User login
Child Psychiatry Consult: Autism assessment
Introduction
Autism spectrum disorder is a neurodevelopmental condition characterized by a heterogeneous grouping of social-communication impairments and behavioral phenomena that are observed in early development and often accompanied by an array of co-occurring issues. The prevalence of autism spectrum disorder (ASD) has risen markedly in the last several years (1 in 68 per a 2014 CDC estimate), and the evidence base for early intervention and other treatment strategies supports the idea that a timely and appropriate diagnosis is critical for promoting positive outcomes for children and their families.
With ASD, there can be wide variety in a young child’s presenting symptoms. Although some youth clearly manifest the hallmark features of ASD, ever-changing development, complicated cognitive profiles, family difficulties, co-occurring mental health problems, and evolving nosology (such as DSM changes) can contribute to the difficulty providers encounter in fully interpreting and identifying ASD symptoms in the course of a typical primary care visit. This case example outlines assessment and diagnostic strategies that may help pediatricians to better understand the complexities of diagnosis, assessment, and treatment for children suspected of having ASD. Ideally, a diagnostic evaluation would quickly follow a standardized screening tool that is positive for concern for ASD (such as the Modified Checklist for Autism in Toddlers – Revised) between the ages of 18 and 24 months.
Case summary
Everett is a 4-year-old boy who presents to an autism diagnostic clinic after his parents expressed concerns about his behavior. Everett is described to be a rigid, stubborn, strong-willed, and easily frustrated boy who began to exhibit aggressive behaviors at 18 months of age. He continues to have almost daily temper tantrums. Notably, Everett did not begin to use single words with communicative intent until he was 24 months old. He will often repeat words nonfunctionally and utter nonsensical verbalizations while spinning in circles and rocking back and forth. Everett enjoys being around peers but has difficulties engaging appropriately with other children, exhibiting poor physical boundaries. Everett’s hearing and vision were previously tested to be without deficit, and there is no history of seizure activity or indication of an underlying metabolic disorder.
Discussion
Everett presents with some signs and symptoms to suggest ASD (namely his communication and language impairments accompanied by some atypical social relatedness and repetitive behaviors). His presentation, however, has many characteristics that while common to ASD are not entirely specific to the diagnosis in a preschooler, and could occur with other disorders. For example, Everett’s social difficulties could be the result of an emerging behavioral disorder (an oppositional defiant disorder) or a primary expressive language disorder, which may manifest with frustration intolerance due to communication difficulties.
With children like Everett, a comprehensive autism diagnostic assessment should be obtained and preferably be comprised of a minimum of two components – a caregiver interview and an observational assessment ideally conducted by an experienced clinical interdisciplinary team. Additionally, evaluations of adaptive skills, cognitive profile, family functioning, social-emotional/behavioral functioning, and sensory issues can be useful to inform treatment planning and diagnosis. Ultimately, the diagnosis of ASD is made after clinicians integrate available information and fully consider the range of differential diagnoses. Clinicians who may participate in the diagnostic process include developmental pediatricians, child psychiatrists, clinical psychologists, speech-language pathologists, and other allied health professionals.
Clinical guidelines suggest that gathering a thorough developmental history, assessing for the characteristic impairments that support an ASD diagnosis, and establishing current levels of functioning can be performed using the semistructured Autism Diagnostic Interview – Revised (ADI-R) with primary caregivers. Information about a child’s social interactions also can be obtained with the use of the Social Responsiveness Scale (SRS), which can yield multi-informant data that helps to capture a youth’s functioning and peer interactions in different settings, including home and school.
The observational assessment ideally utilizes the Autism Diagnostic Observation Schedule (ADOS), a standardized instrument that can evaluate domains of reciprocal social interaction, communication, restricted interests, and repetitive behaviors in a developmentally informed manner. Clinicians should be mindful that certain behaviors may not be displayed during the diagnostic evaluation, and as such, scoring on the ADOS should be integrated with other sources of information and interpreted within a developmental framework; no single result on one instrument is sufficient to make or break an autism diagnosis.
The above-mentioned tools were used in Everett’s assessment. Appraising the collected data, his scoring on the ADOS suggested an autism diagnosis, but information from the ADI-R and SRS were not conclusive. To further evaluate Everett, we incorporated a broad developmental evaluation tool, the Mullen Scales of Early Learning, which provided us with a lens through which to interpret his profile of impairments and strengths. Everett scored with average to above-average skills across all domains, which helped us conceptualize that his social, language, and behavioral struggles were not the result of a global developmental delay or intellectual disability.
Additionally, Everett’s family completed the Vineland Adaptive Behavior Scales (his adaptive socialization skills were a relative weakness, and motor skills were a strength) and Child Behavior Checklists, which revealed the endorsement of emotionally reactive and aggressive behavior symptoms from both parents. Everett’s parents’ mental wellness was assessed with Adult Behavior Checklists in order to provide informed family-based treatment recommendations.
In Everett’s evaluation, enduring challenges in the core symptom domains characterizing ASD were noted. His atypical social affect, limited social awareness, and repetitive patterns of behavior provided evidence that Everett met diagnostic criteria for ASD. Also noted were protective factors that promoted his well-being (he’s verbal, has the capacity to play imaginatively, presents with a supportive family, and demonstrates no significant cognitive deficiencies), which were incorporated into our treatment recommendations. Recommendations included enrollment in structured educational and behavioral interventions, and corresponding parent training treatments to help his caregivers manage his disruptive behaviors while reducing the risk for the development of further emotional/behavioral problems in the future.
Everett’s ASD diagnosis also warranted a referral for genetic testing and/or counseling to help the family to obtain information about the etiology of the disorder, screen for other conditions, and help guide appropriate medical management. There were no other indications to pursue additional medical, imaging, or neurological consultations.
Clinical pearl
For some children, the diagnosis of ASD is unclear. Problems arise in making an accurate diagnosis for a variety of reasons, and fully appreciating a child’s (or adolescent’s) developmental challenges can be difficult, especially given the considerable symptom overlap ASD has with other learning, medical, cognitive, or mental health diagnoses. These children require a diagnostic evaluation using a family-focused and culturally sensitive multidisciplinary approach that incorporates standardized tools. As a primary care provider, it can often be difficult to tease out symptoms and have the time to do a thorough assessment; primary providers should be aware of their local assessment expert resources and referral options.
Jeremiah Dickerson, M.D., a child and adolescent psychiatrist, is an assistant professor of psychiatry at the University of Vermont. Dr. Dickerson is the director of the university’s autism diagnostic clinic. Contact Dr. Dickerson at [email protected].
Introduction
Autism spectrum disorder is a neurodevelopmental condition characterized by a heterogeneous grouping of social-communication impairments and behavioral phenomena that are observed in early development and often accompanied by an array of co-occurring issues. The prevalence of autism spectrum disorder (ASD) has risen markedly in the last several years (1 in 68 per a 2014 CDC estimate), and the evidence base for early intervention and other treatment strategies supports the idea that a timely and appropriate diagnosis is critical for promoting positive outcomes for children and their families.
With ASD, there can be wide variety in a young child’s presenting symptoms. Although some youth clearly manifest the hallmark features of ASD, ever-changing development, complicated cognitive profiles, family difficulties, co-occurring mental health problems, and evolving nosology (such as DSM changes) can contribute to the difficulty providers encounter in fully interpreting and identifying ASD symptoms in the course of a typical primary care visit. This case example outlines assessment and diagnostic strategies that may help pediatricians to better understand the complexities of diagnosis, assessment, and treatment for children suspected of having ASD. Ideally, a diagnostic evaluation would quickly follow a standardized screening tool that is positive for concern for ASD (such as the Modified Checklist for Autism in Toddlers – Revised) between the ages of 18 and 24 months.
Case summary
Everett is a 4-year-old boy who presents to an autism diagnostic clinic after his parents expressed concerns about his behavior. Everett is described to be a rigid, stubborn, strong-willed, and easily frustrated boy who began to exhibit aggressive behaviors at 18 months of age. He continues to have almost daily temper tantrums. Notably, Everett did not begin to use single words with communicative intent until he was 24 months old. He will often repeat words nonfunctionally and utter nonsensical verbalizations while spinning in circles and rocking back and forth. Everett enjoys being around peers but has difficulties engaging appropriately with other children, exhibiting poor physical boundaries. Everett’s hearing and vision were previously tested to be without deficit, and there is no history of seizure activity or indication of an underlying metabolic disorder.
Discussion
Everett presents with some signs and symptoms to suggest ASD (namely his communication and language impairments accompanied by some atypical social relatedness and repetitive behaviors). His presentation, however, has many characteristics that while common to ASD are not entirely specific to the diagnosis in a preschooler, and could occur with other disorders. For example, Everett’s social difficulties could be the result of an emerging behavioral disorder (an oppositional defiant disorder) or a primary expressive language disorder, which may manifest with frustration intolerance due to communication difficulties.
With children like Everett, a comprehensive autism diagnostic assessment should be obtained and preferably be comprised of a minimum of two components – a caregiver interview and an observational assessment ideally conducted by an experienced clinical interdisciplinary team. Additionally, evaluations of adaptive skills, cognitive profile, family functioning, social-emotional/behavioral functioning, and sensory issues can be useful to inform treatment planning and diagnosis. Ultimately, the diagnosis of ASD is made after clinicians integrate available information and fully consider the range of differential diagnoses. Clinicians who may participate in the diagnostic process include developmental pediatricians, child psychiatrists, clinical psychologists, speech-language pathologists, and other allied health professionals.
Clinical guidelines suggest that gathering a thorough developmental history, assessing for the characteristic impairments that support an ASD diagnosis, and establishing current levels of functioning can be performed using the semistructured Autism Diagnostic Interview – Revised (ADI-R) with primary caregivers. Information about a child’s social interactions also can be obtained with the use of the Social Responsiveness Scale (SRS), which can yield multi-informant data that helps to capture a youth’s functioning and peer interactions in different settings, including home and school.
The observational assessment ideally utilizes the Autism Diagnostic Observation Schedule (ADOS), a standardized instrument that can evaluate domains of reciprocal social interaction, communication, restricted interests, and repetitive behaviors in a developmentally informed manner. Clinicians should be mindful that certain behaviors may not be displayed during the diagnostic evaluation, and as such, scoring on the ADOS should be integrated with other sources of information and interpreted within a developmental framework; no single result on one instrument is sufficient to make or break an autism diagnosis.
The above-mentioned tools were used in Everett’s assessment. Appraising the collected data, his scoring on the ADOS suggested an autism diagnosis, but information from the ADI-R and SRS were not conclusive. To further evaluate Everett, we incorporated a broad developmental evaluation tool, the Mullen Scales of Early Learning, which provided us with a lens through which to interpret his profile of impairments and strengths. Everett scored with average to above-average skills across all domains, which helped us conceptualize that his social, language, and behavioral struggles were not the result of a global developmental delay or intellectual disability.
Additionally, Everett’s family completed the Vineland Adaptive Behavior Scales (his adaptive socialization skills were a relative weakness, and motor skills were a strength) and Child Behavior Checklists, which revealed the endorsement of emotionally reactive and aggressive behavior symptoms from both parents. Everett’s parents’ mental wellness was assessed with Adult Behavior Checklists in order to provide informed family-based treatment recommendations.
In Everett’s evaluation, enduring challenges in the core symptom domains characterizing ASD were noted. His atypical social affect, limited social awareness, and repetitive patterns of behavior provided evidence that Everett met diagnostic criteria for ASD. Also noted were protective factors that promoted his well-being (he’s verbal, has the capacity to play imaginatively, presents with a supportive family, and demonstrates no significant cognitive deficiencies), which were incorporated into our treatment recommendations. Recommendations included enrollment in structured educational and behavioral interventions, and corresponding parent training treatments to help his caregivers manage his disruptive behaviors while reducing the risk for the development of further emotional/behavioral problems in the future.
Everett’s ASD diagnosis also warranted a referral for genetic testing and/or counseling to help the family to obtain information about the etiology of the disorder, screen for other conditions, and help guide appropriate medical management. There were no other indications to pursue additional medical, imaging, or neurological consultations.
Clinical pearl
For some children, the diagnosis of ASD is unclear. Problems arise in making an accurate diagnosis for a variety of reasons, and fully appreciating a child’s (or adolescent’s) developmental challenges can be difficult, especially given the considerable symptom overlap ASD has with other learning, medical, cognitive, or mental health diagnoses. These children require a diagnostic evaluation using a family-focused and culturally sensitive multidisciplinary approach that incorporates standardized tools. As a primary care provider, it can often be difficult to tease out symptoms and have the time to do a thorough assessment; primary providers should be aware of their local assessment expert resources and referral options.
Jeremiah Dickerson, M.D., a child and adolescent psychiatrist, is an assistant professor of psychiatry at the University of Vermont. Dr. Dickerson is the director of the university’s autism diagnostic clinic. Contact Dr. Dickerson at [email protected].
Introduction
Autism spectrum disorder is a neurodevelopmental condition characterized by a heterogeneous grouping of social-communication impairments and behavioral phenomena that are observed in early development and often accompanied by an array of co-occurring issues. The prevalence of autism spectrum disorder (ASD) has risen markedly in the last several years (1 in 68 per a 2014 CDC estimate), and the evidence base for early intervention and other treatment strategies supports the idea that a timely and appropriate diagnosis is critical for promoting positive outcomes for children and their families.
With ASD, there can be wide variety in a young child’s presenting symptoms. Although some youth clearly manifest the hallmark features of ASD, ever-changing development, complicated cognitive profiles, family difficulties, co-occurring mental health problems, and evolving nosology (such as DSM changes) can contribute to the difficulty providers encounter in fully interpreting and identifying ASD symptoms in the course of a typical primary care visit. This case example outlines assessment and diagnostic strategies that may help pediatricians to better understand the complexities of diagnosis, assessment, and treatment for children suspected of having ASD. Ideally, a diagnostic evaluation would quickly follow a standardized screening tool that is positive for concern for ASD (such as the Modified Checklist for Autism in Toddlers – Revised) between the ages of 18 and 24 months.
Case summary
Everett is a 4-year-old boy who presents to an autism diagnostic clinic after his parents expressed concerns about his behavior. Everett is described to be a rigid, stubborn, strong-willed, and easily frustrated boy who began to exhibit aggressive behaviors at 18 months of age. He continues to have almost daily temper tantrums. Notably, Everett did not begin to use single words with communicative intent until he was 24 months old. He will often repeat words nonfunctionally and utter nonsensical verbalizations while spinning in circles and rocking back and forth. Everett enjoys being around peers but has difficulties engaging appropriately with other children, exhibiting poor physical boundaries. Everett’s hearing and vision were previously tested to be without deficit, and there is no history of seizure activity or indication of an underlying metabolic disorder.
Discussion
Everett presents with some signs and symptoms to suggest ASD (namely his communication and language impairments accompanied by some atypical social relatedness and repetitive behaviors). His presentation, however, has many characteristics that while common to ASD are not entirely specific to the diagnosis in a preschooler, and could occur with other disorders. For example, Everett’s social difficulties could be the result of an emerging behavioral disorder (an oppositional defiant disorder) or a primary expressive language disorder, which may manifest with frustration intolerance due to communication difficulties.
With children like Everett, a comprehensive autism diagnostic assessment should be obtained and preferably be comprised of a minimum of two components – a caregiver interview and an observational assessment ideally conducted by an experienced clinical interdisciplinary team. Additionally, evaluations of adaptive skills, cognitive profile, family functioning, social-emotional/behavioral functioning, and sensory issues can be useful to inform treatment planning and diagnosis. Ultimately, the diagnosis of ASD is made after clinicians integrate available information and fully consider the range of differential diagnoses. Clinicians who may participate in the diagnostic process include developmental pediatricians, child psychiatrists, clinical psychologists, speech-language pathologists, and other allied health professionals.
Clinical guidelines suggest that gathering a thorough developmental history, assessing for the characteristic impairments that support an ASD diagnosis, and establishing current levels of functioning can be performed using the semistructured Autism Diagnostic Interview – Revised (ADI-R) with primary caregivers. Information about a child’s social interactions also can be obtained with the use of the Social Responsiveness Scale (SRS), which can yield multi-informant data that helps to capture a youth’s functioning and peer interactions in different settings, including home and school.
The observational assessment ideally utilizes the Autism Diagnostic Observation Schedule (ADOS), a standardized instrument that can evaluate domains of reciprocal social interaction, communication, restricted interests, and repetitive behaviors in a developmentally informed manner. Clinicians should be mindful that certain behaviors may not be displayed during the diagnostic evaluation, and as such, scoring on the ADOS should be integrated with other sources of information and interpreted within a developmental framework; no single result on one instrument is sufficient to make or break an autism diagnosis.
The above-mentioned tools were used in Everett’s assessment. Appraising the collected data, his scoring on the ADOS suggested an autism diagnosis, but information from the ADI-R and SRS were not conclusive. To further evaluate Everett, we incorporated a broad developmental evaluation tool, the Mullen Scales of Early Learning, which provided us with a lens through which to interpret his profile of impairments and strengths. Everett scored with average to above-average skills across all domains, which helped us conceptualize that his social, language, and behavioral struggles were not the result of a global developmental delay or intellectual disability.
Additionally, Everett’s family completed the Vineland Adaptive Behavior Scales (his adaptive socialization skills were a relative weakness, and motor skills were a strength) and Child Behavior Checklists, which revealed the endorsement of emotionally reactive and aggressive behavior symptoms from both parents. Everett’s parents’ mental wellness was assessed with Adult Behavior Checklists in order to provide informed family-based treatment recommendations.
In Everett’s evaluation, enduring challenges in the core symptom domains characterizing ASD were noted. His atypical social affect, limited social awareness, and repetitive patterns of behavior provided evidence that Everett met diagnostic criteria for ASD. Also noted were protective factors that promoted his well-being (he’s verbal, has the capacity to play imaginatively, presents with a supportive family, and demonstrates no significant cognitive deficiencies), which were incorporated into our treatment recommendations. Recommendations included enrollment in structured educational and behavioral interventions, and corresponding parent training treatments to help his caregivers manage his disruptive behaviors while reducing the risk for the development of further emotional/behavioral problems in the future.
Everett’s ASD diagnosis also warranted a referral for genetic testing and/or counseling to help the family to obtain information about the etiology of the disorder, screen for other conditions, and help guide appropriate medical management. There were no other indications to pursue additional medical, imaging, or neurological consultations.
Clinical pearl
For some children, the diagnosis of ASD is unclear. Problems arise in making an accurate diagnosis for a variety of reasons, and fully appreciating a child’s (or adolescent’s) developmental challenges can be difficult, especially given the considerable symptom overlap ASD has with other learning, medical, cognitive, or mental health diagnoses. These children require a diagnostic evaluation using a family-focused and culturally sensitive multidisciplinary approach that incorporates standardized tools. As a primary care provider, it can often be difficult to tease out symptoms and have the time to do a thorough assessment; primary providers should be aware of their local assessment expert resources and referral options.
Jeremiah Dickerson, M.D., a child and adolescent psychiatrist, is an assistant professor of psychiatry at the University of Vermont. Dr. Dickerson is the director of the university’s autism diagnostic clinic. Contact Dr. Dickerson at [email protected].
Decompression can save lives in ventricular trapping
BALTIMORE –Aggressive decompression dramatically improved survival in patients who had trapped ventricle syndrome as a result of tumor or intracerebral hemorrhage in a retrospective study.
Overall mortality in the cohort was 70% among those who had no decompression, Dr. Gabriel L. Pagani-Estevez said at the annual meeting of the American Neurological Association. But it dropped to 19% among those who underwent some form of decompression therapy. Even after controlling for confounding factors like age, etiology, and hemorrhage volume, decompression remained a significant independent predictor of survival, said Dr. Pagani-Estevez, a neurology resident at the Mayo Clinic, Rochester, Minn.
Despite all the methodological issues inherent in a retrospective study, the findings “provide at least a suggestion that neurosurgical intervention can markedly reduce mortality in trapped ventricle syndrome,” he said. “Now, research needs to clarify the ideal intervention, the effect of decompression on functional outcome, and which patients might derive the most benefit from treatment.”
The cohort comprised 392 patients who developed ventricular trapping and were treated during 2002-2010. They were a mean of 58 years old. Most (223) were not on anticoagulation therapy. A total of 80 patients were taking aspirin, and the remainder were taking other anticoagulants. The median midline shift was about 10 mm.
Trapping was caused by a tumor in 177 patients. Other etiologies included intracerebral hemorrhage (80), subdural hematoma (55), trauma (26), and stroke (18). Unspecified causes made up the remainder.
The left lateral ventricle was most often involved (176). The right lateral ventricle was trapped in 159 patients and both were involved in 32. Thirteen patients had a trapped fourth ventricle, and 12 had unspecified trapping.
Some kind of decompression procedure was performed on 221 patients. These included craniotomy (126), craniectomy (26), external ventricular drain (30), ventricular-peritoneal shunt (23), and endoscopic septum pellucidum fenestration (16).
Comparisons showed significantly decreased mortality for intervention vs. nonintervention in groups with various causes of ventricular trapping: intracerebral hemorrhage (48% vs. 95%), tumor (12% vs. 47%), and subdural hematoma (20% vs. 90%).
There were nonsignificant declines in mortality among patients who underwent intervention for ventricular trapping caused by trauma or ischemic stroke, but the number of patients in those subgroups were small, which probably confounded the results, Dr. Pagani-Estevez said.
He then conducted a multivariate analysis to determine patient characteristics that might have contributed to survival. Patients who had a decompression procedure were 87% less likely to die than were those who had not – a highly significant finding (P = .0001). A midline shift conferred a slight increase in the risk of death, while having intracerebral hemorrhage as the trapping etiology increased the risk fourfold.
Trapped ventricle carries a notoriously poor prognosis, said Dr. Alejandro A. Rabinstein, a coauthor on the study. “By the time you develop it, it’s a very bad situation, so whatever way you can achieve decompression may improve the situation,” said Dr. Rabinstein, a critical care neurologist who is also at the Mayo Clinic in Rochester. “If you don’t think the patient has enough left to merit the intervention, then you just don’t do it. But despite that limitation, if you think the patient can recover some function, it’s appropriate. An intervention will make patients survive way more often than no intervention. Without something, though, the prospect of survival is bleak.”
Neither Dr. Pagani-Estevez nor Dr. Rabinstein had any financial disclosures.
On Twitter @alz_gal
BALTIMORE –Aggressive decompression dramatically improved survival in patients who had trapped ventricle syndrome as a result of tumor or intracerebral hemorrhage in a retrospective study.
Overall mortality in the cohort was 70% among those who had no decompression, Dr. Gabriel L. Pagani-Estevez said at the annual meeting of the American Neurological Association. But it dropped to 19% among those who underwent some form of decompression therapy. Even after controlling for confounding factors like age, etiology, and hemorrhage volume, decompression remained a significant independent predictor of survival, said Dr. Pagani-Estevez, a neurology resident at the Mayo Clinic, Rochester, Minn.
Despite all the methodological issues inherent in a retrospective study, the findings “provide at least a suggestion that neurosurgical intervention can markedly reduce mortality in trapped ventricle syndrome,” he said. “Now, research needs to clarify the ideal intervention, the effect of decompression on functional outcome, and which patients might derive the most benefit from treatment.”
The cohort comprised 392 patients who developed ventricular trapping and were treated during 2002-2010. They were a mean of 58 years old. Most (223) were not on anticoagulation therapy. A total of 80 patients were taking aspirin, and the remainder were taking other anticoagulants. The median midline shift was about 10 mm.
Trapping was caused by a tumor in 177 patients. Other etiologies included intracerebral hemorrhage (80), subdural hematoma (55), trauma (26), and stroke (18). Unspecified causes made up the remainder.
The left lateral ventricle was most often involved (176). The right lateral ventricle was trapped in 159 patients and both were involved in 32. Thirteen patients had a trapped fourth ventricle, and 12 had unspecified trapping.
Some kind of decompression procedure was performed on 221 patients. These included craniotomy (126), craniectomy (26), external ventricular drain (30), ventricular-peritoneal shunt (23), and endoscopic septum pellucidum fenestration (16).
Comparisons showed significantly decreased mortality for intervention vs. nonintervention in groups with various causes of ventricular trapping: intracerebral hemorrhage (48% vs. 95%), tumor (12% vs. 47%), and subdural hematoma (20% vs. 90%).
There were nonsignificant declines in mortality among patients who underwent intervention for ventricular trapping caused by trauma or ischemic stroke, but the number of patients in those subgroups were small, which probably confounded the results, Dr. Pagani-Estevez said.
He then conducted a multivariate analysis to determine patient characteristics that might have contributed to survival. Patients who had a decompression procedure were 87% less likely to die than were those who had not – a highly significant finding (P = .0001). A midline shift conferred a slight increase in the risk of death, while having intracerebral hemorrhage as the trapping etiology increased the risk fourfold.
Trapped ventricle carries a notoriously poor prognosis, said Dr. Alejandro A. Rabinstein, a coauthor on the study. “By the time you develop it, it’s a very bad situation, so whatever way you can achieve decompression may improve the situation,” said Dr. Rabinstein, a critical care neurologist who is also at the Mayo Clinic in Rochester. “If you don’t think the patient has enough left to merit the intervention, then you just don’t do it. But despite that limitation, if you think the patient can recover some function, it’s appropriate. An intervention will make patients survive way more often than no intervention. Without something, though, the prospect of survival is bleak.”
Neither Dr. Pagani-Estevez nor Dr. Rabinstein had any financial disclosures.
On Twitter @alz_gal
BALTIMORE –Aggressive decompression dramatically improved survival in patients who had trapped ventricle syndrome as a result of tumor or intracerebral hemorrhage in a retrospective study.
Overall mortality in the cohort was 70% among those who had no decompression, Dr. Gabriel L. Pagani-Estevez said at the annual meeting of the American Neurological Association. But it dropped to 19% among those who underwent some form of decompression therapy. Even after controlling for confounding factors like age, etiology, and hemorrhage volume, decompression remained a significant independent predictor of survival, said Dr. Pagani-Estevez, a neurology resident at the Mayo Clinic, Rochester, Minn.
Despite all the methodological issues inherent in a retrospective study, the findings “provide at least a suggestion that neurosurgical intervention can markedly reduce mortality in trapped ventricle syndrome,” he said. “Now, research needs to clarify the ideal intervention, the effect of decompression on functional outcome, and which patients might derive the most benefit from treatment.”
The cohort comprised 392 patients who developed ventricular trapping and were treated during 2002-2010. They were a mean of 58 years old. Most (223) were not on anticoagulation therapy. A total of 80 patients were taking aspirin, and the remainder were taking other anticoagulants. The median midline shift was about 10 mm.
Trapping was caused by a tumor in 177 patients. Other etiologies included intracerebral hemorrhage (80), subdural hematoma (55), trauma (26), and stroke (18). Unspecified causes made up the remainder.
The left lateral ventricle was most often involved (176). The right lateral ventricle was trapped in 159 patients and both were involved in 32. Thirteen patients had a trapped fourth ventricle, and 12 had unspecified trapping.
Some kind of decompression procedure was performed on 221 patients. These included craniotomy (126), craniectomy (26), external ventricular drain (30), ventricular-peritoneal shunt (23), and endoscopic septum pellucidum fenestration (16).
Comparisons showed significantly decreased mortality for intervention vs. nonintervention in groups with various causes of ventricular trapping: intracerebral hemorrhage (48% vs. 95%), tumor (12% vs. 47%), and subdural hematoma (20% vs. 90%).
There were nonsignificant declines in mortality among patients who underwent intervention for ventricular trapping caused by trauma or ischemic stroke, but the number of patients in those subgroups were small, which probably confounded the results, Dr. Pagani-Estevez said.
He then conducted a multivariate analysis to determine patient characteristics that might have contributed to survival. Patients who had a decompression procedure were 87% less likely to die than were those who had not – a highly significant finding (P = .0001). A midline shift conferred a slight increase in the risk of death, while having intracerebral hemorrhage as the trapping etiology increased the risk fourfold.
Trapped ventricle carries a notoriously poor prognosis, said Dr. Alejandro A. Rabinstein, a coauthor on the study. “By the time you develop it, it’s a very bad situation, so whatever way you can achieve decompression may improve the situation,” said Dr. Rabinstein, a critical care neurologist who is also at the Mayo Clinic in Rochester. “If you don’t think the patient has enough left to merit the intervention, then you just don’t do it. But despite that limitation, if you think the patient can recover some function, it’s appropriate. An intervention will make patients survive way more often than no intervention. Without something, though, the prospect of survival is bleak.”
Neither Dr. Pagani-Estevez nor Dr. Rabinstein had any financial disclosures.
On Twitter @alz_gal
AT ANA 2014
Key clinical point: Decompression for a trapped ventricle can be a life-saving procedure.
Major finding: Mortality significantly declined from 70% in patients without decompression to 19% in those who underwent decompression via a variety of methods.
Data source: The retrospective study comprised 392 patients.
Disclosures: Neither Dr. Pagani-Estevez nor Dr. Rabinstein had any financial disclosures.
Residents arrange transfusions despite poor knowledge
PHILADELPHIA—Internal medicine residents are obtaining transfusion consent from patients despite having poor knowledge of transfusion medicine, according to a study of nearly 500 residents in 9 countries.
On an exam assessing transfusion knowledge, the residents’ mean score was 45.7%.
And in a survey, an overwhelming majority of residents said they had “beginner” or “intermediate” transfusion knowledge.
Still, 89% said they had obtained patient consent for a transfusion.
Richard Haspel, MD, PhD, of Beth Israel Deacon Medical Center and Harvard Medical School in Boston, presented these data at the AABB Annual Meeting 2014 (abstract S45-030G).
“We all know there’s a problem with clinicians not knowing how to transfuse blood,” Dr Haspel began. “I would argue, though, that there are a lot of questions we don’t know the answer to. How prevalent is this problem? Are there some places that do it better than others? What areas need improvement?”
With these questions in mind, Dr Haspel and his colleagues used a 23-question survey and a 20-question exam (validated by the BEST Collaborative) to assess 474 internal medicine residents from 23 sites in 9 countries: Australia, Canada, England, Ireland, Italy, Germany, The Netherlands, Spain, and the US.
The mean score of correct responses in the exam was 45.7%. The mean score was significantly lower for first-year residents (43.9%) than for third- (47.1%; P=0.02) and fourth-year residents (50.6%, P=0.002).
However, as 50.6% was the highest mean score, exam scores were poor regardless of a resident’s time served, Dr Haspel noted. Scores were poor across the different study sites as well, ranging from about 32% to 55%.
The exam included questions on red cells, platelets, plasma, allergic reactions, transfusion-related acute lung injury (TRALI), and transfusion-associated circulatory overload (TACO), among other topics.
As an example, Dr Haspel pointed out that, for the 3 questions on TRALI, the percentage of correct responses did not exceed 15%. This was the topic about which residents seemed the least informed.
Dr Haspel noted that, in general, residents with more medical school hours spent learning about transfusion medicine and those with better perceived quality of their training tended to score higher on the exam. Still, there wasn’t much of a difference in exam scores between residents who said they had beginner, intermediate, or advanced knowledge of transfusion medicine.
Twelve percent of residents said they did not receive any transfusion medicine training in medical school, and 28% said they didn’t receive any training during their residency. About 35% said they received more than 2 hours of training in medical school, and 18% said they received more than 2 hours of training during their residency.
“In terms of the quality of the training, most rated it ‘slightly’ or ‘moderately’ effective,” Dr Haspel said. “In terms of attitudes and perceptions, most of them considered themselves a beginner [48%] or intermediate [48%] in regard to transfusion medicine knowledge.”
Ninety-seven percent of residents said they know how to contact the blood bank, and 72% said they know how to contact a transfusion medicine doctor. But 14% percent of residents did not know if their hospital had transfusion guidelines, and 1% wrongly said their hospital did not have guidelines.
Yet 89% of residents said they had obtained consent for a transfusion from a patient.
On the other hand, most residents (77%) said knowledge of transfusion medicine is “very” or “extremely” important in providing appropriate patient care. And 65% said they would find additional training “very” or “extremely” helpful. ![]()
PHILADELPHIA—Internal medicine residents are obtaining transfusion consent from patients despite having poor knowledge of transfusion medicine, according to a study of nearly 500 residents in 9 countries.
On an exam assessing transfusion knowledge, the residents’ mean score was 45.7%.
And in a survey, an overwhelming majority of residents said they had “beginner” or “intermediate” transfusion knowledge.
Still, 89% said they had obtained patient consent for a transfusion.
Richard Haspel, MD, PhD, of Beth Israel Deacon Medical Center and Harvard Medical School in Boston, presented these data at the AABB Annual Meeting 2014 (abstract S45-030G).
“We all know there’s a problem with clinicians not knowing how to transfuse blood,” Dr Haspel began. “I would argue, though, that there are a lot of questions we don’t know the answer to. How prevalent is this problem? Are there some places that do it better than others? What areas need improvement?”
With these questions in mind, Dr Haspel and his colleagues used a 23-question survey and a 20-question exam (validated by the BEST Collaborative) to assess 474 internal medicine residents from 23 sites in 9 countries: Australia, Canada, England, Ireland, Italy, Germany, The Netherlands, Spain, and the US.
The mean score of correct responses in the exam was 45.7%. The mean score was significantly lower for first-year residents (43.9%) than for third- (47.1%; P=0.02) and fourth-year residents (50.6%, P=0.002).
However, as 50.6% was the highest mean score, exam scores were poor regardless of a resident’s time served, Dr Haspel noted. Scores were poor across the different study sites as well, ranging from about 32% to 55%.
The exam included questions on red cells, platelets, plasma, allergic reactions, transfusion-related acute lung injury (TRALI), and transfusion-associated circulatory overload (TACO), among other topics.
As an example, Dr Haspel pointed out that, for the 3 questions on TRALI, the percentage of correct responses did not exceed 15%. This was the topic about which residents seemed the least informed.
Dr Haspel noted that, in general, residents with more medical school hours spent learning about transfusion medicine and those with better perceived quality of their training tended to score higher on the exam. Still, there wasn’t much of a difference in exam scores between residents who said they had beginner, intermediate, or advanced knowledge of transfusion medicine.
Twelve percent of residents said they did not receive any transfusion medicine training in medical school, and 28% said they didn’t receive any training during their residency. About 35% said they received more than 2 hours of training in medical school, and 18% said they received more than 2 hours of training during their residency.
“In terms of the quality of the training, most rated it ‘slightly’ or ‘moderately’ effective,” Dr Haspel said. “In terms of attitudes and perceptions, most of them considered themselves a beginner [48%] or intermediate [48%] in regard to transfusion medicine knowledge.”
Ninety-seven percent of residents said they know how to contact the blood bank, and 72% said they know how to contact a transfusion medicine doctor. But 14% percent of residents did not know if their hospital had transfusion guidelines, and 1% wrongly said their hospital did not have guidelines.
Yet 89% of residents said they had obtained consent for a transfusion from a patient.
On the other hand, most residents (77%) said knowledge of transfusion medicine is “very” or “extremely” important in providing appropriate patient care. And 65% said they would find additional training “very” or “extremely” helpful. ![]()
PHILADELPHIA—Internal medicine residents are obtaining transfusion consent from patients despite having poor knowledge of transfusion medicine, according to a study of nearly 500 residents in 9 countries.
On an exam assessing transfusion knowledge, the residents’ mean score was 45.7%.
And in a survey, an overwhelming majority of residents said they had “beginner” or “intermediate” transfusion knowledge.
Still, 89% said they had obtained patient consent for a transfusion.
Richard Haspel, MD, PhD, of Beth Israel Deacon Medical Center and Harvard Medical School in Boston, presented these data at the AABB Annual Meeting 2014 (abstract S45-030G).
“We all know there’s a problem with clinicians not knowing how to transfuse blood,” Dr Haspel began. “I would argue, though, that there are a lot of questions we don’t know the answer to. How prevalent is this problem? Are there some places that do it better than others? What areas need improvement?”
With these questions in mind, Dr Haspel and his colleagues used a 23-question survey and a 20-question exam (validated by the BEST Collaborative) to assess 474 internal medicine residents from 23 sites in 9 countries: Australia, Canada, England, Ireland, Italy, Germany, The Netherlands, Spain, and the US.
The mean score of correct responses in the exam was 45.7%. The mean score was significantly lower for first-year residents (43.9%) than for third- (47.1%; P=0.02) and fourth-year residents (50.6%, P=0.002).
However, as 50.6% was the highest mean score, exam scores were poor regardless of a resident’s time served, Dr Haspel noted. Scores were poor across the different study sites as well, ranging from about 32% to 55%.
The exam included questions on red cells, platelets, plasma, allergic reactions, transfusion-related acute lung injury (TRALI), and transfusion-associated circulatory overload (TACO), among other topics.
As an example, Dr Haspel pointed out that, for the 3 questions on TRALI, the percentage of correct responses did not exceed 15%. This was the topic about which residents seemed the least informed.
Dr Haspel noted that, in general, residents with more medical school hours spent learning about transfusion medicine and those with better perceived quality of their training tended to score higher on the exam. Still, there wasn’t much of a difference in exam scores between residents who said they had beginner, intermediate, or advanced knowledge of transfusion medicine.
Twelve percent of residents said they did not receive any transfusion medicine training in medical school, and 28% said they didn’t receive any training during their residency. About 35% said they received more than 2 hours of training in medical school, and 18% said they received more than 2 hours of training during their residency.
“In terms of the quality of the training, most rated it ‘slightly’ or ‘moderately’ effective,” Dr Haspel said. “In terms of attitudes and perceptions, most of them considered themselves a beginner [48%] or intermediate [48%] in regard to transfusion medicine knowledge.”
Ninety-seven percent of residents said they know how to contact the blood bank, and 72% said they know how to contact a transfusion medicine doctor. But 14% percent of residents did not know if their hospital had transfusion guidelines, and 1% wrongly said their hospital did not have guidelines.
Yet 89% of residents said they had obtained consent for a transfusion from a patient.
On the other hand, most residents (77%) said knowledge of transfusion medicine is “very” or “extremely” important in providing appropriate patient care. And 65% said they would find additional training “very” or “extremely” helpful. ![]()
Technique cures hemophilia in mice

Credit: Aaron Logan
A new method of genome editing can cure hemophilia B in mice, researchers have reported in Nature.
This new technique doesn’t require the co-delivery of an endonuclease to clip the recipient’s DNA at specific locations, and it doesn’t rely on the co-insertion of genetic promoters to activate the new gene’s expression.
These differences may make the new approach both safer and longer-lasting than other genome editing methods, according to researchers.
“It appears that we may be able to achieve lifelong expression of the inserted gene, which is particularly important when treating genetic diseases like hemophilia and severe combined immunodeficiency,” said study author Mark Kay, MD, PhD, of the Stanford University School of Medicine in California.
“We’re able to do this without using promoters or nucleases, which significantly reduces the chances of cancers that can result if the new gene inserts itself at random places in the genome.”
Using their new technique, Dr Kay and his colleagues were able to insert a working copy of the human coagulation factor IX gene into the DNA of mice with hemophilia B. Although the insertion was accomplished in only about 1% of liver cells, those cells made enough factor IX to ameliorate the disorder.
Instead of using nucleases to cut the DNA or a promoter to drive expression of the factor IX gene, the researchers hitched the expression of the new gene to that of albumin.
They used a modified version of adeno-associated virus and relied on homologous recombination to insert the factor IX gene near the albumin gene.
Using a special DNA linker between the genes, the researchers were able to ensure that the clotting factor protein was made hand-in-hand with the highly expressed albumin protein.
During homologous recombination, the cell takes advantage of the fact that it has two copies of every chromosome. By lining up the damaged and undamaged chromosomes, the cell can “crib” off the intact copy to repair the damage without losing vital genetic information.
The researchers used this natural process to copy sequences from the viral vector into the genome at places they chose—in this case, after the albumin gene.
When they tested their approach in newborn lab mice with hemophilia, the team found the animals began to express levels of factor IX that were between 7% and 20% of normal. That amount of clotting factor has been shown in previous studies to be therapeutic in mice.
The researchers further showed that the technique worked as well in adult animals, even though the gene was successfully inserted in fewer than 1 in every 100 liver cells.
“We expected this approach to work best in newborn animals because the liver is still growing,” Dr Kay said. “However, because homologous recombination has been thought to occur mostly in proliferating cells, we didn’t expect it to work as well as it did in adult animals.”
The researchers are now planning to test the technique in mice with livers composed of human and mouse cells, a model that may be a good surrogate to further predict what will happen in humans. ![]()

Credit: Aaron Logan
A new method of genome editing can cure hemophilia B in mice, researchers have reported in Nature.
This new technique doesn’t require the co-delivery of an endonuclease to clip the recipient’s DNA at specific locations, and it doesn’t rely on the co-insertion of genetic promoters to activate the new gene’s expression.
These differences may make the new approach both safer and longer-lasting than other genome editing methods, according to researchers.
“It appears that we may be able to achieve lifelong expression of the inserted gene, which is particularly important when treating genetic diseases like hemophilia and severe combined immunodeficiency,” said study author Mark Kay, MD, PhD, of the Stanford University School of Medicine in California.
“We’re able to do this without using promoters or nucleases, which significantly reduces the chances of cancers that can result if the new gene inserts itself at random places in the genome.”
Using their new technique, Dr Kay and his colleagues were able to insert a working copy of the human coagulation factor IX gene into the DNA of mice with hemophilia B. Although the insertion was accomplished in only about 1% of liver cells, those cells made enough factor IX to ameliorate the disorder.
Instead of using nucleases to cut the DNA or a promoter to drive expression of the factor IX gene, the researchers hitched the expression of the new gene to that of albumin.
They used a modified version of adeno-associated virus and relied on homologous recombination to insert the factor IX gene near the albumin gene.
Using a special DNA linker between the genes, the researchers were able to ensure that the clotting factor protein was made hand-in-hand with the highly expressed albumin protein.
During homologous recombination, the cell takes advantage of the fact that it has two copies of every chromosome. By lining up the damaged and undamaged chromosomes, the cell can “crib” off the intact copy to repair the damage without losing vital genetic information.
The researchers used this natural process to copy sequences from the viral vector into the genome at places they chose—in this case, after the albumin gene.
When they tested their approach in newborn lab mice with hemophilia, the team found the animals began to express levels of factor IX that were between 7% and 20% of normal. That amount of clotting factor has been shown in previous studies to be therapeutic in mice.
The researchers further showed that the technique worked as well in adult animals, even though the gene was successfully inserted in fewer than 1 in every 100 liver cells.
“We expected this approach to work best in newborn animals because the liver is still growing,” Dr Kay said. “However, because homologous recombination has been thought to occur mostly in proliferating cells, we didn’t expect it to work as well as it did in adult animals.”
The researchers are now planning to test the technique in mice with livers composed of human and mouse cells, a model that may be a good surrogate to further predict what will happen in humans. ![]()

Credit: Aaron Logan
A new method of genome editing can cure hemophilia B in mice, researchers have reported in Nature.
This new technique doesn’t require the co-delivery of an endonuclease to clip the recipient’s DNA at specific locations, and it doesn’t rely on the co-insertion of genetic promoters to activate the new gene’s expression.
These differences may make the new approach both safer and longer-lasting than other genome editing methods, according to researchers.
“It appears that we may be able to achieve lifelong expression of the inserted gene, which is particularly important when treating genetic diseases like hemophilia and severe combined immunodeficiency,” said study author Mark Kay, MD, PhD, of the Stanford University School of Medicine in California.
“We’re able to do this without using promoters or nucleases, which significantly reduces the chances of cancers that can result if the new gene inserts itself at random places in the genome.”
Using their new technique, Dr Kay and his colleagues were able to insert a working copy of the human coagulation factor IX gene into the DNA of mice with hemophilia B. Although the insertion was accomplished in only about 1% of liver cells, those cells made enough factor IX to ameliorate the disorder.
Instead of using nucleases to cut the DNA or a promoter to drive expression of the factor IX gene, the researchers hitched the expression of the new gene to that of albumin.
They used a modified version of adeno-associated virus and relied on homologous recombination to insert the factor IX gene near the albumin gene.
Using a special DNA linker between the genes, the researchers were able to ensure that the clotting factor protein was made hand-in-hand with the highly expressed albumin protein.
During homologous recombination, the cell takes advantage of the fact that it has two copies of every chromosome. By lining up the damaged and undamaged chromosomes, the cell can “crib” off the intact copy to repair the damage without losing vital genetic information.
The researchers used this natural process to copy sequences from the viral vector into the genome at places they chose—in this case, after the albumin gene.
When they tested their approach in newborn lab mice with hemophilia, the team found the animals began to express levels of factor IX that were between 7% and 20% of normal. That amount of clotting factor has been shown in previous studies to be therapeutic in mice.
The researchers further showed that the technique worked as well in adult animals, even though the gene was successfully inserted in fewer than 1 in every 100 liver cells.
“We expected this approach to work best in newborn animals because the liver is still growing,” Dr Kay said. “However, because homologous recombination has been thought to occur mostly in proliferating cells, we didn’t expect it to work as well as it did in adult animals.”
The researchers are now planning to test the technique in mice with livers composed of human and mouse cells, a model that may be a good surrogate to further predict what will happen in humans. ![]()
Chlorambucil’s role in untreated CLL debated

NEW YORK—With both the pro and con positions drawing on data from the phase 3 CLL11 trial, two speakers at the Lymphoma & Myeloma2014 congress faced off on whether it’s necessary to use chlorambucil with obinutuzumab in untreated chronic lymphocytic leukemia (CLL).
Myron S. Czuczman, MD, of Roswell Park Cancer Institute in Buffalo, New York, argued in favor of using chlorambucil. And Richard R. Furman, MD, of Weill Cornell Medical College in New York, argued against it.
Obinutuzumab is a glycoengineered, humanized, monoclonal antibody that selectively binds to the extracellular domain of the CD20 antigen on B cells.
It was approved by the US Food and Drug Administration based on initial results from the phase 3 CLL11 study, in which 781 patients were randomized to receive chlorambucil alone or chlorambucil with either obinutuzumab or rituximab.
Pro
Dr Czuczman pointed out that while the obinutuzumab-chlorambucil combination had more toxicity than the rituximab-chlorambucil combination, the overall response rate and complete response rate with obinutuzumab were significantly higher than with rituximab (P<0.0001).
Progression-free survival (PFS), which was the primary endpoint, was significantly higher with either obinutuzumab at 26.7 months, or rituximab, at 16.3 months, than with chlorambucil alone, at 11.1 months.
And in the head-to-head portion of CLL11, PFS with obinutuzumab-chlorambucil was significantly better at 26.7 months than with rituximab-chlorambucil, at 15.2 months (P<0.001).
Dr Czuczman also reviewed data on obinutuzumab combined with drugs other than chlorambucil.
The GALTON trial, a small, phase 1b trial in untreated CLL, compared obinutuzumab plus fludarabine and cyclophosphamide to obinutuzumab plus bendamustine.
Dr Czuczman showed that while there is more toxicity when obinutuzumab is combined with cyclophosphamide or bendamustine than with chlorambucil, “there is not much more activity.”
He said it’s not clear whether obinutuzumab with cyclophosphamide is better than rituximab with cyclophosphamide or if obinutuzumab with bendamustine is better than rituximab with bendamustine in upfront CLL.
“For now,” he said, “chloramubucil should be the only chemo agent combined with obinutuzumab to treat upfront CLL—outside of clinical trial participation.”
Con
Dr Furman also reviewed the CLL11 trial, noting that rituximab did not add very much to chlorambucil, but obinutuzumab did, in terms of overall survival and PFS. He cautioned, however, that additive or synergistic effects cannot be ruled out in the combination studies.
He then reviewed the GAGE trial, which compared 2 doses of single-agent obinutuzumab in untreated CLL. The 2000 mg dose produced a greater overall response rate than the 1000 mg dose, but the difference between the 2 arms was not significant (P=0.08).
PFS was 21 months in the 1000 mg arm and 20 months in the 2000 mg arm (P=0.07). PFS for obinutuzumab plus chlorambucil in the CLL11 trial was 26.7 months.
However, second cancers may be more of an issue with chlorambucil. In CALGB 9011, investigators reported 27 epithelial cancers, 9 with fludarabine, 11 with chlorambucil, and 7 with fludarabine plus chlorambucil.
Dr Furman concluded that while chlorambucil may aid obinutuzumab by reducing bulk, it may be unnecessary if higher doses of the antibody are used. Single-agent obinutuzumab produces a similar PFS as the combination with chlorambucil, and there are greater toxicities with chlorambucil. ![]()

NEW YORK—With both the pro and con positions drawing on data from the phase 3 CLL11 trial, two speakers at the Lymphoma & Myeloma2014 congress faced off on whether it’s necessary to use chlorambucil with obinutuzumab in untreated chronic lymphocytic leukemia (CLL).
Myron S. Czuczman, MD, of Roswell Park Cancer Institute in Buffalo, New York, argued in favor of using chlorambucil. And Richard R. Furman, MD, of Weill Cornell Medical College in New York, argued against it.
Obinutuzumab is a glycoengineered, humanized, monoclonal antibody that selectively binds to the extracellular domain of the CD20 antigen on B cells.
It was approved by the US Food and Drug Administration based on initial results from the phase 3 CLL11 study, in which 781 patients were randomized to receive chlorambucil alone or chlorambucil with either obinutuzumab or rituximab.
Pro
Dr Czuczman pointed out that while the obinutuzumab-chlorambucil combination had more toxicity than the rituximab-chlorambucil combination, the overall response rate and complete response rate with obinutuzumab were significantly higher than with rituximab (P<0.0001).
Progression-free survival (PFS), which was the primary endpoint, was significantly higher with either obinutuzumab at 26.7 months, or rituximab, at 16.3 months, than with chlorambucil alone, at 11.1 months.
And in the head-to-head portion of CLL11, PFS with obinutuzumab-chlorambucil was significantly better at 26.7 months than with rituximab-chlorambucil, at 15.2 months (P<0.001).
Dr Czuczman also reviewed data on obinutuzumab combined with drugs other than chlorambucil.
The GALTON trial, a small, phase 1b trial in untreated CLL, compared obinutuzumab plus fludarabine and cyclophosphamide to obinutuzumab plus bendamustine.
Dr Czuczman showed that while there is more toxicity when obinutuzumab is combined with cyclophosphamide or bendamustine than with chlorambucil, “there is not much more activity.”
He said it’s not clear whether obinutuzumab with cyclophosphamide is better than rituximab with cyclophosphamide or if obinutuzumab with bendamustine is better than rituximab with bendamustine in upfront CLL.
“For now,” he said, “chloramubucil should be the only chemo agent combined with obinutuzumab to treat upfront CLL—outside of clinical trial participation.”
Con
Dr Furman also reviewed the CLL11 trial, noting that rituximab did not add very much to chlorambucil, but obinutuzumab did, in terms of overall survival and PFS. He cautioned, however, that additive or synergistic effects cannot be ruled out in the combination studies.
He then reviewed the GAGE trial, which compared 2 doses of single-agent obinutuzumab in untreated CLL. The 2000 mg dose produced a greater overall response rate than the 1000 mg dose, but the difference between the 2 arms was not significant (P=0.08).
PFS was 21 months in the 1000 mg arm and 20 months in the 2000 mg arm (P=0.07). PFS for obinutuzumab plus chlorambucil in the CLL11 trial was 26.7 months.
However, second cancers may be more of an issue with chlorambucil. In CALGB 9011, investigators reported 27 epithelial cancers, 9 with fludarabine, 11 with chlorambucil, and 7 with fludarabine plus chlorambucil.
Dr Furman concluded that while chlorambucil may aid obinutuzumab by reducing bulk, it may be unnecessary if higher doses of the antibody are used. Single-agent obinutuzumab produces a similar PFS as the combination with chlorambucil, and there are greater toxicities with chlorambucil. ![]()

NEW YORK—With both the pro and con positions drawing on data from the phase 3 CLL11 trial, two speakers at the Lymphoma & Myeloma2014 congress faced off on whether it’s necessary to use chlorambucil with obinutuzumab in untreated chronic lymphocytic leukemia (CLL).
Myron S. Czuczman, MD, of Roswell Park Cancer Institute in Buffalo, New York, argued in favor of using chlorambucil. And Richard R. Furman, MD, of Weill Cornell Medical College in New York, argued against it.
Obinutuzumab is a glycoengineered, humanized, monoclonal antibody that selectively binds to the extracellular domain of the CD20 antigen on B cells.
It was approved by the US Food and Drug Administration based on initial results from the phase 3 CLL11 study, in which 781 patients were randomized to receive chlorambucil alone or chlorambucil with either obinutuzumab or rituximab.
Pro
Dr Czuczman pointed out that while the obinutuzumab-chlorambucil combination had more toxicity than the rituximab-chlorambucil combination, the overall response rate and complete response rate with obinutuzumab were significantly higher than with rituximab (P<0.0001).
Progression-free survival (PFS), which was the primary endpoint, was significantly higher with either obinutuzumab at 26.7 months, or rituximab, at 16.3 months, than with chlorambucil alone, at 11.1 months.
And in the head-to-head portion of CLL11, PFS with obinutuzumab-chlorambucil was significantly better at 26.7 months than with rituximab-chlorambucil, at 15.2 months (P<0.001).
Dr Czuczman also reviewed data on obinutuzumab combined with drugs other than chlorambucil.
The GALTON trial, a small, phase 1b trial in untreated CLL, compared obinutuzumab plus fludarabine and cyclophosphamide to obinutuzumab plus bendamustine.
Dr Czuczman showed that while there is more toxicity when obinutuzumab is combined with cyclophosphamide or bendamustine than with chlorambucil, “there is not much more activity.”
He said it’s not clear whether obinutuzumab with cyclophosphamide is better than rituximab with cyclophosphamide or if obinutuzumab with bendamustine is better than rituximab with bendamustine in upfront CLL.
“For now,” he said, “chloramubucil should be the only chemo agent combined with obinutuzumab to treat upfront CLL—outside of clinical trial participation.”
Con
Dr Furman also reviewed the CLL11 trial, noting that rituximab did not add very much to chlorambucil, but obinutuzumab did, in terms of overall survival and PFS. He cautioned, however, that additive or synergistic effects cannot be ruled out in the combination studies.
He then reviewed the GAGE trial, which compared 2 doses of single-agent obinutuzumab in untreated CLL. The 2000 mg dose produced a greater overall response rate than the 1000 mg dose, but the difference between the 2 arms was not significant (P=0.08).
PFS was 21 months in the 1000 mg arm and 20 months in the 2000 mg arm (P=0.07). PFS for obinutuzumab plus chlorambucil in the CLL11 trial was 26.7 months.
However, second cancers may be more of an issue with chlorambucil. In CALGB 9011, investigators reported 27 epithelial cancers, 9 with fludarabine, 11 with chlorambucil, and 7 with fludarabine plus chlorambucil.
Dr Furman concluded that while chlorambucil may aid obinutuzumab by reducing bulk, it may be unnecessary if higher doses of the antibody are used. Single-agent obinutuzumab produces a similar PFS as the combination with chlorambucil, and there are greater toxicities with chlorambucil. ![]()
Paperwork consumes docs’ time, erodes morale

A survey of nearly 5000 US physicians showed that the average doctor spent 16.6% of his or her working hours on non-patient-related paperwork.
This includes tasks such as billing, obtaining insurance approvals, financial and personnel management, and negotiating contracts.
The more time doctors spent on such tasks, the less satisfied they were with medicine as a career.
Researchers detailed these findings in the International Journal of Health Services.
“Our crazy health financing system is demoralizing doctors and wasting vast resources,” said study author David Himmelstein, MD, a professor at Hunter College of the City University of New York.
“Turning healthcare into a business means we spend more and more time on billing, insurance paperwork, and the bottom line. We need to move to a simple, nonprofit national health insurance system that lets doctors and hospitals focus on patients, not finances.”
Dr Himmelstein and colleague Steffie Woolhandler, MD, analyzed confidential data from the 2008 Health Tracking Physician Survey (the most recent data available). The survey included information from a nationally representative sample of 4720 physicians who practiced at least 20 hours per week.
The data showed that the average doctor spent 8.7 hours per week, or 16.6% of his or her working time, on non-patient-related administration. This excludes tasks such as writing chart notes, communicating with other doctors, and ordering lab tests.
In total, patient-care physicians spent 168.4 million hours on non-patient-related administrative tasks in 2008. Drs Himmelstein and Woolhandler estimate that the total cost of physician time spent on administration in 2014 will amount to $102 billion.
Career satisfaction was lower for physicians who spent more time on administration. “Very satisfied” doctors spent, on average, 16.1% of their time on administration. “Very dissatisfied” doctors spent 20.6% of their time on such tasks.
Among various specialties, psychiatrists spent the most time on administration (20.3%), followed by internists (17.3%) and family/general practitioners (17.3%). Pediatricians spent the least amount of time (14.1%) on non-patient-related administrative tasks and were the most satisfied group of doctors.
Solo practice was associated with more administrative work, but small group practice was not. Doctors practicing in groups of 100 or more actually spent more time (19.7%) on such tasks than those in small groups (16.3%).
The researchers were surprised to find that physicians who used electronic health records spent more time (17.2% for those using entirely electronic records, 18% for those using a mix of paper and electronic) on administration than those who used only paper records (15.5%).
The pair noted that physicians in Canada spend far less time on administration than US doctors, and they attributed the difference to Canada’s single-payer system, which has greatly simplified billing and reduced bureaucracy.
The researchers pointed out that the only previous nationally representative survey of this kind was carried out in 1995, and that study showed that administration and insurance-related matters accounted for 13.5% of physicians’ total work time. Other, less representative studies also suggest the bureaucratic burden on physicians has grown in the past two decades.
“American doctors are drowning in paperwork,” Dr Woolhandler said. “Our study almost certainly understates physicians’ current administrative burden.”
“Since 2008, when the survey we analyzed was collected, tens of thousands of doctors have moved from small private practices with minimal bureaucracy into giant group practices where bureaucracy is rampant. And under the accountable care organizations favored by insurers, more doctors are facing HMO-type incentives to deny care to their patients, a move that our data shows drives up administrative work.” ![]()

A survey of nearly 5000 US physicians showed that the average doctor spent 16.6% of his or her working hours on non-patient-related paperwork.
This includes tasks such as billing, obtaining insurance approvals, financial and personnel management, and negotiating contracts.
The more time doctors spent on such tasks, the less satisfied they were with medicine as a career.
Researchers detailed these findings in the International Journal of Health Services.
“Our crazy health financing system is demoralizing doctors and wasting vast resources,” said study author David Himmelstein, MD, a professor at Hunter College of the City University of New York.
“Turning healthcare into a business means we spend more and more time on billing, insurance paperwork, and the bottom line. We need to move to a simple, nonprofit national health insurance system that lets doctors and hospitals focus on patients, not finances.”
Dr Himmelstein and colleague Steffie Woolhandler, MD, analyzed confidential data from the 2008 Health Tracking Physician Survey (the most recent data available). The survey included information from a nationally representative sample of 4720 physicians who practiced at least 20 hours per week.
The data showed that the average doctor spent 8.7 hours per week, or 16.6% of his or her working time, on non-patient-related administration. This excludes tasks such as writing chart notes, communicating with other doctors, and ordering lab tests.
In total, patient-care physicians spent 168.4 million hours on non-patient-related administrative tasks in 2008. Drs Himmelstein and Woolhandler estimate that the total cost of physician time spent on administration in 2014 will amount to $102 billion.
Career satisfaction was lower for physicians who spent more time on administration. “Very satisfied” doctors spent, on average, 16.1% of their time on administration. “Very dissatisfied” doctors spent 20.6% of their time on such tasks.
Among various specialties, psychiatrists spent the most time on administration (20.3%), followed by internists (17.3%) and family/general practitioners (17.3%). Pediatricians spent the least amount of time (14.1%) on non-patient-related administrative tasks and were the most satisfied group of doctors.
Solo practice was associated with more administrative work, but small group practice was not. Doctors practicing in groups of 100 or more actually spent more time (19.7%) on such tasks than those in small groups (16.3%).
The researchers were surprised to find that physicians who used electronic health records spent more time (17.2% for those using entirely electronic records, 18% for those using a mix of paper and electronic) on administration than those who used only paper records (15.5%).
The pair noted that physicians in Canada spend far less time on administration than US doctors, and they attributed the difference to Canada’s single-payer system, which has greatly simplified billing and reduced bureaucracy.
The researchers pointed out that the only previous nationally representative survey of this kind was carried out in 1995, and that study showed that administration and insurance-related matters accounted for 13.5% of physicians’ total work time. Other, less representative studies also suggest the bureaucratic burden on physicians has grown in the past two decades.
“American doctors are drowning in paperwork,” Dr Woolhandler said. “Our study almost certainly understates physicians’ current administrative burden.”
“Since 2008, when the survey we analyzed was collected, tens of thousands of doctors have moved from small private practices with minimal bureaucracy into giant group practices where bureaucracy is rampant. And under the accountable care organizations favored by insurers, more doctors are facing HMO-type incentives to deny care to their patients, a move that our data shows drives up administrative work.” ![]()

A survey of nearly 5000 US physicians showed that the average doctor spent 16.6% of his or her working hours on non-patient-related paperwork.
This includes tasks such as billing, obtaining insurance approvals, financial and personnel management, and negotiating contracts.
The more time doctors spent on such tasks, the less satisfied they were with medicine as a career.
Researchers detailed these findings in the International Journal of Health Services.
“Our crazy health financing system is demoralizing doctors and wasting vast resources,” said study author David Himmelstein, MD, a professor at Hunter College of the City University of New York.
“Turning healthcare into a business means we spend more and more time on billing, insurance paperwork, and the bottom line. We need to move to a simple, nonprofit national health insurance system that lets doctors and hospitals focus on patients, not finances.”
Dr Himmelstein and colleague Steffie Woolhandler, MD, analyzed confidential data from the 2008 Health Tracking Physician Survey (the most recent data available). The survey included information from a nationally representative sample of 4720 physicians who practiced at least 20 hours per week.
The data showed that the average doctor spent 8.7 hours per week, or 16.6% of his or her working time, on non-patient-related administration. This excludes tasks such as writing chart notes, communicating with other doctors, and ordering lab tests.
In total, patient-care physicians spent 168.4 million hours on non-patient-related administrative tasks in 2008. Drs Himmelstein and Woolhandler estimate that the total cost of physician time spent on administration in 2014 will amount to $102 billion.
Career satisfaction was lower for physicians who spent more time on administration. “Very satisfied” doctors spent, on average, 16.1% of their time on administration. “Very dissatisfied” doctors spent 20.6% of their time on such tasks.
Among various specialties, psychiatrists spent the most time on administration (20.3%), followed by internists (17.3%) and family/general practitioners (17.3%). Pediatricians spent the least amount of time (14.1%) on non-patient-related administrative tasks and were the most satisfied group of doctors.
Solo practice was associated with more administrative work, but small group practice was not. Doctors practicing in groups of 100 or more actually spent more time (19.7%) on such tasks than those in small groups (16.3%).
The researchers were surprised to find that physicians who used electronic health records spent more time (17.2% for those using entirely electronic records, 18% for those using a mix of paper and electronic) on administration than those who used only paper records (15.5%).
The pair noted that physicians in Canada spend far less time on administration than US doctors, and they attributed the difference to Canada’s single-payer system, which has greatly simplified billing and reduced bureaucracy.
The researchers pointed out that the only previous nationally representative survey of this kind was carried out in 1995, and that study showed that administration and insurance-related matters accounted for 13.5% of physicians’ total work time. Other, less representative studies also suggest the bureaucratic burden on physicians has grown in the past two decades.
“American doctors are drowning in paperwork,” Dr Woolhandler said. “Our study almost certainly understates physicians’ current administrative burden.”
“Since 2008, when the survey we analyzed was collected, tens of thousands of doctors have moved from small private practices with minimal bureaucracy into giant group practices where bureaucracy is rampant. And under the accountable care organizations favored by insurers, more doctors are facing HMO-type incentives to deny care to their patients, a move that our data shows drives up administrative work.” ![]()
Housestaff Teams and Patient Outcomes
Since the Institute of Medicine Report To Err is Human, increased attention has been paid to improving the care of hospitalized patients.[1] Strategies include utilization of guidelines and pathways, and the application of quality improvement techniques to improve or standardize processes. Despite improvements in focused areas such as prevention of hospital‐acquired infections, evidence suggests that outcomes for hospitalized patients remain suboptimal.[2] Rates of errors and hospital‐related complications such as falls, decubitus ulcers, and infections remain high,[3, 4, 5] and not all patients receive what is known to be appropriate care.[6]
Many attempts to improve inpatient care have used process‐improvement approaches, focusing on impacting individuals' behaviors, or on breaking down processes into component parts. Examples include central line bundles or checklists.[7, 8] These approaches attempt to ensure that providers do things in a standardized way, but are implicitly based on the reductionist assumption that we can break processes down into predictable parts to improve the system. An alternative way to understand clinical systems is based on interdependencies between individuals in the system, or the ways in which parts of the system interact with each other, which may be unpredictable over time.[1, 9] Whereas these interdependencies include care processes, they also encompass the providers who care for patients. Providers working together vary in terms of the kinds of relationships they have with each other. Those relationships are crucial to system function because they are the foundation for the interactions that lead to effective patient care.
The application of several frameworks or approaches for considering healthcare systems in terms of relationships highlights the importance of this way of understanding system function. The include complexity science,[1, 7] relational coordination (which is grounded in complexity science),[10] high reliability,[11] and the Big Five for teamwork.[12]
Research indicates that interactions among healthcare providers can have important influences on outcomes.[13, 14, 15, 16, 17] Additionally, the initial implementation of checklists to prevent central‐line associated infections appeared to change provider relationships in a way that significantly influenced their success.[18] For example, positive primary care clinic member relationships as assessed by the Lanham framework have been associated with better chronic care model implementation, learning, and patient experience of care.[19, 20] This framework, which we apply here, identifies 7 relationship characteristics: (1) trust; (2) diversity; (3) respect; (4) mindfulness, or being open to new ideas from others; (5) heedfulness, or an understanding of how one's roles influence those of others; (6) use of rich in‐person or verbal communication, particularly for potentially ambiguous information open to multiple interpretations; and (7) having a mixture of social and task relatedness among teams, or relatedness outside of only work‐related tasks.[19] Relationships within surgical teams that are characterized by psychological safety and diversity are associated with successful uptake of new techniques and decreased mortality.[13, 14] Relationships are important because the ability of patients and providers to learn and make sense of their patients' illnesses is grounded in relationships.
We sought to better understand and characterize inpatient physician teams' relationships, and assess the association between team relationships as evaluated by Lanham's framework and outcomes for hospitalized patients. Data on relationships among inpatient medical teams are few, despite the fact that these teams provide a great proportion of inpatient care. Additionally, the care of hospitalized medical patients is complex and uncertain, often involving multiple providers, making provider relationships potentially even more important to outcomes than in other settings.
METHODS
Overview
We conducted an observational, convergent mixed‐methods study of inpatient medicine teams.[21, 22, 23] We focused on inpatient physician teams, defining them as the functional work group responsible for medical decision making in academic medical centers. Physician teams in this context have been studied in terms of social hierarchy, authority, and delegation.[24, 25, 26] Focusing on the relationships within these groups could provide insights into strategies to mitigate potential negative effects of hierarchy. We recognize that other providers are closely involved in the care of hospitalized patients, and although we did not have standard interactions between physicians, nurses, case managers, and other providers that we could consistently observe, we did include interactions with these other providers in our observations and assessments of team relationships. Because this work is among the first in inpatient medical teams, we chose to study a small number of teams in great depth, allowing us to make rich assessments of team relationships.
We chose patient outcomes of length of stay (LOS), unnecessary LOS (ULOS), and complication rates, adjusted for patient characteristics and team workload. LOS is an important metric of inpatient care delivery. We feel ULOS is an aspect of LOS that is dependent on the physician team, as it reflects their preparation of the patient for discharge. Finally, we chose complication rates because hospital‐acquired conditions and complications are important contributors to inpatient morbidity, and because recent surgical literature has identified complication rates as a contributor to mortality that could be related to providers' collective ability to recognize complications and act quickly.
This study was approved by the institutional review board at the University of Texas Health Science Center at San Antonio (UTHSCSA), the Research and Development Committee for the South Texas Veterans Health Care System (STVHCS), and the Research Committee at University Health System (UHS). All physicians consented to participate in the study. We obtained a waiver of consent for inclusion of patient data.
Setting and Study Participants
This study was conducted at the 2 UTHSCSA primary teaching affiliates. The Audie L. Murphy Veterans Affairs Hospital is the 220‐bed acute‐care hospital of the STVHCS. University Hospital is the 614‐bed, level‐I trauma, acute‐care facility for UHS, the county system for Bexar County, which includes the San Antonio, Texas major metropolitan area.
The inpatient internal medicine physician team was our unit of study. Inpatient medicine teams consisted of 1 faculty attending physician, 1 postgraduate year (PGY)‐2 or PGY‐3 resident, and 2 PGY‐1 members. In addition, typically 2 to 3 third‐year medical students were part of the team, and a subintern was sometimes present. Doctor of Pharmacy faculty and students were also occasionally part of the team. Social workers and case managers often joined team rounds for portions of the time, and nurses sometimes joined bedside rounds on specific patients. These teams admit all medicine patients with the exception of those with acute coronary syndromes, new onset congestive heart failure, or arrhythmias. Patients are randomly assigned to teams based on time of admission and call schedules.
Between these 2 hospitals, there are 10 inpatient medicine teams caring for patients, with a pool of over 40 potential faculty attendings. Our goal was to observe teams that would be most likely to vary in terms of their relationship characteristics and patient outcomes through observing teams with a range of individual members. We used a purposeful sampling approach to obtain a diverse sample, sampling based on physician attributes and time of year.[16, 17] Three characteristics were most important: attending physician years of experience, attending involvement in educational and administrative leadership, and the presence of struggling resident members, as defined by being on probation or having been discussed in the residency Clinical Competency Committee. We did not set explicit thresholds in terms of attending experience, but instead sought to ensure a range. The attendings we observed were more likely to be involved in education and administrative leadership activities, but were otherwise similar to those we did not observe in terms of years of experience. We included struggling residents to observe individuals with a range of skill sets, and not just high‐performing individuals. We obtained attending information based on our knowledge of the attending faculty pool, and from the internal medicine residency program. We sampled across the year to ensure a diversity of trainee experience, but did not observe teams in either July or August, as these months were early in the academic year. Interns spend approximately 5 months per year on inpatient services, whereas residents spend 2 to 3 months per year. Thus, interns but not residents observed later in the year might have spent significantly more time on an inpatient service. However, in all instances, none of the team members observed had worked together previously.
Data Collection
Data were collected over nine 1‐month periods from September 2008 through June 2011. Teams were observed daily for 2‐ to 4‐week periods during morning rounds, the time when the team discusses each patient and makes clinical decisions. Data collection started on the first day of the month, the first day that all team members worked together, and continued for approximately 27 days, the last day before the resident rotated to a different service. By comprehensively and systematically observing these teams' daily rounds, we obtained rich, in‐depth data with multiple data points, enabling us to assess specific team behaviors and interactions.
During the third and fourth months, we collected data on teams in which the attending changed partway through. We did this to understand the impact of individual attending change on team relationships. Because the team relationships differed with each attending, we analyzed them separately. Thus, we observed 7 teams for approximately 4‐week periods and 4 teams for approximately 2‐week periods.
Observers arrived in the team room prior to rounds to begin observations, staying until after rounds were completed. Detailed free‐text field notes were taken regarding team activities and behaviors, including how the teams made patient care decisions. Field notes included: length of rounds, which team members spoke during each patient discussion, who contributed to management discussions, how information from consultants was incorporated, how communication with others outside of the team occurred, how team members spoke with each other including the types of words used, and team member willingness to perform tasks outside of their usually defined role, among others. Field notes were collected in an open‐ended format to allow for inductive observations. Observers also recorded clinical data daily regarding each patient, including admission and discharge dates, and presenting complaint.
The observation team consisted of the principle investigator (PI) (hospitalist) and 2 research assistants (a graduate‐level medical anthropologist and social psychologist), all of whom were trained by a qualitative research expert to systematically collect data related to topics of interest. Observers were instructed to record what the teams were doing and talking about at all times, noting any behaviors that they felt reflected how team members related to each other and came to decisions about their patients, or that were characteristic of the team. To ensure consistency, the PI and 1 research assistant conducted observations jointly at the start of data collection for each team, checking concordance of observations daily using a percent agreement until general agreement on field note content and patient information reached 90%. Two individuals observed 24 days of data collection, representing 252 patient discussions (13% of observed discussions).
An age‐adjusted Charlson‐Deyo comorbidity score was calculated for each patient admitted to each team, using data from rounds and from each hospital's electronic health records (EHR).[27] We collected data regarding mental health conditions for each patient (substance use, mood disorder, cognitive disorder, or a combination) because these comorbidities could impact LOS or ULOS. Discharge diagnoses were based on the discharge summary in the EHR. We also collected data daily regarding team census and numbers of admissions to and discharges from each team to assess workload.
Three patient outcomes were measured: LOS, ULOS, and complications. LOS was defined as the total number of days the patient was in the hospital. ULOS was defined as the number of days a patient remained in the hospital after the day the team determined the patient was medically ready for discharge (assessed by either discussion on rounds or EHR documentation). ULOS may occur when postdischarge needs have been adequately assessed, or because of delays in care, which may be related to provider communication during the hospitalization. Complications were defined on a per‐patient, per‐day basis in 2 ways: the development of a new problem in the hospital, for example acute kidney injury, a hospital‐acquired infection, or delirium, or by the team noting a clinical deterioration after at least 24 hours of clinical stability, such as the patient requiring transfer to a higher level of care. Complications were determined based on discussions during rounds, with EHR verification if needed.
Analysis Phase I: Assessment of Relationship Characteristics
After the completion of data collection, field notes were reviewed by a research team member not involved in the original study design or primary data collection (senior medical student). We took this approach to guard against biasing the reviewer's view of team behaviors, both in terms of not having conducted observations of the teams and being blinded to patient outcomes.
The reviewer completed a series of 3 readings of all field notes. The first reading provided a summary of the content of the data and the individual teams. Behavioral patterns of each team were used to create an initial team profile. The field notes and profiles were reviewed by the PI and a coauthor not involved in data collection to ensure that the profiles adequately reflected the field notes. No significant changes to the profiles were made based on this review. The profiles were discussed at a meeting with members of the larger research team, including the PI, research assistants, and coinvestigators (with backgrounds in medicine, anthropology, and information and organization management). Behavior characteristics that could be used to distinguish teams were identified in the profiles using a grounded theory approach.
The second review of field notes was conducted to test the applicability of the characteristics identified in the first review. To systematically record the appearance of the behaviors, we created a matrix with a row for each behavior and columns for each team to note whether they exhibited each behavior. If the behavior was exhibited, specific examples were cataloged in the matrix. This matrix was reviewed and refined by the research team. During the final field note review meeting, the research team compared the summary matrix for each team, with the specific behaviors noted during the first reading of the field notes to ensure that all behaviors were recorded.
After cataloging behaviors, the research team assigned each behavior to 1 of the 7 Lanham relationship characteristics. We wanted to assess our observations against a relationship framework to ensure that we were able to systematically assess all aspects of relationships. The Lanham framework was initially developed based on a systematic review of the organizational and educational literatures, making it relevant to the complex environment of an academic medical inpatient team and allowing us to assess relationships at a fine‐grained, richly detailed level. This assignment was done by the author team as a group. Any questions were discussed and different interpretations resolved through consensus. The Lanham framework has 7 characteristics.[19] Based on the presence of behaviors associated with each relationship characteristic, we assigned a point to each team for each relationship characteristic observed. We considered a behavior type to be present if we observed it on at least 3 occasions on separate days. Though we used a threshold of at least 3 occurrences, most teams that did not receive a point for a particular characteristic did not have any instances in which we observed the characteristic. This was particularly true for trust and mindfulness, and least so for social/task relatedness. By summing these points, we calculated a total relationship score for each team, with potential scores ranging from 0 (for teams exhibiting no behaviors reflecting a particular relationship characteristic) to 7.
Analysis Phase II: Factor Analysis
To formally determine which relationship characteristics were most highly related, data were submitted to a principal components factor analysis using oblique rotation. Item separation was determined by visual inspection of the scree plot and eigenvalues over 1.
Analysis Phase III: Assessing the Association between Physician Team Relationship Characteristics and Patient Outcomes
We examined the association between team relationships and patient outcomes using team relationship scores. For the LOS/ULOS analysis, we only included patients whose entire hospitalization occurred under the care of the team we observed. Patients who were on the team at the start of the month, were transferred from another service, or who remained hospitalized after the end of the team's time together were excluded. The longest possible LOS for patients whose entire hospitalization occurred on teams that were observed for half a month was 12 days. To facilitate accurate comparison between teams, we only included patients whose LOS was 12 days.
Complication rates were defined on a per‐patient per‐day basis to normalize for different team volumes and days of observation. For this analysis, we included patients who remained on the team after data collection completion, patients transferred to another team, or patients transferred from another team. However, we only counted complications that occurred at least 24 hours following transfer to minimize the likelihood that the complication was related to the care of other physicians.
Preliminary analysis involved inspection and assessment of the distribution of all variables followed, by a general linear modeling approach to assess the association between patient and workload covariates and outcomes.[28, 29] Because we anticipated that outcome variables would be markedly skewed, we also planned to assess the association between relationship characteristics with outcomes using the Kruskal‐Wallis rank sum test to compare groups with Dunn's test[30] for pairwise comparisons if overall significance occurred.[31] There are no known acceptable methods for covariate adjustments using the Kruskal‐Wallis method. All models were run using SAS software (SAS Institute Inc., Cary, NC).[32]
RESULTS
The research team observed 1941 discussions of 576 individual patients. Observations were conducted over 352 hours and 54 minutes, resulting in 741 pages of notes (see Supporting Table 1 in the online version of this article for data regarding individual team members). Teams observed over half‐months are referred to with a and b designations.
| Relationship Characteristic | Definition | Thirteen Types of Behaviors Observed in Field Notes | Observed Examples |
|---|---|---|---|
| Trust | Willingness to be vulnerable to others | Use of we instead of you or I by the attending | Where are we going with this guy? |
| Attending admitting I don't know | Let's go talk to him, I can't figure this out | ||
| Asking questions to help team members to think through problems | Will the echo change our management? How will it help us? | ||
| Diversity | Including different perspectives and different thinking | Team member participation in conversations about patients that are not theirs | One intern is presenting, another intern asks a question, and the resident joins the discussion |
| Inclusion of perspectives of those outside the team (nursing and family members) | Taking a break to call the nurse, having a family meeting | ||
| Respect | Valuing the opinions of others, honest and tactful interactions | Use of positive reinforcement by the attending | Being encouraging of the medical student's differential, saying excellent |
| How the team talks with patients | Asking if the patient has any concerns, what they can do to make them comfortable | ||
| Heedfulness | Awareness of how each person's roles impact the rest of the team | Team members performing tasks not expected of their role | One intern helping another with changing orders to transfer a patient |
| Summarizing plans and strategizing | Attending recaps the plan for the day, asks what they can do | ||
| Mindfulness | Openness to new ideas/free discussion about what is and is not working | Entire team engaged in discussion | Attending asks the medical student, intern, and resident what they think is going on |
| Social relatedness | Having socially related interactions | Social conversation among team members | Intern talks about their day off |
| Jokes by the attending | Showers and a bowel movement is the key to making people happy | ||
| Appropriate use of rich communication | Use of in‐person communication for sensitive or difficult issues | Using verbal communication with consultants or family | Intern is on the phone with the pharm D because there is a problem with the medication |
Creation of team profiles yielded 13 common behavior characteristics that were inductively identified and that could potentially distinguish teams, including consideration of perspectives outside of the team and team members performing tasks normally outside of their roles. Table 1 provides examples of and summarizes observed behaviors using examples from the field notes, mapping these behavior characteristics onto the Lanham relationship characteristics. The distribution of relationship characteristics and scores for each team are shown in Table 2.
| Relationship Characteristic | Team | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3a | 3b | 4a | 4b | 5 | 6 | 7 | 8 | 9 | |
| Trust | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 |
| Diversity | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 |
| Respect | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 |
| Heedfulness | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 1 |
| Mindfulness | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 |
| Social/task relatedness | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 |
| Rich/lean communication | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 |
| Relationship score (no. of characteristics observed) | 0 | 5 | 7 | 2 | 2 | 3 | 5 | 0 | 7 | 7 | 6 |
Correlation between relationship characteristics ranged from 0.32 to 0.95 (see Supporting Table 2 in the online version of this article). Mindfulness and trust are more highly correlated with each other than with other variables, as are diversity and respect. We performed a principal components factor analysis. Based on scree plot inspection and eigenvalues >1, we kept 3 factors that explained 85% of the total variance (see Supporting Table 3 in the online version of this article).
| No. of Relationship Characteristics | |||
|---|---|---|---|
| 02 | 35 | 67 | |
| |||
| LOS, d, n=293 | |||
| Median | 4 | 5 | 3 |
| IQR | 5 | 4 | 3 |
| Mean | 4.7 (2.72) | 4.7 (2.52) | 4.1 (2.51), P=0.12a |
| ULOS, d, n=293 | |||
| Median | 0 | 0 | 0 |
| IQR | 0 | 0 | 0 |
| Mean | 0.37 (0.99) | 0.33 (0.96) | 0.13 (0.56), P=0.09a |
| Complications (per patient per day), n=398 | |||
| Median | 0 | 0 | 0 |
| IQR | 1 | 1 | 0 |
| Mean | 0.58 (1.06) | 0.45 (0.77) | 0.18 (0.59), P=0.001 compared to teams with 02 or 35 characteristics |
Our analyses of LOS and ULOS included 298 of the 576 patients. Two hundred sixty‐seven patients were excluded because their entire LOS did not occur while under the care of the observed teams. Eleven patients were removed from the analysis because their LOS was >12 days. The analysis of complications included 398 patients. In our preliminary general linear modeling approach, only patient workload was significantly associated with outcomes using a cutoff of P=0.05. Charlson‐Deyo score and mental health comorbidities were not associated with outcomes.
The results of the Kruskal‐Wallis test show the patient average ranking on each of the outcome variables by 3 groups (Table 3). Overall, teams with higher relationship scores had lower rank scores on all outcomes measures. However, the only statistically significant comparisons were for complications. Teams having 6 to 7 characteristics had a significantly lower complication rate ranking than teams with 0 to 2 and 3 to 5 (P=0.001). We did not find consistent differences between individual teams or groups of teams with relationship scores from 0 to 2, 3 to 5, and 6 to 7 with regard to Charlson score, mental health issues, or workload. The only significant differences were between Charlson‐Deyo scores for patients admitted to teams with low relationship scores of 0 to 2 versus high relationship scores of 6 to 7 (6.7 vs 5.1); scores for teams with relationship scores of 3 to 5 were not significantly different from the low or high groups.
Table 4 shows the Kruskal‐Wallace rank test results for each group of relationship characteristics identified in the factor analysis based on whether teams displayed all or none of the characteristics in the factor. There were no differences in these groupings for LOS. Teams that exhibited both mindfulness and trust had lower ranks on ULOS than teams that did not have either. Similarly, teams with heedfulness, social‐task relatedness, and more rich communication demonstrated lower ULOS rankings than teams who did not have all 3 characteristics.
| Mind/Trust | Diversity/Respect | Heed/Relate/Communicate | ||||
|---|---|---|---|---|---|---|
| Patient Outcome | None | Both | None | Both | None | All 3 |
| ||||||
| LOS, d, n=293 | ||||||
| Median | 4 | 4 | 4 | 4 | 4 | 4 |
| IQR | 5 | 3 | 4.5 | 3 | 4 | 4 |
| Mean | 4.7 (2.6) | 4.2 (2.5) | 4.7 (2.6) | 4.3 (2.5) | 4.4 (2.6) | 4.4 (2.6) |
| P value | 0.06a | 0.23a | 0.85a | |||
| ULOS, d, n=293 | ||||||
| Median | 0 | 0 | 0 | 0 | 0 | 0 |
| IQR | 0 | 0 | 0 | 0 | 0 | 0 |
| Mean | 0.39 (1.01) | 0.15 (0.62) | 0.33 (0.92) | 0.18 (0.71) | 0.32 (0.93) | 0.18 (0.69) |
| P value | 0.009 | 0.06 | 0.03 | |||
| Complications (per patient), n=389 | ||||||
| Median | 0 | 0 | 0 | 0 | 0 | 0 |
| IQR | 1 | 0 | 1 | 0 | 1 | 0 |
| Mean | 0.58 (1.01) | 0.19 (0.58) | 0.47 (0.81) | 0.29 (0.82) | 0.26 (0.92) | 0.28 (0.70) |
| P value | <0.0001 | 0.001 | 0.02 | |||
DISCUSSION
Relationships are critical to team function because they are the basis for the social interactions that are central to patient care. These interactions include how providers recognize and make sense of what is happening with patients, and how they learn to care for patients more effectively. Additionally, the high task interdependencies among inpatient providers require effective relationships for optimal care. In our study, inpatient medicine physician teams' relationships varied, and these differences were associated with ULOS and complications. Relationship characteristics are not mutually exclusive, and as our factor analysis demonstrates, are intercorrelated. Trust and mindfulness appear to be particularly important. Trust may foster psychological safety that in turn promotes the willingness of individuals to contribute their thoughts and ideas.[13] In low‐trust teams, providers may fear a negative impact for bringing forward a concern based on limited data. Mindful teams may be more likely to notice nuanced changes, or are more likely to talk when things just do not appear to be going in the right direction with the patient. In the case of acutely ill medical patients, trust and mindfulness may lead to an increased likelihood that clinical changes are recognized and discussed quickly. For example, on a team characterized by trust and mindfulness, the entire team was typically involved in care discussions, and the interns and students frequently asked a lot of questions, even regarding the care of patients they were not directly following. We observed that these questions and discussions often led the team to realize that they needed to make a change in management decisions (eg, discontinuing Bactrim, lowering insulin doses, adjusting antihypertensives, premedicating for intravenous contrast) that they had not caught in the assessment and plan portion of the patient care discussion. In another example, a medical student asked a tentative question after a patient needed to go quickly to the bathroom while they were examining her, leading the team to ask more questions that led to a more rapid evaluation of a potential urinary tract infection. This finding is consistent with the description of failure to rescue among surgical patients, in which mortality has been associated with the failure to recognize complications rapidly and act effectively.[33]
Our findings are limited in several ways. First, these data are from a single academic institution. Although we sought diversity among our teams and collected data across 2 hospitals, there may be local contextual factors that influenced our results. Second, our data demonstrate an association, but not causality. Our findings should be tested in studies that assess causality and potential mechanisms through which relationships influence outcomes. Third, the individuals observing the teams had some knowledge of patient outcomes through hearing patient discussions. However, by involving individuals who did not participate in observations and were blinded to outcomes in assessing team relationships, we addressed this potential bias. Fourth, our observations were largely focused on physician teams, not directly including other providers. Our difficulty in observing regular interactions between physicians and other providers underscores the need to increase contact among those caring for hospitalized patients, such as occurs through multidisciplinary rounds. We did include team communication with other disciplines in our assessment of the relationship characteristics of diversity and rich communication. Finally, our analysis was limited by our sample size. We observed a relatively small number of teams. Although we benefitted from seeing the change in team relationships that occurred with attending changes halfway through some of our data collection months, this did limit the number of patients we could include in our analyses. Though we did not observe obvious differences in relationships between the teams observed across the 2 hospitals, the small number of teams and hospitals precluded our ability to perform multilevel modeling analyses, which would have allowed us to assess or account for the influence of team or organizational factors. However, this small sample size did allow for a richer assessment of team behaviors.
Although preliminary, our findings are an important step in understanding the function of inpatient medical teams not only in terms of processes of care, but also in terms of relationships. Patient care is a social activity, requiring effective communication to develop working diagnoses, recognize changes in patients' clinical courses, and formulate effective treatment plans during and after hospitalization. Future work could follow several directions. One would be to assess the causal mechanisms through which relationships influence patient outcomes. These may include sensemaking, learning, and improved coordination. Positive relationships may facilitate interaction of tacit and explicit information, facilitating the creation of understandings that foster more effective patient care.[34] The dynamic nature of relationships and how patient outcomes in turn feed back into relationships could be an area of exploration. This line of research could build on the idea of teaming.[35] Understanding relationships across multidisciplinary teams or with patients and families would be another direction. Finally, our results could point to potential interventions to improve patient outcomes through improving relationships. Better understanding of the nature of effective relationships among providers should enable us to develop more effective strategies to improve the care of hospitalized patients. In the larger context of payment reforms that require greater coordination and communication among and across providers, a greater understanding of how relationships influence patient outcomes will be important.
Acknowledgements
The authors thank the physicians involved in this study and Ms. Shannon Provost for her involvement in discussions of this work.
Disclosures: The research reported herein was supported by the Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development Service (CDA 07‐022). Investigator salary support was provided through this funding, and through the South Texas Veterans Health Care System. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs. Dr. McDaniel receives support from the IC[2] Institute of the University of Texas at Austin. Dr. Luci Leykum had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The authors report no conflicts of interest.
- . Redesigning health care with insights from the science of complex adaptive systems. In: Crossing the Quality Chasm: A New Heath System for the 21st Century. Washington, DC: National Academy of Sciences; 2000:309–322.
- , , , , , . Temporal trends in rates of patient harm resulting from medical care. N Engl J Med. 2010;323(22):2124–2135.
- , , , et al. Circumstances of patient falls and injuries in 9 hospitals in a mid‐western healthcare system. Infect Control Hosp Epidemiol. 2007;28(5):544–550.
- , . Point prevalence of wounds in a sample of acute hospitals in Canada. Int Wound J. 2009;6(4):287–293.
- , , , , , . Non‐adherence to guidelines: an avoidable cause of failure of empirical antimicrobial therapy in the presence of difficult‐to‐treat bacteria. Intensive Care Med. 2010;36(1):75–82.
- , , , , . Quality of care in U.S. hospitals as reflected by standardized measures, 2002–2004. N Engl J Med. 2005;353(3):255–264.
- Centers for Disease Control and Prevention. National Center for Emerging and Zoonotic Infectious Diseases. Division of Healthcare Quality Promotion. Checklist for prevention of central line associated blood stream infections. Available at: http://www.cdc.gov/HAI/pdfs/bsi/checklist‐for‐CLABSI.pdf. Accessed August 3, 2014.
- Safer Healthcare Partners, LLC. Checklists: a critical patient safety tool. Available at: http://www.saferhealthcare.com/high‐reliability‐topics/checklists. Accessed July 31, 2014.
- . Making Things Work: Solving Complex Problems in a Complex World. Boston, MA: Knowledge Press; 2004:117–160.
- . High Performance Healthcare: Using The Power of Relationships to Achieve Quality, Efficiency, and Resilience. 1st ed. New York, NY: McGraw‐Hill; 2009.
- , . Design of high reliability organizations in health care. Qual Saf Health Care. 2006;15(suppl 1):i4–i9.
- , , , . Does team training work? Principles for health care. Acad Emerg Med. 2008;15(11):1002–1009.
- . Speaking up in the operating room: how team leaders promote learning in interdisciplinary action teams. J Manag Stud. 2003;40(6):1419–1452.
- , , , et al. Association between implementation of a medical team training program and surgical mortality. JAMA. 2010;304(15):1693–1700.
- , , , . Group cognition, membership change, and performance: Investigating the benefits and detriments of collective knowledge. Organ Behav Hum Decis Process. 2007;103(2):159–178.
- , , , , , . Reciprocal learning and chronic care model implementation in primary care: results from a new scale of learning in primary care settings. BMC Health Serv Res. 2011;11:44.
- , , , , . The importance of relational coordination and reciprocal learning for chronic illness care within primary care teams. Health Care Manage Rev. 2012;38(1):20–28.
- , , , , . Explaining Michigan: developing an ex post theory of a quality improvement program. Milbank Q. 2011;89(2):167–205.
- , , , et al. How improving practice relationships among clinicians and nonclinicians can improve quality in primary care. Jt Comm J Qual Patient Saf. 2009;35(9):457–466.
- , , , et al. Relationship quality and patient‐assessed quality of care in VA primary care clinics: development and validation of the work relationships scale. Ann Fam Med. 2013;11(6):543–549.
- , . Designing and Conducting Mixed Methods Research. 2nd ed. Thousand Oaks, CA: Sage; 2011.
- . Qualitative Evaluation Methods. Thousand Oaks, CA: Sage; 2002.
- , , . Qualitative methods in research on health care quality. Qual Saf Health Care. 2002;11:148–152.
- . Managing the negatives of experience in physician teams. Health Care Manage Rev. 2010;35(1):65–76.
- , , , , . Rethinking resident supervision to improve safety: from hierarchical to interprofessional models. J Hosp Med. 2011;6(8):445 b452.
- , , , . Dynamic delegation: shared, hierarchical, and deindividualized leadership in extreme action teams. Adm Sci Q. 2006;51(4):590–621.
- , , . Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619.
- . Exploratory Data Analysis. Reading, MA: Addison‐Wesley; 1977.
- . Biostatistical Analysis. 4th ed. Upper Saddle River, NJ: Pearson Prentice‐Hall; 2010.
- . Multiple contrasts using rank sums. Technometrics. 1964;6:241–252.
- , . A SAS macro implementation of a multiple comparison post hoc test for a Kruskal–Wallis analysis. Comput Methods Programs Biomed. 2011;102:75–80.
- SAS/STAT Software [computer program]. Version 9.1. Cary, NC: SAS Institute Inc.; 2003.
- , , . Complications, failure to rescue, and mortality with major inpatient surgery in Medicare patients. Ann Surg. 2009;250(6):1029–1034.
- . A dynamic theory of organizational knowledge creation. Org Sci. 1994;5(1):14–37.
- . Teaming: How Organizations Learn, Innovate, and Compete in the Knowledge Economy. 1st ed. Boston, MA: Harvard Business School; 2012.
Since the Institute of Medicine Report To Err is Human, increased attention has been paid to improving the care of hospitalized patients.[1] Strategies include utilization of guidelines and pathways, and the application of quality improvement techniques to improve or standardize processes. Despite improvements in focused areas such as prevention of hospital‐acquired infections, evidence suggests that outcomes for hospitalized patients remain suboptimal.[2] Rates of errors and hospital‐related complications such as falls, decubitus ulcers, and infections remain high,[3, 4, 5] and not all patients receive what is known to be appropriate care.[6]
Many attempts to improve inpatient care have used process‐improvement approaches, focusing on impacting individuals' behaviors, or on breaking down processes into component parts. Examples include central line bundles or checklists.[7, 8] These approaches attempt to ensure that providers do things in a standardized way, but are implicitly based on the reductionist assumption that we can break processes down into predictable parts to improve the system. An alternative way to understand clinical systems is based on interdependencies between individuals in the system, or the ways in which parts of the system interact with each other, which may be unpredictable over time.[1, 9] Whereas these interdependencies include care processes, they also encompass the providers who care for patients. Providers working together vary in terms of the kinds of relationships they have with each other. Those relationships are crucial to system function because they are the foundation for the interactions that lead to effective patient care.
The application of several frameworks or approaches for considering healthcare systems in terms of relationships highlights the importance of this way of understanding system function. The include complexity science,[1, 7] relational coordination (which is grounded in complexity science),[10] high reliability,[11] and the Big Five for teamwork.[12]
Research indicates that interactions among healthcare providers can have important influences on outcomes.[13, 14, 15, 16, 17] Additionally, the initial implementation of checklists to prevent central‐line associated infections appeared to change provider relationships in a way that significantly influenced their success.[18] For example, positive primary care clinic member relationships as assessed by the Lanham framework have been associated with better chronic care model implementation, learning, and patient experience of care.[19, 20] This framework, which we apply here, identifies 7 relationship characteristics: (1) trust; (2) diversity; (3) respect; (4) mindfulness, or being open to new ideas from others; (5) heedfulness, or an understanding of how one's roles influence those of others; (6) use of rich in‐person or verbal communication, particularly for potentially ambiguous information open to multiple interpretations; and (7) having a mixture of social and task relatedness among teams, or relatedness outside of only work‐related tasks.[19] Relationships within surgical teams that are characterized by psychological safety and diversity are associated with successful uptake of new techniques and decreased mortality.[13, 14] Relationships are important because the ability of patients and providers to learn and make sense of their patients' illnesses is grounded in relationships.
We sought to better understand and characterize inpatient physician teams' relationships, and assess the association between team relationships as evaluated by Lanham's framework and outcomes for hospitalized patients. Data on relationships among inpatient medical teams are few, despite the fact that these teams provide a great proportion of inpatient care. Additionally, the care of hospitalized medical patients is complex and uncertain, often involving multiple providers, making provider relationships potentially even more important to outcomes than in other settings.
METHODS
Overview
We conducted an observational, convergent mixed‐methods study of inpatient medicine teams.[21, 22, 23] We focused on inpatient physician teams, defining them as the functional work group responsible for medical decision making in academic medical centers. Physician teams in this context have been studied in terms of social hierarchy, authority, and delegation.[24, 25, 26] Focusing on the relationships within these groups could provide insights into strategies to mitigate potential negative effects of hierarchy. We recognize that other providers are closely involved in the care of hospitalized patients, and although we did not have standard interactions between physicians, nurses, case managers, and other providers that we could consistently observe, we did include interactions with these other providers in our observations and assessments of team relationships. Because this work is among the first in inpatient medical teams, we chose to study a small number of teams in great depth, allowing us to make rich assessments of team relationships.
We chose patient outcomes of length of stay (LOS), unnecessary LOS (ULOS), and complication rates, adjusted for patient characteristics and team workload. LOS is an important metric of inpatient care delivery. We feel ULOS is an aspect of LOS that is dependent on the physician team, as it reflects their preparation of the patient for discharge. Finally, we chose complication rates because hospital‐acquired conditions and complications are important contributors to inpatient morbidity, and because recent surgical literature has identified complication rates as a contributor to mortality that could be related to providers' collective ability to recognize complications and act quickly.
This study was approved by the institutional review board at the University of Texas Health Science Center at San Antonio (UTHSCSA), the Research and Development Committee for the South Texas Veterans Health Care System (STVHCS), and the Research Committee at University Health System (UHS). All physicians consented to participate in the study. We obtained a waiver of consent for inclusion of patient data.
Setting and Study Participants
This study was conducted at the 2 UTHSCSA primary teaching affiliates. The Audie L. Murphy Veterans Affairs Hospital is the 220‐bed acute‐care hospital of the STVHCS. University Hospital is the 614‐bed, level‐I trauma, acute‐care facility for UHS, the county system for Bexar County, which includes the San Antonio, Texas major metropolitan area.
The inpatient internal medicine physician team was our unit of study. Inpatient medicine teams consisted of 1 faculty attending physician, 1 postgraduate year (PGY)‐2 or PGY‐3 resident, and 2 PGY‐1 members. In addition, typically 2 to 3 third‐year medical students were part of the team, and a subintern was sometimes present. Doctor of Pharmacy faculty and students were also occasionally part of the team. Social workers and case managers often joined team rounds for portions of the time, and nurses sometimes joined bedside rounds on specific patients. These teams admit all medicine patients with the exception of those with acute coronary syndromes, new onset congestive heart failure, or arrhythmias. Patients are randomly assigned to teams based on time of admission and call schedules.
Between these 2 hospitals, there are 10 inpatient medicine teams caring for patients, with a pool of over 40 potential faculty attendings. Our goal was to observe teams that would be most likely to vary in terms of their relationship characteristics and patient outcomes through observing teams with a range of individual members. We used a purposeful sampling approach to obtain a diverse sample, sampling based on physician attributes and time of year.[16, 17] Three characteristics were most important: attending physician years of experience, attending involvement in educational and administrative leadership, and the presence of struggling resident members, as defined by being on probation or having been discussed in the residency Clinical Competency Committee. We did not set explicit thresholds in terms of attending experience, but instead sought to ensure a range. The attendings we observed were more likely to be involved in education and administrative leadership activities, but were otherwise similar to those we did not observe in terms of years of experience. We included struggling residents to observe individuals with a range of skill sets, and not just high‐performing individuals. We obtained attending information based on our knowledge of the attending faculty pool, and from the internal medicine residency program. We sampled across the year to ensure a diversity of trainee experience, but did not observe teams in either July or August, as these months were early in the academic year. Interns spend approximately 5 months per year on inpatient services, whereas residents spend 2 to 3 months per year. Thus, interns but not residents observed later in the year might have spent significantly more time on an inpatient service. However, in all instances, none of the team members observed had worked together previously.
Data Collection
Data were collected over nine 1‐month periods from September 2008 through June 2011. Teams were observed daily for 2‐ to 4‐week periods during morning rounds, the time when the team discusses each patient and makes clinical decisions. Data collection started on the first day of the month, the first day that all team members worked together, and continued for approximately 27 days, the last day before the resident rotated to a different service. By comprehensively and systematically observing these teams' daily rounds, we obtained rich, in‐depth data with multiple data points, enabling us to assess specific team behaviors and interactions.
During the third and fourth months, we collected data on teams in which the attending changed partway through. We did this to understand the impact of individual attending change on team relationships. Because the team relationships differed with each attending, we analyzed them separately. Thus, we observed 7 teams for approximately 4‐week periods and 4 teams for approximately 2‐week periods.
Observers arrived in the team room prior to rounds to begin observations, staying until after rounds were completed. Detailed free‐text field notes were taken regarding team activities and behaviors, including how the teams made patient care decisions. Field notes included: length of rounds, which team members spoke during each patient discussion, who contributed to management discussions, how information from consultants was incorporated, how communication with others outside of the team occurred, how team members spoke with each other including the types of words used, and team member willingness to perform tasks outside of their usually defined role, among others. Field notes were collected in an open‐ended format to allow for inductive observations. Observers also recorded clinical data daily regarding each patient, including admission and discharge dates, and presenting complaint.
The observation team consisted of the principle investigator (PI) (hospitalist) and 2 research assistants (a graduate‐level medical anthropologist and social psychologist), all of whom were trained by a qualitative research expert to systematically collect data related to topics of interest. Observers were instructed to record what the teams were doing and talking about at all times, noting any behaviors that they felt reflected how team members related to each other and came to decisions about their patients, or that were characteristic of the team. To ensure consistency, the PI and 1 research assistant conducted observations jointly at the start of data collection for each team, checking concordance of observations daily using a percent agreement until general agreement on field note content and patient information reached 90%. Two individuals observed 24 days of data collection, representing 252 patient discussions (13% of observed discussions).
An age‐adjusted Charlson‐Deyo comorbidity score was calculated for each patient admitted to each team, using data from rounds and from each hospital's electronic health records (EHR).[27] We collected data regarding mental health conditions for each patient (substance use, mood disorder, cognitive disorder, or a combination) because these comorbidities could impact LOS or ULOS. Discharge diagnoses were based on the discharge summary in the EHR. We also collected data daily regarding team census and numbers of admissions to and discharges from each team to assess workload.
Three patient outcomes were measured: LOS, ULOS, and complications. LOS was defined as the total number of days the patient was in the hospital. ULOS was defined as the number of days a patient remained in the hospital after the day the team determined the patient was medically ready for discharge (assessed by either discussion on rounds or EHR documentation). ULOS may occur when postdischarge needs have been adequately assessed, or because of delays in care, which may be related to provider communication during the hospitalization. Complications were defined on a per‐patient, per‐day basis in 2 ways: the development of a new problem in the hospital, for example acute kidney injury, a hospital‐acquired infection, or delirium, or by the team noting a clinical deterioration after at least 24 hours of clinical stability, such as the patient requiring transfer to a higher level of care. Complications were determined based on discussions during rounds, with EHR verification if needed.
Analysis Phase I: Assessment of Relationship Characteristics
After the completion of data collection, field notes were reviewed by a research team member not involved in the original study design or primary data collection (senior medical student). We took this approach to guard against biasing the reviewer's view of team behaviors, both in terms of not having conducted observations of the teams and being blinded to patient outcomes.
The reviewer completed a series of 3 readings of all field notes. The first reading provided a summary of the content of the data and the individual teams. Behavioral patterns of each team were used to create an initial team profile. The field notes and profiles were reviewed by the PI and a coauthor not involved in data collection to ensure that the profiles adequately reflected the field notes. No significant changes to the profiles were made based on this review. The profiles were discussed at a meeting with members of the larger research team, including the PI, research assistants, and coinvestigators (with backgrounds in medicine, anthropology, and information and organization management). Behavior characteristics that could be used to distinguish teams were identified in the profiles using a grounded theory approach.
The second review of field notes was conducted to test the applicability of the characteristics identified in the first review. To systematically record the appearance of the behaviors, we created a matrix with a row for each behavior and columns for each team to note whether they exhibited each behavior. If the behavior was exhibited, specific examples were cataloged in the matrix. This matrix was reviewed and refined by the research team. During the final field note review meeting, the research team compared the summary matrix for each team, with the specific behaviors noted during the first reading of the field notes to ensure that all behaviors were recorded.
After cataloging behaviors, the research team assigned each behavior to 1 of the 7 Lanham relationship characteristics. We wanted to assess our observations against a relationship framework to ensure that we were able to systematically assess all aspects of relationships. The Lanham framework was initially developed based on a systematic review of the organizational and educational literatures, making it relevant to the complex environment of an academic medical inpatient team and allowing us to assess relationships at a fine‐grained, richly detailed level. This assignment was done by the author team as a group. Any questions were discussed and different interpretations resolved through consensus. The Lanham framework has 7 characteristics.[19] Based on the presence of behaviors associated with each relationship characteristic, we assigned a point to each team for each relationship characteristic observed. We considered a behavior type to be present if we observed it on at least 3 occasions on separate days. Though we used a threshold of at least 3 occurrences, most teams that did not receive a point for a particular characteristic did not have any instances in which we observed the characteristic. This was particularly true for trust and mindfulness, and least so for social/task relatedness. By summing these points, we calculated a total relationship score for each team, with potential scores ranging from 0 (for teams exhibiting no behaviors reflecting a particular relationship characteristic) to 7.
Analysis Phase II: Factor Analysis
To formally determine which relationship characteristics were most highly related, data were submitted to a principal components factor analysis using oblique rotation. Item separation was determined by visual inspection of the scree plot and eigenvalues over 1.
Analysis Phase III: Assessing the Association between Physician Team Relationship Characteristics and Patient Outcomes
We examined the association between team relationships and patient outcomes using team relationship scores. For the LOS/ULOS analysis, we only included patients whose entire hospitalization occurred under the care of the team we observed. Patients who were on the team at the start of the month, were transferred from another service, or who remained hospitalized after the end of the team's time together were excluded. The longest possible LOS for patients whose entire hospitalization occurred on teams that were observed for half a month was 12 days. To facilitate accurate comparison between teams, we only included patients whose LOS was 12 days.
Complication rates were defined on a per‐patient per‐day basis to normalize for different team volumes and days of observation. For this analysis, we included patients who remained on the team after data collection completion, patients transferred to another team, or patients transferred from another team. However, we only counted complications that occurred at least 24 hours following transfer to minimize the likelihood that the complication was related to the care of other physicians.
Preliminary analysis involved inspection and assessment of the distribution of all variables followed, by a general linear modeling approach to assess the association between patient and workload covariates and outcomes.[28, 29] Because we anticipated that outcome variables would be markedly skewed, we also planned to assess the association between relationship characteristics with outcomes using the Kruskal‐Wallis rank sum test to compare groups with Dunn's test[30] for pairwise comparisons if overall significance occurred.[31] There are no known acceptable methods for covariate adjustments using the Kruskal‐Wallis method. All models were run using SAS software (SAS Institute Inc., Cary, NC).[32]
RESULTS
The research team observed 1941 discussions of 576 individual patients. Observations were conducted over 352 hours and 54 minutes, resulting in 741 pages of notes (see Supporting Table 1 in the online version of this article for data regarding individual team members). Teams observed over half‐months are referred to with a and b designations.
| Relationship Characteristic | Definition | Thirteen Types of Behaviors Observed in Field Notes | Observed Examples |
|---|---|---|---|
| Trust | Willingness to be vulnerable to others | Use of we instead of you or I by the attending | Where are we going with this guy? |
| Attending admitting I don't know | Let's go talk to him, I can't figure this out | ||
| Asking questions to help team members to think through problems | Will the echo change our management? How will it help us? | ||
| Diversity | Including different perspectives and different thinking | Team member participation in conversations about patients that are not theirs | One intern is presenting, another intern asks a question, and the resident joins the discussion |
| Inclusion of perspectives of those outside the team (nursing and family members) | Taking a break to call the nurse, having a family meeting | ||
| Respect | Valuing the opinions of others, honest and tactful interactions | Use of positive reinforcement by the attending | Being encouraging of the medical student's differential, saying excellent |
| How the team talks with patients | Asking if the patient has any concerns, what they can do to make them comfortable | ||
| Heedfulness | Awareness of how each person's roles impact the rest of the team | Team members performing tasks not expected of their role | One intern helping another with changing orders to transfer a patient |
| Summarizing plans and strategizing | Attending recaps the plan for the day, asks what they can do | ||
| Mindfulness | Openness to new ideas/free discussion about what is and is not working | Entire team engaged in discussion | Attending asks the medical student, intern, and resident what they think is going on |
| Social relatedness | Having socially related interactions | Social conversation among team members | Intern talks about their day off |
| Jokes by the attending | Showers and a bowel movement is the key to making people happy | ||
| Appropriate use of rich communication | Use of in‐person communication for sensitive or difficult issues | Using verbal communication with consultants or family | Intern is on the phone with the pharm D because there is a problem with the medication |
Creation of team profiles yielded 13 common behavior characteristics that were inductively identified and that could potentially distinguish teams, including consideration of perspectives outside of the team and team members performing tasks normally outside of their roles. Table 1 provides examples of and summarizes observed behaviors using examples from the field notes, mapping these behavior characteristics onto the Lanham relationship characteristics. The distribution of relationship characteristics and scores for each team are shown in Table 2.
| Relationship Characteristic | Team | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3a | 3b | 4a | 4b | 5 | 6 | 7 | 8 | 9 | |
| Trust | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 |
| Diversity | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 |
| Respect | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 |
| Heedfulness | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 1 |
| Mindfulness | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 |
| Social/task relatedness | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 |
| Rich/lean communication | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 |
| Relationship score (no. of characteristics observed) | 0 | 5 | 7 | 2 | 2 | 3 | 5 | 0 | 7 | 7 | 6 |
Correlation between relationship characteristics ranged from 0.32 to 0.95 (see Supporting Table 2 in the online version of this article). Mindfulness and trust are more highly correlated with each other than with other variables, as are diversity and respect. We performed a principal components factor analysis. Based on scree plot inspection and eigenvalues >1, we kept 3 factors that explained 85% of the total variance (see Supporting Table 3 in the online version of this article).
| No. of Relationship Characteristics | |||
|---|---|---|---|
| 02 | 35 | 67 | |
| |||
| LOS, d, n=293 | |||
| Median | 4 | 5 | 3 |
| IQR | 5 | 4 | 3 |
| Mean | 4.7 (2.72) | 4.7 (2.52) | 4.1 (2.51), P=0.12a |
| ULOS, d, n=293 | |||
| Median | 0 | 0 | 0 |
| IQR | 0 | 0 | 0 |
| Mean | 0.37 (0.99) | 0.33 (0.96) | 0.13 (0.56), P=0.09a |
| Complications (per patient per day), n=398 | |||
| Median | 0 | 0 | 0 |
| IQR | 1 | 1 | 0 |
| Mean | 0.58 (1.06) | 0.45 (0.77) | 0.18 (0.59), P=0.001 compared to teams with 02 or 35 characteristics |
Our analyses of LOS and ULOS included 298 of the 576 patients. Two hundred sixty‐seven patients were excluded because their entire LOS did not occur while under the care of the observed teams. Eleven patients were removed from the analysis because their LOS was >12 days. The analysis of complications included 398 patients. In our preliminary general linear modeling approach, only patient workload was significantly associated with outcomes using a cutoff of P=0.05. Charlson‐Deyo score and mental health comorbidities were not associated with outcomes.
The results of the Kruskal‐Wallis test show the patient average ranking on each of the outcome variables by 3 groups (Table 3). Overall, teams with higher relationship scores had lower rank scores on all outcomes measures. However, the only statistically significant comparisons were for complications. Teams having 6 to 7 characteristics had a significantly lower complication rate ranking than teams with 0 to 2 and 3 to 5 (P=0.001). We did not find consistent differences between individual teams or groups of teams with relationship scores from 0 to 2, 3 to 5, and 6 to 7 with regard to Charlson score, mental health issues, or workload. The only significant differences were between Charlson‐Deyo scores for patients admitted to teams with low relationship scores of 0 to 2 versus high relationship scores of 6 to 7 (6.7 vs 5.1); scores for teams with relationship scores of 3 to 5 were not significantly different from the low or high groups.
Table 4 shows the Kruskal‐Wallace rank test results for each group of relationship characteristics identified in the factor analysis based on whether teams displayed all or none of the characteristics in the factor. There were no differences in these groupings for LOS. Teams that exhibited both mindfulness and trust had lower ranks on ULOS than teams that did not have either. Similarly, teams with heedfulness, social‐task relatedness, and more rich communication demonstrated lower ULOS rankings than teams who did not have all 3 characteristics.
| Mind/Trust | Diversity/Respect | Heed/Relate/Communicate | ||||
|---|---|---|---|---|---|---|
| Patient Outcome | None | Both | None | Both | None | All 3 |
| ||||||
| LOS, d, n=293 | ||||||
| Median | 4 | 4 | 4 | 4 | 4 | 4 |
| IQR | 5 | 3 | 4.5 | 3 | 4 | 4 |
| Mean | 4.7 (2.6) | 4.2 (2.5) | 4.7 (2.6) | 4.3 (2.5) | 4.4 (2.6) | 4.4 (2.6) |
| P value | 0.06a | 0.23a | 0.85a | |||
| ULOS, d, n=293 | ||||||
| Median | 0 | 0 | 0 | 0 | 0 | 0 |
| IQR | 0 | 0 | 0 | 0 | 0 | 0 |
| Mean | 0.39 (1.01) | 0.15 (0.62) | 0.33 (0.92) | 0.18 (0.71) | 0.32 (0.93) | 0.18 (0.69) |
| P value | 0.009 | 0.06 | 0.03 | |||
| Complications (per patient), n=389 | ||||||
| Median | 0 | 0 | 0 | 0 | 0 | 0 |
| IQR | 1 | 0 | 1 | 0 | 1 | 0 |
| Mean | 0.58 (1.01) | 0.19 (0.58) | 0.47 (0.81) | 0.29 (0.82) | 0.26 (0.92) | 0.28 (0.70) |
| P value | <0.0001 | 0.001 | 0.02 | |||
DISCUSSION
Relationships are critical to team function because they are the basis for the social interactions that are central to patient care. These interactions include how providers recognize and make sense of what is happening with patients, and how they learn to care for patients more effectively. Additionally, the high task interdependencies among inpatient providers require effective relationships for optimal care. In our study, inpatient medicine physician teams' relationships varied, and these differences were associated with ULOS and complications. Relationship characteristics are not mutually exclusive, and as our factor analysis demonstrates, are intercorrelated. Trust and mindfulness appear to be particularly important. Trust may foster psychological safety that in turn promotes the willingness of individuals to contribute their thoughts and ideas.[13] In low‐trust teams, providers may fear a negative impact for bringing forward a concern based on limited data. Mindful teams may be more likely to notice nuanced changes, or are more likely to talk when things just do not appear to be going in the right direction with the patient. In the case of acutely ill medical patients, trust and mindfulness may lead to an increased likelihood that clinical changes are recognized and discussed quickly. For example, on a team characterized by trust and mindfulness, the entire team was typically involved in care discussions, and the interns and students frequently asked a lot of questions, even regarding the care of patients they were not directly following. We observed that these questions and discussions often led the team to realize that they needed to make a change in management decisions (eg, discontinuing Bactrim, lowering insulin doses, adjusting antihypertensives, premedicating for intravenous contrast) that they had not caught in the assessment and plan portion of the patient care discussion. In another example, a medical student asked a tentative question after a patient needed to go quickly to the bathroom while they were examining her, leading the team to ask more questions that led to a more rapid evaluation of a potential urinary tract infection. This finding is consistent with the description of failure to rescue among surgical patients, in which mortality has been associated with the failure to recognize complications rapidly and act effectively.[33]
Our findings are limited in several ways. First, these data are from a single academic institution. Although we sought diversity among our teams and collected data across 2 hospitals, there may be local contextual factors that influenced our results. Second, our data demonstrate an association, but not causality. Our findings should be tested in studies that assess causality and potential mechanisms through which relationships influence outcomes. Third, the individuals observing the teams had some knowledge of patient outcomes through hearing patient discussions. However, by involving individuals who did not participate in observations and were blinded to outcomes in assessing team relationships, we addressed this potential bias. Fourth, our observations were largely focused on physician teams, not directly including other providers. Our difficulty in observing regular interactions between physicians and other providers underscores the need to increase contact among those caring for hospitalized patients, such as occurs through multidisciplinary rounds. We did include team communication with other disciplines in our assessment of the relationship characteristics of diversity and rich communication. Finally, our analysis was limited by our sample size. We observed a relatively small number of teams. Although we benefitted from seeing the change in team relationships that occurred with attending changes halfway through some of our data collection months, this did limit the number of patients we could include in our analyses. Though we did not observe obvious differences in relationships between the teams observed across the 2 hospitals, the small number of teams and hospitals precluded our ability to perform multilevel modeling analyses, which would have allowed us to assess or account for the influence of team or organizational factors. However, this small sample size did allow for a richer assessment of team behaviors.
Although preliminary, our findings are an important step in understanding the function of inpatient medical teams not only in terms of processes of care, but also in terms of relationships. Patient care is a social activity, requiring effective communication to develop working diagnoses, recognize changes in patients' clinical courses, and formulate effective treatment plans during and after hospitalization. Future work could follow several directions. One would be to assess the causal mechanisms through which relationships influence patient outcomes. These may include sensemaking, learning, and improved coordination. Positive relationships may facilitate interaction of tacit and explicit information, facilitating the creation of understandings that foster more effective patient care.[34] The dynamic nature of relationships and how patient outcomes in turn feed back into relationships could be an area of exploration. This line of research could build on the idea of teaming.[35] Understanding relationships across multidisciplinary teams or with patients and families would be another direction. Finally, our results could point to potential interventions to improve patient outcomes through improving relationships. Better understanding of the nature of effective relationships among providers should enable us to develop more effective strategies to improve the care of hospitalized patients. In the larger context of payment reforms that require greater coordination and communication among and across providers, a greater understanding of how relationships influence patient outcomes will be important.
Acknowledgements
The authors thank the physicians involved in this study and Ms. Shannon Provost for her involvement in discussions of this work.
Disclosures: The research reported herein was supported by the Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development Service (CDA 07‐022). Investigator salary support was provided through this funding, and through the South Texas Veterans Health Care System. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs. Dr. McDaniel receives support from the IC[2] Institute of the University of Texas at Austin. Dr. Luci Leykum had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The authors report no conflicts of interest.
Since the Institute of Medicine Report To Err is Human, increased attention has been paid to improving the care of hospitalized patients.[1] Strategies include utilization of guidelines and pathways, and the application of quality improvement techniques to improve or standardize processes. Despite improvements in focused areas such as prevention of hospital‐acquired infections, evidence suggests that outcomes for hospitalized patients remain suboptimal.[2] Rates of errors and hospital‐related complications such as falls, decubitus ulcers, and infections remain high,[3, 4, 5] and not all patients receive what is known to be appropriate care.[6]
Many attempts to improve inpatient care have used process‐improvement approaches, focusing on impacting individuals' behaviors, or on breaking down processes into component parts. Examples include central line bundles or checklists.[7, 8] These approaches attempt to ensure that providers do things in a standardized way, but are implicitly based on the reductionist assumption that we can break processes down into predictable parts to improve the system. An alternative way to understand clinical systems is based on interdependencies between individuals in the system, or the ways in which parts of the system interact with each other, which may be unpredictable over time.[1, 9] Whereas these interdependencies include care processes, they also encompass the providers who care for patients. Providers working together vary in terms of the kinds of relationships they have with each other. Those relationships are crucial to system function because they are the foundation for the interactions that lead to effective patient care.
The application of several frameworks or approaches for considering healthcare systems in terms of relationships highlights the importance of this way of understanding system function. The include complexity science,[1, 7] relational coordination (which is grounded in complexity science),[10] high reliability,[11] and the Big Five for teamwork.[12]
Research indicates that interactions among healthcare providers can have important influences on outcomes.[13, 14, 15, 16, 17] Additionally, the initial implementation of checklists to prevent central‐line associated infections appeared to change provider relationships in a way that significantly influenced their success.[18] For example, positive primary care clinic member relationships as assessed by the Lanham framework have been associated with better chronic care model implementation, learning, and patient experience of care.[19, 20] This framework, which we apply here, identifies 7 relationship characteristics: (1) trust; (2) diversity; (3) respect; (4) mindfulness, or being open to new ideas from others; (5) heedfulness, or an understanding of how one's roles influence those of others; (6) use of rich in‐person or verbal communication, particularly for potentially ambiguous information open to multiple interpretations; and (7) having a mixture of social and task relatedness among teams, or relatedness outside of only work‐related tasks.[19] Relationships within surgical teams that are characterized by psychological safety and diversity are associated with successful uptake of new techniques and decreased mortality.[13, 14] Relationships are important because the ability of patients and providers to learn and make sense of their patients' illnesses is grounded in relationships.
We sought to better understand and characterize inpatient physician teams' relationships, and assess the association between team relationships as evaluated by Lanham's framework and outcomes for hospitalized patients. Data on relationships among inpatient medical teams are few, despite the fact that these teams provide a great proportion of inpatient care. Additionally, the care of hospitalized medical patients is complex and uncertain, often involving multiple providers, making provider relationships potentially even more important to outcomes than in other settings.
METHODS
Overview
We conducted an observational, convergent mixed‐methods study of inpatient medicine teams.[21, 22, 23] We focused on inpatient physician teams, defining them as the functional work group responsible for medical decision making in academic medical centers. Physician teams in this context have been studied in terms of social hierarchy, authority, and delegation.[24, 25, 26] Focusing on the relationships within these groups could provide insights into strategies to mitigate potential negative effects of hierarchy. We recognize that other providers are closely involved in the care of hospitalized patients, and although we did not have standard interactions between physicians, nurses, case managers, and other providers that we could consistently observe, we did include interactions with these other providers in our observations and assessments of team relationships. Because this work is among the first in inpatient medical teams, we chose to study a small number of teams in great depth, allowing us to make rich assessments of team relationships.
We chose patient outcomes of length of stay (LOS), unnecessary LOS (ULOS), and complication rates, adjusted for patient characteristics and team workload. LOS is an important metric of inpatient care delivery. We feel ULOS is an aspect of LOS that is dependent on the physician team, as it reflects their preparation of the patient for discharge. Finally, we chose complication rates because hospital‐acquired conditions and complications are important contributors to inpatient morbidity, and because recent surgical literature has identified complication rates as a contributor to mortality that could be related to providers' collective ability to recognize complications and act quickly.
This study was approved by the institutional review board at the University of Texas Health Science Center at San Antonio (UTHSCSA), the Research and Development Committee for the South Texas Veterans Health Care System (STVHCS), and the Research Committee at University Health System (UHS). All physicians consented to participate in the study. We obtained a waiver of consent for inclusion of patient data.
Setting and Study Participants
This study was conducted at the 2 UTHSCSA primary teaching affiliates. The Audie L. Murphy Veterans Affairs Hospital is the 220‐bed acute‐care hospital of the STVHCS. University Hospital is the 614‐bed, level‐I trauma, acute‐care facility for UHS, the county system for Bexar County, which includes the San Antonio, Texas major metropolitan area.
The inpatient internal medicine physician team was our unit of study. Inpatient medicine teams consisted of 1 faculty attending physician, 1 postgraduate year (PGY)‐2 or PGY‐3 resident, and 2 PGY‐1 members. In addition, typically 2 to 3 third‐year medical students were part of the team, and a subintern was sometimes present. Doctor of Pharmacy faculty and students were also occasionally part of the team. Social workers and case managers often joined team rounds for portions of the time, and nurses sometimes joined bedside rounds on specific patients. These teams admit all medicine patients with the exception of those with acute coronary syndromes, new onset congestive heart failure, or arrhythmias. Patients are randomly assigned to teams based on time of admission and call schedules.
Between these 2 hospitals, there are 10 inpatient medicine teams caring for patients, with a pool of over 40 potential faculty attendings. Our goal was to observe teams that would be most likely to vary in terms of their relationship characteristics and patient outcomes through observing teams with a range of individual members. We used a purposeful sampling approach to obtain a diverse sample, sampling based on physician attributes and time of year.[16, 17] Three characteristics were most important: attending physician years of experience, attending involvement in educational and administrative leadership, and the presence of struggling resident members, as defined by being on probation or having been discussed in the residency Clinical Competency Committee. We did not set explicit thresholds in terms of attending experience, but instead sought to ensure a range. The attendings we observed were more likely to be involved in education and administrative leadership activities, but were otherwise similar to those we did not observe in terms of years of experience. We included struggling residents to observe individuals with a range of skill sets, and not just high‐performing individuals. We obtained attending information based on our knowledge of the attending faculty pool, and from the internal medicine residency program. We sampled across the year to ensure a diversity of trainee experience, but did not observe teams in either July or August, as these months were early in the academic year. Interns spend approximately 5 months per year on inpatient services, whereas residents spend 2 to 3 months per year. Thus, interns but not residents observed later in the year might have spent significantly more time on an inpatient service. However, in all instances, none of the team members observed had worked together previously.
Data Collection
Data were collected over nine 1‐month periods from September 2008 through June 2011. Teams were observed daily for 2‐ to 4‐week periods during morning rounds, the time when the team discusses each patient and makes clinical decisions. Data collection started on the first day of the month, the first day that all team members worked together, and continued for approximately 27 days, the last day before the resident rotated to a different service. By comprehensively and systematically observing these teams' daily rounds, we obtained rich, in‐depth data with multiple data points, enabling us to assess specific team behaviors and interactions.
During the third and fourth months, we collected data on teams in which the attending changed partway through. We did this to understand the impact of individual attending change on team relationships. Because the team relationships differed with each attending, we analyzed them separately. Thus, we observed 7 teams for approximately 4‐week periods and 4 teams for approximately 2‐week periods.
Observers arrived in the team room prior to rounds to begin observations, staying until after rounds were completed. Detailed free‐text field notes were taken regarding team activities and behaviors, including how the teams made patient care decisions. Field notes included: length of rounds, which team members spoke during each patient discussion, who contributed to management discussions, how information from consultants was incorporated, how communication with others outside of the team occurred, how team members spoke with each other including the types of words used, and team member willingness to perform tasks outside of their usually defined role, among others. Field notes were collected in an open‐ended format to allow for inductive observations. Observers also recorded clinical data daily regarding each patient, including admission and discharge dates, and presenting complaint.
The observation team consisted of the principle investigator (PI) (hospitalist) and 2 research assistants (a graduate‐level medical anthropologist and social psychologist), all of whom were trained by a qualitative research expert to systematically collect data related to topics of interest. Observers were instructed to record what the teams were doing and talking about at all times, noting any behaviors that they felt reflected how team members related to each other and came to decisions about their patients, or that were characteristic of the team. To ensure consistency, the PI and 1 research assistant conducted observations jointly at the start of data collection for each team, checking concordance of observations daily using a percent agreement until general agreement on field note content and patient information reached 90%. Two individuals observed 24 days of data collection, representing 252 patient discussions (13% of observed discussions).
An age‐adjusted Charlson‐Deyo comorbidity score was calculated for each patient admitted to each team, using data from rounds and from each hospital's electronic health records (EHR).[27] We collected data regarding mental health conditions for each patient (substance use, mood disorder, cognitive disorder, or a combination) because these comorbidities could impact LOS or ULOS. Discharge diagnoses were based on the discharge summary in the EHR. We also collected data daily regarding team census and numbers of admissions to and discharges from each team to assess workload.
Three patient outcomes were measured: LOS, ULOS, and complications. LOS was defined as the total number of days the patient was in the hospital. ULOS was defined as the number of days a patient remained in the hospital after the day the team determined the patient was medically ready for discharge (assessed by either discussion on rounds or EHR documentation). ULOS may occur when postdischarge needs have been adequately assessed, or because of delays in care, which may be related to provider communication during the hospitalization. Complications were defined on a per‐patient, per‐day basis in 2 ways: the development of a new problem in the hospital, for example acute kidney injury, a hospital‐acquired infection, or delirium, or by the team noting a clinical deterioration after at least 24 hours of clinical stability, such as the patient requiring transfer to a higher level of care. Complications were determined based on discussions during rounds, with EHR verification if needed.
Analysis Phase I: Assessment of Relationship Characteristics
After the completion of data collection, field notes were reviewed by a research team member not involved in the original study design or primary data collection (senior medical student). We took this approach to guard against biasing the reviewer's view of team behaviors, both in terms of not having conducted observations of the teams and being blinded to patient outcomes.
The reviewer completed a series of 3 readings of all field notes. The first reading provided a summary of the content of the data and the individual teams. Behavioral patterns of each team were used to create an initial team profile. The field notes and profiles were reviewed by the PI and a coauthor not involved in data collection to ensure that the profiles adequately reflected the field notes. No significant changes to the profiles were made based on this review. The profiles were discussed at a meeting with members of the larger research team, including the PI, research assistants, and coinvestigators (with backgrounds in medicine, anthropology, and information and organization management). Behavior characteristics that could be used to distinguish teams were identified in the profiles using a grounded theory approach.
The second review of field notes was conducted to test the applicability of the characteristics identified in the first review. To systematically record the appearance of the behaviors, we created a matrix with a row for each behavior and columns for each team to note whether they exhibited each behavior. If the behavior was exhibited, specific examples were cataloged in the matrix. This matrix was reviewed and refined by the research team. During the final field note review meeting, the research team compared the summary matrix for each team, with the specific behaviors noted during the first reading of the field notes to ensure that all behaviors were recorded.
After cataloging behaviors, the research team assigned each behavior to 1 of the 7 Lanham relationship characteristics. We wanted to assess our observations against a relationship framework to ensure that we were able to systematically assess all aspects of relationships. The Lanham framework was initially developed based on a systematic review of the organizational and educational literatures, making it relevant to the complex environment of an academic medical inpatient team and allowing us to assess relationships at a fine‐grained, richly detailed level. This assignment was done by the author team as a group. Any questions were discussed and different interpretations resolved through consensus. The Lanham framework has 7 characteristics.[19] Based on the presence of behaviors associated with each relationship characteristic, we assigned a point to each team for each relationship characteristic observed. We considered a behavior type to be present if we observed it on at least 3 occasions on separate days. Though we used a threshold of at least 3 occurrences, most teams that did not receive a point for a particular characteristic did not have any instances in which we observed the characteristic. This was particularly true for trust and mindfulness, and least so for social/task relatedness. By summing these points, we calculated a total relationship score for each team, with potential scores ranging from 0 (for teams exhibiting no behaviors reflecting a particular relationship characteristic) to 7.
Analysis Phase II: Factor Analysis
To formally determine which relationship characteristics were most highly related, data were submitted to a principal components factor analysis using oblique rotation. Item separation was determined by visual inspection of the scree plot and eigenvalues over 1.
Analysis Phase III: Assessing the Association between Physician Team Relationship Characteristics and Patient Outcomes
We examined the association between team relationships and patient outcomes using team relationship scores. For the LOS/ULOS analysis, we only included patients whose entire hospitalization occurred under the care of the team we observed. Patients who were on the team at the start of the month, were transferred from another service, or who remained hospitalized after the end of the team's time together were excluded. The longest possible LOS for patients whose entire hospitalization occurred on teams that were observed for half a month was 12 days. To facilitate accurate comparison between teams, we only included patients whose LOS was 12 days.
Complication rates were defined on a per‐patient per‐day basis to normalize for different team volumes and days of observation. For this analysis, we included patients who remained on the team after data collection completion, patients transferred to another team, or patients transferred from another team. However, we only counted complications that occurred at least 24 hours following transfer to minimize the likelihood that the complication was related to the care of other physicians.
Preliminary analysis involved inspection and assessment of the distribution of all variables followed, by a general linear modeling approach to assess the association between patient and workload covariates and outcomes.[28, 29] Because we anticipated that outcome variables would be markedly skewed, we also planned to assess the association between relationship characteristics with outcomes using the Kruskal‐Wallis rank sum test to compare groups with Dunn's test[30] for pairwise comparisons if overall significance occurred.[31] There are no known acceptable methods for covariate adjustments using the Kruskal‐Wallis method. All models were run using SAS software (SAS Institute Inc., Cary, NC).[32]
RESULTS
The research team observed 1941 discussions of 576 individual patients. Observations were conducted over 352 hours and 54 minutes, resulting in 741 pages of notes (see Supporting Table 1 in the online version of this article for data regarding individual team members). Teams observed over half‐months are referred to with a and b designations.
| Relationship Characteristic | Definition | Thirteen Types of Behaviors Observed in Field Notes | Observed Examples |
|---|---|---|---|
| Trust | Willingness to be vulnerable to others | Use of we instead of you or I by the attending | Where are we going with this guy? |
| Attending admitting I don't know | Let's go talk to him, I can't figure this out | ||
| Asking questions to help team members to think through problems | Will the echo change our management? How will it help us? | ||
| Diversity | Including different perspectives and different thinking | Team member participation in conversations about patients that are not theirs | One intern is presenting, another intern asks a question, and the resident joins the discussion |
| Inclusion of perspectives of those outside the team (nursing and family members) | Taking a break to call the nurse, having a family meeting | ||
| Respect | Valuing the opinions of others, honest and tactful interactions | Use of positive reinforcement by the attending | Being encouraging of the medical student's differential, saying excellent |
| How the team talks with patients | Asking if the patient has any concerns, what they can do to make them comfortable | ||
| Heedfulness | Awareness of how each person's roles impact the rest of the team | Team members performing tasks not expected of their role | One intern helping another with changing orders to transfer a patient |
| Summarizing plans and strategizing | Attending recaps the plan for the day, asks what they can do | ||
| Mindfulness | Openness to new ideas/free discussion about what is and is not working | Entire team engaged in discussion | Attending asks the medical student, intern, and resident what they think is going on |
| Social relatedness | Having socially related interactions | Social conversation among team members | Intern talks about their day off |
| Jokes by the attending | Showers and a bowel movement is the key to making people happy | ||
| Appropriate use of rich communication | Use of in‐person communication for sensitive or difficult issues | Using verbal communication with consultants or family | Intern is on the phone with the pharm D because there is a problem with the medication |
Creation of team profiles yielded 13 common behavior characteristics that were inductively identified and that could potentially distinguish teams, including consideration of perspectives outside of the team and team members performing tasks normally outside of their roles. Table 1 provides examples of and summarizes observed behaviors using examples from the field notes, mapping these behavior characteristics onto the Lanham relationship characteristics. The distribution of relationship characteristics and scores for each team are shown in Table 2.
| Relationship Characteristic | Team | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3a | 3b | 4a | 4b | 5 | 6 | 7 | 8 | 9 | |
| Trust | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 1 |
| Diversity | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 |
| Respect | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 |
| Heedfulness | 0 | 1 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 1 |
| Mindfulness | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 1 | 1 | 1 |
| Social/task relatedness | 0 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 1 | 1 | 1 |
| Rich/lean communication | 0 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 |
| Relationship score (no. of characteristics observed) | 0 | 5 | 7 | 2 | 2 | 3 | 5 | 0 | 7 | 7 | 6 |
Correlation between relationship characteristics ranged from 0.32 to 0.95 (see Supporting Table 2 in the online version of this article). Mindfulness and trust are more highly correlated with each other than with other variables, as are diversity and respect. We performed a principal components factor analysis. Based on scree plot inspection and eigenvalues >1, we kept 3 factors that explained 85% of the total variance (see Supporting Table 3 in the online version of this article).
| No. of Relationship Characteristics | |||
|---|---|---|---|
| 02 | 35 | 67 | |
| |||
| LOS, d, n=293 | |||
| Median | 4 | 5 | 3 |
| IQR | 5 | 4 | 3 |
| Mean | 4.7 (2.72) | 4.7 (2.52) | 4.1 (2.51), P=0.12a |
| ULOS, d, n=293 | |||
| Median | 0 | 0 | 0 |
| IQR | 0 | 0 | 0 |
| Mean | 0.37 (0.99) | 0.33 (0.96) | 0.13 (0.56), P=0.09a |
| Complications (per patient per day), n=398 | |||
| Median | 0 | 0 | 0 |
| IQR | 1 | 1 | 0 |
| Mean | 0.58 (1.06) | 0.45 (0.77) | 0.18 (0.59), P=0.001 compared to teams with 02 or 35 characteristics |
Our analyses of LOS and ULOS included 298 of the 576 patients. Two hundred sixty‐seven patients were excluded because their entire LOS did not occur while under the care of the observed teams. Eleven patients were removed from the analysis because their LOS was >12 days. The analysis of complications included 398 patients. In our preliminary general linear modeling approach, only patient workload was significantly associated with outcomes using a cutoff of P=0.05. Charlson‐Deyo score and mental health comorbidities were not associated with outcomes.
The results of the Kruskal‐Wallis test show the patient average ranking on each of the outcome variables by 3 groups (Table 3). Overall, teams with higher relationship scores had lower rank scores on all outcomes measures. However, the only statistically significant comparisons were for complications. Teams having 6 to 7 characteristics had a significantly lower complication rate ranking than teams with 0 to 2 and 3 to 5 (P=0.001). We did not find consistent differences between individual teams or groups of teams with relationship scores from 0 to 2, 3 to 5, and 6 to 7 with regard to Charlson score, mental health issues, or workload. The only significant differences were between Charlson‐Deyo scores for patients admitted to teams with low relationship scores of 0 to 2 versus high relationship scores of 6 to 7 (6.7 vs 5.1); scores for teams with relationship scores of 3 to 5 were not significantly different from the low or high groups.
Table 4 shows the Kruskal‐Wallace rank test results for each group of relationship characteristics identified in the factor analysis based on whether teams displayed all or none of the characteristics in the factor. There were no differences in these groupings for LOS. Teams that exhibited both mindfulness and trust had lower ranks on ULOS than teams that did not have either. Similarly, teams with heedfulness, social‐task relatedness, and more rich communication demonstrated lower ULOS rankings than teams who did not have all 3 characteristics.
| Mind/Trust | Diversity/Respect | Heed/Relate/Communicate | ||||
|---|---|---|---|---|---|---|
| Patient Outcome | None | Both | None | Both | None | All 3 |
| ||||||
| LOS, d, n=293 | ||||||
| Median | 4 | 4 | 4 | 4 | 4 | 4 |
| IQR | 5 | 3 | 4.5 | 3 | 4 | 4 |
| Mean | 4.7 (2.6) | 4.2 (2.5) | 4.7 (2.6) | 4.3 (2.5) | 4.4 (2.6) | 4.4 (2.6) |
| P value | 0.06a | 0.23a | 0.85a | |||
| ULOS, d, n=293 | ||||||
| Median | 0 | 0 | 0 | 0 | 0 | 0 |
| IQR | 0 | 0 | 0 | 0 | 0 | 0 |
| Mean | 0.39 (1.01) | 0.15 (0.62) | 0.33 (0.92) | 0.18 (0.71) | 0.32 (0.93) | 0.18 (0.69) |
| P value | 0.009 | 0.06 | 0.03 | |||
| Complications (per patient), n=389 | ||||||
| Median | 0 | 0 | 0 | 0 | 0 | 0 |
| IQR | 1 | 0 | 1 | 0 | 1 | 0 |
| Mean | 0.58 (1.01) | 0.19 (0.58) | 0.47 (0.81) | 0.29 (0.82) | 0.26 (0.92) | 0.28 (0.70) |
| P value | <0.0001 | 0.001 | 0.02 | |||
DISCUSSION
Relationships are critical to team function because they are the basis for the social interactions that are central to patient care. These interactions include how providers recognize and make sense of what is happening with patients, and how they learn to care for patients more effectively. Additionally, the high task interdependencies among inpatient providers require effective relationships for optimal care. In our study, inpatient medicine physician teams' relationships varied, and these differences were associated with ULOS and complications. Relationship characteristics are not mutually exclusive, and as our factor analysis demonstrates, are intercorrelated. Trust and mindfulness appear to be particularly important. Trust may foster psychological safety that in turn promotes the willingness of individuals to contribute their thoughts and ideas.[13] In low‐trust teams, providers may fear a negative impact for bringing forward a concern based on limited data. Mindful teams may be more likely to notice nuanced changes, or are more likely to talk when things just do not appear to be going in the right direction with the patient. In the case of acutely ill medical patients, trust and mindfulness may lead to an increased likelihood that clinical changes are recognized and discussed quickly. For example, on a team characterized by trust and mindfulness, the entire team was typically involved in care discussions, and the interns and students frequently asked a lot of questions, even regarding the care of patients they were not directly following. We observed that these questions and discussions often led the team to realize that they needed to make a change in management decisions (eg, discontinuing Bactrim, lowering insulin doses, adjusting antihypertensives, premedicating for intravenous contrast) that they had not caught in the assessment and plan portion of the patient care discussion. In another example, a medical student asked a tentative question after a patient needed to go quickly to the bathroom while they were examining her, leading the team to ask more questions that led to a more rapid evaluation of a potential urinary tract infection. This finding is consistent with the description of failure to rescue among surgical patients, in which mortality has been associated with the failure to recognize complications rapidly and act effectively.[33]
Our findings are limited in several ways. First, these data are from a single academic institution. Although we sought diversity among our teams and collected data across 2 hospitals, there may be local contextual factors that influenced our results. Second, our data demonstrate an association, but not causality. Our findings should be tested in studies that assess causality and potential mechanisms through which relationships influence outcomes. Third, the individuals observing the teams had some knowledge of patient outcomes through hearing patient discussions. However, by involving individuals who did not participate in observations and were blinded to outcomes in assessing team relationships, we addressed this potential bias. Fourth, our observations were largely focused on physician teams, not directly including other providers. Our difficulty in observing regular interactions between physicians and other providers underscores the need to increase contact among those caring for hospitalized patients, such as occurs through multidisciplinary rounds. We did include team communication with other disciplines in our assessment of the relationship characteristics of diversity and rich communication. Finally, our analysis was limited by our sample size. We observed a relatively small number of teams. Although we benefitted from seeing the change in team relationships that occurred with attending changes halfway through some of our data collection months, this did limit the number of patients we could include in our analyses. Though we did not observe obvious differences in relationships between the teams observed across the 2 hospitals, the small number of teams and hospitals precluded our ability to perform multilevel modeling analyses, which would have allowed us to assess or account for the influence of team or organizational factors. However, this small sample size did allow for a richer assessment of team behaviors.
Although preliminary, our findings are an important step in understanding the function of inpatient medical teams not only in terms of processes of care, but also in terms of relationships. Patient care is a social activity, requiring effective communication to develop working diagnoses, recognize changes in patients' clinical courses, and formulate effective treatment plans during and after hospitalization. Future work could follow several directions. One would be to assess the causal mechanisms through which relationships influence patient outcomes. These may include sensemaking, learning, and improved coordination. Positive relationships may facilitate interaction of tacit and explicit information, facilitating the creation of understandings that foster more effective patient care.[34] The dynamic nature of relationships and how patient outcomes in turn feed back into relationships could be an area of exploration. This line of research could build on the idea of teaming.[35] Understanding relationships across multidisciplinary teams or with patients and families would be another direction. Finally, our results could point to potential interventions to improve patient outcomes through improving relationships. Better understanding of the nature of effective relationships among providers should enable us to develop more effective strategies to improve the care of hospitalized patients. In the larger context of payment reforms that require greater coordination and communication among and across providers, a greater understanding of how relationships influence patient outcomes will be important.
Acknowledgements
The authors thank the physicians involved in this study and Ms. Shannon Provost for her involvement in discussions of this work.
Disclosures: The research reported herein was supported by the Department of Veterans Affairs, Veterans Health Administration, Health Services Research and Development Service (CDA 07‐022). Investigator salary support was provided through this funding, and through the South Texas Veterans Health Care System. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs. Dr. McDaniel receives support from the IC[2] Institute of the University of Texas at Austin. Dr. Luci Leykum had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The authors report no conflicts of interest.
- . Redesigning health care with insights from the science of complex adaptive systems. In: Crossing the Quality Chasm: A New Heath System for the 21st Century. Washington, DC: National Academy of Sciences; 2000:309–322.
- , , , , , . Temporal trends in rates of patient harm resulting from medical care. N Engl J Med. 2010;323(22):2124–2135.
- , , , et al. Circumstances of patient falls and injuries in 9 hospitals in a mid‐western healthcare system. Infect Control Hosp Epidemiol. 2007;28(5):544–550.
- , . Point prevalence of wounds in a sample of acute hospitals in Canada. Int Wound J. 2009;6(4):287–293.
- , , , , , . Non‐adherence to guidelines: an avoidable cause of failure of empirical antimicrobial therapy in the presence of difficult‐to‐treat bacteria. Intensive Care Med. 2010;36(1):75–82.
- , , , , . Quality of care in U.S. hospitals as reflected by standardized measures, 2002–2004. N Engl J Med. 2005;353(3):255–264.
- Centers for Disease Control and Prevention. National Center for Emerging and Zoonotic Infectious Diseases. Division of Healthcare Quality Promotion. Checklist for prevention of central line associated blood stream infections. Available at: http://www.cdc.gov/HAI/pdfs/bsi/checklist‐for‐CLABSI.pdf. Accessed August 3, 2014.
- Safer Healthcare Partners, LLC. Checklists: a critical patient safety tool. Available at: http://www.saferhealthcare.com/high‐reliability‐topics/checklists. Accessed July 31, 2014.
- . Making Things Work: Solving Complex Problems in a Complex World. Boston, MA: Knowledge Press; 2004:117–160.
- . High Performance Healthcare: Using The Power of Relationships to Achieve Quality, Efficiency, and Resilience. 1st ed. New York, NY: McGraw‐Hill; 2009.
- , . Design of high reliability organizations in health care. Qual Saf Health Care. 2006;15(suppl 1):i4–i9.
- , , , . Does team training work? Principles for health care. Acad Emerg Med. 2008;15(11):1002–1009.
- . Speaking up in the operating room: how team leaders promote learning in interdisciplinary action teams. J Manag Stud. 2003;40(6):1419–1452.
- , , , et al. Association between implementation of a medical team training program and surgical mortality. JAMA. 2010;304(15):1693–1700.
- , , , . Group cognition, membership change, and performance: Investigating the benefits and detriments of collective knowledge. Organ Behav Hum Decis Process. 2007;103(2):159–178.
- , , , , , . Reciprocal learning and chronic care model implementation in primary care: results from a new scale of learning in primary care settings. BMC Health Serv Res. 2011;11:44.
- , , , , . The importance of relational coordination and reciprocal learning for chronic illness care within primary care teams. Health Care Manage Rev. 2012;38(1):20–28.
- , , , , . Explaining Michigan: developing an ex post theory of a quality improvement program. Milbank Q. 2011;89(2):167–205.
- , , , et al. How improving practice relationships among clinicians and nonclinicians can improve quality in primary care. Jt Comm J Qual Patient Saf. 2009;35(9):457–466.
- , , , et al. Relationship quality and patient‐assessed quality of care in VA primary care clinics: development and validation of the work relationships scale. Ann Fam Med. 2013;11(6):543–549.
- , . Designing and Conducting Mixed Methods Research. 2nd ed. Thousand Oaks, CA: Sage; 2011.
- . Qualitative Evaluation Methods. Thousand Oaks, CA: Sage; 2002.
- , , . Qualitative methods in research on health care quality. Qual Saf Health Care. 2002;11:148–152.
- . Managing the negatives of experience in physician teams. Health Care Manage Rev. 2010;35(1):65–76.
- , , , , . Rethinking resident supervision to improve safety: from hierarchical to interprofessional models. J Hosp Med. 2011;6(8):445 b452.
- , , , . Dynamic delegation: shared, hierarchical, and deindividualized leadership in extreme action teams. Adm Sci Q. 2006;51(4):590–621.
- , , . Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619.
- . Exploratory Data Analysis. Reading, MA: Addison‐Wesley; 1977.
- . Biostatistical Analysis. 4th ed. Upper Saddle River, NJ: Pearson Prentice‐Hall; 2010.
- . Multiple contrasts using rank sums. Technometrics. 1964;6:241–252.
- , . A SAS macro implementation of a multiple comparison post hoc test for a Kruskal–Wallis analysis. Comput Methods Programs Biomed. 2011;102:75–80.
- SAS/STAT Software [computer program]. Version 9.1. Cary, NC: SAS Institute Inc.; 2003.
- , , . Complications, failure to rescue, and mortality with major inpatient surgery in Medicare patients. Ann Surg. 2009;250(6):1029–1034.
- . A dynamic theory of organizational knowledge creation. Org Sci. 1994;5(1):14–37.
- . Teaming: How Organizations Learn, Innovate, and Compete in the Knowledge Economy. 1st ed. Boston, MA: Harvard Business School; 2012.
- . Redesigning health care with insights from the science of complex adaptive systems. In: Crossing the Quality Chasm: A New Heath System for the 21st Century. Washington, DC: National Academy of Sciences; 2000:309–322.
- , , , , , . Temporal trends in rates of patient harm resulting from medical care. N Engl J Med. 2010;323(22):2124–2135.
- , , , et al. Circumstances of patient falls and injuries in 9 hospitals in a mid‐western healthcare system. Infect Control Hosp Epidemiol. 2007;28(5):544–550.
- , . Point prevalence of wounds in a sample of acute hospitals in Canada. Int Wound J. 2009;6(4):287–293.
- , , , , , . Non‐adherence to guidelines: an avoidable cause of failure of empirical antimicrobial therapy in the presence of difficult‐to‐treat bacteria. Intensive Care Med. 2010;36(1):75–82.
- , , , , . Quality of care in U.S. hospitals as reflected by standardized measures, 2002–2004. N Engl J Med. 2005;353(3):255–264.
- Centers for Disease Control and Prevention. National Center for Emerging and Zoonotic Infectious Diseases. Division of Healthcare Quality Promotion. Checklist for prevention of central line associated blood stream infections. Available at: http://www.cdc.gov/HAI/pdfs/bsi/checklist‐for‐CLABSI.pdf. Accessed August 3, 2014.
- Safer Healthcare Partners, LLC. Checklists: a critical patient safety tool. Available at: http://www.saferhealthcare.com/high‐reliability‐topics/checklists. Accessed July 31, 2014.
- . Making Things Work: Solving Complex Problems in a Complex World. Boston, MA: Knowledge Press; 2004:117–160.
- . High Performance Healthcare: Using The Power of Relationships to Achieve Quality, Efficiency, and Resilience. 1st ed. New York, NY: McGraw‐Hill; 2009.
- , . Design of high reliability organizations in health care. Qual Saf Health Care. 2006;15(suppl 1):i4–i9.
- , , , . Does team training work? Principles for health care. Acad Emerg Med. 2008;15(11):1002–1009.
- . Speaking up in the operating room: how team leaders promote learning in interdisciplinary action teams. J Manag Stud. 2003;40(6):1419–1452.
- , , , et al. Association between implementation of a medical team training program and surgical mortality. JAMA. 2010;304(15):1693–1700.
- , , , . Group cognition, membership change, and performance: Investigating the benefits and detriments of collective knowledge. Organ Behav Hum Decis Process. 2007;103(2):159–178.
- , , , , , . Reciprocal learning and chronic care model implementation in primary care: results from a new scale of learning in primary care settings. BMC Health Serv Res. 2011;11:44.
- , , , , . The importance of relational coordination and reciprocal learning for chronic illness care within primary care teams. Health Care Manage Rev. 2012;38(1):20–28.
- , , , , . Explaining Michigan: developing an ex post theory of a quality improvement program. Milbank Q. 2011;89(2):167–205.
- , , , et al. How improving practice relationships among clinicians and nonclinicians can improve quality in primary care. Jt Comm J Qual Patient Saf. 2009;35(9):457–466.
- , , , et al. Relationship quality and patient‐assessed quality of care in VA primary care clinics: development and validation of the work relationships scale. Ann Fam Med. 2013;11(6):543–549.
- , . Designing and Conducting Mixed Methods Research. 2nd ed. Thousand Oaks, CA: Sage; 2011.
- . Qualitative Evaluation Methods. Thousand Oaks, CA: Sage; 2002.
- , , . Qualitative methods in research on health care quality. Qual Saf Health Care. 2002;11:148–152.
- . Managing the negatives of experience in physician teams. Health Care Manage Rev. 2010;35(1):65–76.
- , , , , . Rethinking resident supervision to improve safety: from hierarchical to interprofessional models. J Hosp Med. 2011;6(8):445 b452.
- , , , . Dynamic delegation: shared, hierarchical, and deindividualized leadership in extreme action teams. Adm Sci Q. 2006;51(4):590–621.
- , , . Adapting a clinical comorbidity index for use with ICD‐9‐CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619.
- . Exploratory Data Analysis. Reading, MA: Addison‐Wesley; 1977.
- . Biostatistical Analysis. 4th ed. Upper Saddle River, NJ: Pearson Prentice‐Hall; 2010.
- . Multiple contrasts using rank sums. Technometrics. 1964;6:241–252.
- , . A SAS macro implementation of a multiple comparison post hoc test for a Kruskal–Wallis analysis. Comput Methods Programs Biomed. 2011;102:75–80.
- SAS/STAT Software [computer program]. Version 9.1. Cary, NC: SAS Institute Inc.; 2003.
- , , . Complications, failure to rescue, and mortality with major inpatient surgery in Medicare patients. Ann Surg. 2009;250(6):1029–1034.
- . A dynamic theory of organizational knowledge creation. Org Sci. 1994;5(1):14–37.
- . Teaming: How Organizations Learn, Innovate, and Compete in the Knowledge Economy. 1st ed. Boston, MA: Harvard Business School; 2012.
© 2014 Society of Hospital Medicine
How to find certified mammography facilities
The Food and Drug Administration has released an online resource to help consumers find FDA-certified mammography facilities by location, the agency announced Oct. 29.
The FDA hopes that women will continue to keep screening and prevention in mind well beyond Breast Cancer Awareness Month, they said. Mammograms are the best way to screen for cancer early, because they can help detect lumps that may be too small for a patient or physician to find during a self-breast exam.
The FDA conducts annual inspections of mammography facilities to ensure they meet standards for equipment and staff training under the Mammography Quality Standards Act. Facilities must be FDA-certified to legally perform mammogram services in the United States.
The agency has also recently approved new 3-D imaging technology that creates cross-sectional images to help doctors evaluate dense tissue and that may even find hidden tumors, they said. The FDA warns that other tools such as thermograms and nipple aspirate tests are not substitutes for mammograms.
The FDA has more information about breast cancer screening at its website, as well as a list of certified mammography facility near you.
The Food and Drug Administration has released an online resource to help consumers find FDA-certified mammography facilities by location, the agency announced Oct. 29.
The FDA hopes that women will continue to keep screening and prevention in mind well beyond Breast Cancer Awareness Month, they said. Mammograms are the best way to screen for cancer early, because they can help detect lumps that may be too small for a patient or physician to find during a self-breast exam.
The FDA conducts annual inspections of mammography facilities to ensure they meet standards for equipment and staff training under the Mammography Quality Standards Act. Facilities must be FDA-certified to legally perform mammogram services in the United States.
The agency has also recently approved new 3-D imaging technology that creates cross-sectional images to help doctors evaluate dense tissue and that may even find hidden tumors, they said. The FDA warns that other tools such as thermograms and nipple aspirate tests are not substitutes for mammograms.
The FDA has more information about breast cancer screening at its website, as well as a list of certified mammography facility near you.
The Food and Drug Administration has released an online resource to help consumers find FDA-certified mammography facilities by location, the agency announced Oct. 29.
The FDA hopes that women will continue to keep screening and prevention in mind well beyond Breast Cancer Awareness Month, they said. Mammograms are the best way to screen for cancer early, because they can help detect lumps that may be too small for a patient or physician to find during a self-breast exam.
The FDA conducts annual inspections of mammography facilities to ensure they meet standards for equipment and staff training under the Mammography Quality Standards Act. Facilities must be FDA-certified to legally perform mammogram services in the United States.
The agency has also recently approved new 3-D imaging technology that creates cross-sectional images to help doctors evaluate dense tissue and that may even find hidden tumors, they said. The FDA warns that other tools such as thermograms and nipple aspirate tests are not substitutes for mammograms.
The FDA has more information about breast cancer screening at its website, as well as a list of certified mammography facility near you.
AHA guidelines recommend Mediterranean diet to prevent stroke
Lifestyle modifications, including eating a Mediterranean or DASH-style diet, should be encouraged to lower an individual’s risk of first-time stroke, according to new guidelines for the primary prevention of stroke from the American Heart Association and American Stroke Association.
Mediterranean and DASH (Dietary Approaches to Stop Hypertension) dietary plans are characterized by their emphasis on fruits, vegetables, whole grains, legumes, nuts, seeds, poultry, and fish, while limiting red meats, sweets, and any foods with saturated fats. This new guidelines – which have been endorsed by the American Academy of Neurology, American Association of Neurological Surgeons, Congress of Neurological Surgeons, and Preventive Cardiovascular Nurses Association – suggest that adopting either of these diets in addition to a few other healthy living habits can dramatically reduce an individual’s odds of suffering a stroke (Stroke 2014;45 [doi: 10.1161/STR.0000000000000046]).
“We have a huge opportunity to improve how we prevent new strokes, because risk factors that can be changed or controlled – especially high blood pressure – account for 90% of strokes,” said Dr. James Meschia, chair of the writing committee and chairman of neurology at the Mayo Clinic in Jacksonville, Fla., in a statement. The last such guidelines were released 3 years ago (Stroke 2011;42:517-84)
The writing committee gathered data pertaining to the age, birth weight, race/ethnicity, and genetic factors, among several others. Studies examined included a U.S. Nationwide Inpatient Sample, which showed that stroke hospitalizations increased between 1998 and 2007 for individuals aged 25-34 years and 35-44 years; the Framingham Heart Study, which estimated that the odds of a middle-aged adult suffering a stroke are 1 in 6; an analysis of South Carolina Medicaid beneficiaries under age 50, which revealed that individuals born weighing less than 2,500 g were twice as likely to have a stroke as those born heavier; and an Atherosclerosis Risk in Communities (ARIC) study that showed Latino and African American populations being at higher risk for stroke due to hypertension, obesity, and diabetes.
Because blood pressure, hypertension, diabetes, and obesity are so commonly linked to stroke risk, the new American Heart Association/American Stroke Association (AHA/ASA) guidelines highly recommend a Mediterranean or DASH-style diet supplemented with nuts. Additionally, the guidelines advise health care professionals to advise patients to cut down on sodium intake, regularly monitor their blood pressure, talk to their physicians immediately if any medication does not do what it is intended to or creates negative side effects, and quit smoking, and, for women, consider an alternative to oral birth control pills.
Hypertension, “the most important, well-documented, modifiable stroke risk factor,” should be treated with antihypertensive medication to a target blood pressure of less than 140/90 mm Hg, the guidelines state.
Furthermore, the ASA/AHA guidelines continue to recommend regular physical exercise and acute monitoring of individuals’ cholesterol levels, such as LDL, HDL, and triglycerides, as failure to keep these numbers in check can easily lead to a serious stroke. The guidelines also state that although heavy alcohol consumption can increase the chance of stroke, “light to moderate” alcohol consumption can actually decrease the odds of suffering a stroke.
The AHA/ASA guidelines also examine factors such as migraines, which are associated with stroke in women under age 55, and hyperhomocysteinemia, which is also associated with an increased risk of stroke. Other factors like hypercoagulability and sleep apnea were not shown to have any identifiable relationship with an increased risk of stroke.
“As health professionals, we must ensure that progress in preventing stroke does not lead to complacency,” say the guidelines. “We must acknowledge that several recommendations remain vague because of suboptimal clinical trial evidence or, even more concerning, may be out of date and therefore irrelevant.”
Dr. Meschia and his associates warn that although medications are helpful, the best way to safeguard against a stroke is to change a person’s lifestyle into one of healthy eating and exercise habits. Unfortunately, say the authors, “it is easier to convince a patient to take a pill than to radically change his or her lifestyle, [but] we must expect the same standards of evidence for lifestyle interventions.”
Dr. Meschia disclosed that his research grant comes from the National Institute of Neurological Disorders and Stroke. He had no other relevant financial disclosures of interest. Several of the guidelines’ coauthors had disclosures of their own, which are listed in the statement.
Lifestyle modifications, including eating a Mediterranean or DASH-style diet, should be encouraged to lower an individual’s risk of first-time stroke, according to new guidelines for the primary prevention of stroke from the American Heart Association and American Stroke Association.
Mediterranean and DASH (Dietary Approaches to Stop Hypertension) dietary plans are characterized by their emphasis on fruits, vegetables, whole grains, legumes, nuts, seeds, poultry, and fish, while limiting red meats, sweets, and any foods with saturated fats. This new guidelines – which have been endorsed by the American Academy of Neurology, American Association of Neurological Surgeons, Congress of Neurological Surgeons, and Preventive Cardiovascular Nurses Association – suggest that adopting either of these diets in addition to a few other healthy living habits can dramatically reduce an individual’s odds of suffering a stroke (Stroke 2014;45 [doi: 10.1161/STR.0000000000000046]).
“We have a huge opportunity to improve how we prevent new strokes, because risk factors that can be changed or controlled – especially high blood pressure – account for 90% of strokes,” said Dr. James Meschia, chair of the writing committee and chairman of neurology at the Mayo Clinic in Jacksonville, Fla., in a statement. The last such guidelines were released 3 years ago (Stroke 2011;42:517-84)
The writing committee gathered data pertaining to the age, birth weight, race/ethnicity, and genetic factors, among several others. Studies examined included a U.S. Nationwide Inpatient Sample, which showed that stroke hospitalizations increased between 1998 and 2007 for individuals aged 25-34 years and 35-44 years; the Framingham Heart Study, which estimated that the odds of a middle-aged adult suffering a stroke are 1 in 6; an analysis of South Carolina Medicaid beneficiaries under age 50, which revealed that individuals born weighing less than 2,500 g were twice as likely to have a stroke as those born heavier; and an Atherosclerosis Risk in Communities (ARIC) study that showed Latino and African American populations being at higher risk for stroke due to hypertension, obesity, and diabetes.
Because blood pressure, hypertension, diabetes, and obesity are so commonly linked to stroke risk, the new American Heart Association/American Stroke Association (AHA/ASA) guidelines highly recommend a Mediterranean or DASH-style diet supplemented with nuts. Additionally, the guidelines advise health care professionals to advise patients to cut down on sodium intake, regularly monitor their blood pressure, talk to their physicians immediately if any medication does not do what it is intended to or creates negative side effects, and quit smoking, and, for women, consider an alternative to oral birth control pills.
Hypertension, “the most important, well-documented, modifiable stroke risk factor,” should be treated with antihypertensive medication to a target blood pressure of less than 140/90 mm Hg, the guidelines state.
Furthermore, the ASA/AHA guidelines continue to recommend regular physical exercise and acute monitoring of individuals’ cholesterol levels, such as LDL, HDL, and triglycerides, as failure to keep these numbers in check can easily lead to a serious stroke. The guidelines also state that although heavy alcohol consumption can increase the chance of stroke, “light to moderate” alcohol consumption can actually decrease the odds of suffering a stroke.
The AHA/ASA guidelines also examine factors such as migraines, which are associated with stroke in women under age 55, and hyperhomocysteinemia, which is also associated with an increased risk of stroke. Other factors like hypercoagulability and sleep apnea were not shown to have any identifiable relationship with an increased risk of stroke.
“As health professionals, we must ensure that progress in preventing stroke does not lead to complacency,” say the guidelines. “We must acknowledge that several recommendations remain vague because of suboptimal clinical trial evidence or, even more concerning, may be out of date and therefore irrelevant.”
Dr. Meschia and his associates warn that although medications are helpful, the best way to safeguard against a stroke is to change a person’s lifestyle into one of healthy eating and exercise habits. Unfortunately, say the authors, “it is easier to convince a patient to take a pill than to radically change his or her lifestyle, [but] we must expect the same standards of evidence for lifestyle interventions.”
Dr. Meschia disclosed that his research grant comes from the National Institute of Neurological Disorders and Stroke. He had no other relevant financial disclosures of interest. Several of the guidelines’ coauthors had disclosures of their own, which are listed in the statement.
Lifestyle modifications, including eating a Mediterranean or DASH-style diet, should be encouraged to lower an individual’s risk of first-time stroke, according to new guidelines for the primary prevention of stroke from the American Heart Association and American Stroke Association.
Mediterranean and DASH (Dietary Approaches to Stop Hypertension) dietary plans are characterized by their emphasis on fruits, vegetables, whole grains, legumes, nuts, seeds, poultry, and fish, while limiting red meats, sweets, and any foods with saturated fats. This new guidelines – which have been endorsed by the American Academy of Neurology, American Association of Neurological Surgeons, Congress of Neurological Surgeons, and Preventive Cardiovascular Nurses Association – suggest that adopting either of these diets in addition to a few other healthy living habits can dramatically reduce an individual’s odds of suffering a stroke (Stroke 2014;45 [doi: 10.1161/STR.0000000000000046]).
“We have a huge opportunity to improve how we prevent new strokes, because risk factors that can be changed or controlled – especially high blood pressure – account for 90% of strokes,” said Dr. James Meschia, chair of the writing committee and chairman of neurology at the Mayo Clinic in Jacksonville, Fla., in a statement. The last such guidelines were released 3 years ago (Stroke 2011;42:517-84)
The writing committee gathered data pertaining to the age, birth weight, race/ethnicity, and genetic factors, among several others. Studies examined included a U.S. Nationwide Inpatient Sample, which showed that stroke hospitalizations increased between 1998 and 2007 for individuals aged 25-34 years and 35-44 years; the Framingham Heart Study, which estimated that the odds of a middle-aged adult suffering a stroke are 1 in 6; an analysis of South Carolina Medicaid beneficiaries under age 50, which revealed that individuals born weighing less than 2,500 g were twice as likely to have a stroke as those born heavier; and an Atherosclerosis Risk in Communities (ARIC) study that showed Latino and African American populations being at higher risk for stroke due to hypertension, obesity, and diabetes.
Because blood pressure, hypertension, diabetes, and obesity are so commonly linked to stroke risk, the new American Heart Association/American Stroke Association (AHA/ASA) guidelines highly recommend a Mediterranean or DASH-style diet supplemented with nuts. Additionally, the guidelines advise health care professionals to advise patients to cut down on sodium intake, regularly monitor their blood pressure, talk to their physicians immediately if any medication does not do what it is intended to or creates negative side effects, and quit smoking, and, for women, consider an alternative to oral birth control pills.
Hypertension, “the most important, well-documented, modifiable stroke risk factor,” should be treated with antihypertensive medication to a target blood pressure of less than 140/90 mm Hg, the guidelines state.
Furthermore, the ASA/AHA guidelines continue to recommend regular physical exercise and acute monitoring of individuals’ cholesterol levels, such as LDL, HDL, and triglycerides, as failure to keep these numbers in check can easily lead to a serious stroke. The guidelines also state that although heavy alcohol consumption can increase the chance of stroke, “light to moderate” alcohol consumption can actually decrease the odds of suffering a stroke.
The AHA/ASA guidelines also examine factors such as migraines, which are associated with stroke in women under age 55, and hyperhomocysteinemia, which is also associated with an increased risk of stroke. Other factors like hypercoagulability and sleep apnea were not shown to have any identifiable relationship with an increased risk of stroke.
“As health professionals, we must ensure that progress in preventing stroke does not lead to complacency,” say the guidelines. “We must acknowledge that several recommendations remain vague because of suboptimal clinical trial evidence or, even more concerning, may be out of date and therefore irrelevant.”
Dr. Meschia and his associates warn that although medications are helpful, the best way to safeguard against a stroke is to change a person’s lifestyle into one of healthy eating and exercise habits. Unfortunately, say the authors, “it is easier to convince a patient to take a pill than to radically change his or her lifestyle, [but] we must expect the same standards of evidence for lifestyle interventions.”
Dr. Meschia disclosed that his research grant comes from the National Institute of Neurological Disorders and Stroke. He had no other relevant financial disclosures of interest. Several of the guidelines’ coauthors had disclosures of their own, which are listed in the statement.
FROM THE AMERICAN HEART ASSOCIATION AND THE AMERICAN STROKE ASSOCIATION
ACC project seeks to reduce heart failure, MI readmissions
A program that’s designed to help hospitals reduce readmissions after inpatient treatment for a heart attack or heart failure is being launched in 35 selected hospitals.
The Patient Navigator Program is sponsored by the American College of Cardiology and AstraZeneca, which provided $10 million in funding for the 2-year pilot program, but was not involved in selection of facilities or any other aspect.
It’s “a unique collaboration between the cardiovascular care team, patients, and families to manage the stress of hospitalization for complex conditions in a way that allows patients to return home, remain healthy, and avoid the need for readmission whenever possible,” said ACC President Patrick T. O’Gara, in a statement.
Hospitals have been under pressure to reduce readmissions since the fall of 2012. That’s when Medicare began penalizing facilities up to 1% of their inpatient admissions for excess readmissions within 30 days of patients with acute myocardial infarctions, heart failure, and pneumonia. The penalty increased to 2% in fiscal year 2014, and went up to 3% in the fiscal year that started Oct. 1. For this year, chronic obstructive pulmonary disease and hip/knee arthroplasty were added to the list of conditions being monitored for readmissions.
The penalties have already been assessed for fiscal year 2015.
Medicare’s Readmissions Reduction Program bases penalties on a prior 3-year period. Fiscal 2015 penalties were based on readmissions from 2010 to 2013.
The Medicare penalties were the driving force behind the creation of the program a few years ago, said Dr. Mary Norine Walsh, chair of the ACC’s Care Transition Oversight Program. But it also represented a chance “to pursue excellence,” said Dr. Walsh in an interview.
The 35 hospitals that are participating were selected by ACC senior staff and cardiologists like Dr. Walsh who are involved in the ACC’s quality improvement efforts. To be eligible, they had to be participants in the ACC’s National Cardiovascular Data Registry ACTION Registry–GWTG, which, according to the ACC, “is a risk-adjusted, outcomes-based quality improvement program that focuses exclusively on high-risk STEMI/NSTEMI myocardial infarction patients.” The registry helps hospitals apply ACC and American Heart Association clinical guideline recommendations and provides quality improvement tools.
They also had to be part of the ACC’s Hospital to Home Initiative, which helps hospitals and cardiovascular care providers improve transitions from hospital to homes.
All 35 hospitals are eligible to receive $80,000 a year for 2 years. Most likely, the facilities will use that money to hire an individual or individuals who can act as a navigator for heart failure and MI patients, said Dr. Walsh, who is the medical director of the heart failure and cardiac transplant program at St. Vincent Heart Center, Indianapolis, Ind.
While there are few randomized, controlled trials that examine what works to reduce readmission rates, there are several interventions that have been shown to help, she said. Patient eduction and getting patients in for follow-up care within 7 days are two key components that can make a difference, said Dr. Walsh. Multidisciplinary heart failure programs also have an impact.
The participating hospitals will share their processes and models, and at the end of the 2 years, the hope is that the facilities will continue to fund the program, said Dr. Walsh.
The ACC will also “be interested to find out what success looks like,” she said.
The Patient Navigator Program probably won’t help hospitals avoid penalties until fiscal year 2017 at the earliest, Dr. Walsh noted. However, the model will still be important, she said.
“We know that value-based purchasing is moving on, and the penalties will almost certainly extend to other diagnoses each sequential year, so hospitals are interested in preparing for the future,” said Dr. Walsh.
On Twitter @aliciaault
A program that’s designed to help hospitals reduce readmissions after inpatient treatment for a heart attack or heart failure is being launched in 35 selected hospitals.
The Patient Navigator Program is sponsored by the American College of Cardiology and AstraZeneca, which provided $10 million in funding for the 2-year pilot program, but was not involved in selection of facilities or any other aspect.
It’s “a unique collaboration between the cardiovascular care team, patients, and families to manage the stress of hospitalization for complex conditions in a way that allows patients to return home, remain healthy, and avoid the need for readmission whenever possible,” said ACC President Patrick T. O’Gara, in a statement.
Hospitals have been under pressure to reduce readmissions since the fall of 2012. That’s when Medicare began penalizing facilities up to 1% of their inpatient admissions for excess readmissions within 30 days of patients with acute myocardial infarctions, heart failure, and pneumonia. The penalty increased to 2% in fiscal year 2014, and went up to 3% in the fiscal year that started Oct. 1. For this year, chronic obstructive pulmonary disease and hip/knee arthroplasty were added to the list of conditions being monitored for readmissions.
The penalties have already been assessed for fiscal year 2015.
Medicare’s Readmissions Reduction Program bases penalties on a prior 3-year period. Fiscal 2015 penalties were based on readmissions from 2010 to 2013.
The Medicare penalties were the driving force behind the creation of the program a few years ago, said Dr. Mary Norine Walsh, chair of the ACC’s Care Transition Oversight Program. But it also represented a chance “to pursue excellence,” said Dr. Walsh in an interview.
The 35 hospitals that are participating were selected by ACC senior staff and cardiologists like Dr. Walsh who are involved in the ACC’s quality improvement efforts. To be eligible, they had to be participants in the ACC’s National Cardiovascular Data Registry ACTION Registry–GWTG, which, according to the ACC, “is a risk-adjusted, outcomes-based quality improvement program that focuses exclusively on high-risk STEMI/NSTEMI myocardial infarction patients.” The registry helps hospitals apply ACC and American Heart Association clinical guideline recommendations and provides quality improvement tools.
They also had to be part of the ACC’s Hospital to Home Initiative, which helps hospitals and cardiovascular care providers improve transitions from hospital to homes.
All 35 hospitals are eligible to receive $80,000 a year for 2 years. Most likely, the facilities will use that money to hire an individual or individuals who can act as a navigator for heart failure and MI patients, said Dr. Walsh, who is the medical director of the heart failure and cardiac transplant program at St. Vincent Heart Center, Indianapolis, Ind.
While there are few randomized, controlled trials that examine what works to reduce readmission rates, there are several interventions that have been shown to help, she said. Patient eduction and getting patients in for follow-up care within 7 days are two key components that can make a difference, said Dr. Walsh. Multidisciplinary heart failure programs also have an impact.
The participating hospitals will share their processes and models, and at the end of the 2 years, the hope is that the facilities will continue to fund the program, said Dr. Walsh.
The ACC will also “be interested to find out what success looks like,” she said.
The Patient Navigator Program probably won’t help hospitals avoid penalties until fiscal year 2017 at the earliest, Dr. Walsh noted. However, the model will still be important, she said.
“We know that value-based purchasing is moving on, and the penalties will almost certainly extend to other diagnoses each sequential year, so hospitals are interested in preparing for the future,” said Dr. Walsh.
On Twitter @aliciaault
A program that’s designed to help hospitals reduce readmissions after inpatient treatment for a heart attack or heart failure is being launched in 35 selected hospitals.
The Patient Navigator Program is sponsored by the American College of Cardiology and AstraZeneca, which provided $10 million in funding for the 2-year pilot program, but was not involved in selection of facilities or any other aspect.
It’s “a unique collaboration between the cardiovascular care team, patients, and families to manage the stress of hospitalization for complex conditions in a way that allows patients to return home, remain healthy, and avoid the need for readmission whenever possible,” said ACC President Patrick T. O’Gara, in a statement.
Hospitals have been under pressure to reduce readmissions since the fall of 2012. That’s when Medicare began penalizing facilities up to 1% of their inpatient admissions for excess readmissions within 30 days of patients with acute myocardial infarctions, heart failure, and pneumonia. The penalty increased to 2% in fiscal year 2014, and went up to 3% in the fiscal year that started Oct. 1. For this year, chronic obstructive pulmonary disease and hip/knee arthroplasty were added to the list of conditions being monitored for readmissions.
The penalties have already been assessed for fiscal year 2015.
Medicare’s Readmissions Reduction Program bases penalties on a prior 3-year period. Fiscal 2015 penalties were based on readmissions from 2010 to 2013.
The Medicare penalties were the driving force behind the creation of the program a few years ago, said Dr. Mary Norine Walsh, chair of the ACC’s Care Transition Oversight Program. But it also represented a chance “to pursue excellence,” said Dr. Walsh in an interview.
The 35 hospitals that are participating were selected by ACC senior staff and cardiologists like Dr. Walsh who are involved in the ACC’s quality improvement efforts. To be eligible, they had to be participants in the ACC’s National Cardiovascular Data Registry ACTION Registry–GWTG, which, according to the ACC, “is a risk-adjusted, outcomes-based quality improvement program that focuses exclusively on high-risk STEMI/NSTEMI myocardial infarction patients.” The registry helps hospitals apply ACC and American Heart Association clinical guideline recommendations and provides quality improvement tools.
They also had to be part of the ACC’s Hospital to Home Initiative, which helps hospitals and cardiovascular care providers improve transitions from hospital to homes.
All 35 hospitals are eligible to receive $80,000 a year for 2 years. Most likely, the facilities will use that money to hire an individual or individuals who can act as a navigator for heart failure and MI patients, said Dr. Walsh, who is the medical director of the heart failure and cardiac transplant program at St. Vincent Heart Center, Indianapolis, Ind.
While there are few randomized, controlled trials that examine what works to reduce readmission rates, there are several interventions that have been shown to help, she said. Patient eduction and getting patients in for follow-up care within 7 days are two key components that can make a difference, said Dr. Walsh. Multidisciplinary heart failure programs also have an impact.
The participating hospitals will share their processes and models, and at the end of the 2 years, the hope is that the facilities will continue to fund the program, said Dr. Walsh.
The ACC will also “be interested to find out what success looks like,” she said.
The Patient Navigator Program probably won’t help hospitals avoid penalties until fiscal year 2017 at the earliest, Dr. Walsh noted. However, the model will still be important, she said.
“We know that value-based purchasing is moving on, and the penalties will almost certainly extend to other diagnoses each sequential year, so hospitals are interested in preparing for the future,” said Dr. Walsh.
On Twitter @aliciaault





