in to see all their messages. Users can also add a signature block, which will appear on all correspondence. They may also designate a surrogate to answer messages when they are unavailable, such as during administrative or personal leave. The HCPs also have the ability to create a SM even if the patient has not yet messaged them. Users can also send copies of messages to other staff members. Providers and staff have the ability to attach a file, which can be a test result, letter, records, etc. Messages can then be saved in the CPRS if desired.
Patients, however, cannot send attachments to their HCP. Only those HCPs who have seen the patient will be available for communication. This system eliminates the possibility of patients self-referring to a specialist and asking questions of HCPs who have never seen them. The HCPs and staff may also forward messages to the appropriate person.
Secure messaging can provide unique opportunities for communication and improvement in outcome measures in certain specialties. For example, in endocrinology patients may be asked to send home blood sugar or blood pressure (BP) readings in between visits, to allow for more rapid medication titration and achievement of treatment goals. A study by Harris and colleagues showed that the frequent use of electronic SM was associated with improved glycemic control.2
At the Atlanta VAMC, SM was implemented in the Primary Care Service Line prior to the Medicine Specialty Care Service Line. The implementation was a natural fit for the organized Primary Care teams. Implementation within the specialties brought forth a new set of issues. Many specialties were not formally organized with a team leader. There were often multiple HCPs in a division, some full time, some part time, in addition to subspecialty pharmacists, physician assistants, and nurse practitioners. Because the Atlanta VAMC is also a training hospital for the Emory University School of Medicine, new residents and fellows are included in the teams each month. It was, therefore, necessary for each specialty to design a message flow that would best fit its needs. Initially, there was concern that SM would add yet another layer of responsibilities to the already stretched HCPs.
The reality has been the opposite. Secure messaging was found to be an additional type of communication, which could be completed more rapidly than a phone call or generating a results letter. The HCPs were also concerned that patients would attempt to use them as primary care providers (PCPs). However, as patients were able to view both their PCP and their specialty care provider in the drop-down menu, they were generally able to direct their questions appropriately.
At the Atlanta VAMC, 60% of the messages were completed by the provider, 29% by a clinical team member, and 11% by the triage staff from 2013 to 2014 (Figure 1). Some HCPs were concerned that once SM was in place, they would be inundated with messages. The reality seems to be that most patients use SM judiciously, and although they are comfortable in the knowledge that they can communicate directly with their HCP, the need is infrequent. The number of messages has slowly increased over the past year as more patients join MHV and SM (Table). Surprisingly, as the number of inbound messages increased, the percentage of escalated messages (messages not answered within 3 days) declined, indicating a learning curve as HCPs begin using SM.
There are 3 steps to patient enrollment in SM. The first is enrollment in MHV, which can be done either online or at the VAMC. The second step requires the patient to go to the VAMC and present identification to complete the IPA. Finally, the enrolled patients must opt-in to the program. Enrollment in MHV has steadily increased through advertising campaigns on the VAMC website, within the VAMC, and through HCPs and staff (Figure 2).
However, barriers still exist. Some patients do not have an Internet connection and are not computer savvy. Other patients express interest but put it off to another visit. Some patients have been confused about the additional step of IPA that is required for SM and stop at enrollment in MHV only.
Therefore the key challenges for implementing SM are facilitating MHV enrollment, IPA, and completion of the opt-in feature. To encourage participation, VISN 7 mailed postcards to all 33,000 patients who had undergone IPA but had not yet opted-in. The number of patients who opted-in quadrupled, demonstrating that this type of promotion is an effective recruitment tool.
Another ongoing challenge is developing a method to easily generate workload credit for the HCPs’ time spent using SM for patient care. This will be an important parameter to track, as the time spent on SM per provider is expected to increase. It has also been suggested that there be an out-of-office response for nonemergent messages and the assignment of a surrogate to handle incoming messages for HCPs who are on leave. An unforeseen example of a nonemergent message occurs when a patient replies “Thank you” to a message from an HCP. That message is then counted as a new message and must be viewed and completed like any other message. It can also become an escalated message, even though there is no important information being transmitted.
Conclusions
Secure messaging provides a simple means of rapid communication and feedback between HCPs and their patients. An e-mail notification is generated, HCPs access SM through the link, the reply is sent, and a CPRS note is automatically generated. That same communication would require a far more time-consuming and complicated process without SM: The patient must contact the service, usually the program assistant, and leave a message; that message would be passed on via voicemail or e-mail to the appropriate HCP; the provider would need to access the CPRS, phone the patient, discuss the issue if the patient is available, and then document the contact with a note in the CPRS. If the patient was unavailable, this process would require multiple phone calls.
With respect to patients, the benefits of SM are significant and include easy access to prescription refills and a quick response to questions about medications, dosages, or tests. Patients are able to change or cancel appointments, thereby avoiding no-shows. Frustration concerning the inability to reach the correct party or to speak with staff directly is reduced with SM, and overall communication between HCP and patient is streamlined.
Author disclosures
The authors report no actual or potential conflicts of interest with regard to this article.
Disclaimer
The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the U.S. Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.
Secure messaging (SM) is an encrypted, web-based mode of communication within the My HealtheVet (MHV) website. It was developed for the nonurgent, nonemergency communication of test results and other health information as well as for scheduling appointments and renewing medication prescriptions. Secure messaging is asynchronous, which means that communication between parties is not done at the same time. It was designed to address the need for a secure means of communication between patient and provider.1 Messages can be triaged across teams and saved to the Computerized Patient Record System (CPRS).
The VA patients who use MHV can upgrade their account through an in-person authentication process (IPA), which takes about 10 minutes. Any health care provider (HCP) team or administrator can use SM if set up in the system. Health care providers can only receive messages from patients who have been associated with their triage care group. Patients may only message an HCP with which they are associated. In general, this group would comprise their HCP and 1 or more specialty clinics where they have already been seen. Patients can choose an HCP from a limited drop-down menu.
Patients using SM choose a subject, such as appointments, medications, tests, or general. Patients are then able to type a message, and they are also able to see the threads of previous messages. They may access test results or attachments sent to them by the HCPs. Patients are notified of messages through their previously registered e-mail account, which displays a message asking them to log on to MHV.
Health care providers may access MHV either through the CPRS on the tools menu or as a link in an e-mail. Once HCPs log on, they will see their inbox and messages listed by sender and type of inquiry (ie, prescription refill, test question, and so forth). The HCPs can view escalated messages (those that have not been answered within 3 days), drafts, and sent and completed messages. Health care providers can also create special folders to store their messages.
The health care team can personalize how and to whom messages appear. There are 2 main models used by Specialty Care. The first involves a staff member designated to triage messages for the team. This staff member will see all incoming messages and forward them appropriately. For example, in one clinic model, the program assistant reviews all messages and then forwards them to the appropriate provider. The team pharmacist receives prescription requests, the HCP receives general or test inquiries from patients, and the program assistant retains and answers all communication related to appointments and cancellations. Another model involves employing a staff person or administrator as a co-user with each HCP. The HCP can then forward messages that may need administrative action.
The HCPs receive an e-mail notification with a link when a message has been received. Clicking on the link takes them directly to SM within MHV, where they can sign in to see all their messages. Users can also add a signature block, which will appear on all correspondence. They may also designate a surrogate to answer messages when they are unavailable, such as during administrative or personal leave. The HCPs also have the ability to create a SM even if the patient has not yet messaged them. Users can also send copies of messages to other staff members. Providers and staff have the ability to attach a file, which can be a test result, letter, records, etc. Messages can then be saved in the CPRS if desired.
Patients, however, cannot send attachments to their HCP. Only those HCPs who have seen the patient will be available for communication. This system eliminates the possibility of patients self-referring to a specialist and asking questions of HCPs who have never seen them. The HCPs and staff may also forward messages to the appropriate person.
Secure messaging can provide unique opportunities for communication and improvement in outcome measures in certain specialties. For example, in endocrinology patients may be asked to send home blood sugar or blood pressure (BP) readings in between visits, to allow for more rapid medication titration and achievement of treatment goals. A study by Harris and colleagues showed that the frequent use of electronic SM was associated with improved glycemic control.2
At the Atlanta VAMC, SM was implemented in the Primary Care Service Line prior to the Medicine Specialty Care Service Line. The implementation was a natural fit for the organized Primary Care teams. Implementation within the specialties brought forth a new set of issues. Many specialties were not formally organized with a team leader. There were often multiple HCPs in a division, some full time, some part time, in addition to subspecialty pharmacists, physician assistants, and nurse practitioners. Because the Atlanta VAMC is also a training hospital for the Emory University School of Medicine, new residents and fellows are included in the teams each month. It was, therefore, necessary for each specialty to design a message flow that would best fit its needs. Initially, there was concern that SM would add yet another layer of responsibilities to the already stretched HCPs.
The reality has been the opposite. Secure messaging was found to be an additional type of communication, which could be completed more rapidly than a phone call or generating a results letter. The HCPs were also concerned that patients would attempt to use them as primary care providers (PCPs). However, as patients were able to view both their PCP and their specialty care provider in the drop-down menu, they were generally able to direct their questions appropriately.
At the Atlanta VAMC, 60% of the messages were completed by the provider, 29% by a clinical team member, and 11% by the triage staff from 2013 to 2014 (Figure 1). Some HCPs were concerned that once SM was in place, they would be inundated with messages. The reality seems to be that most patients use SM judiciously, and although they are comfortable in the knowledge that they can communicate directly with their HCP, the need is infrequent. The number of messages has slowly increased over the past year as more patients join MHV and SM (Table). Surprisingly, as the number of inbound messages increased, the percentage of escalated messages (messages not answered within 3 days) declined, indicating a learning curve as HCPs begin using SM.
There are 3 steps to patient enrollment in SM. The first is enrollment in MHV, which can be done either online or at the VAMC. The second step requires the patient to go to the VAMC and present identification to complete the IPA. Finally, the enrolled patients must opt-in to the program. Enrollment in MHV has steadily increased through advertising campaigns on the VAMC website, within the VAMC, and through HCPs and staff (Figure 2).
However, barriers still exist. Some patients do not have an Internet connection and are not computer savvy. Other patients express interest but put it off to another visit. Some patients have been confused about the additional step of IPA that is required for SM and stop at enrollment in MHV only.
Therefore the key challenges for implementing SM are facilitating MHV enrollment, IPA, and completion of the opt-in feature. To encourage participation, VISN 7 mailed postcards to all 33,000 patients who had undergone IPA but had not yet opted-in. The number of patients who opted-in quadrupled, demonstrating that this type of promotion is an effective recruitment tool.
Another ongoing challenge is developing a method to easily generate workload credit for the HCPs’ time spent using SM for patient care. This will be an important parameter to track, as the time spent on SM per provider is expected to increase. It has also been suggested that there be an out-of-office response for nonemergent messages and the assignment of a surrogate to handle incoming messages for HCPs who are on leave. An unforeseen example of a nonemergent message occurs when a patient replies “Thank you” to a message from an HCP. That message is then counted as a new message and must be viewed and completed like any other message. It can also become an escalated message, even though there is no important information being transmitted.
Conclusions
Secure messaging provides a simple means of rapid communication and feedback between HCPs and their patients. An e-mail notification is generated, HCPs access SM through the link, the reply is sent, and a CPRS note is automatically generated. That same communication would require a far more time-consuming and complicated process without SM: The patient must contact the service, usually the program assistant, and leave a message; that message would be passed on via voicemail or e-mail to the appropriate HCP; the provider would need to access the CPRS, phone the patient, discuss the issue if the patient is available, and then document the contact with a note in the CPRS. If the patient was unavailable, this process would require multiple phone calls.
With respect to patients, the benefits of SM are significant and include easy access to prescription refills and a quick response to questions about medications, dosages, or tests. Patients are able to change or cancel appointments, thereby avoiding no-shows. Frustration concerning the inability to reach the correct party or to speak with staff directly is reduced with SM, and overall communication between HCP and patient is streamlined.
Author disclosures
The authors report no actual or potential conflicts of interest with regard to this article.
Disclaimer
The opinions expressed herein are those of the authors and do not necessarily reflect those of Federal Practitioner, Frontline Medical Communications Inc., the U.S. Government, or any of its agencies. This article may discuss unlabeled or investigational use of certain drugs. Please review complete prescribing information for specific drugs or drug combinations—including indications, contraindications, warnings, and adverse effects—before administering pharmacologic therapy to patients.
1. Delbanco T, Sands DZ. Electrons in flight—e-mail between doctors and patients. New Engl J Med. 2004;350(17):1705-1707.
2. Harris LT, Haneuse SJ, Martin DP, Ralston JD. Diabetes quality of care and outpatient utilization associated with electronic patient-provider messaging: A cross-sectional analysis. Diabetes Care. 2009;32(7):1182-1187.
1. Delbanco T, Sands DZ. Electrons in flight—e-mail between doctors and patients. New Engl J Med. 2004;350(17):1705-1707.
2. Harris LT, Haneuse SJ, Martin DP, Ralston JD. Diabetes quality of care and outpatient utilization associated with electronic patient-provider messaging: A cross-sectional analysis. Diabetes Care. 2009;32(7):1182-1187.