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Portable spectrometers can detect malaria early, group says

Credit: St Jude Children’s
Research Hospital
An infrared spectroscopy technique can detect malaria parasites at early stages of development, according to preclinical research published in Analytical Chemistry.
A group of biochemists found this method could detect Plasmodium falciparum in red blood cells by picking up on a fatty acid signature.
This allowed the researchers to identify and quantify parasites at various stages of development, including the ring and gametocyte stages.
The team also pointed out that the spectrometer they used is portable, inexpensive, and does not require highly trained staff for operation. It could therefore prove useful in the field.
“Current tests for malaria suffer from serious limitations,” said study author Bayden Wood, PhD, of Monash University in Victoria, Australia.
“Many are expensive [and] require specialist instruments and highly trained staff to judge whether blood samples contain the parasite. What’s been holding us back is the lack of an accurate and inexpensive test to detect malaria early and stop it in its tracks. We believe we’ve found it.”
Dr Wood and his colleagues already knew that fatty acids were a marker for malaria from previous studies conducted at the Australian Synchrotron. The Synchrotron allowed the team to see the different life stages of the parasite and the variation in its fatty acids.
The researchers thought they might be able to use this information for diagnosis, but they needed a more portable detection method.
So they decided to test whether attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FT-IR) could detect the fatty acid signature. The technique utilizes infrared light to pick up on the vibrations of molecules.
The researchers spiked red blood cells with parasites of different numbers and life stages and observed them using ATR-FT-IR.
Dr Wood said the method produced results within minutes. And it gave a clear indication of malaria at a much earlier stage of infection than current tests on the market—at the ring and gametocyte stages.
The absolute detection limit was 0.00001% parasitemia (<1 parasite/μL of blood) for cultured early ring-stage parasites in a suspension of normal red blood cells.
“Now that we can detect the early stages of a parasite’s life in the bloodstream, the disease will be much easier to test and treat,” Dr Wood said. “The big advantage of our test is that it doesn’t need scientists and expensive equipment. This has the potential to dramatically reduce the number of people dying from this disease in remote communities.”
The method also shows the potential to detect a number of other blood-borne diseases, according to the researchers. Dr Wood and his colleagues are now planning a clinical trial of ATR-FT-IR in Thailand. ![]()

Credit: St Jude Children’s
Research Hospital
An infrared spectroscopy technique can detect malaria parasites at early stages of development, according to preclinical research published in Analytical Chemistry.
A group of biochemists found this method could detect Plasmodium falciparum in red blood cells by picking up on a fatty acid signature.
This allowed the researchers to identify and quantify parasites at various stages of development, including the ring and gametocyte stages.
The team also pointed out that the spectrometer they used is portable, inexpensive, and does not require highly trained staff for operation. It could therefore prove useful in the field.
“Current tests for malaria suffer from serious limitations,” said study author Bayden Wood, PhD, of Monash University in Victoria, Australia.
“Many are expensive [and] require specialist instruments and highly trained staff to judge whether blood samples contain the parasite. What’s been holding us back is the lack of an accurate and inexpensive test to detect malaria early and stop it in its tracks. We believe we’ve found it.”
Dr Wood and his colleagues already knew that fatty acids were a marker for malaria from previous studies conducted at the Australian Synchrotron. The Synchrotron allowed the team to see the different life stages of the parasite and the variation in its fatty acids.
The researchers thought they might be able to use this information for diagnosis, but they needed a more portable detection method.
So they decided to test whether attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FT-IR) could detect the fatty acid signature. The technique utilizes infrared light to pick up on the vibrations of molecules.
The researchers spiked red blood cells with parasites of different numbers and life stages and observed them using ATR-FT-IR.
Dr Wood said the method produced results within minutes. And it gave a clear indication of malaria at a much earlier stage of infection than current tests on the market—at the ring and gametocyte stages.
The absolute detection limit was 0.00001% parasitemia (<1 parasite/μL of blood) for cultured early ring-stage parasites in a suspension of normal red blood cells.
“Now that we can detect the early stages of a parasite’s life in the bloodstream, the disease will be much easier to test and treat,” Dr Wood said. “The big advantage of our test is that it doesn’t need scientists and expensive equipment. This has the potential to dramatically reduce the number of people dying from this disease in remote communities.”
The method also shows the potential to detect a number of other blood-borne diseases, according to the researchers. Dr Wood and his colleagues are now planning a clinical trial of ATR-FT-IR in Thailand. ![]()

Credit: St Jude Children’s
Research Hospital
An infrared spectroscopy technique can detect malaria parasites at early stages of development, according to preclinical research published in Analytical Chemistry.
A group of biochemists found this method could detect Plasmodium falciparum in red blood cells by picking up on a fatty acid signature.
This allowed the researchers to identify and quantify parasites at various stages of development, including the ring and gametocyte stages.
The team also pointed out that the spectrometer they used is portable, inexpensive, and does not require highly trained staff for operation. It could therefore prove useful in the field.
“Current tests for malaria suffer from serious limitations,” said study author Bayden Wood, PhD, of Monash University in Victoria, Australia.
“Many are expensive [and] require specialist instruments and highly trained staff to judge whether blood samples contain the parasite. What’s been holding us back is the lack of an accurate and inexpensive test to detect malaria early and stop it in its tracks. We believe we’ve found it.”
Dr Wood and his colleagues already knew that fatty acids were a marker for malaria from previous studies conducted at the Australian Synchrotron. The Synchrotron allowed the team to see the different life stages of the parasite and the variation in its fatty acids.
The researchers thought they might be able to use this information for diagnosis, but they needed a more portable detection method.
So they decided to test whether attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FT-IR) could detect the fatty acid signature. The technique utilizes infrared light to pick up on the vibrations of molecules.
The researchers spiked red blood cells with parasites of different numbers and life stages and observed them using ATR-FT-IR.
Dr Wood said the method produced results within minutes. And it gave a clear indication of malaria at a much earlier stage of infection than current tests on the market—at the ring and gametocyte stages.
The absolute detection limit was 0.00001% parasitemia (<1 parasite/μL of blood) for cultured early ring-stage parasites in a suspension of normal red blood cells.
“Now that we can detect the early stages of a parasite’s life in the bloodstream, the disease will be much easier to test and treat,” Dr Wood said. “The big advantage of our test is that it doesn’t need scientists and expensive equipment. This has the potential to dramatically reduce the number of people dying from this disease in remote communities.”
The method also shows the potential to detect a number of other blood-borne diseases, according to the researchers. Dr Wood and his colleagues are now planning a clinical trial of ATR-FT-IR in Thailand. ![]()
Hospira issues Class I recall of infusion pumps

Credit: Daniel Gay
Hospira, Inc., has issued a Class I recall of Abbott Acclaim infusion pumps and Hospira Acclaim Encore infusion pumps, after receiving reports of broken door assemblies on these products.
If a door assembly breaks, the door may not close properly and an over-infusion or a delay of therapy may occur.
If the door cannot be closed, the pump cannot be used, and this can result in a delay of therapy.
Use of these products may cause serious adverse events, including death.
These pumps are used to deliver hydration fluids, drugs, blood and blood fractions, intravenous nutritionals, and enteral nutritionals.
The affected Abbott Acclaim Infusion Pumps, list Number 12032, were manufactured from February 1998 to November 1998 and distributed from September 1998 through February 2004.
The affected Hospira Acclaim Encore infusion pumps, list Number 12237, were manufactured from February 1997 to February 2010 and distributed from July 1999 through November 2013.
Hospira is recommending that users inspect each Hospira/Abbott Acclaim Encore infusion pump for door handle cracks prior to programming a therapy, by taking the following steps:
1. After inserting the tubing (with the roller clamp closed) and closing the door handle against the infusion pump, check that the door is fully closed.
If a pump has a door that does not close properly and a gap or separation exists between the completely closed door and the pump itself, remove the pump from clinical service, and call Hospira at 1-800-441-4100 (M-F, 8am-5pm, CT). If the door closes correctly, proceed to Step 2.
2. If the door closes correctly and a gap or separation does not exist between the completely closed door and the pump itself, check that there is no free flow activity in the drip chamber of the administration set by opening the roller clamp.
If free flow is detected, close the roller clamp, remove the pump from clinical service, and call Hospira at 1-800-441-4100 (M-F, 8am-5pm, CT).
3. If no issues are found through steps 1 and 2, the pump is acceptable for use. However, healthcare professionals should still ensure that anyone in their facility who might use these products is made aware of this safety notification and the recommended actions.
Healthcare professionals and patients can report adverse events or side effects related to the use of these products to the FDA’s MedWatch Program. ![]()

Credit: Daniel Gay
Hospira, Inc., has issued a Class I recall of Abbott Acclaim infusion pumps and Hospira Acclaim Encore infusion pumps, after receiving reports of broken door assemblies on these products.
If a door assembly breaks, the door may not close properly and an over-infusion or a delay of therapy may occur.
If the door cannot be closed, the pump cannot be used, and this can result in a delay of therapy.
Use of these products may cause serious adverse events, including death.
These pumps are used to deliver hydration fluids, drugs, blood and blood fractions, intravenous nutritionals, and enteral nutritionals.
The affected Abbott Acclaim Infusion Pumps, list Number 12032, were manufactured from February 1998 to November 1998 and distributed from September 1998 through February 2004.
The affected Hospira Acclaim Encore infusion pumps, list Number 12237, were manufactured from February 1997 to February 2010 and distributed from July 1999 through November 2013.
Hospira is recommending that users inspect each Hospira/Abbott Acclaim Encore infusion pump for door handle cracks prior to programming a therapy, by taking the following steps:
1. After inserting the tubing (with the roller clamp closed) and closing the door handle against the infusion pump, check that the door is fully closed.
If a pump has a door that does not close properly and a gap or separation exists between the completely closed door and the pump itself, remove the pump from clinical service, and call Hospira at 1-800-441-4100 (M-F, 8am-5pm, CT). If the door closes correctly, proceed to Step 2.
2. If the door closes correctly and a gap or separation does not exist between the completely closed door and the pump itself, check that there is no free flow activity in the drip chamber of the administration set by opening the roller clamp.
If free flow is detected, close the roller clamp, remove the pump from clinical service, and call Hospira at 1-800-441-4100 (M-F, 8am-5pm, CT).
3. If no issues are found through steps 1 and 2, the pump is acceptable for use. However, healthcare professionals should still ensure that anyone in their facility who might use these products is made aware of this safety notification and the recommended actions.
Healthcare professionals and patients can report adverse events or side effects related to the use of these products to the FDA’s MedWatch Program. ![]()

Credit: Daniel Gay
Hospira, Inc., has issued a Class I recall of Abbott Acclaim infusion pumps and Hospira Acclaim Encore infusion pumps, after receiving reports of broken door assemblies on these products.
If a door assembly breaks, the door may not close properly and an over-infusion or a delay of therapy may occur.
If the door cannot be closed, the pump cannot be used, and this can result in a delay of therapy.
Use of these products may cause serious adverse events, including death.
These pumps are used to deliver hydration fluids, drugs, blood and blood fractions, intravenous nutritionals, and enteral nutritionals.
The affected Abbott Acclaim Infusion Pumps, list Number 12032, were manufactured from February 1998 to November 1998 and distributed from September 1998 through February 2004.
The affected Hospira Acclaim Encore infusion pumps, list Number 12237, were manufactured from February 1997 to February 2010 and distributed from July 1999 through November 2013.
Hospira is recommending that users inspect each Hospira/Abbott Acclaim Encore infusion pump for door handle cracks prior to programming a therapy, by taking the following steps:
1. After inserting the tubing (with the roller clamp closed) and closing the door handle against the infusion pump, check that the door is fully closed.
If a pump has a door that does not close properly and a gap or separation exists between the completely closed door and the pump itself, remove the pump from clinical service, and call Hospira at 1-800-441-4100 (M-F, 8am-5pm, CT). If the door closes correctly, proceed to Step 2.
2. If the door closes correctly and a gap or separation does not exist between the completely closed door and the pump itself, check that there is no free flow activity in the drip chamber of the administration set by opening the roller clamp.
If free flow is detected, close the roller clamp, remove the pump from clinical service, and call Hospira at 1-800-441-4100 (M-F, 8am-5pm, CT).
3. If no issues are found through steps 1 and 2, the pump is acceptable for use. However, healthcare professionals should still ensure that anyone in their facility who might use these products is made aware of this safety notification and the recommended actions.
Healthcare professionals and patients can report adverse events or side effects related to the use of these products to the FDA’s MedWatch Program. ![]()
FDA approves CML drug for home administration

The US Food and Drug Administration (FDA) has expanded the approval of omacetaxine mepesuccinate (Synribo) to include home administration.
The drug is already FDA-approved to treat adults with chronic or accelerated phase chronic myeloid leukemia (CML) who do not respond to or cannot tolerate 2 or more tyrosine kinase inhibitors.
The new approval allows CML patients to self-administer subcutaneous injections of omacetaxine mepesuccinate at home.
“It had been necessary for adults living with chronic or accelerated phase CML who are prescribed Synribo to travel to their doctor’s office twice a day for 2 weeks, which can be extremely burdensome and inconvenient to both patients and their caregivers,” said Meir Wetzler, MD, FACP, Chief of the Leukemia Section at Roswell Park Cancer Institute in Buffalo, New York.
“Now, physicians can decide if their patients are candidates for self-administration and, if so, provide their patients with guidance on how to properly administer reconstituted Synribo in the home.”
The drug’s maker, Teva Pharmaceutical Industries, Ltd., is working to finalize a pharmacy support program that will help facilitate successful home administration of omacetaxine mepesuccinate. The program is expected to “go live” this month or next.
About omacetaxine mepesuccinate
Omacetaxine mepesuccinate is a protein synthesis inhibitor. Although the drug’s mechanism of action is not fully understood, it is known to prevent the production of Bcr-Abl and Mcl-1, which help drive CML.
In October 2012, the FDA granted omacetaxine mepesuccinate accelerated approval for the treatment of adult patients with chronic or accelerated phase CML with resistance and/or intolerance to 2 or more tyrosine kinase inhibitors. Omacetaxine mepesuccinate gained full FDA approval in February.
The drug has been associated with severe and fatal myelosuppression, including thrombocytopenia, neutropenia, and anemia in some patients. So healthcare professionals should monitor patients’ complete blood counts weekly during induction and initial maintenance cycles and every 2 weeks during later maintenance cycles, as clinically indicated.
Omacetaxine mepesuccinate has been known to cause severe thrombocytopenia, which increases the risk of hemorrhage. Fatalities from cerebral hemorrhage have occurred. And severe, non-fatal gastrointestinal hemorrhages have occurred.
So healthcare professionals should monitor platelet counts as part of the complete blood count as recommended. Patients should not receive anticoagulants, aspirin, or non-steroidal anti-inflammatory drugs when their platelet counts are <50,000/μL, as these drugs may increase the risk of bleeding.
Omacetaxine mepesuccinate can induce glucose intolerance as well. So healthcare professionals should monitor blood glucose levels frequently, especially in patients with diabetes or risk factors for diabetes. Patients with poorly controlled diabetes mellitus should not receive omacetaxine mepesuccinate until good glycemic control has been established.
Omacetaxine mepesuccinate can cause fetal harm when administered to a pregnant woman. So women should be advised to avoid becoming pregnant while using the drug.
For more details on omacetaxine mepesuccinate, see the full prescribing information. ![]()

The US Food and Drug Administration (FDA) has expanded the approval of omacetaxine mepesuccinate (Synribo) to include home administration.
The drug is already FDA-approved to treat adults with chronic or accelerated phase chronic myeloid leukemia (CML) who do not respond to or cannot tolerate 2 or more tyrosine kinase inhibitors.
The new approval allows CML patients to self-administer subcutaneous injections of omacetaxine mepesuccinate at home.
“It had been necessary for adults living with chronic or accelerated phase CML who are prescribed Synribo to travel to their doctor’s office twice a day for 2 weeks, which can be extremely burdensome and inconvenient to both patients and their caregivers,” said Meir Wetzler, MD, FACP, Chief of the Leukemia Section at Roswell Park Cancer Institute in Buffalo, New York.
“Now, physicians can decide if their patients are candidates for self-administration and, if so, provide their patients with guidance on how to properly administer reconstituted Synribo in the home.”
The drug’s maker, Teva Pharmaceutical Industries, Ltd., is working to finalize a pharmacy support program that will help facilitate successful home administration of omacetaxine mepesuccinate. The program is expected to “go live” this month or next.
About omacetaxine mepesuccinate
Omacetaxine mepesuccinate is a protein synthesis inhibitor. Although the drug’s mechanism of action is not fully understood, it is known to prevent the production of Bcr-Abl and Mcl-1, which help drive CML.
In October 2012, the FDA granted omacetaxine mepesuccinate accelerated approval for the treatment of adult patients with chronic or accelerated phase CML with resistance and/or intolerance to 2 or more tyrosine kinase inhibitors. Omacetaxine mepesuccinate gained full FDA approval in February.
The drug has been associated with severe and fatal myelosuppression, including thrombocytopenia, neutropenia, and anemia in some patients. So healthcare professionals should monitor patients’ complete blood counts weekly during induction and initial maintenance cycles and every 2 weeks during later maintenance cycles, as clinically indicated.
Omacetaxine mepesuccinate has been known to cause severe thrombocytopenia, which increases the risk of hemorrhage. Fatalities from cerebral hemorrhage have occurred. And severe, non-fatal gastrointestinal hemorrhages have occurred.
So healthcare professionals should monitor platelet counts as part of the complete blood count as recommended. Patients should not receive anticoagulants, aspirin, or non-steroidal anti-inflammatory drugs when their platelet counts are <50,000/μL, as these drugs may increase the risk of bleeding.
Omacetaxine mepesuccinate can induce glucose intolerance as well. So healthcare professionals should monitor blood glucose levels frequently, especially in patients with diabetes or risk factors for diabetes. Patients with poorly controlled diabetes mellitus should not receive omacetaxine mepesuccinate until good glycemic control has been established.
Omacetaxine mepesuccinate can cause fetal harm when administered to a pregnant woman. So women should be advised to avoid becoming pregnant while using the drug.
For more details on omacetaxine mepesuccinate, see the full prescribing information. ![]()

The US Food and Drug Administration (FDA) has expanded the approval of omacetaxine mepesuccinate (Synribo) to include home administration.
The drug is already FDA-approved to treat adults with chronic or accelerated phase chronic myeloid leukemia (CML) who do not respond to or cannot tolerate 2 or more tyrosine kinase inhibitors.
The new approval allows CML patients to self-administer subcutaneous injections of omacetaxine mepesuccinate at home.
“It had been necessary for adults living with chronic or accelerated phase CML who are prescribed Synribo to travel to their doctor’s office twice a day for 2 weeks, which can be extremely burdensome and inconvenient to both patients and their caregivers,” said Meir Wetzler, MD, FACP, Chief of the Leukemia Section at Roswell Park Cancer Institute in Buffalo, New York.
“Now, physicians can decide if their patients are candidates for self-administration and, if so, provide their patients with guidance on how to properly administer reconstituted Synribo in the home.”
The drug’s maker, Teva Pharmaceutical Industries, Ltd., is working to finalize a pharmacy support program that will help facilitate successful home administration of omacetaxine mepesuccinate. The program is expected to “go live” this month or next.
About omacetaxine mepesuccinate
Omacetaxine mepesuccinate is a protein synthesis inhibitor. Although the drug’s mechanism of action is not fully understood, it is known to prevent the production of Bcr-Abl and Mcl-1, which help drive CML.
In October 2012, the FDA granted omacetaxine mepesuccinate accelerated approval for the treatment of adult patients with chronic or accelerated phase CML with resistance and/or intolerance to 2 or more tyrosine kinase inhibitors. Omacetaxine mepesuccinate gained full FDA approval in February.
The drug has been associated with severe and fatal myelosuppression, including thrombocytopenia, neutropenia, and anemia in some patients. So healthcare professionals should monitor patients’ complete blood counts weekly during induction and initial maintenance cycles and every 2 weeks during later maintenance cycles, as clinically indicated.
Omacetaxine mepesuccinate has been known to cause severe thrombocytopenia, which increases the risk of hemorrhage. Fatalities from cerebral hemorrhage have occurred. And severe, non-fatal gastrointestinal hemorrhages have occurred.
So healthcare professionals should monitor platelet counts as part of the complete blood count as recommended. Patients should not receive anticoagulants, aspirin, or non-steroidal anti-inflammatory drugs when their platelet counts are <50,000/μL, as these drugs may increase the risk of bleeding.
Omacetaxine mepesuccinate can induce glucose intolerance as well. So healthcare professionals should monitor blood glucose levels frequently, especially in patients with diabetes or risk factors for diabetes. Patients with poorly controlled diabetes mellitus should not receive omacetaxine mepesuccinate until good glycemic control has been established.
Omacetaxine mepesuccinate can cause fetal harm when administered to a pregnant woman. So women should be advised to avoid becoming pregnant while using the drug.
For more details on omacetaxine mepesuccinate, see the full prescribing information. ![]()
Teaching Cases Perception vs Reality
The advent of work‐hour restrictions and admission limits for teaching services has led many academic hospitals to implement hospitalist‐run staff (ie, nonteaching) services.[1] Although this practice is not new,[2] it is growing in popularity[3] and has been endorsed as a way to protect resident teaching and prevent excessive workload.[4] One potential benefit is the assignment of more educational cases to teaching services, whereas the nonteaching services receive more patients whose care is presumably relatively mundane or routine.[5]
Despite the rapid growth of this system of educational triage,[6] little is known about the factors considered when teaching versus nonteaching decisions are made. Studies of clinical outcomes for patients assigned to teaching versus nonteaching services have understandably used random assignment,[7, 8] whereas a study finding that patients with unhealthy substance use were more likely to be on teaching services than nonteaching services relied on patient assignment based on the identity of the patient's primary care provider or insurer.[9] In 2009, O'Connor et al. reported that implementation of nonteaching services at 2 hospitals had led to unequal distribution of patients in terms of demographics, diagnosis, and illness severity.[10] Triage decisions were made by either a nurse coordinator or a medical chief resident, and sicker patients (and occasionally good teaching cases) were preferentially placed on the teaching services, reportedly out of respect for the comfort level of the midlevel providers who staffed the nonteaching services.
Our institution has used a system of hospitalist educational triage since 1998. Over that time, residents have often expressed concerns about the assignment of patients to the teaching services, reporting in particular that they receive a disproportionate number of complex cases and outside transfers. In 2006, the hospitalist group attempted to address these concerns by collecting real‐time admission data, but the application of the data was limited by suspicion on both sides of a Hawthorne effect (data not published).
If trainee and hospitalist expectations for what constitutes a great teaching case differ substantially, that difference can have significant implications for resident and medical student teaching, self‐perceived roles, and satisfaction. More significantly, an understanding of what faculty perceive as ideal teaching cases would provide valuable information about the strengths and weaknesses of the teachingnonteaching model, which may prove useful to other academic institutions considering such a system. In this study, we endeavored to understand what residents and hospitalists consider an educational admission and to compare these expectations to the actual triage decisions of hospitalists.
METHODS
Mayo Clinic Hospital (Phoenix, Arizona) has used separate teaching and nonteaching services since opening in 1998. At our institution, like many others,[11] a hospitalist is assigned to take all calls for emergency department (ED) admissions, admissions from outpatient clinics, and transfer requests; this physician directs patients to the teaching or nonteaching service. At the time of our study, the 2 teaching services alternated days in which they admitted up to 7 patients, and the 5 nonteaching services admitted all other patients and provided medicine consultative services for the hospital. Teaching services consisted of 1 hospitalist, 2 senior residents, 2 or 3 first‐year residents, and sometimes 1 third‐ or fourth‐year medical student. Nonteaching services consisted of a hospitalist with intermittent assistance from a physician assistant or nurse practitioner.
Although there are no formal guidelines for the hospitalist triage role, hospitalists are encouraged to assign more educational cases to the teaching services and to allow the residents enough time to address the acute needs of the prior admission before receiving the next admission. Residents are not assigned any patients between 4:00 am and 7:00 am. The goals and objectives for the resident rotation on the medicine teaching service include a list of diagnoses with which residents are expected to become familiar during their residency; triage hospitalists have on‐line access to these goals and objectives.
To assess resident and hospitalist opinions about what types of patients should or should not be admitted to teaching services and to compare those characteristics with those of the patients actually admitted to teaching services, we began by administering a simple, open‐ended survey and asked both groups: (1) In an ideal world, what kinds of patients should be admitted to the internal medicine teaching services at Mayo Clinic Hospital? (2) In the real world, what kinds of patients are admitted to the internal medicine teaching services at Mayo Clinic Hospital?
Ample space was provided for free‐text entries. Residents were additionally asked their postgraduate year level. The survey was administered in April 2011, at which time all residents would have rotated on the medicine teaching services several times. Survey responses were anonymous and were compiled and retyped by someone unfamiliar with the subjects' handwriting.
Two authors (D.L.R. and H.R.L.) reviewed the results of the first survey and used conventional content analysis to group responses into categories and tally them.[12] Responses from hospitalists and residents were used to determine the content for a second, quantitative survey that asked respondents to rate specific possible factors that affected triage decisions on a Likert scale from 1 (Argues against teaching admission) to 5 (Argues for teaching admission). The second survey, administered to the same residents and hospitalists in May 2011, asked: (1) In an ideal world, how do these factors contribute to the decision about which patients should be admitted to the internal medicine teaching services at Mayo Clinic Hospital? (2) In the real world, how do these factors contribute to the decision about which patients are admitted to the internal medicine teaching services at Mayo Clinic Hospital?
Assuming a 3:1 ratio of nonteaching to teaching admissions, we calculated that we would need to analyze 1028 admissions to detect a 10% difference in the proportion of a specific trait present in 50% of patients admitted to the nonteaching service, with the use of a 2‐sided test with 80% statistical power and a significance level of 0.05.
We collected data on patient assignment via retrospective chart review to avoid the possibility of a Hawthorne effect. We studied all admissions to the internal medicine services for a 3‐month period before the administration of the first survey (January 1, 2011 through March 31, 2011). The following patient data were collected: service assignment (teaching vs nonteaching), age, sex, source of admission (ED, direct from clinic, outside transfer, internal transfer from another hospital service), first visit to our institution, prior hematology or oncology visit at our institution (as a surrogate for cancer), prior psychiatry visit at our institution (as a surrogate for psychiatric disease), transplantation history, human immunodeficiency virus (HIV) or acquired immune deficiency syndrome (AIDS) history, chronic or functional pain mentioned in ED or admission note, need for translator, and benefactor status. Additionally, an online calculator was used to determine the Charlson Comorbidity Index score for each patient.[13] We collected actual patient data corresponding to factors reported by survey respondents whenever possible and practical, but not every factor reported by survey respondents was amenable to rigorous analysis; for example, no unbiased method could be devised to rigorously categorize patients whose admissions are likely to take more time or difficult patients and families.
Responses to the second (quantitative) survey and patient data were compared using the Pearson [2] and Fisher exact test for categorical variables and the Student t test or Wilcoxon rank sum test for continuous variables. Categorical variables that achieved statistical significance for overall difference were analyzed on a post hoc basis using the Bonferroni method to control for the overall type I error rate. We also examined the differences between actual and ideal triage decisions using the Wilcoxon signed rank test. Data were analyzed using SAS 9.3 (SAS Institute, Inc., Cary, NC). Statistical significance was defined as P<0.05.
The project was deemed exempt by the Mayo Clinic institutional review board.
RESULTS
We surveyed all categorical internal medicine residents (n=30, 10 each from postgraduate year [PGY]‐1, PGY‐2, and PGY‐3) and hospitalists except the authors (n=21; average years since completing training=13.3; range, 129 years). For both surveys, responses were collected from 29 (96.7%) residents. The nonresponding resident was a PGY‐2. The response rate for hospitalists was 20/21 (95.2%) for the first survey and 16/21 (76.2%) for the second survey.
First Survey
Table 1 compares the most frequent resident and faculty responses to the initial, open‐ended survey about what types of patients should or should not be admitted to teaching services. Residents most commonly indicated that ideal patients were traditional medicine cases (ie, bread‐and‐butter admissions, with 13 residents using that exact phrase), and others supplied specific examples of such cases, including chronic obstructive pulmonary disease, pneumonia, diabetic ketoacidosis, congestive heart failure, chest pain, and gastrointestinal tract bleeding. Only 1 faculty member mentioned bread‐and‐butter admissions, although several listed examples like chest pain and pneumonia. A smaller number of residents pointed to the importance of rare cases, whereas faculty considered rare cases to be ideal for teaching services, followed by variety of pathology and complexity.
| Residents (n=29) | Faculty (n=20) | |||
|---|---|---|---|---|
| Question | Characteristic | No. (%) | Characteristic | No. (%) |
| ||||
| In an ideal world, what kinds of patients should be admitted to the internal medicine teaching services at Mayo Clinic Hospital? | Bread‐and butter admissionsb | 14 (44.8) | Rare cases | 9 (45.0) |
| Rare cases | 9 (31.0) | Variety of pathology | 7 (35.0) | |
| No social admissions | 7 (24.1) | Complex cases | 5 (25.0) | |
| New diagnoses instead of chronic management | 4 (13.8) | Variety of complexity | 5 (25.0) | |
| Variety of complexity | 4 (13.8) | Patients with HIV/AIDS | 3 (15.0) | |
| Diagnostic dilemmas | 3 (15.0) | |||
| New diagnoses instead of chronic management | 3 (15.0) | |||
| In the real world, what kinds of patients are admitted to the internal medicine teaching services at Mayo Clinic Hospital? | Patients with cancer | 11 (37.9) | Complex patients | 6 (30.0) |
| Complex patients | 10 (34.5) | Difficult patients | 5 (25.0) | |
| Social admissions | 9 (31.0) | Patients whose admissions are expected to be time consuming | 5 (25.0) | |
| Acutely ill patients | 6 (20.7) | Rare cases | 3 (15.0) | |
| Variety of pathology | 6 (20.7) | Cases determined by the time of day | 3 (15.0) | |
With regard to actual admissions, residents and faculty agreed that they often were complex, but residents were more likely to suggest high rates of patients with cancer (11 residents vs 2 hospitalists) and social admissions (9 residents vs 2 hospitalists). Four residents each believed that they preferentially received elderly patients, outside transfers, and patients with functional pain, and 2 perceived a disproportionate number of patients making their first visit to Mayo Clinic. One hospitalist believed that residents were more likely to receive non‐English speakers.
Second Survey
Table 2 compares the resident and faculty responses to the second, numerical survey regarding ideal admissions to the teaching services. In contrast to the first survey, residents prioritized rare cases as the feature they most associated with ideal teaching admissions. They also placed a premium on variety of pathology, patients with unique findings, and patients likely to be written up or presented. The patients they believed were least appropriate for a teaching service were social admissions or those with placement issues, patients with functional or chronic pain, and benefactors or public figures.
| Factor | Resident, n=29 | Faculty, n=16 | P Value |
|---|---|---|---|
| |||
| Rare diseases | 0.22 | ||
| Mean (SD) | 4.8 (0.5) | 4.9 (0.3) | |
| Median | 5 | 5 | |
| Variety of pathology | 0.22 | ||
| Mean (SD) | 4.7 (0.5) | 4.5 (0.5) | |
| Median | 5 | 5 | |
| Cases that might be written up or presented | 0.35 | ||
| Mean (SD) | 4.7 (0.5) | 4.8 (0.6) | |
| Median | 5 | 5 | |
| Bread‐and‐butter cases | 0.001 | ||
| Mean (SD) | 4.6 (0.7) | 3.7 (0.9) | |
| Median | 5 | 4 | |
| Unique physical findings | 0.67 | ||
| Mean (SD) | 4.6 (0.6) | 4.7 (0.5) | |
| Median | 5 | 5 | |
| Variety of complexity | 0.21 | ||
| Mean (SD) | 4.3 (0.7) | 4.1 (0.6) | |
| Median | 4 | 4 | |
| Variety of acuity | 0.40 | ||
| Mean (SD) | 4.2 (0.7) | 4.1 (0.7) | |
| Median | 4 | 4 | |
| Spectrum of ages | 0.046 | ||
| Mean (SD) | 4.1 (0.8) | 3.6 (0.8) | |
| Median | 4 | 3 | |
| HIV or AIDS | 0.39 | ||
| Mean (SD) | 4.1 (0.9) | 4.4 (0.5) | |
| Median | 4 | 4 | |
| Acutely ill or unstable | 0.54 | ||
| Mean (SD) | 4.0 (0.9) | 3.9 (0.6) | |
| Median | 4 | 4 | |
| Complex patients | 0.94 | ||
| Mean (SD) | 4.0 (0.8) | 3.9 (0.6) | |
| Median | 4 | 4 | |
| Patients at end of life | 0.16 | ||
| Mean (SD) | 3.5 (0.8) | 3.1 (0.6) | |
| Median | 3 | 3 | |
| First‐time Mayo patients | 0.45 | ||
| Mean (SD) | 3.5 (0.7) | 3.3 (0.5) | |
| Median | 3 | 3 | |
| Younger patients | 0.50 | ||
| Mean (SD) | 3.5 (0.9) | 3.3 (0.6) | |
| Median | 3 | 3 | |
| Stable patients | 0.21 | ||
| Mean (SD) | 3.3 (0.8) | 3.1 (0.3) | |
| Median | 3 | 3 | |
| Patients with cancer | 0.67 | ||
| Mean (SD) | 3.3 (0.8) | 3.1 (0.4) | |
| Median | 3 | 3 | |
| Straightforward patients | 0.64 | ||
| Mean (SD) | 3.2 (0.8) | 3.1 (0.8) | |
| Median | 3 | 3 | |
| Older patients | 0.73 | ||
| Mean (SD) | 3.2 (0.7) | 3.1 (0.3) | |
| Median | 3 | 3 | |
| Patients with a history of transplantation | 0.67 | ||
| Mean (SD) | 3.1 (1.1) | 3.3 (0.6) | |
| Median | 3 | 3 | |
| Time of day of admission | 0.71 | ||
| Mean (SD) | 3.1 (1.0) | 3.1 (0.5) | |
| Median | 3 | 3 | |
| Patients with a history of psychiatric illness | 0.59 | ||
| Mean (SD) | 3.1 (1.0) | 3.1 (0.6) | |
| Median | 3 | 3 | |
| Patients who require a translator | 0.49 | ||
| Mean (SD) | 3.0 (0.9) | 3.1 (0.5) | |
| Median | 3 | 3 | |
| Patients whose admissions are expected to take more time | 0.13 | ||
| Mean (SD) | 2.9 (0.8) | 3.2 (0.6) | |
| Median | 3 | 3 | |
| Difficult patients and families | 0.55 | ||
| Mean (SD) | 2.8 (1.0) | 2.6 (0.8) | |
| Median | 3 | 3 | |
| Transfers from other hospitals | 0.11 | ||
| Mean (SD) | 2.7 (1.1) | 3.1 (0.3) | |
| Median | 3 | 3 | |
| Benefactors and public figures | 0.49 | ||
| Mean (SD) | 2.7 (1.0) | 2.5 (0.7) | |
| Median | 3 | 3 | |
| Patients with functional or chronic pain | 0.87 | ||
| Mean (SD) | 2.4 (1.1) | 2.4 (1.0) | |
| Median | 2 | 3 | |
| Social admissions or placement issues | 0.99 | ||
| Mean (SD) | 2.1 (1.1) | 2.0 (1.0) | |
| Median | 2 | 2 | |
Faculty prioritized many of the same features for ideal teaching cases as residents; 4 of their 5 highest‐scoring factors were the same (rare diseases, patients whose cases might be written up or presented, patients with unique physical findings, and variety of pathology). They also agreed on the least ideal features (social admissions or placement issues, patients with functional or chronic pain, and benefactors or public figures). The only significant differences between resident and faculty ratings for ideal teaching cases were for bread‐and‐butter cases and a spectrum of ages.
Discordance between resident and faculty survey responses on actual admission decisions (Table 3) was starker; residents rated several features significantly higher than faculty as features contributing to triage decisions including older patients; patients with functional or chronic pain, social admissions, or placement issues; patients with cancer; transfers from other hospitals; and difficult patients and families. Relative to residents, faculty reported that patients with HIV or AIDS, and patients whose cases were likely to be written up or presented, were more likely to be admitted to teaching services.
| Factor | Resident, n=29 | Faculty, n=16 | P Value |
|---|---|---|---|
| |||
| Rare diseases | 0.14 | ||
| Mean (SD) | 4.4 (0.6) | 4.7 (0.6) | |
| Median | 4 | 5 | |
| Complex patients | 0.83 | ||
| Mean (SD) | 4.3 (0.6) | 4.3 (0.6) | |
| Median | 4 | 4 | |
| Acutely ill or unstable | 0.18 | ||
| Mean (SD) | 4.3 (0.7) | 3.9 (0.9) | |
| Median | 4 | 4 | |
| Unique physical findings | 0.18 | ||
| Mean (SD) | 4.1 (0.8) | 4.5 (0.6) | |
| Median | 4 | 5 | |
| Transfers from other hospitals | 0.003 | ||
| Mean (SD) | 4.1 (1.0) | 3.5 (0.5) | |
| Median | 4 | 3 | |
| Cases that might be written up or presented | 0.03 | ||
| Mean (SD) | 4.1 (0.7) | 4.6 (0.6) | |
| Median | 4 | 5 | |
| Older patients | <0.001 | ||
| Mean (SD) | 3.9 (0.8) | 3.0 (0.7) | |
| Median | 4 | 3 | |
| Time of day of admission | 0.50 | ||
| Mean (SD) | 3.9 (1.1) | 3.7 (0.9) | |
| Median | 4 | 4 | |
| Patients with cancer | 0.01 | ||
| Mean (SD) | 3.9 (0.9) | 3.3 (0.5) | |
| Median | 4 | 3 | |
| Variety of pathology | 0.21 | ||
| Mean (SD) | 3.9 (0.8) | 4.2 (0.7) | |
| Median | 4 | 4 | |
| Patients whose admissions are expected to take more time | 0.13 | ||
| Mean (SD) | 3.9 (1.0) | 3.4 (0.9) | |
| Median | 4 | 3 | |
| HIV or AIDS | 0.008 | ||
| Mean (SD) | 3.8 (0.9) | 4.5 (0.5) | |
| Median | 4 | 4.5 | |
| Variety of complexity | 0.31 | ||
| Mean (SD) | 3.7 (0.9) | 3.9 (0.6) | |
| Median | 3.5 | 4 | |
| Bread‐and‐butter cases | 0.07 | ||
| Mean (SD) | 3.6 (1.0) | 2.9 (1.2) | |
| Median | 3 | 3 | |
| First‐time Mayo patients | 0.82 | ||
| Mean (SD) | 3.6 (0.9) | 3.5 (0.7) | |
| Median | 3 | 3 | |
| Patients with functional or chronic pain | 0.004 | ||
| Mean (SD) | 3.6 (1.0) | 2.8 (0.7) | |
| Median | 4 | 3 | |
| Social admissions or placement issues | 0.03 | ||
| Mean (SD) | 3.5 (1.2) | 2.7 (0.9) | |
| Median | 4 | 3 | |
| Variety of acuity | 0.25 | ||
| Mean (SD) | 3.5 (0.8) | 3.7 (0.6) | |
| Median | 3 | 4 | |
| Difficult patients and families | 0.03 | ||
| Mean (SD) | 3.4 (0.9) | 2.8 (0.7) | |
| Median | 3 | 3 | |
| Patients at end of life | 0.10 | ||
| Mean (SD) | 3.4 (0.8) | 3.0 (0.5) | |
| Median | 3 | 3 | |
| Spectrum of ages | 0.80 | ||
| Mean (SD) | 3.3 (0.7) | 3.3 (0.6) | |
| Median | 3 | 3 | |
| Patients with a history of psychiatric illness | 0.81 | ||
| Mean (SD) | 3.3 (0.9) | 3.1 (0.6) | |
| Median | 3 | 3 | |
| Patients with a history of transplantation | 0.25 | ||
| Mean (SD) | 3.2 (0.9) | 3.5 (0.5) | |
| Median | 3 | 3 | |
| Patients who require a translator | 0.60 | ||
| Mean (SD) | 3.2 (0.7) | 3.2 (0.6) | |
| Median | 3 | 3 | |
| Younger patients | 0.42 | ||
| Mean (SD) | 3.0 (0.9) | 3.1 (0.4) | |
| Median | 3 | 3 | |
| Benefactors and public figures | 0.09 | ||
| Mean (SD) | 2.9 (1.0) | 2.3 (0.7) | |
| Median | 3 | 2 | |
| Straightforward patients | 0.18 | ||
| Mean (SD) | 2.8 (1.0) | 2.4 (1.0) | |
| Median | 2.5 | 2 | |
| Stable patients | 0.53 | ||
| Mean (SD) | 2.7 (1.0) | 2.8 (0.7) | |
| Median | 3 | 3 | |
Comparing resident survey ratings for ideal versus actual triage decisions gave some insight into the features that they thought were inappropriately emphasized or ignored when triage decisions were made. Differences in resident scores for ideal versus actual admissions were significantly different for 16 of 28 items (data available upon request), suggesting a degree of perceived discordance. The largest positive differences (ie, features they valued in teaching admissions but thought were less represented in actual admissions) were for bread‐and‐butter admissions, variety of pathology, a spectrum of ages, and variety of acuity. The largest negative differences (ie, features they thought were well represented in actual admissions but were less valuable) were for social admissions or placement issues, transfers from other hospitals, patients with functional or chronic pain, and patients whose admissions were expected to take more time.
In terms of ideal versus actual triage decisions, faculty reported less discordance than residents; ideal and actual triage behavior differed significantly only for 4 of 28 items (data available upon request). They did agree with residents about the relative lack of bread‐and‐butter admissions and the over‐representation of social admissions or placement issues and transfers from other hospitals. They additionally noted a lack of straightforward cases.
We reviewed records of the 1426 patients admitted to the internal medicine services during the study period. Of these, 359 (25.2%) were assigned to the teaching services. Patient characteristics are summarized in Table 4.
| Characteristic | Teaching Service, n=359 | Nonteaching Service, n=1,067 | P Value |
|---|---|---|---|
| |||
| Age, y, mean (SD) | 66.7 (16.5) | 69.3 (15.7) | 0.008 |
| Admission type, No. (%) | 0.049 | ||
| Admission from the emergency department | 315 (87.7) | 915 (85.8) | 0.34 |
| Direct admission from Mayo outpatient clinic | 27 (7.5) | 114 (10.7) | 0.08 |
| Transfer from another institution | 16 (4.5) | 27 (2.5) | 0.06 |
| Internal transfer from a different hospital service | 1 (0.3) | 11 (1.0) | 0.31 |
| First‐time Mayo patient, No. (%) | 61 (17.0) | 175 (16.4) | 0.79 |
| Prior hematology or oncology visit, No. (%) | 86 (24.0) | 235 (22.0) | 0.45 |
| History of transplantation, No. (%) | 20 (5.6) | 52 (4.9) | 0.60 |
| Prior psychiatry visit, No. (%) | 53 (14.8) | 122 (11.4) | 0.10 |
| History of chronic or functional pain, No. (%) | 122 (34.0) | 330 (30.9) | 0.28 |
| Required translator, No. (%) | 5 (1.4) | 14 (1.3) | 0.91 |
| Benefactor, No. (%) | 5 (1.4) | 24 (2.2) | 0.32 |
| Charlson comorbidity score, mean (SD) | 2.7 (2.5) | 2.6 (2.5) | 0.49 |
DISCUSSION
The results of our qualitative and quantitative surveys showed significant differences between resident and staff perceptions of the faculty triage role. Although both groups similarly valued many features, residents expressed a clear preference for more bread‐and‐butter admissions, whereas the staff prioritized selecting the most complex, challenging, and rare cases from among the day's admissions to give to the residents. (Residents were also very interested in rare cases, suggesting that they saw benefit to admitting patients with a variety of degrees of rarity and complexity.) Residents and faculty seemed to agree that the number of social admissions and outside transfers admitted to teaching services was not ideal.
These perceptions have substantial implications. If the current triage process is to continue, there may be benefit to designing a faculty development project focused on the triage process, which previously has been largely unexamined. Efforts to remove or limit time barriers that prevent perceived educational cases from being admitted to teaching services is also a worthy endeavor (eg, structuring the 2 teams to admit simultaneously so that teaching teams can admit patients back to back without exceeding capacity). In addition, residents may benefit from teaching hospitalists who concentrate educational efforts on the learning that can be extracted from the care of any patient, including admissions that initially seem mundane or purely social.[14] A concerted effort to divert more traditional medicine admissions and fewer unusual cases to the teaching service might improve resident perceptions of the triage process. Further, although the care of any patient can have education benefit, the fact that both groups perceived excessive social admissions in the teaching service suggests that a potential benefit of a nonteaching service (ie, absorbing the most mundane admissions) may not yet be fully realized.
Despite the perceived differences noted on the surveys, we found remarkably few differences between patients admitted to the teaching and nonteaching services. Although both groups rated complexity; outside transfers; being seen at the institution for the first time; and histories of transplantation, cancer, chronic or functional pain, and psychiatric disease as increasing the likelihood of admission to a teaching service, no differences were observed for these factors or their quantifiable surrogates. (Although the overall test for admission type achieved marginal statistical significance, none of the individual admission types were significantly different in post hoc analysis.) Residents, but not faculty, thought that older patients were over‐represented on the teaching service, but their assigned patients were significantly younger than those on the nonteaching service.
These findings have several possible explanations. First, although most hospitalists spend time on teaching and nonteaching services (and therefore are familiar with the patient composition of each), residents get very little exposure to the nonteaching services (until they are senior residents with a rotation on a consulting service). Their impression of inequity may be due to misunderstanding the patient composition of the nonteaching services. Second, the mere existence of a triage role may create false expectations about patient composition; that is, simply by knowing that every admission was chosen for its educational merit, residents may have disproportionate perceptions about those cases judged to have less educational value, even ifas our data suggestassignments to teaching versus nonteaching services are occurring fairly equitably.
Study Limitations
We acknowledge several limitations of our study. First, many factors that were reported as important in the qualitative survey did not lend themselves to objective abstraction from patient records. For example, providers did not specifically document when an admission is purely social, nor was there an objective way to identify difficult patients or families or admissions that were expected to take more time. We attempted to limit the analysis to objective patient metrics that were (1) not influenced by the teaching or nonteaching assignment itself (eg, we avoided discharge diagnoses, which might be entered differently by residents and staff hospitalists) and (2) easily available to triage hospitalists. For the latter reason, we used a prior appointment in the hematology or oncology clinic as a surrogate for cancer patients and a prior psychiatry visit as a surrogate for patients with a history of psychiatric disease. These are naturally inexact surrogates, but they reflect the information a busy hospitalist is likely to access when making patient assignment decisions.
Second, it may well be that assigning patients equitably according to a certain trait is not the same as assigning patients ideally for the educational needs of residents. The patients admitted to our medicine services (teaching and nonteaching) were generally older than 60 years, had complex diagnoses, and had substantial pain. Residents on the teaching services potentially would benefit from an intentionally unbalanced admission policy that shunted patients to the teaching services on the basis of features other than individual perceived educational merit. It must also be borne in mind that resident, and for that matter faculty, perceptions of ideal teaching cases are likely inexact correlates of educational best practices; the ideal role of the triage hospitalist is to admit to the teaching services those patients that will best advance the education of the learners, including a consideration of the goals and objectives of the rotation. Future studies correlating different triage practices to actual educational outcomes would be very helpful.
Third, the analysis could not reliably eliminate patients whose admissions did not represent genuine triage decisions (eg, those assigned to the hospitalist service after the teaching service had reached its capacity or immediately after they had received a complex case). Studying admission decisions prospectively could eliminate this variability, but it could introduce a Hawthorne effect, the negative effects of which likely would outweigh this benefit.
CONCLUSION
Triage hospitalists distributed patients fairly evenly between teaching and nonteaching services, but residents and faculty alike perceived that residents would benefit from more bread‐and‐butter cases. Hospitals considering the addition of a nonteaching service may want to incorporate a faculty development project focused on the triage process to ensure that these traditional medicine cases are assigned to resident services and to ensure that the great teaching case is not considered such because of complexity and acuity alone.
Acknowledgements
The authors thank Elizabeth Jones and Lois Bell for their assistance with survey collection and collation.
Disclosures: This study (institutional review board application #11‐3;002635) was deemed exempt by the Mayo Clinic institutional review board on May 16, 2011. The authors report no conflicts of interest.
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The advent of work‐hour restrictions and admission limits for teaching services has led many academic hospitals to implement hospitalist‐run staff (ie, nonteaching) services.[1] Although this practice is not new,[2] it is growing in popularity[3] and has been endorsed as a way to protect resident teaching and prevent excessive workload.[4] One potential benefit is the assignment of more educational cases to teaching services, whereas the nonteaching services receive more patients whose care is presumably relatively mundane or routine.[5]
Despite the rapid growth of this system of educational triage,[6] little is known about the factors considered when teaching versus nonteaching decisions are made. Studies of clinical outcomes for patients assigned to teaching versus nonteaching services have understandably used random assignment,[7, 8] whereas a study finding that patients with unhealthy substance use were more likely to be on teaching services than nonteaching services relied on patient assignment based on the identity of the patient's primary care provider or insurer.[9] In 2009, O'Connor et al. reported that implementation of nonteaching services at 2 hospitals had led to unequal distribution of patients in terms of demographics, diagnosis, and illness severity.[10] Triage decisions were made by either a nurse coordinator or a medical chief resident, and sicker patients (and occasionally good teaching cases) were preferentially placed on the teaching services, reportedly out of respect for the comfort level of the midlevel providers who staffed the nonteaching services.
Our institution has used a system of hospitalist educational triage since 1998. Over that time, residents have often expressed concerns about the assignment of patients to the teaching services, reporting in particular that they receive a disproportionate number of complex cases and outside transfers. In 2006, the hospitalist group attempted to address these concerns by collecting real‐time admission data, but the application of the data was limited by suspicion on both sides of a Hawthorne effect (data not published).
If trainee and hospitalist expectations for what constitutes a great teaching case differ substantially, that difference can have significant implications for resident and medical student teaching, self‐perceived roles, and satisfaction. More significantly, an understanding of what faculty perceive as ideal teaching cases would provide valuable information about the strengths and weaknesses of the teachingnonteaching model, which may prove useful to other academic institutions considering such a system. In this study, we endeavored to understand what residents and hospitalists consider an educational admission and to compare these expectations to the actual triage decisions of hospitalists.
METHODS
Mayo Clinic Hospital (Phoenix, Arizona) has used separate teaching and nonteaching services since opening in 1998. At our institution, like many others,[11] a hospitalist is assigned to take all calls for emergency department (ED) admissions, admissions from outpatient clinics, and transfer requests; this physician directs patients to the teaching or nonteaching service. At the time of our study, the 2 teaching services alternated days in which they admitted up to 7 patients, and the 5 nonteaching services admitted all other patients and provided medicine consultative services for the hospital. Teaching services consisted of 1 hospitalist, 2 senior residents, 2 or 3 first‐year residents, and sometimes 1 third‐ or fourth‐year medical student. Nonteaching services consisted of a hospitalist with intermittent assistance from a physician assistant or nurse practitioner.
Although there are no formal guidelines for the hospitalist triage role, hospitalists are encouraged to assign more educational cases to the teaching services and to allow the residents enough time to address the acute needs of the prior admission before receiving the next admission. Residents are not assigned any patients between 4:00 am and 7:00 am. The goals and objectives for the resident rotation on the medicine teaching service include a list of diagnoses with which residents are expected to become familiar during their residency; triage hospitalists have on‐line access to these goals and objectives.
To assess resident and hospitalist opinions about what types of patients should or should not be admitted to teaching services and to compare those characteristics with those of the patients actually admitted to teaching services, we began by administering a simple, open‐ended survey and asked both groups: (1) In an ideal world, what kinds of patients should be admitted to the internal medicine teaching services at Mayo Clinic Hospital? (2) In the real world, what kinds of patients are admitted to the internal medicine teaching services at Mayo Clinic Hospital?
Ample space was provided for free‐text entries. Residents were additionally asked their postgraduate year level. The survey was administered in April 2011, at which time all residents would have rotated on the medicine teaching services several times. Survey responses were anonymous and were compiled and retyped by someone unfamiliar with the subjects' handwriting.
Two authors (D.L.R. and H.R.L.) reviewed the results of the first survey and used conventional content analysis to group responses into categories and tally them.[12] Responses from hospitalists and residents were used to determine the content for a second, quantitative survey that asked respondents to rate specific possible factors that affected triage decisions on a Likert scale from 1 (Argues against teaching admission) to 5 (Argues for teaching admission). The second survey, administered to the same residents and hospitalists in May 2011, asked: (1) In an ideal world, how do these factors contribute to the decision about which patients should be admitted to the internal medicine teaching services at Mayo Clinic Hospital? (2) In the real world, how do these factors contribute to the decision about which patients are admitted to the internal medicine teaching services at Mayo Clinic Hospital?
Assuming a 3:1 ratio of nonteaching to teaching admissions, we calculated that we would need to analyze 1028 admissions to detect a 10% difference in the proportion of a specific trait present in 50% of patients admitted to the nonteaching service, with the use of a 2‐sided test with 80% statistical power and a significance level of 0.05.
We collected data on patient assignment via retrospective chart review to avoid the possibility of a Hawthorne effect. We studied all admissions to the internal medicine services for a 3‐month period before the administration of the first survey (January 1, 2011 through March 31, 2011). The following patient data were collected: service assignment (teaching vs nonteaching), age, sex, source of admission (ED, direct from clinic, outside transfer, internal transfer from another hospital service), first visit to our institution, prior hematology or oncology visit at our institution (as a surrogate for cancer), prior psychiatry visit at our institution (as a surrogate for psychiatric disease), transplantation history, human immunodeficiency virus (HIV) or acquired immune deficiency syndrome (AIDS) history, chronic or functional pain mentioned in ED or admission note, need for translator, and benefactor status. Additionally, an online calculator was used to determine the Charlson Comorbidity Index score for each patient.[13] We collected actual patient data corresponding to factors reported by survey respondents whenever possible and practical, but not every factor reported by survey respondents was amenable to rigorous analysis; for example, no unbiased method could be devised to rigorously categorize patients whose admissions are likely to take more time or difficult patients and families.
Responses to the second (quantitative) survey and patient data were compared using the Pearson [2] and Fisher exact test for categorical variables and the Student t test or Wilcoxon rank sum test for continuous variables. Categorical variables that achieved statistical significance for overall difference were analyzed on a post hoc basis using the Bonferroni method to control for the overall type I error rate. We also examined the differences between actual and ideal triage decisions using the Wilcoxon signed rank test. Data were analyzed using SAS 9.3 (SAS Institute, Inc., Cary, NC). Statistical significance was defined as P<0.05.
The project was deemed exempt by the Mayo Clinic institutional review board.
RESULTS
We surveyed all categorical internal medicine residents (n=30, 10 each from postgraduate year [PGY]‐1, PGY‐2, and PGY‐3) and hospitalists except the authors (n=21; average years since completing training=13.3; range, 129 years). For both surveys, responses were collected from 29 (96.7%) residents. The nonresponding resident was a PGY‐2. The response rate for hospitalists was 20/21 (95.2%) for the first survey and 16/21 (76.2%) for the second survey.
First Survey
Table 1 compares the most frequent resident and faculty responses to the initial, open‐ended survey about what types of patients should or should not be admitted to teaching services. Residents most commonly indicated that ideal patients were traditional medicine cases (ie, bread‐and‐butter admissions, with 13 residents using that exact phrase), and others supplied specific examples of such cases, including chronic obstructive pulmonary disease, pneumonia, diabetic ketoacidosis, congestive heart failure, chest pain, and gastrointestinal tract bleeding. Only 1 faculty member mentioned bread‐and‐butter admissions, although several listed examples like chest pain and pneumonia. A smaller number of residents pointed to the importance of rare cases, whereas faculty considered rare cases to be ideal for teaching services, followed by variety of pathology and complexity.
| Residents (n=29) | Faculty (n=20) | |||
|---|---|---|---|---|
| Question | Characteristic | No. (%) | Characteristic | No. (%) |
| ||||
| In an ideal world, what kinds of patients should be admitted to the internal medicine teaching services at Mayo Clinic Hospital? | Bread‐and butter admissionsb | 14 (44.8) | Rare cases | 9 (45.0) |
| Rare cases | 9 (31.0) | Variety of pathology | 7 (35.0) | |
| No social admissions | 7 (24.1) | Complex cases | 5 (25.0) | |
| New diagnoses instead of chronic management | 4 (13.8) | Variety of complexity | 5 (25.0) | |
| Variety of complexity | 4 (13.8) | Patients with HIV/AIDS | 3 (15.0) | |
| Diagnostic dilemmas | 3 (15.0) | |||
| New diagnoses instead of chronic management | 3 (15.0) | |||
| In the real world, what kinds of patients are admitted to the internal medicine teaching services at Mayo Clinic Hospital? | Patients with cancer | 11 (37.9) | Complex patients | 6 (30.0) |
| Complex patients | 10 (34.5) | Difficult patients | 5 (25.0) | |
| Social admissions | 9 (31.0) | Patients whose admissions are expected to be time consuming | 5 (25.0) | |
| Acutely ill patients | 6 (20.7) | Rare cases | 3 (15.0) | |
| Variety of pathology | 6 (20.7) | Cases determined by the time of day | 3 (15.0) | |
With regard to actual admissions, residents and faculty agreed that they often were complex, but residents were more likely to suggest high rates of patients with cancer (11 residents vs 2 hospitalists) and social admissions (9 residents vs 2 hospitalists). Four residents each believed that they preferentially received elderly patients, outside transfers, and patients with functional pain, and 2 perceived a disproportionate number of patients making their first visit to Mayo Clinic. One hospitalist believed that residents were more likely to receive non‐English speakers.
Second Survey
Table 2 compares the resident and faculty responses to the second, numerical survey regarding ideal admissions to the teaching services. In contrast to the first survey, residents prioritized rare cases as the feature they most associated with ideal teaching admissions. They also placed a premium on variety of pathology, patients with unique findings, and patients likely to be written up or presented. The patients they believed were least appropriate for a teaching service were social admissions or those with placement issues, patients with functional or chronic pain, and benefactors or public figures.
| Factor | Resident, n=29 | Faculty, n=16 | P Value |
|---|---|---|---|
| |||
| Rare diseases | 0.22 | ||
| Mean (SD) | 4.8 (0.5) | 4.9 (0.3) | |
| Median | 5 | 5 | |
| Variety of pathology | 0.22 | ||
| Mean (SD) | 4.7 (0.5) | 4.5 (0.5) | |
| Median | 5 | 5 | |
| Cases that might be written up or presented | 0.35 | ||
| Mean (SD) | 4.7 (0.5) | 4.8 (0.6) | |
| Median | 5 | 5 | |
| Bread‐and‐butter cases | 0.001 | ||
| Mean (SD) | 4.6 (0.7) | 3.7 (0.9) | |
| Median | 5 | 4 | |
| Unique physical findings | 0.67 | ||
| Mean (SD) | 4.6 (0.6) | 4.7 (0.5) | |
| Median | 5 | 5 | |
| Variety of complexity | 0.21 | ||
| Mean (SD) | 4.3 (0.7) | 4.1 (0.6) | |
| Median | 4 | 4 | |
| Variety of acuity | 0.40 | ||
| Mean (SD) | 4.2 (0.7) | 4.1 (0.7) | |
| Median | 4 | 4 | |
| Spectrum of ages | 0.046 | ||
| Mean (SD) | 4.1 (0.8) | 3.6 (0.8) | |
| Median | 4 | 3 | |
| HIV or AIDS | 0.39 | ||
| Mean (SD) | 4.1 (0.9) | 4.4 (0.5) | |
| Median | 4 | 4 | |
| Acutely ill or unstable | 0.54 | ||
| Mean (SD) | 4.0 (0.9) | 3.9 (0.6) | |
| Median | 4 | 4 | |
| Complex patients | 0.94 | ||
| Mean (SD) | 4.0 (0.8) | 3.9 (0.6) | |
| Median | 4 | 4 | |
| Patients at end of life | 0.16 | ||
| Mean (SD) | 3.5 (0.8) | 3.1 (0.6) | |
| Median | 3 | 3 | |
| First‐time Mayo patients | 0.45 | ||
| Mean (SD) | 3.5 (0.7) | 3.3 (0.5) | |
| Median | 3 | 3 | |
| Younger patients | 0.50 | ||
| Mean (SD) | 3.5 (0.9) | 3.3 (0.6) | |
| Median | 3 | 3 | |
| Stable patients | 0.21 | ||
| Mean (SD) | 3.3 (0.8) | 3.1 (0.3) | |
| Median | 3 | 3 | |
| Patients with cancer | 0.67 | ||
| Mean (SD) | 3.3 (0.8) | 3.1 (0.4) | |
| Median | 3 | 3 | |
| Straightforward patients | 0.64 | ||
| Mean (SD) | 3.2 (0.8) | 3.1 (0.8) | |
| Median | 3 | 3 | |
| Older patients | 0.73 | ||
| Mean (SD) | 3.2 (0.7) | 3.1 (0.3) | |
| Median | 3 | 3 | |
| Patients with a history of transplantation | 0.67 | ||
| Mean (SD) | 3.1 (1.1) | 3.3 (0.6) | |
| Median | 3 | 3 | |
| Time of day of admission | 0.71 | ||
| Mean (SD) | 3.1 (1.0) | 3.1 (0.5) | |
| Median | 3 | 3 | |
| Patients with a history of psychiatric illness | 0.59 | ||
| Mean (SD) | 3.1 (1.0) | 3.1 (0.6) | |
| Median | 3 | 3 | |
| Patients who require a translator | 0.49 | ||
| Mean (SD) | 3.0 (0.9) | 3.1 (0.5) | |
| Median | 3 | 3 | |
| Patients whose admissions are expected to take more time | 0.13 | ||
| Mean (SD) | 2.9 (0.8) | 3.2 (0.6) | |
| Median | 3 | 3 | |
| Difficult patients and families | 0.55 | ||
| Mean (SD) | 2.8 (1.0) | 2.6 (0.8) | |
| Median | 3 | 3 | |
| Transfers from other hospitals | 0.11 | ||
| Mean (SD) | 2.7 (1.1) | 3.1 (0.3) | |
| Median | 3 | 3 | |
| Benefactors and public figures | 0.49 | ||
| Mean (SD) | 2.7 (1.0) | 2.5 (0.7) | |
| Median | 3 | 3 | |
| Patients with functional or chronic pain | 0.87 | ||
| Mean (SD) | 2.4 (1.1) | 2.4 (1.0) | |
| Median | 2 | 3 | |
| Social admissions or placement issues | 0.99 | ||
| Mean (SD) | 2.1 (1.1) | 2.0 (1.0) | |
| Median | 2 | 2 | |
Faculty prioritized many of the same features for ideal teaching cases as residents; 4 of their 5 highest‐scoring factors were the same (rare diseases, patients whose cases might be written up or presented, patients with unique physical findings, and variety of pathology). They also agreed on the least ideal features (social admissions or placement issues, patients with functional or chronic pain, and benefactors or public figures). The only significant differences between resident and faculty ratings for ideal teaching cases were for bread‐and‐butter cases and a spectrum of ages.
Discordance between resident and faculty survey responses on actual admission decisions (Table 3) was starker; residents rated several features significantly higher than faculty as features contributing to triage decisions including older patients; patients with functional or chronic pain, social admissions, or placement issues; patients with cancer; transfers from other hospitals; and difficult patients and families. Relative to residents, faculty reported that patients with HIV or AIDS, and patients whose cases were likely to be written up or presented, were more likely to be admitted to teaching services.
| Factor | Resident, n=29 | Faculty, n=16 | P Value |
|---|---|---|---|
| |||
| Rare diseases | 0.14 | ||
| Mean (SD) | 4.4 (0.6) | 4.7 (0.6) | |
| Median | 4 | 5 | |
| Complex patients | 0.83 | ||
| Mean (SD) | 4.3 (0.6) | 4.3 (0.6) | |
| Median | 4 | 4 | |
| Acutely ill or unstable | 0.18 | ||
| Mean (SD) | 4.3 (0.7) | 3.9 (0.9) | |
| Median | 4 | 4 | |
| Unique physical findings | 0.18 | ||
| Mean (SD) | 4.1 (0.8) | 4.5 (0.6) | |
| Median | 4 | 5 | |
| Transfers from other hospitals | 0.003 | ||
| Mean (SD) | 4.1 (1.0) | 3.5 (0.5) | |
| Median | 4 | 3 | |
| Cases that might be written up or presented | 0.03 | ||
| Mean (SD) | 4.1 (0.7) | 4.6 (0.6) | |
| Median | 4 | 5 | |
| Older patients | <0.001 | ||
| Mean (SD) | 3.9 (0.8) | 3.0 (0.7) | |
| Median | 4 | 3 | |
| Time of day of admission | 0.50 | ||
| Mean (SD) | 3.9 (1.1) | 3.7 (0.9) | |
| Median | 4 | 4 | |
| Patients with cancer | 0.01 | ||
| Mean (SD) | 3.9 (0.9) | 3.3 (0.5) | |
| Median | 4 | 3 | |
| Variety of pathology | 0.21 | ||
| Mean (SD) | 3.9 (0.8) | 4.2 (0.7) | |
| Median | 4 | 4 | |
| Patients whose admissions are expected to take more time | 0.13 | ||
| Mean (SD) | 3.9 (1.0) | 3.4 (0.9) | |
| Median | 4 | 3 | |
| HIV or AIDS | 0.008 | ||
| Mean (SD) | 3.8 (0.9) | 4.5 (0.5) | |
| Median | 4 | 4.5 | |
| Variety of complexity | 0.31 | ||
| Mean (SD) | 3.7 (0.9) | 3.9 (0.6) | |
| Median | 3.5 | 4 | |
| Bread‐and‐butter cases | 0.07 | ||
| Mean (SD) | 3.6 (1.0) | 2.9 (1.2) | |
| Median | 3 | 3 | |
| First‐time Mayo patients | 0.82 | ||
| Mean (SD) | 3.6 (0.9) | 3.5 (0.7) | |
| Median | 3 | 3 | |
| Patients with functional or chronic pain | 0.004 | ||
| Mean (SD) | 3.6 (1.0) | 2.8 (0.7) | |
| Median | 4 | 3 | |
| Social admissions or placement issues | 0.03 | ||
| Mean (SD) | 3.5 (1.2) | 2.7 (0.9) | |
| Median | 4 | 3 | |
| Variety of acuity | 0.25 | ||
| Mean (SD) | 3.5 (0.8) | 3.7 (0.6) | |
| Median | 3 | 4 | |
| Difficult patients and families | 0.03 | ||
| Mean (SD) | 3.4 (0.9) | 2.8 (0.7) | |
| Median | 3 | 3 | |
| Patients at end of life | 0.10 | ||
| Mean (SD) | 3.4 (0.8) | 3.0 (0.5) | |
| Median | 3 | 3 | |
| Spectrum of ages | 0.80 | ||
| Mean (SD) | 3.3 (0.7) | 3.3 (0.6) | |
| Median | 3 | 3 | |
| Patients with a history of psychiatric illness | 0.81 | ||
| Mean (SD) | 3.3 (0.9) | 3.1 (0.6) | |
| Median | 3 | 3 | |
| Patients with a history of transplantation | 0.25 | ||
| Mean (SD) | 3.2 (0.9) | 3.5 (0.5) | |
| Median | 3 | 3 | |
| Patients who require a translator | 0.60 | ||
| Mean (SD) | 3.2 (0.7) | 3.2 (0.6) | |
| Median | 3 | 3 | |
| Younger patients | 0.42 | ||
| Mean (SD) | 3.0 (0.9) | 3.1 (0.4) | |
| Median | 3 | 3 | |
| Benefactors and public figures | 0.09 | ||
| Mean (SD) | 2.9 (1.0) | 2.3 (0.7) | |
| Median | 3 | 2 | |
| Straightforward patients | 0.18 | ||
| Mean (SD) | 2.8 (1.0) | 2.4 (1.0) | |
| Median | 2.5 | 2 | |
| Stable patients | 0.53 | ||
| Mean (SD) | 2.7 (1.0) | 2.8 (0.7) | |
| Median | 3 | 3 | |
Comparing resident survey ratings for ideal versus actual triage decisions gave some insight into the features that they thought were inappropriately emphasized or ignored when triage decisions were made. Differences in resident scores for ideal versus actual admissions were significantly different for 16 of 28 items (data available upon request), suggesting a degree of perceived discordance. The largest positive differences (ie, features they valued in teaching admissions but thought were less represented in actual admissions) were for bread‐and‐butter admissions, variety of pathology, a spectrum of ages, and variety of acuity. The largest negative differences (ie, features they thought were well represented in actual admissions but were less valuable) were for social admissions or placement issues, transfers from other hospitals, patients with functional or chronic pain, and patients whose admissions were expected to take more time.
In terms of ideal versus actual triage decisions, faculty reported less discordance than residents; ideal and actual triage behavior differed significantly only for 4 of 28 items (data available upon request). They did agree with residents about the relative lack of bread‐and‐butter admissions and the over‐representation of social admissions or placement issues and transfers from other hospitals. They additionally noted a lack of straightforward cases.
We reviewed records of the 1426 patients admitted to the internal medicine services during the study period. Of these, 359 (25.2%) were assigned to the teaching services. Patient characteristics are summarized in Table 4.
| Characteristic | Teaching Service, n=359 | Nonteaching Service, n=1,067 | P Value |
|---|---|---|---|
| |||
| Age, y, mean (SD) | 66.7 (16.5) | 69.3 (15.7) | 0.008 |
| Admission type, No. (%) | 0.049 | ||
| Admission from the emergency department | 315 (87.7) | 915 (85.8) | 0.34 |
| Direct admission from Mayo outpatient clinic | 27 (7.5) | 114 (10.7) | 0.08 |
| Transfer from another institution | 16 (4.5) | 27 (2.5) | 0.06 |
| Internal transfer from a different hospital service | 1 (0.3) | 11 (1.0) | 0.31 |
| First‐time Mayo patient, No. (%) | 61 (17.0) | 175 (16.4) | 0.79 |
| Prior hematology or oncology visit, No. (%) | 86 (24.0) | 235 (22.0) | 0.45 |
| History of transplantation, No. (%) | 20 (5.6) | 52 (4.9) | 0.60 |
| Prior psychiatry visit, No. (%) | 53 (14.8) | 122 (11.4) | 0.10 |
| History of chronic or functional pain, No. (%) | 122 (34.0) | 330 (30.9) | 0.28 |
| Required translator, No. (%) | 5 (1.4) | 14 (1.3) | 0.91 |
| Benefactor, No. (%) | 5 (1.4) | 24 (2.2) | 0.32 |
| Charlson comorbidity score, mean (SD) | 2.7 (2.5) | 2.6 (2.5) | 0.49 |
DISCUSSION
The results of our qualitative and quantitative surveys showed significant differences between resident and staff perceptions of the faculty triage role. Although both groups similarly valued many features, residents expressed a clear preference for more bread‐and‐butter admissions, whereas the staff prioritized selecting the most complex, challenging, and rare cases from among the day's admissions to give to the residents. (Residents were also very interested in rare cases, suggesting that they saw benefit to admitting patients with a variety of degrees of rarity and complexity.) Residents and faculty seemed to agree that the number of social admissions and outside transfers admitted to teaching services was not ideal.
These perceptions have substantial implications. If the current triage process is to continue, there may be benefit to designing a faculty development project focused on the triage process, which previously has been largely unexamined. Efforts to remove or limit time barriers that prevent perceived educational cases from being admitted to teaching services is also a worthy endeavor (eg, structuring the 2 teams to admit simultaneously so that teaching teams can admit patients back to back without exceeding capacity). In addition, residents may benefit from teaching hospitalists who concentrate educational efforts on the learning that can be extracted from the care of any patient, including admissions that initially seem mundane or purely social.[14] A concerted effort to divert more traditional medicine admissions and fewer unusual cases to the teaching service might improve resident perceptions of the triage process. Further, although the care of any patient can have education benefit, the fact that both groups perceived excessive social admissions in the teaching service suggests that a potential benefit of a nonteaching service (ie, absorbing the most mundane admissions) may not yet be fully realized.
Despite the perceived differences noted on the surveys, we found remarkably few differences between patients admitted to the teaching and nonteaching services. Although both groups rated complexity; outside transfers; being seen at the institution for the first time; and histories of transplantation, cancer, chronic or functional pain, and psychiatric disease as increasing the likelihood of admission to a teaching service, no differences were observed for these factors or their quantifiable surrogates. (Although the overall test for admission type achieved marginal statistical significance, none of the individual admission types were significantly different in post hoc analysis.) Residents, but not faculty, thought that older patients were over‐represented on the teaching service, but their assigned patients were significantly younger than those on the nonteaching service.
These findings have several possible explanations. First, although most hospitalists spend time on teaching and nonteaching services (and therefore are familiar with the patient composition of each), residents get very little exposure to the nonteaching services (until they are senior residents with a rotation on a consulting service). Their impression of inequity may be due to misunderstanding the patient composition of the nonteaching services. Second, the mere existence of a triage role may create false expectations about patient composition; that is, simply by knowing that every admission was chosen for its educational merit, residents may have disproportionate perceptions about those cases judged to have less educational value, even ifas our data suggestassignments to teaching versus nonteaching services are occurring fairly equitably.
Study Limitations
We acknowledge several limitations of our study. First, many factors that were reported as important in the qualitative survey did not lend themselves to objective abstraction from patient records. For example, providers did not specifically document when an admission is purely social, nor was there an objective way to identify difficult patients or families or admissions that were expected to take more time. We attempted to limit the analysis to objective patient metrics that were (1) not influenced by the teaching or nonteaching assignment itself (eg, we avoided discharge diagnoses, which might be entered differently by residents and staff hospitalists) and (2) easily available to triage hospitalists. For the latter reason, we used a prior appointment in the hematology or oncology clinic as a surrogate for cancer patients and a prior psychiatry visit as a surrogate for patients with a history of psychiatric disease. These are naturally inexact surrogates, but they reflect the information a busy hospitalist is likely to access when making patient assignment decisions.
Second, it may well be that assigning patients equitably according to a certain trait is not the same as assigning patients ideally for the educational needs of residents. The patients admitted to our medicine services (teaching and nonteaching) were generally older than 60 years, had complex diagnoses, and had substantial pain. Residents on the teaching services potentially would benefit from an intentionally unbalanced admission policy that shunted patients to the teaching services on the basis of features other than individual perceived educational merit. It must also be borne in mind that resident, and for that matter faculty, perceptions of ideal teaching cases are likely inexact correlates of educational best practices; the ideal role of the triage hospitalist is to admit to the teaching services those patients that will best advance the education of the learners, including a consideration of the goals and objectives of the rotation. Future studies correlating different triage practices to actual educational outcomes would be very helpful.
Third, the analysis could not reliably eliminate patients whose admissions did not represent genuine triage decisions (eg, those assigned to the hospitalist service after the teaching service had reached its capacity or immediately after they had received a complex case). Studying admission decisions prospectively could eliminate this variability, but it could introduce a Hawthorne effect, the negative effects of which likely would outweigh this benefit.
CONCLUSION
Triage hospitalists distributed patients fairly evenly between teaching and nonteaching services, but residents and faculty alike perceived that residents would benefit from more bread‐and‐butter cases. Hospitals considering the addition of a nonteaching service may want to incorporate a faculty development project focused on the triage process to ensure that these traditional medicine cases are assigned to resident services and to ensure that the great teaching case is not considered such because of complexity and acuity alone.
Acknowledgements
The authors thank Elizabeth Jones and Lois Bell for their assistance with survey collection and collation.
Disclosures: This study (institutional review board application #11‐3;002635) was deemed exempt by the Mayo Clinic institutional review board on May 16, 2011. The authors report no conflicts of interest.
The advent of work‐hour restrictions and admission limits for teaching services has led many academic hospitals to implement hospitalist‐run staff (ie, nonteaching) services.[1] Although this practice is not new,[2] it is growing in popularity[3] and has been endorsed as a way to protect resident teaching and prevent excessive workload.[4] One potential benefit is the assignment of more educational cases to teaching services, whereas the nonteaching services receive more patients whose care is presumably relatively mundane or routine.[5]
Despite the rapid growth of this system of educational triage,[6] little is known about the factors considered when teaching versus nonteaching decisions are made. Studies of clinical outcomes for patients assigned to teaching versus nonteaching services have understandably used random assignment,[7, 8] whereas a study finding that patients with unhealthy substance use were more likely to be on teaching services than nonteaching services relied on patient assignment based on the identity of the patient's primary care provider or insurer.[9] In 2009, O'Connor et al. reported that implementation of nonteaching services at 2 hospitals had led to unequal distribution of patients in terms of demographics, diagnosis, and illness severity.[10] Triage decisions were made by either a nurse coordinator or a medical chief resident, and sicker patients (and occasionally good teaching cases) were preferentially placed on the teaching services, reportedly out of respect for the comfort level of the midlevel providers who staffed the nonteaching services.
Our institution has used a system of hospitalist educational triage since 1998. Over that time, residents have often expressed concerns about the assignment of patients to the teaching services, reporting in particular that they receive a disproportionate number of complex cases and outside transfers. In 2006, the hospitalist group attempted to address these concerns by collecting real‐time admission data, but the application of the data was limited by suspicion on both sides of a Hawthorne effect (data not published).
If trainee and hospitalist expectations for what constitutes a great teaching case differ substantially, that difference can have significant implications for resident and medical student teaching, self‐perceived roles, and satisfaction. More significantly, an understanding of what faculty perceive as ideal teaching cases would provide valuable information about the strengths and weaknesses of the teachingnonteaching model, which may prove useful to other academic institutions considering such a system. In this study, we endeavored to understand what residents and hospitalists consider an educational admission and to compare these expectations to the actual triage decisions of hospitalists.
METHODS
Mayo Clinic Hospital (Phoenix, Arizona) has used separate teaching and nonteaching services since opening in 1998. At our institution, like many others,[11] a hospitalist is assigned to take all calls for emergency department (ED) admissions, admissions from outpatient clinics, and transfer requests; this physician directs patients to the teaching or nonteaching service. At the time of our study, the 2 teaching services alternated days in which they admitted up to 7 patients, and the 5 nonteaching services admitted all other patients and provided medicine consultative services for the hospital. Teaching services consisted of 1 hospitalist, 2 senior residents, 2 or 3 first‐year residents, and sometimes 1 third‐ or fourth‐year medical student. Nonteaching services consisted of a hospitalist with intermittent assistance from a physician assistant or nurse practitioner.
Although there are no formal guidelines for the hospitalist triage role, hospitalists are encouraged to assign more educational cases to the teaching services and to allow the residents enough time to address the acute needs of the prior admission before receiving the next admission. Residents are not assigned any patients between 4:00 am and 7:00 am. The goals and objectives for the resident rotation on the medicine teaching service include a list of diagnoses with which residents are expected to become familiar during their residency; triage hospitalists have on‐line access to these goals and objectives.
To assess resident and hospitalist opinions about what types of patients should or should not be admitted to teaching services and to compare those characteristics with those of the patients actually admitted to teaching services, we began by administering a simple, open‐ended survey and asked both groups: (1) In an ideal world, what kinds of patients should be admitted to the internal medicine teaching services at Mayo Clinic Hospital? (2) In the real world, what kinds of patients are admitted to the internal medicine teaching services at Mayo Clinic Hospital?
Ample space was provided for free‐text entries. Residents were additionally asked their postgraduate year level. The survey was administered in April 2011, at which time all residents would have rotated on the medicine teaching services several times. Survey responses were anonymous and were compiled and retyped by someone unfamiliar with the subjects' handwriting.
Two authors (D.L.R. and H.R.L.) reviewed the results of the first survey and used conventional content analysis to group responses into categories and tally them.[12] Responses from hospitalists and residents were used to determine the content for a second, quantitative survey that asked respondents to rate specific possible factors that affected triage decisions on a Likert scale from 1 (Argues against teaching admission) to 5 (Argues for teaching admission). The second survey, administered to the same residents and hospitalists in May 2011, asked: (1) In an ideal world, how do these factors contribute to the decision about which patients should be admitted to the internal medicine teaching services at Mayo Clinic Hospital? (2) In the real world, how do these factors contribute to the decision about which patients are admitted to the internal medicine teaching services at Mayo Clinic Hospital?
Assuming a 3:1 ratio of nonteaching to teaching admissions, we calculated that we would need to analyze 1028 admissions to detect a 10% difference in the proportion of a specific trait present in 50% of patients admitted to the nonteaching service, with the use of a 2‐sided test with 80% statistical power and a significance level of 0.05.
We collected data on patient assignment via retrospective chart review to avoid the possibility of a Hawthorne effect. We studied all admissions to the internal medicine services for a 3‐month period before the administration of the first survey (January 1, 2011 through March 31, 2011). The following patient data were collected: service assignment (teaching vs nonteaching), age, sex, source of admission (ED, direct from clinic, outside transfer, internal transfer from another hospital service), first visit to our institution, prior hematology or oncology visit at our institution (as a surrogate for cancer), prior psychiatry visit at our institution (as a surrogate for psychiatric disease), transplantation history, human immunodeficiency virus (HIV) or acquired immune deficiency syndrome (AIDS) history, chronic or functional pain mentioned in ED or admission note, need for translator, and benefactor status. Additionally, an online calculator was used to determine the Charlson Comorbidity Index score for each patient.[13] We collected actual patient data corresponding to factors reported by survey respondents whenever possible and practical, but not every factor reported by survey respondents was amenable to rigorous analysis; for example, no unbiased method could be devised to rigorously categorize patients whose admissions are likely to take more time or difficult patients and families.
Responses to the second (quantitative) survey and patient data were compared using the Pearson [2] and Fisher exact test for categorical variables and the Student t test or Wilcoxon rank sum test for continuous variables. Categorical variables that achieved statistical significance for overall difference were analyzed on a post hoc basis using the Bonferroni method to control for the overall type I error rate. We also examined the differences between actual and ideal triage decisions using the Wilcoxon signed rank test. Data were analyzed using SAS 9.3 (SAS Institute, Inc., Cary, NC). Statistical significance was defined as P<0.05.
The project was deemed exempt by the Mayo Clinic institutional review board.
RESULTS
We surveyed all categorical internal medicine residents (n=30, 10 each from postgraduate year [PGY]‐1, PGY‐2, and PGY‐3) and hospitalists except the authors (n=21; average years since completing training=13.3; range, 129 years). For both surveys, responses were collected from 29 (96.7%) residents. The nonresponding resident was a PGY‐2. The response rate for hospitalists was 20/21 (95.2%) for the first survey and 16/21 (76.2%) for the second survey.
First Survey
Table 1 compares the most frequent resident and faculty responses to the initial, open‐ended survey about what types of patients should or should not be admitted to teaching services. Residents most commonly indicated that ideal patients were traditional medicine cases (ie, bread‐and‐butter admissions, with 13 residents using that exact phrase), and others supplied specific examples of such cases, including chronic obstructive pulmonary disease, pneumonia, diabetic ketoacidosis, congestive heart failure, chest pain, and gastrointestinal tract bleeding. Only 1 faculty member mentioned bread‐and‐butter admissions, although several listed examples like chest pain and pneumonia. A smaller number of residents pointed to the importance of rare cases, whereas faculty considered rare cases to be ideal for teaching services, followed by variety of pathology and complexity.
| Residents (n=29) | Faculty (n=20) | |||
|---|---|---|---|---|
| Question | Characteristic | No. (%) | Characteristic | No. (%) |
| ||||
| In an ideal world, what kinds of patients should be admitted to the internal medicine teaching services at Mayo Clinic Hospital? | Bread‐and butter admissionsb | 14 (44.8) | Rare cases | 9 (45.0) |
| Rare cases | 9 (31.0) | Variety of pathology | 7 (35.0) | |
| No social admissions | 7 (24.1) | Complex cases | 5 (25.0) | |
| New diagnoses instead of chronic management | 4 (13.8) | Variety of complexity | 5 (25.0) | |
| Variety of complexity | 4 (13.8) | Patients with HIV/AIDS | 3 (15.0) | |
| Diagnostic dilemmas | 3 (15.0) | |||
| New diagnoses instead of chronic management | 3 (15.0) | |||
| In the real world, what kinds of patients are admitted to the internal medicine teaching services at Mayo Clinic Hospital? | Patients with cancer | 11 (37.9) | Complex patients | 6 (30.0) |
| Complex patients | 10 (34.5) | Difficult patients | 5 (25.0) | |
| Social admissions | 9 (31.0) | Patients whose admissions are expected to be time consuming | 5 (25.0) | |
| Acutely ill patients | 6 (20.7) | Rare cases | 3 (15.0) | |
| Variety of pathology | 6 (20.7) | Cases determined by the time of day | 3 (15.0) | |
With regard to actual admissions, residents and faculty agreed that they often were complex, but residents were more likely to suggest high rates of patients with cancer (11 residents vs 2 hospitalists) and social admissions (9 residents vs 2 hospitalists). Four residents each believed that they preferentially received elderly patients, outside transfers, and patients with functional pain, and 2 perceived a disproportionate number of patients making their first visit to Mayo Clinic. One hospitalist believed that residents were more likely to receive non‐English speakers.
Second Survey
Table 2 compares the resident and faculty responses to the second, numerical survey regarding ideal admissions to the teaching services. In contrast to the first survey, residents prioritized rare cases as the feature they most associated with ideal teaching admissions. They also placed a premium on variety of pathology, patients with unique findings, and patients likely to be written up or presented. The patients they believed were least appropriate for a teaching service were social admissions or those with placement issues, patients with functional or chronic pain, and benefactors or public figures.
| Factor | Resident, n=29 | Faculty, n=16 | P Value |
|---|---|---|---|
| |||
| Rare diseases | 0.22 | ||
| Mean (SD) | 4.8 (0.5) | 4.9 (0.3) | |
| Median | 5 | 5 | |
| Variety of pathology | 0.22 | ||
| Mean (SD) | 4.7 (0.5) | 4.5 (0.5) | |
| Median | 5 | 5 | |
| Cases that might be written up or presented | 0.35 | ||
| Mean (SD) | 4.7 (0.5) | 4.8 (0.6) | |
| Median | 5 | 5 | |
| Bread‐and‐butter cases | 0.001 | ||
| Mean (SD) | 4.6 (0.7) | 3.7 (0.9) | |
| Median | 5 | 4 | |
| Unique physical findings | 0.67 | ||
| Mean (SD) | 4.6 (0.6) | 4.7 (0.5) | |
| Median | 5 | 5 | |
| Variety of complexity | 0.21 | ||
| Mean (SD) | 4.3 (0.7) | 4.1 (0.6) | |
| Median | 4 | 4 | |
| Variety of acuity | 0.40 | ||
| Mean (SD) | 4.2 (0.7) | 4.1 (0.7) | |
| Median | 4 | 4 | |
| Spectrum of ages | 0.046 | ||
| Mean (SD) | 4.1 (0.8) | 3.6 (0.8) | |
| Median | 4 | 3 | |
| HIV or AIDS | 0.39 | ||
| Mean (SD) | 4.1 (0.9) | 4.4 (0.5) | |
| Median | 4 | 4 | |
| Acutely ill or unstable | 0.54 | ||
| Mean (SD) | 4.0 (0.9) | 3.9 (0.6) | |
| Median | 4 | 4 | |
| Complex patients | 0.94 | ||
| Mean (SD) | 4.0 (0.8) | 3.9 (0.6) | |
| Median | 4 | 4 | |
| Patients at end of life | 0.16 | ||
| Mean (SD) | 3.5 (0.8) | 3.1 (0.6) | |
| Median | 3 | 3 | |
| First‐time Mayo patients | 0.45 | ||
| Mean (SD) | 3.5 (0.7) | 3.3 (0.5) | |
| Median | 3 | 3 | |
| Younger patients | 0.50 | ||
| Mean (SD) | 3.5 (0.9) | 3.3 (0.6) | |
| Median | 3 | 3 | |
| Stable patients | 0.21 | ||
| Mean (SD) | 3.3 (0.8) | 3.1 (0.3) | |
| Median | 3 | 3 | |
| Patients with cancer | 0.67 | ||
| Mean (SD) | 3.3 (0.8) | 3.1 (0.4) | |
| Median | 3 | 3 | |
| Straightforward patients | 0.64 | ||
| Mean (SD) | 3.2 (0.8) | 3.1 (0.8) | |
| Median | 3 | 3 | |
| Older patients | 0.73 | ||
| Mean (SD) | 3.2 (0.7) | 3.1 (0.3) | |
| Median | 3 | 3 | |
| Patients with a history of transplantation | 0.67 | ||
| Mean (SD) | 3.1 (1.1) | 3.3 (0.6) | |
| Median | 3 | 3 | |
| Time of day of admission | 0.71 | ||
| Mean (SD) | 3.1 (1.0) | 3.1 (0.5) | |
| Median | 3 | 3 | |
| Patients with a history of psychiatric illness | 0.59 | ||
| Mean (SD) | 3.1 (1.0) | 3.1 (0.6) | |
| Median | 3 | 3 | |
| Patients who require a translator | 0.49 | ||
| Mean (SD) | 3.0 (0.9) | 3.1 (0.5) | |
| Median | 3 | 3 | |
| Patients whose admissions are expected to take more time | 0.13 | ||
| Mean (SD) | 2.9 (0.8) | 3.2 (0.6) | |
| Median | 3 | 3 | |
| Difficult patients and families | 0.55 | ||
| Mean (SD) | 2.8 (1.0) | 2.6 (0.8) | |
| Median | 3 | 3 | |
| Transfers from other hospitals | 0.11 | ||
| Mean (SD) | 2.7 (1.1) | 3.1 (0.3) | |
| Median | 3 | 3 | |
| Benefactors and public figures | 0.49 | ||
| Mean (SD) | 2.7 (1.0) | 2.5 (0.7) | |
| Median | 3 | 3 | |
| Patients with functional or chronic pain | 0.87 | ||
| Mean (SD) | 2.4 (1.1) | 2.4 (1.0) | |
| Median | 2 | 3 | |
| Social admissions or placement issues | 0.99 | ||
| Mean (SD) | 2.1 (1.1) | 2.0 (1.0) | |
| Median | 2 | 2 | |
Faculty prioritized many of the same features for ideal teaching cases as residents; 4 of their 5 highest‐scoring factors were the same (rare diseases, patients whose cases might be written up or presented, patients with unique physical findings, and variety of pathology). They also agreed on the least ideal features (social admissions or placement issues, patients with functional or chronic pain, and benefactors or public figures). The only significant differences between resident and faculty ratings for ideal teaching cases were for bread‐and‐butter cases and a spectrum of ages.
Discordance between resident and faculty survey responses on actual admission decisions (Table 3) was starker; residents rated several features significantly higher than faculty as features contributing to triage decisions including older patients; patients with functional or chronic pain, social admissions, or placement issues; patients with cancer; transfers from other hospitals; and difficult patients and families. Relative to residents, faculty reported that patients with HIV or AIDS, and patients whose cases were likely to be written up or presented, were more likely to be admitted to teaching services.
| Factor | Resident, n=29 | Faculty, n=16 | P Value |
|---|---|---|---|
| |||
| Rare diseases | 0.14 | ||
| Mean (SD) | 4.4 (0.6) | 4.7 (0.6) | |
| Median | 4 | 5 | |
| Complex patients | 0.83 | ||
| Mean (SD) | 4.3 (0.6) | 4.3 (0.6) | |
| Median | 4 | 4 | |
| Acutely ill or unstable | 0.18 | ||
| Mean (SD) | 4.3 (0.7) | 3.9 (0.9) | |
| Median | 4 | 4 | |
| Unique physical findings | 0.18 | ||
| Mean (SD) | 4.1 (0.8) | 4.5 (0.6) | |
| Median | 4 | 5 | |
| Transfers from other hospitals | 0.003 | ||
| Mean (SD) | 4.1 (1.0) | 3.5 (0.5) | |
| Median | 4 | 3 | |
| Cases that might be written up or presented | 0.03 | ||
| Mean (SD) | 4.1 (0.7) | 4.6 (0.6) | |
| Median | 4 | 5 | |
| Older patients | <0.001 | ||
| Mean (SD) | 3.9 (0.8) | 3.0 (0.7) | |
| Median | 4 | 3 | |
| Time of day of admission | 0.50 | ||
| Mean (SD) | 3.9 (1.1) | 3.7 (0.9) | |
| Median | 4 | 4 | |
| Patients with cancer | 0.01 | ||
| Mean (SD) | 3.9 (0.9) | 3.3 (0.5) | |
| Median | 4 | 3 | |
| Variety of pathology | 0.21 | ||
| Mean (SD) | 3.9 (0.8) | 4.2 (0.7) | |
| Median | 4 | 4 | |
| Patients whose admissions are expected to take more time | 0.13 | ||
| Mean (SD) | 3.9 (1.0) | 3.4 (0.9) | |
| Median | 4 | 3 | |
| HIV or AIDS | 0.008 | ||
| Mean (SD) | 3.8 (0.9) | 4.5 (0.5) | |
| Median | 4 | 4.5 | |
| Variety of complexity | 0.31 | ||
| Mean (SD) | 3.7 (0.9) | 3.9 (0.6) | |
| Median | 3.5 | 4 | |
| Bread‐and‐butter cases | 0.07 | ||
| Mean (SD) | 3.6 (1.0) | 2.9 (1.2) | |
| Median | 3 | 3 | |
| First‐time Mayo patients | 0.82 | ||
| Mean (SD) | 3.6 (0.9) | 3.5 (0.7) | |
| Median | 3 | 3 | |
| Patients with functional or chronic pain | 0.004 | ||
| Mean (SD) | 3.6 (1.0) | 2.8 (0.7) | |
| Median | 4 | 3 | |
| Social admissions or placement issues | 0.03 | ||
| Mean (SD) | 3.5 (1.2) | 2.7 (0.9) | |
| Median | 4 | 3 | |
| Variety of acuity | 0.25 | ||
| Mean (SD) | 3.5 (0.8) | 3.7 (0.6) | |
| Median | 3 | 4 | |
| Difficult patients and families | 0.03 | ||
| Mean (SD) | 3.4 (0.9) | 2.8 (0.7) | |
| Median | 3 | 3 | |
| Patients at end of life | 0.10 | ||
| Mean (SD) | 3.4 (0.8) | 3.0 (0.5) | |
| Median | 3 | 3 | |
| Spectrum of ages | 0.80 | ||
| Mean (SD) | 3.3 (0.7) | 3.3 (0.6) | |
| Median | 3 | 3 | |
| Patients with a history of psychiatric illness | 0.81 | ||
| Mean (SD) | 3.3 (0.9) | 3.1 (0.6) | |
| Median | 3 | 3 | |
| Patients with a history of transplantation | 0.25 | ||
| Mean (SD) | 3.2 (0.9) | 3.5 (0.5) | |
| Median | 3 | 3 | |
| Patients who require a translator | 0.60 | ||
| Mean (SD) | 3.2 (0.7) | 3.2 (0.6) | |
| Median | 3 | 3 | |
| Younger patients | 0.42 | ||
| Mean (SD) | 3.0 (0.9) | 3.1 (0.4) | |
| Median | 3 | 3 | |
| Benefactors and public figures | 0.09 | ||
| Mean (SD) | 2.9 (1.0) | 2.3 (0.7) | |
| Median | 3 | 2 | |
| Straightforward patients | 0.18 | ||
| Mean (SD) | 2.8 (1.0) | 2.4 (1.0) | |
| Median | 2.5 | 2 | |
| Stable patients | 0.53 | ||
| Mean (SD) | 2.7 (1.0) | 2.8 (0.7) | |
| Median | 3 | 3 | |
Comparing resident survey ratings for ideal versus actual triage decisions gave some insight into the features that they thought were inappropriately emphasized or ignored when triage decisions were made. Differences in resident scores for ideal versus actual admissions were significantly different for 16 of 28 items (data available upon request), suggesting a degree of perceived discordance. The largest positive differences (ie, features they valued in teaching admissions but thought were less represented in actual admissions) were for bread‐and‐butter admissions, variety of pathology, a spectrum of ages, and variety of acuity. The largest negative differences (ie, features they thought were well represented in actual admissions but were less valuable) were for social admissions or placement issues, transfers from other hospitals, patients with functional or chronic pain, and patients whose admissions were expected to take more time.
In terms of ideal versus actual triage decisions, faculty reported less discordance than residents; ideal and actual triage behavior differed significantly only for 4 of 28 items (data available upon request). They did agree with residents about the relative lack of bread‐and‐butter admissions and the over‐representation of social admissions or placement issues and transfers from other hospitals. They additionally noted a lack of straightforward cases.
We reviewed records of the 1426 patients admitted to the internal medicine services during the study period. Of these, 359 (25.2%) were assigned to the teaching services. Patient characteristics are summarized in Table 4.
| Characteristic | Teaching Service, n=359 | Nonteaching Service, n=1,067 | P Value |
|---|---|---|---|
| |||
| Age, y, mean (SD) | 66.7 (16.5) | 69.3 (15.7) | 0.008 |
| Admission type, No. (%) | 0.049 | ||
| Admission from the emergency department | 315 (87.7) | 915 (85.8) | 0.34 |
| Direct admission from Mayo outpatient clinic | 27 (7.5) | 114 (10.7) | 0.08 |
| Transfer from another institution | 16 (4.5) | 27 (2.5) | 0.06 |
| Internal transfer from a different hospital service | 1 (0.3) | 11 (1.0) | 0.31 |
| First‐time Mayo patient, No. (%) | 61 (17.0) | 175 (16.4) | 0.79 |
| Prior hematology or oncology visit, No. (%) | 86 (24.0) | 235 (22.0) | 0.45 |
| History of transplantation, No. (%) | 20 (5.6) | 52 (4.9) | 0.60 |
| Prior psychiatry visit, No. (%) | 53 (14.8) | 122 (11.4) | 0.10 |
| History of chronic or functional pain, No. (%) | 122 (34.0) | 330 (30.9) | 0.28 |
| Required translator, No. (%) | 5 (1.4) | 14 (1.3) | 0.91 |
| Benefactor, No. (%) | 5 (1.4) | 24 (2.2) | 0.32 |
| Charlson comorbidity score, mean (SD) | 2.7 (2.5) | 2.6 (2.5) | 0.49 |
DISCUSSION
The results of our qualitative and quantitative surveys showed significant differences between resident and staff perceptions of the faculty triage role. Although both groups similarly valued many features, residents expressed a clear preference for more bread‐and‐butter admissions, whereas the staff prioritized selecting the most complex, challenging, and rare cases from among the day's admissions to give to the residents. (Residents were also very interested in rare cases, suggesting that they saw benefit to admitting patients with a variety of degrees of rarity and complexity.) Residents and faculty seemed to agree that the number of social admissions and outside transfers admitted to teaching services was not ideal.
These perceptions have substantial implications. If the current triage process is to continue, there may be benefit to designing a faculty development project focused on the triage process, which previously has been largely unexamined. Efforts to remove or limit time barriers that prevent perceived educational cases from being admitted to teaching services is also a worthy endeavor (eg, structuring the 2 teams to admit simultaneously so that teaching teams can admit patients back to back without exceeding capacity). In addition, residents may benefit from teaching hospitalists who concentrate educational efforts on the learning that can be extracted from the care of any patient, including admissions that initially seem mundane or purely social.[14] A concerted effort to divert more traditional medicine admissions and fewer unusual cases to the teaching service might improve resident perceptions of the triage process. Further, although the care of any patient can have education benefit, the fact that both groups perceived excessive social admissions in the teaching service suggests that a potential benefit of a nonteaching service (ie, absorbing the most mundane admissions) may not yet be fully realized.
Despite the perceived differences noted on the surveys, we found remarkably few differences between patients admitted to the teaching and nonteaching services. Although both groups rated complexity; outside transfers; being seen at the institution for the first time; and histories of transplantation, cancer, chronic or functional pain, and psychiatric disease as increasing the likelihood of admission to a teaching service, no differences were observed for these factors or their quantifiable surrogates. (Although the overall test for admission type achieved marginal statistical significance, none of the individual admission types were significantly different in post hoc analysis.) Residents, but not faculty, thought that older patients were over‐represented on the teaching service, but their assigned patients were significantly younger than those on the nonteaching service.
These findings have several possible explanations. First, although most hospitalists spend time on teaching and nonteaching services (and therefore are familiar with the patient composition of each), residents get very little exposure to the nonteaching services (until they are senior residents with a rotation on a consulting service). Their impression of inequity may be due to misunderstanding the patient composition of the nonteaching services. Second, the mere existence of a triage role may create false expectations about patient composition; that is, simply by knowing that every admission was chosen for its educational merit, residents may have disproportionate perceptions about those cases judged to have less educational value, even ifas our data suggestassignments to teaching versus nonteaching services are occurring fairly equitably.
Study Limitations
We acknowledge several limitations of our study. First, many factors that were reported as important in the qualitative survey did not lend themselves to objective abstraction from patient records. For example, providers did not specifically document when an admission is purely social, nor was there an objective way to identify difficult patients or families or admissions that were expected to take more time. We attempted to limit the analysis to objective patient metrics that were (1) not influenced by the teaching or nonteaching assignment itself (eg, we avoided discharge diagnoses, which might be entered differently by residents and staff hospitalists) and (2) easily available to triage hospitalists. For the latter reason, we used a prior appointment in the hematology or oncology clinic as a surrogate for cancer patients and a prior psychiatry visit as a surrogate for patients with a history of psychiatric disease. These are naturally inexact surrogates, but they reflect the information a busy hospitalist is likely to access when making patient assignment decisions.
Second, it may well be that assigning patients equitably according to a certain trait is not the same as assigning patients ideally for the educational needs of residents. The patients admitted to our medicine services (teaching and nonteaching) were generally older than 60 years, had complex diagnoses, and had substantial pain. Residents on the teaching services potentially would benefit from an intentionally unbalanced admission policy that shunted patients to the teaching services on the basis of features other than individual perceived educational merit. It must also be borne in mind that resident, and for that matter faculty, perceptions of ideal teaching cases are likely inexact correlates of educational best practices; the ideal role of the triage hospitalist is to admit to the teaching services those patients that will best advance the education of the learners, including a consideration of the goals and objectives of the rotation. Future studies correlating different triage practices to actual educational outcomes would be very helpful.
Third, the analysis could not reliably eliminate patients whose admissions did not represent genuine triage decisions (eg, those assigned to the hospitalist service after the teaching service had reached its capacity or immediately after they had received a complex case). Studying admission decisions prospectively could eliminate this variability, but it could introduce a Hawthorne effect, the negative effects of which likely would outweigh this benefit.
CONCLUSION
Triage hospitalists distributed patients fairly evenly between teaching and nonteaching services, but residents and faculty alike perceived that residents would benefit from more bread‐and‐butter cases. Hospitals considering the addition of a nonteaching service may want to incorporate a faculty development project focused on the triage process to ensure that these traditional medicine cases are assigned to resident services and to ensure that the great teaching case is not considered such because of complexity and acuity alone.
Acknowledgements
The authors thank Elizabeth Jones and Lois Bell for their assistance with survey collection and collation.
Disclosures: This study (institutional review board application #11‐3;002635) was deemed exempt by the Mayo Clinic institutional review board on May 16, 2011. The authors report no conflicts of interest.
- . Duty hours for resident physicians: tough choices for teaching hospitals. N Engl J Med. 2002;347(16):1275–1278.
- , , , , . A randomized, controlled trial of an attending staff service in general internal medicine. Med Care. 1991;29(7 suppl):JS31–JS40.
- , , , , . Non‐housestaff medicine services in academic centers: models and challenges. J Hosp Med. 2008;3(3):247–255.
- , , ; Education Committee of the American College of Physicians. Redesigning training for internal medicine. Ann Intern Med. 2006;144(12):927–932.
- , , , , . Improving resource utilization in a teaching hospital: development of a nonteaching service for chest pain admissions. Acad Med. 2006;81(5):432–435.
- , , . Hospitalists and an innovative emergency department admission process. J Gen Intern Med. 2004;19(3):266–268.
- , , , , . Comparison of resource utilization and clinical outcomes between teaching and nonteaching medical services. J Hosp Med. 2007;2(3):150–157.
- , , , . A comparative study of unscheduled hospital readmissions in a resident‐staffed teaching service and a hospitalist‐based service. South Med J. 2009;102(2):145–149.
- , , , et al. Prevalence of unhealthy substance use on teaching and hospitalist medical services: implications for education. Am J Addict. 2012;21(2):111–119.
- , , , et al. The effect of nonteaching services on the distribution of inpatient cases for internal medicine residents. Acad Med. 2009;84(2):220–225.
- Teaching and nonteaching services: separate no more? Today's Hospitalist website. Available at: http://www.todayshospita list.com/index.php?b=articles_read15(9):1277–1288.
- Charlson comorbidity scoring system: estimating prognosis for dialysis patients. Touchcalc website. Available at: http://www.touchcalc.com/calculators/cci_js#t2_probability. Accessed January 15, 2014.
- . Curiosity. Ann Intern Med. 1999;130(1):70–71.
- . Duty hours for resident physicians: tough choices for teaching hospitals. N Engl J Med. 2002;347(16):1275–1278.
- , , , , . A randomized, controlled trial of an attending staff service in general internal medicine. Med Care. 1991;29(7 suppl):JS31–JS40.
- , , , , . Non‐housestaff medicine services in academic centers: models and challenges. J Hosp Med. 2008;3(3):247–255.
- , , ; Education Committee of the American College of Physicians. Redesigning training for internal medicine. Ann Intern Med. 2006;144(12):927–932.
- , , , , . Improving resource utilization in a teaching hospital: development of a nonteaching service for chest pain admissions. Acad Med. 2006;81(5):432–435.
- , , . Hospitalists and an innovative emergency department admission process. J Gen Intern Med. 2004;19(3):266–268.
- , , , , . Comparison of resource utilization and clinical outcomes between teaching and nonteaching medical services. J Hosp Med. 2007;2(3):150–157.
- , , , . A comparative study of unscheduled hospital readmissions in a resident‐staffed teaching service and a hospitalist‐based service. South Med J. 2009;102(2):145–149.
- , , , et al. Prevalence of unhealthy substance use on teaching and hospitalist medical services: implications for education. Am J Addict. 2012;21(2):111–119.
- , , , et al. The effect of nonteaching services on the distribution of inpatient cases for internal medicine residents. Acad Med. 2009;84(2):220–225.
- Teaching and nonteaching services: separate no more? Today's Hospitalist website. Available at: http://www.todayshospita list.com/index.php?b=articles_read15(9):1277–1288.
- Charlson comorbidity scoring system: estimating prognosis for dialysis patients. Touchcalc website. Available at: http://www.touchcalc.com/calculators/cci_js#t2_probability. Accessed January 15, 2014.
- . Curiosity. Ann Intern Med. 1999;130(1):70–71.
© 2014 Society of Hospital Medicine
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A day in the life of a rheumatologist
7:00 a.m. When they called me for this consult on this young female with known lupus presenting with pleuritic chest pain, they didn’t tell me that (a) she has a history of pleural effusions, and (b) her creatinine is 4.9 mg/dL.
8:00 a.m. Waiting for my patient to be roomed. We’re implementing a new electronic health record, so I have to wait for the medical assistant (MA) to finish her tasks: input the patient’s medications, take his vital signs, and ask for his chief complaint.
8:20 a.m. Patient is still not ready for me. Who thought it would be a good idea for the MA to take the patient’s medications? It’d be so much more efficient if I did it myself.
9:00 a.m. Finally finished with the first patient. It was a follow-up visit that was scheduled as 15 minutes. I am now 45 minutes behind schedule. Thankfully, the MA managed to use the 45 minutes to room the 8:15 patient.
12:30 p.m. Whew, I just finished my morning. I start again in 30 minutes. I am never going to finish these 12 charts in 30 minutes. Also, I am hungry. If I don’t eat now, I am going to have my MA for lunch.
12:45 p.m. Speaking to Dr. Winchester from Blue Cross to get approval for a contrast MRI of the right foot. (This call may be recorded. What did your x-rays show? Have you failed conservative treatment? Will it change management? Here’s your approval number.)
1:00 p.m. The new patient is here. She is the proud owner of a very long med list. It’ll probably take the MA 30 minutes to get through all that. Let me call dermatology in the meantime; I need a full-thickness skin biopsy on Mrs. Rodrigues. (One week later, biopsy shows polyarteritis nodosa.)
3:15 p.m. I just finished a visit with Silvi. Her rheumatoid arthritis is quiescent, but she is in tears. Not only did her mother die unexpectedly from a ruptured aneurysm 2 months ago, she has just received a new diagnosis of breast cancer, and her husband lost his job. I can’t make this stuff up. That was an emotionally draining visit. I need a drink. Oh wait, there are no drinks to be had at a doctor’s office. Maybe the drug rep brought some ice cream.
3:20 p.m. Some ice cream regret going on here.
4:40 p.m. Just got done with a new-patient consultation for a "positive" antinuclear antibody test of 1:40 and a positive systems review. I’m exhausted.
6:15 p.m. Returning phone calls. Mrs. Greggerson is regaling me with details of her ablutions.
6:35 p.m. Filling out prior authorization forms for a biologic. Among the questions: A1c, T score, growth velocity, Mini-Mental State Exam, free and total testosterone, hepatitis C viral load and genotype. I would like to officially nominate this form for Most Number of Irrelevant Questions Ever.
7:00 p.m. Finally, last prior-authorization form for the day. Wait ... it’s for methotrexate? Since when have I needed to get prior authorization for methotrexate? I didn’t think it was even possible for me to get any angrier after the Mini-Mental State question.
8:00 p.m. Finally home. I’m too beat to go to the gym. My good decision–making reserves are exhausted. I would rather have a glass of red. The resveratrol will do me more good than a workout.
Dr. Chan practices rheumatology in Pawtucket, R.I.
7:00 a.m. When they called me for this consult on this young female with known lupus presenting with pleuritic chest pain, they didn’t tell me that (a) she has a history of pleural effusions, and (b) her creatinine is 4.9 mg/dL.
8:00 a.m. Waiting for my patient to be roomed. We’re implementing a new electronic health record, so I have to wait for the medical assistant (MA) to finish her tasks: input the patient’s medications, take his vital signs, and ask for his chief complaint.
8:20 a.m. Patient is still not ready for me. Who thought it would be a good idea for the MA to take the patient’s medications? It’d be so much more efficient if I did it myself.
9:00 a.m. Finally finished with the first patient. It was a follow-up visit that was scheduled as 15 minutes. I am now 45 minutes behind schedule. Thankfully, the MA managed to use the 45 minutes to room the 8:15 patient.
12:30 p.m. Whew, I just finished my morning. I start again in 30 minutes. I am never going to finish these 12 charts in 30 minutes. Also, I am hungry. If I don’t eat now, I am going to have my MA for lunch.
12:45 p.m. Speaking to Dr. Winchester from Blue Cross to get approval for a contrast MRI of the right foot. (This call may be recorded. What did your x-rays show? Have you failed conservative treatment? Will it change management? Here’s your approval number.)
1:00 p.m. The new patient is here. She is the proud owner of a very long med list. It’ll probably take the MA 30 minutes to get through all that. Let me call dermatology in the meantime; I need a full-thickness skin biopsy on Mrs. Rodrigues. (One week later, biopsy shows polyarteritis nodosa.)
3:15 p.m. I just finished a visit with Silvi. Her rheumatoid arthritis is quiescent, but she is in tears. Not only did her mother die unexpectedly from a ruptured aneurysm 2 months ago, she has just received a new diagnosis of breast cancer, and her husband lost his job. I can’t make this stuff up. That was an emotionally draining visit. I need a drink. Oh wait, there are no drinks to be had at a doctor’s office. Maybe the drug rep brought some ice cream.
3:20 p.m. Some ice cream regret going on here.
4:40 p.m. Just got done with a new-patient consultation for a "positive" antinuclear antibody test of 1:40 and a positive systems review. I’m exhausted.
6:15 p.m. Returning phone calls. Mrs. Greggerson is regaling me with details of her ablutions.
6:35 p.m. Filling out prior authorization forms for a biologic. Among the questions: A1c, T score, growth velocity, Mini-Mental State Exam, free and total testosterone, hepatitis C viral load and genotype. I would like to officially nominate this form for Most Number of Irrelevant Questions Ever.
7:00 p.m. Finally, last prior-authorization form for the day. Wait ... it’s for methotrexate? Since when have I needed to get prior authorization for methotrexate? I didn’t think it was even possible for me to get any angrier after the Mini-Mental State question.
8:00 p.m. Finally home. I’m too beat to go to the gym. My good decision–making reserves are exhausted. I would rather have a glass of red. The resveratrol will do me more good than a workout.
Dr. Chan practices rheumatology in Pawtucket, R.I.
7:00 a.m. When they called me for this consult on this young female with known lupus presenting with pleuritic chest pain, they didn’t tell me that (a) she has a history of pleural effusions, and (b) her creatinine is 4.9 mg/dL.
8:00 a.m. Waiting for my patient to be roomed. We’re implementing a new electronic health record, so I have to wait for the medical assistant (MA) to finish her tasks: input the patient’s medications, take his vital signs, and ask for his chief complaint.
8:20 a.m. Patient is still not ready for me. Who thought it would be a good idea for the MA to take the patient’s medications? It’d be so much more efficient if I did it myself.
9:00 a.m. Finally finished with the first patient. It was a follow-up visit that was scheduled as 15 minutes. I am now 45 minutes behind schedule. Thankfully, the MA managed to use the 45 minutes to room the 8:15 patient.
12:30 p.m. Whew, I just finished my morning. I start again in 30 minutes. I am never going to finish these 12 charts in 30 minutes. Also, I am hungry. If I don’t eat now, I am going to have my MA for lunch.
12:45 p.m. Speaking to Dr. Winchester from Blue Cross to get approval for a contrast MRI of the right foot. (This call may be recorded. What did your x-rays show? Have you failed conservative treatment? Will it change management? Here’s your approval number.)
1:00 p.m. The new patient is here. She is the proud owner of a very long med list. It’ll probably take the MA 30 minutes to get through all that. Let me call dermatology in the meantime; I need a full-thickness skin biopsy on Mrs. Rodrigues. (One week later, biopsy shows polyarteritis nodosa.)
3:15 p.m. I just finished a visit with Silvi. Her rheumatoid arthritis is quiescent, but she is in tears. Not only did her mother die unexpectedly from a ruptured aneurysm 2 months ago, she has just received a new diagnosis of breast cancer, and her husband lost his job. I can’t make this stuff up. That was an emotionally draining visit. I need a drink. Oh wait, there are no drinks to be had at a doctor’s office. Maybe the drug rep brought some ice cream.
3:20 p.m. Some ice cream regret going on here.
4:40 p.m. Just got done with a new-patient consultation for a "positive" antinuclear antibody test of 1:40 and a positive systems review. I’m exhausted.
6:15 p.m. Returning phone calls. Mrs. Greggerson is regaling me with details of her ablutions.
6:35 p.m. Filling out prior authorization forms for a biologic. Among the questions: A1c, T score, growth velocity, Mini-Mental State Exam, free and total testosterone, hepatitis C viral load and genotype. I would like to officially nominate this form for Most Number of Irrelevant Questions Ever.
7:00 p.m. Finally, last prior-authorization form for the day. Wait ... it’s for methotrexate? Since when have I needed to get prior authorization for methotrexate? I didn’t think it was even possible for me to get any angrier after the Mini-Mental State question.
8:00 p.m. Finally home. I’m too beat to go to the gym. My good decision–making reserves are exhausted. I would rather have a glass of red. The resveratrol will do me more good than a workout.
Dr. Chan practices rheumatology in Pawtucket, R.I.
Legislation’s privacy exceptions for psychiatric patients are concerning
Like many of you, I’m currently in New York City for 5 days of psychiatry and psychiatrists, 24/7. I’m hoping there will be a bagel with lox in there somewhere as well.
I wanted to talk about one section of Rep. Tim Murphy’s (R-Pa.) proposed legislation, H.R. 3717, the Helping Families in Mental Health Crisis Act. If you’re not familiar with it, the legislation intends to overhaul a broken mental health system in the United States. One component of the bill, Section 301 located on page 44, deals with modifying HIPAA such that mental health providers can speak with caregivers and family members. Rep. Murphy – who is also a psychologist – has noted in his television appearance and in public testimony that HIPAA is misinterpreted such that families are sometimes told they may not provide historical information about the patient. HIPAA does not actually prevent a mental health professional from listening to anyone’s free speech, but there seem to be times when the involved parties believe this is the case.
In addition, Rep. Murphy noted that HIPAA prevents clinicians from releasing information to caretakers that might help in providing for outpatient care – specifically for releasing medication information and follow-up appointments to those who may be responsible for helping patients negotiate these crucial items.
The proposed legislation reads:
"Caregiver Access to Information: ...to an individual with a serious mental illness who does not provide consent for the disclosure of protected health information to a caregiver of such individual, the caregiver shall be treated by a covered entity as a personal representative ... when the provider furnishing services to the individual reasonably believes it is necessary for protected health information of the individual to be made available to the caregiver in order to protect the health, safety, or welfare of such individuals or the safety of one or more other individuals."
The bill goes on to define "caregiver" as an immediate family member, an individual who assumes primary responsibility for providing for the patient’s basic needs, or a personal representative as determined by law. I think we all agree that collaboration and communication are essential to the care of our patients, and so I applaud these efforts. I worry, however, about the unintended consequences and what roads this might lead us down.
Long before we had HIPAA, we had requirements for patient confidentiality. I, like Rep. Murphy, believe that HIPAA gets distorted. "We need to let your family know your discharge medications and follow-up appointments," is not often met with resistance, but if it is, shouldn’t that be respected? What if patients have valid reasons for not wanting family to know their medications? What if they feel their family is too intrusive, or is part of the problem? Such legislation might suggest that the family is always right and the patient is always wrong.
While the intent (as I’ve understood it from Rep. Murphy’s speeches) is to allow hospitals to tell families, "Yes, your loved [one] has been admitted to our inpatient unit," or to allow well-negotiated follow-up to prevent relapse, might such legislation lead patients to believe that the content of their discussions with mental health professionals can be relayed to others against their will? Might it serve as one more reason for a troubled individual to avoid care?
From a psychiatrist’s point of view, I might be concerned that I would agree with a patient that information should not be released to family, and nothing about this law would then force me to release it. But would family members feel the law says otherwise? Will they contend, "My family member is mentally ill so HIPAA does not apply, and you must release information to me?" While any given psychiatrist might choose not to release information on any given patient, I wonder if this might be setting us up to be at odds with families, and that would not be a good thing. Much as I’m no fan of HIPAA for many reasons, people do understand the concept that confidentiality is required by law. Perhaps I’m reading too much into this?
Finally, we all agree that eliminating stigma is a good thing when it comes to facilitating voluntary care for those who might need it. But I wonder if we can say that people with mental illnesses are just like everyone else, that this is a medical condition just like other conditions, but for this select group of people they lose their right to privacy, much as children have no right to medical privacy. Might that add to the stigma of mental illness?
I don’t have an answer. I believe the intentions of the Helping Families in Mental Health Crisis Act are good, and I believe they target weaknesses in our system. But I also worry that the legislation might create as many problems as it might fix.
The comments feature of the Clinical Psychiatry News website is turned off for the moment, and I would love to hear your thoughts. Please do e-mail with your comments; I can be reached at [email protected], or you may comment on a similar post here.
Dr. Miller is a coauthor of "Shrink Rap: Three Psychiatrists Explain Their Work" (Baltimore: the Johns Hopkins University Press, 2011).
Like many of you, I’m currently in New York City for 5 days of psychiatry and psychiatrists, 24/7. I’m hoping there will be a bagel with lox in there somewhere as well.
I wanted to talk about one section of Rep. Tim Murphy’s (R-Pa.) proposed legislation, H.R. 3717, the Helping Families in Mental Health Crisis Act. If you’re not familiar with it, the legislation intends to overhaul a broken mental health system in the United States. One component of the bill, Section 301 located on page 44, deals with modifying HIPAA such that mental health providers can speak with caregivers and family members. Rep. Murphy – who is also a psychologist – has noted in his television appearance and in public testimony that HIPAA is misinterpreted such that families are sometimes told they may not provide historical information about the patient. HIPAA does not actually prevent a mental health professional from listening to anyone’s free speech, but there seem to be times when the involved parties believe this is the case.
In addition, Rep. Murphy noted that HIPAA prevents clinicians from releasing information to caretakers that might help in providing for outpatient care – specifically for releasing medication information and follow-up appointments to those who may be responsible for helping patients negotiate these crucial items.
The proposed legislation reads:
"Caregiver Access to Information: ...to an individual with a serious mental illness who does not provide consent for the disclosure of protected health information to a caregiver of such individual, the caregiver shall be treated by a covered entity as a personal representative ... when the provider furnishing services to the individual reasonably believes it is necessary for protected health information of the individual to be made available to the caregiver in order to protect the health, safety, or welfare of such individuals or the safety of one or more other individuals."
The bill goes on to define "caregiver" as an immediate family member, an individual who assumes primary responsibility for providing for the patient’s basic needs, or a personal representative as determined by law. I think we all agree that collaboration and communication are essential to the care of our patients, and so I applaud these efforts. I worry, however, about the unintended consequences and what roads this might lead us down.
Long before we had HIPAA, we had requirements for patient confidentiality. I, like Rep. Murphy, believe that HIPAA gets distorted. "We need to let your family know your discharge medications and follow-up appointments," is not often met with resistance, but if it is, shouldn’t that be respected? What if patients have valid reasons for not wanting family to know their medications? What if they feel their family is too intrusive, or is part of the problem? Such legislation might suggest that the family is always right and the patient is always wrong.
While the intent (as I’ve understood it from Rep. Murphy’s speeches) is to allow hospitals to tell families, "Yes, your loved [one] has been admitted to our inpatient unit," or to allow well-negotiated follow-up to prevent relapse, might such legislation lead patients to believe that the content of their discussions with mental health professionals can be relayed to others against their will? Might it serve as one more reason for a troubled individual to avoid care?
From a psychiatrist’s point of view, I might be concerned that I would agree with a patient that information should not be released to family, and nothing about this law would then force me to release it. But would family members feel the law says otherwise? Will they contend, "My family member is mentally ill so HIPAA does not apply, and you must release information to me?" While any given psychiatrist might choose not to release information on any given patient, I wonder if this might be setting us up to be at odds with families, and that would not be a good thing. Much as I’m no fan of HIPAA for many reasons, people do understand the concept that confidentiality is required by law. Perhaps I’m reading too much into this?
Finally, we all agree that eliminating stigma is a good thing when it comes to facilitating voluntary care for those who might need it. But I wonder if we can say that people with mental illnesses are just like everyone else, that this is a medical condition just like other conditions, but for this select group of people they lose their right to privacy, much as children have no right to medical privacy. Might that add to the stigma of mental illness?
I don’t have an answer. I believe the intentions of the Helping Families in Mental Health Crisis Act are good, and I believe they target weaknesses in our system. But I also worry that the legislation might create as many problems as it might fix.
The comments feature of the Clinical Psychiatry News website is turned off for the moment, and I would love to hear your thoughts. Please do e-mail with your comments; I can be reached at [email protected], or you may comment on a similar post here.
Dr. Miller is a coauthor of "Shrink Rap: Three Psychiatrists Explain Their Work" (Baltimore: the Johns Hopkins University Press, 2011).
Like many of you, I’m currently in New York City for 5 days of psychiatry and psychiatrists, 24/7. I’m hoping there will be a bagel with lox in there somewhere as well.
I wanted to talk about one section of Rep. Tim Murphy’s (R-Pa.) proposed legislation, H.R. 3717, the Helping Families in Mental Health Crisis Act. If you’re not familiar with it, the legislation intends to overhaul a broken mental health system in the United States. One component of the bill, Section 301 located on page 44, deals with modifying HIPAA such that mental health providers can speak with caregivers and family members. Rep. Murphy – who is also a psychologist – has noted in his television appearance and in public testimony that HIPAA is misinterpreted such that families are sometimes told they may not provide historical information about the patient. HIPAA does not actually prevent a mental health professional from listening to anyone’s free speech, but there seem to be times when the involved parties believe this is the case.
In addition, Rep. Murphy noted that HIPAA prevents clinicians from releasing information to caretakers that might help in providing for outpatient care – specifically for releasing medication information and follow-up appointments to those who may be responsible for helping patients negotiate these crucial items.
The proposed legislation reads:
"Caregiver Access to Information: ...to an individual with a serious mental illness who does not provide consent for the disclosure of protected health information to a caregiver of such individual, the caregiver shall be treated by a covered entity as a personal representative ... when the provider furnishing services to the individual reasonably believes it is necessary for protected health information of the individual to be made available to the caregiver in order to protect the health, safety, or welfare of such individuals or the safety of one or more other individuals."
The bill goes on to define "caregiver" as an immediate family member, an individual who assumes primary responsibility for providing for the patient’s basic needs, or a personal representative as determined by law. I think we all agree that collaboration and communication are essential to the care of our patients, and so I applaud these efforts. I worry, however, about the unintended consequences and what roads this might lead us down.
Long before we had HIPAA, we had requirements for patient confidentiality. I, like Rep. Murphy, believe that HIPAA gets distorted. "We need to let your family know your discharge medications and follow-up appointments," is not often met with resistance, but if it is, shouldn’t that be respected? What if patients have valid reasons for not wanting family to know their medications? What if they feel their family is too intrusive, or is part of the problem? Such legislation might suggest that the family is always right and the patient is always wrong.
While the intent (as I’ve understood it from Rep. Murphy’s speeches) is to allow hospitals to tell families, "Yes, your loved [one] has been admitted to our inpatient unit," or to allow well-negotiated follow-up to prevent relapse, might such legislation lead patients to believe that the content of their discussions with mental health professionals can be relayed to others against their will? Might it serve as one more reason for a troubled individual to avoid care?
From a psychiatrist’s point of view, I might be concerned that I would agree with a patient that information should not be released to family, and nothing about this law would then force me to release it. But would family members feel the law says otherwise? Will they contend, "My family member is mentally ill so HIPAA does not apply, and you must release information to me?" While any given psychiatrist might choose not to release information on any given patient, I wonder if this might be setting us up to be at odds with families, and that would not be a good thing. Much as I’m no fan of HIPAA for many reasons, people do understand the concept that confidentiality is required by law. Perhaps I’m reading too much into this?
Finally, we all agree that eliminating stigma is a good thing when it comes to facilitating voluntary care for those who might need it. But I wonder if we can say that people with mental illnesses are just like everyone else, that this is a medical condition just like other conditions, but for this select group of people they lose their right to privacy, much as children have no right to medical privacy. Might that add to the stigma of mental illness?
I don’t have an answer. I believe the intentions of the Helping Families in Mental Health Crisis Act are good, and I believe they target weaknesses in our system. But I also worry that the legislation might create as many problems as it might fix.
The comments feature of the Clinical Psychiatry News website is turned off for the moment, and I would love to hear your thoughts. Please do e-mail with your comments; I can be reached at [email protected], or you may comment on a similar post here.
Dr. Miller is a coauthor of "Shrink Rap: Three Psychiatrists Explain Their Work" (Baltimore: the Johns Hopkins University Press, 2011).
Liquid droplets help explain cell migration

Scientists have discovered an unexpected link between the shape of a cell and its migration efficiency, and they’ve explained its physics using a model of a liquid droplet.
Cell migration is achieved through the movement of the cell’s membrane, which is powered by the action of a protein network inside the cell.
This interaction is affected by the cell’s overall shape, but exactly how this takes place has been unclear.
Research published in Current Biology provides some insight.
The first step in cell migration occurs when the cell extends its front edge—a process called protrusion. This is driven by the growth of actin filaments, which push the cell membrane from inside. At the same time, membrane tension controls protrusion by providing resistance and protecting the cell from over-extending.
But physical laws dictate that the shape of the cell membrane must play a role in the balance between force exerted by actin and the resisting membrane tension. This was not taken into account in previous studies, which used 2D models of migrating cells.
Now, Chiara Gabella, PhD, of Ecole Polytechnique Fédérale de Lausanne in Switzerland, and her colleagues have used a 3D model to better describe the relationship between cell protrusion, shape, and membrane tension.
The scientists developed a way to evaluate the 3D shape of migrating fish epidermal keratocytes by observing the cells in a chamber filled with a fluorescent solution.
The team applied various treatments to swell, shrink, or stretch the cells. And they were able to observe the treatment’s impact on membrane tension, shape, and protrusion velocity.
The treatments only affected the cells’ shape and migration speed, not membrane tension. The results also showed that the more spherical a cell is, the faster it moves.
To interpret these unexpected findings, the scientists modeled a migrating cell as a liquid droplet spreading on a surface.
“It is well known that a droplet’s shape and, in particular, the contact angle that it makes with the surface are determined by the tension forces between the droplet, its environmental medium (eg, air or a different liquid), and the surface on which it moves,” Dr Gabella said.
Results of the modeling experiment suggested that the leading edge could be considered a triple interface between the substrate, membrane, and extracellular medium. And the contact angle between the membrane and the substrate determines the load on actin polymerization and, therefore, the protrusion rate.
“From this point of view, a more spherical cell means less load for actin filaments to overcome and, therefore, faster actin growth and migration,” said Alexander Verkhovsky, PhD, also of Ecole Polytechnique Fédérale de Lausanne.
In support of this idea, the scientists found the cells were sensitive to the surface characteristics, just as droplets would be, by slowing down or being pinned at ridges.
“The emphasis of many studies has been on discovering and characterizing individual cellular components,” Dr Verkhovsky said. “This is rooted in the common belief that a cell’s behavior is determined by intricate networks of genes and proteins.”
In contrast, this work shows that, despite their molecular complexity, cells can be described as physical objects. The findings point to a new relationship between a cell’s shape and its dynamics and may help us to understand how cell migration is guided by the cell’s 3D environment. ![]()

Scientists have discovered an unexpected link between the shape of a cell and its migration efficiency, and they’ve explained its physics using a model of a liquid droplet.
Cell migration is achieved through the movement of the cell’s membrane, which is powered by the action of a protein network inside the cell.
This interaction is affected by the cell’s overall shape, but exactly how this takes place has been unclear.
Research published in Current Biology provides some insight.
The first step in cell migration occurs when the cell extends its front edge—a process called protrusion. This is driven by the growth of actin filaments, which push the cell membrane from inside. At the same time, membrane tension controls protrusion by providing resistance and protecting the cell from over-extending.
But physical laws dictate that the shape of the cell membrane must play a role in the balance between force exerted by actin and the resisting membrane tension. This was not taken into account in previous studies, which used 2D models of migrating cells.
Now, Chiara Gabella, PhD, of Ecole Polytechnique Fédérale de Lausanne in Switzerland, and her colleagues have used a 3D model to better describe the relationship between cell protrusion, shape, and membrane tension.
The scientists developed a way to evaluate the 3D shape of migrating fish epidermal keratocytes by observing the cells in a chamber filled with a fluorescent solution.
The team applied various treatments to swell, shrink, or stretch the cells. And they were able to observe the treatment’s impact on membrane tension, shape, and protrusion velocity.
The treatments only affected the cells’ shape and migration speed, not membrane tension. The results also showed that the more spherical a cell is, the faster it moves.
To interpret these unexpected findings, the scientists modeled a migrating cell as a liquid droplet spreading on a surface.
“It is well known that a droplet’s shape and, in particular, the contact angle that it makes with the surface are determined by the tension forces between the droplet, its environmental medium (eg, air or a different liquid), and the surface on which it moves,” Dr Gabella said.
Results of the modeling experiment suggested that the leading edge could be considered a triple interface between the substrate, membrane, and extracellular medium. And the contact angle between the membrane and the substrate determines the load on actin polymerization and, therefore, the protrusion rate.
“From this point of view, a more spherical cell means less load for actin filaments to overcome and, therefore, faster actin growth and migration,” said Alexander Verkhovsky, PhD, also of Ecole Polytechnique Fédérale de Lausanne.
In support of this idea, the scientists found the cells were sensitive to the surface characteristics, just as droplets would be, by slowing down or being pinned at ridges.
“The emphasis of many studies has been on discovering and characterizing individual cellular components,” Dr Verkhovsky said. “This is rooted in the common belief that a cell’s behavior is determined by intricate networks of genes and proteins.”
In contrast, this work shows that, despite their molecular complexity, cells can be described as physical objects. The findings point to a new relationship between a cell’s shape and its dynamics and may help us to understand how cell migration is guided by the cell’s 3D environment. ![]()

Scientists have discovered an unexpected link between the shape of a cell and its migration efficiency, and they’ve explained its physics using a model of a liquid droplet.
Cell migration is achieved through the movement of the cell’s membrane, which is powered by the action of a protein network inside the cell.
This interaction is affected by the cell’s overall shape, but exactly how this takes place has been unclear.
Research published in Current Biology provides some insight.
The first step in cell migration occurs when the cell extends its front edge—a process called protrusion. This is driven by the growth of actin filaments, which push the cell membrane from inside. At the same time, membrane tension controls protrusion by providing resistance and protecting the cell from over-extending.
But physical laws dictate that the shape of the cell membrane must play a role in the balance between force exerted by actin and the resisting membrane tension. This was not taken into account in previous studies, which used 2D models of migrating cells.
Now, Chiara Gabella, PhD, of Ecole Polytechnique Fédérale de Lausanne in Switzerland, and her colleagues have used a 3D model to better describe the relationship between cell protrusion, shape, and membrane tension.
The scientists developed a way to evaluate the 3D shape of migrating fish epidermal keratocytes by observing the cells in a chamber filled with a fluorescent solution.
The team applied various treatments to swell, shrink, or stretch the cells. And they were able to observe the treatment’s impact on membrane tension, shape, and protrusion velocity.
The treatments only affected the cells’ shape and migration speed, not membrane tension. The results also showed that the more spherical a cell is, the faster it moves.
To interpret these unexpected findings, the scientists modeled a migrating cell as a liquid droplet spreading on a surface.
“It is well known that a droplet’s shape and, in particular, the contact angle that it makes with the surface are determined by the tension forces between the droplet, its environmental medium (eg, air or a different liquid), and the surface on which it moves,” Dr Gabella said.
Results of the modeling experiment suggested that the leading edge could be considered a triple interface between the substrate, membrane, and extracellular medium. And the contact angle between the membrane and the substrate determines the load on actin polymerization and, therefore, the protrusion rate.
“From this point of view, a more spherical cell means less load for actin filaments to overcome and, therefore, faster actin growth and migration,” said Alexander Verkhovsky, PhD, also of Ecole Polytechnique Fédérale de Lausanne.
In support of this idea, the scientists found the cells were sensitive to the surface characteristics, just as droplets would be, by slowing down or being pinned at ridges.
“The emphasis of many studies has been on discovering and characterizing individual cellular components,” Dr Verkhovsky said. “This is rooted in the common belief that a cell’s behavior is determined by intricate networks of genes and proteins.”
In contrast, this work shows that, despite their molecular complexity, cells can be described as physical objects. The findings point to a new relationship between a cell’s shape and its dynamics and may help us to understand how cell migration is guided by the cell’s 3D environment. ![]()
Combo can overcome resistance in MM

Credit: PNAS
A 2-drug combination can overcome Mcl-1-dependent treatment resistance in multiple myeloma (MM), preclinical research suggests.
The therapy consists of the Chk1 inhibitor CEP3891 and the MEK1/2 inhibitor PD184352.
Chk1 inhibitors prevent cells from arresting in stages of the cell cycle that facilitate DNA repair. And MEK inhibitors prevent cells from activating proteins that regulate DNA repair, while promoting the accumulation of pro-apoptotic proteins.
Researchers recounted their results with the 2 inhibitors in PLOS ONE.
The team noted that, although several drugs are effective against MM, the cancer cells can often survive treatment by increasing production of Mcl-1. This protein regulates processes that promote cell survival and has been implicated in resistance to bortezomib and other anti-myeloma drugs that were initially effective.
With their experiments, the researchers discovered that CEP3891 and PD184352 can reduce Mcl-1 expression and disrupt its interactions with other proteins to effectively kill MM cells.
“This research builds on our previous studies that showed exposing multiple myeloma and leukemia cells to Chk1 inhibitors activated a protective response through the Ras/MEK/ERK signaling pathway,” said Xin-Yan Pei, MD, PhD, of Virginia Commonwealth University and the Massey Cancer Center in Richmond.
“By combining a Chk1 inhibitor with a MEK inhibitor, we have developed one of only a limited number of strategies shown to circumvent therapeutic resistance caused by high expressions of Mcl-1.”
The team began this research by forcing overexpression of Mcl-1 in human MM cells. This caused the cells to become highly resistant to bortezomib, but it failed to protect them from CEP3891 and PD184352.
Furthermore, CEP3891 and PD184352 completely overcame resistance due to microenvironmental factors associated with increased expression of Mcl-1.
“Not only was the combination therapy effective against multiple myeloma cells, it notably did not harm normal bone marrow cells, raising the possibility of therapeutic selectivity,” said study author Steven Grant, MD, also of Virginia Commonwealth University and the Massey Cancer Center.
“We are hopeful that this research will lead to better therapies for multiple myeloma and help make current therapies more effective by overcoming resistance caused by Mcl-1.”
The researchers have started initial discussions with clinical investigators and drug manufacturers about a clinical trial testing a combination of Chk1 and MEK inhibitors in patients with refractory MM. ![]()

Credit: PNAS
A 2-drug combination can overcome Mcl-1-dependent treatment resistance in multiple myeloma (MM), preclinical research suggests.
The therapy consists of the Chk1 inhibitor CEP3891 and the MEK1/2 inhibitor PD184352.
Chk1 inhibitors prevent cells from arresting in stages of the cell cycle that facilitate DNA repair. And MEK inhibitors prevent cells from activating proteins that regulate DNA repair, while promoting the accumulation of pro-apoptotic proteins.
Researchers recounted their results with the 2 inhibitors in PLOS ONE.
The team noted that, although several drugs are effective against MM, the cancer cells can often survive treatment by increasing production of Mcl-1. This protein regulates processes that promote cell survival and has been implicated in resistance to bortezomib and other anti-myeloma drugs that were initially effective.
With their experiments, the researchers discovered that CEP3891 and PD184352 can reduce Mcl-1 expression and disrupt its interactions with other proteins to effectively kill MM cells.
“This research builds on our previous studies that showed exposing multiple myeloma and leukemia cells to Chk1 inhibitors activated a protective response through the Ras/MEK/ERK signaling pathway,” said Xin-Yan Pei, MD, PhD, of Virginia Commonwealth University and the Massey Cancer Center in Richmond.
“By combining a Chk1 inhibitor with a MEK inhibitor, we have developed one of only a limited number of strategies shown to circumvent therapeutic resistance caused by high expressions of Mcl-1.”
The team began this research by forcing overexpression of Mcl-1 in human MM cells. This caused the cells to become highly resistant to bortezomib, but it failed to protect them from CEP3891 and PD184352.
Furthermore, CEP3891 and PD184352 completely overcame resistance due to microenvironmental factors associated with increased expression of Mcl-1.
“Not only was the combination therapy effective against multiple myeloma cells, it notably did not harm normal bone marrow cells, raising the possibility of therapeutic selectivity,” said study author Steven Grant, MD, also of Virginia Commonwealth University and the Massey Cancer Center.
“We are hopeful that this research will lead to better therapies for multiple myeloma and help make current therapies more effective by overcoming resistance caused by Mcl-1.”
The researchers have started initial discussions with clinical investigators and drug manufacturers about a clinical trial testing a combination of Chk1 and MEK inhibitors in patients with refractory MM. ![]()

Credit: PNAS
A 2-drug combination can overcome Mcl-1-dependent treatment resistance in multiple myeloma (MM), preclinical research suggests.
The therapy consists of the Chk1 inhibitor CEP3891 and the MEK1/2 inhibitor PD184352.
Chk1 inhibitors prevent cells from arresting in stages of the cell cycle that facilitate DNA repair. And MEK inhibitors prevent cells from activating proteins that regulate DNA repair, while promoting the accumulation of pro-apoptotic proteins.
Researchers recounted their results with the 2 inhibitors in PLOS ONE.
The team noted that, although several drugs are effective against MM, the cancer cells can often survive treatment by increasing production of Mcl-1. This protein regulates processes that promote cell survival and has been implicated in resistance to bortezomib and other anti-myeloma drugs that were initially effective.
With their experiments, the researchers discovered that CEP3891 and PD184352 can reduce Mcl-1 expression and disrupt its interactions with other proteins to effectively kill MM cells.
“This research builds on our previous studies that showed exposing multiple myeloma and leukemia cells to Chk1 inhibitors activated a protective response through the Ras/MEK/ERK signaling pathway,” said Xin-Yan Pei, MD, PhD, of Virginia Commonwealth University and the Massey Cancer Center in Richmond.
“By combining a Chk1 inhibitor with a MEK inhibitor, we have developed one of only a limited number of strategies shown to circumvent therapeutic resistance caused by high expressions of Mcl-1.”
The team began this research by forcing overexpression of Mcl-1 in human MM cells. This caused the cells to become highly resistant to bortezomib, but it failed to protect them from CEP3891 and PD184352.
Furthermore, CEP3891 and PD184352 completely overcame resistance due to microenvironmental factors associated with increased expression of Mcl-1.
“Not only was the combination therapy effective against multiple myeloma cells, it notably did not harm normal bone marrow cells, raising the possibility of therapeutic selectivity,” said study author Steven Grant, MD, also of Virginia Commonwealth University and the Massey Cancer Center.
“We are hopeful that this research will lead to better therapies for multiple myeloma and help make current therapies more effective by overcoming resistance caused by Mcl-1.”
The researchers have started initial discussions with clinical investigators and drug manufacturers about a clinical trial testing a combination of Chk1 and MEK inhibitors in patients with refractory MM. ![]()