Susan C. Day

Evaluation and outcome of emergency room patients with transient loss of consciousness.

We identified 198 patients who presented to our emergency room with transient loss of consciousness. Seizures (29 percent of patients) and vasovagal/psychogenic episodes (40 percent of patients) were the most common presumptive causes of loss of consciousness, but the cause of loss of consciousness remained uncertain even at follow-up in 11 +/- 6 months in 13 percent of the patients. The history and physical examinations were sufficient for diagnosis in 85 percent of the patients in whom a diagnosis could be established. These data guided inpatient and outpatient with potentially dangerous causes of loss of consciousness except for one patient who had pulmonary embolism. In selected patient, diagnostic tests such as blood chemistries (three patients), electrocardiograms (four patients) electroencephalograms (three patients), and Holter monitoring (four patients) provided crucial information, and CT scans identified new brain tumors in four patients with focal neurologic presentations. At the time of follow-up, 7.5 percent of patients had suffered either major morbidity or death related to the cause of the index episode of loss of consciousness. Patients with cardiac causes represented a high risk (33 percent) group for such poor outcome, whereas patients who were under age 30, or who were under age 70 and had loss of consciousness on a vasovagal/psychogenic or unknown basis, constituted a low risk (1 percent) subgroup.

A controlled trial to improve delivery of preventive care - Physician or patient reminders?

Objective:To improve the delivery of preventive care in a medical clinic, a controlled trial was conducted of two interventions that were expected to influence delivery of preventive services differently, depending on level of initiative required of the physician or patient to complete a service.Design:A prospective, controlled trial of five-months’ duration.Setting:A university hospital-based, general medical clinic.Participants:Thirty-nine junior and senior medical residents who saw patients in stable clinic teams throughout the study.Intervention:A computerized reminder system for physicians and a patient questionnaire and educational handout on preventive care.Measurements and main results:Delivery of five of six audited preventive services improved significantly after the interventions were introduced. The computerized reminder alone increased completion rates of services that relied primarily on physician initiative; the questionnaire alone increased completion rate of the service that depended more on patient compliance as well as on some physician-dependent services. Both interventions used together were slightly less effective in improving performance of physician-dependent services than the computerized reminder used alone.Conclusions:These interventions can improve the delivery of preventive care but they differ in their impacts on physician and patient behaviors. Overall, the computer reminder was the more effective intervention.

A primary care intervention for weight loss: results of a randomized controlled pilot study.

Most primary care providers (PCPs), constrained by time and resources, cannot provide intensive behavioral counseling for obesity. This study evaluated the effect of using medical assistants (MAs) as weight loss counselors. The study was a randomized controlled trial conducted in two primary care offices at an academic medical center. Patients (n = 50) had a BMI of 27–50 kg/m2 and no contraindications to weight loss. They were randomized to quarterly PCP visits and weight loss materials (Control group) or to the same approach combined with eight visits with a MA over 6 months (Brief Counseling). Outcomes included change in weight and cardiovascular risk factors (glucose, lipids, blood pressure, and waist circumference). Patients in the Brief Counseling and Control groups lost 4.4 ± 0.6 kg (5.1 ± 0.7% of initial weight) and 0.9 ± 0.6 kg (1.0 ± 0.7%), respectively, at month 6 (P < 0.001). There were no significant differences between groups for changes in cardiovascular risk factors. Brief Counseling patients regained weight between month 6 and month 12, when MA visits were discontinued. Attrition was 10% after 6 months and 6% after 12 months. Brief Counseling by MAs induced significant weight loss during 6 months. Office‐based obesity treatment should be tested in larger trials and should include weight loss maintenance counseling.

Residents’ perception of evaluation procedures used by their training program

Objective:To determine the methods of evaluation used routinely by training programs and to obtain information concerning the frequencies with which various evaluation methods were used.Design:Survey of residents who had recently completed internal medicine training.Participants:5,693 respondents who completed residencies in 1987 and 1988 and were registered as first-time takers for the 1988 Certifying Examination in Internal Medicine. This constituted a 76% response rate.Main results:Virtually all residents were aware that routine evaluations were submitted on inpatient rotations, but were more uncertain about the evaluation process in the outpatient setting and the methods used to assess their bumanistic qualities. Most residents had undergone a Clinical Evaluation Exercise (CEX); residents’ clinical skills were less likely to be evaluated by direct observation of history or physical examination skills. Resident responses were aggregated within training programs to determine the pattern of evaluation across programs. The majority of programs used Advanced Cardiac Life Support (ACLS) certification, medical record audit, and the national In-Training Examination to assess most of their residents. Performance-based tests were used selectively by a third or more of the programs. Breast and pelvic examination skills and ability to perform sigmoidoscopy were thought not to be adequately assessed by the majority of residents in almost half of the programs.Conclusions:While most residents are receiving routine evaluation, including a CEX, increased efforts to educate residents about their evaluation system, to strengthen evaluation in the outpatient setting, and to evaluate certain procedural skills are recommended.

Gender differences in the clinical competence of residents in internal medicine.

Objective:To study the differences between cognitive and noncognitive skills of men and those of women entering internal medicine.Design:Comparison of program directors’ ratings of overall clinical competence and its specific components and pass rates for men and women taking the Certifying Examinations in Internal Medicine in 1984–1987.Participants:14,340 U.S. and Canadian graduates taking the Certifying Examinations of the American Board of Internal Medicine for the first time in 1984–1987.Measurements/results:Average program directors’ ratings of overall competence were 6.70–6.78 for men and 6.60–6.71 for women. The greatest differences in ratings of specific components of competence were in the areas of medical knowledge and procedural skills, where men were rated higher than women, and humanistic qualities, where women were rated higher than men. Pass rates were stable over the four years of the study, and ranged from 85 to 86% for men and from 79 to 81% for women. Men consistently performed slightly better than women regardless of the type of residency or quality of medical school attended.Conclusions:Small but consistent differences were found in the performances of men and those of women completing training in Internal Medicine as measured by program directors’ ratings and ABIM Certifying Examination performances.

Using Default Options Within the Electronic Health Record to Increase the Prescribing of Generic-Equivalent Medications: A Quasi-experimental Study

Health care costs in the United States continue to rise and now account for more than

Redesigning the practice model for general internal medicine. A proposal for coordinated care: A policy monograph of the Society of General Internal Medicine

2.8 trillion annually (1). It is estimated that as much as one third of this spending is due to unnecessary waste in the health care system (2). The Choosing Wisely campaign is a joint initiative launched in 2012 by the American Board of Internal Medicine Foundation, Consumer Reports, and several medical societies (3). Its premise is to decrease unnecessary spending by focusing on reducing the use of low-value services. Similarly, the American College of Physicians (ACP) has launched the High Value Care Initiative with the goal of helping physicians provide the best possible care for their patients while simultaneously reducing unnecessary costs to the health care system (4). Prescribing brand-name medications that have existing generic equivalents is a prime example of a low-value service. These medications are often more expensive than their generic equivalents, yet in most cases evidence suggests they are similar in effectiveness (5). A recent study of 20 popular multisource drugs found that in 2009, Medicaid spent an additional

The scoring and reproducibility of an essay test of clinical judgment.

329 million that could have been saved by using existing generic equivalents instead of brand-name medications (6). The use of default options has been recognized as a successful strategy to change behavior in many settings, including health care (710). Default options are effective because they are often viewed as an implicit recommendation and because people tend to choose the path of least resistance. If a decision maker does not opt out, the defaults action takes place (7). Small changes in default settings can substantially affect medical decision making and provider behavior. In January 2012, the Division of General Internal Medicine at the University of Pennsylvania in Philadelphia implemented an intervention to change the default in the electronic health record (EHR) medication prescriber with the goal of increasing the prescribing of generic equivalents when available. Before the intervention, if a provider searched for a brand-name medication, the options for prescribing brand names were shown near the top with generic equivalents listed below. After the intervention, if a provider searched for a brand-name medication, only the generic equivalent was listed. Providers still maintained the ability to opt out and conduct a broader search that listed the brand if warranted. The objective of this study was to evaluate the effect of this intervention on physician behavior related to the ordering of brand-name medications versus existing generic equivalents. Methods The institutional review board at the University of Pennsylvania approved this study. Setting and Participants The sample comprised attending faculty and residents at 2 ambulatory clinics in the Division of General Internal Medicine (IM) and 2 ambulatory clinics in the Department of Family Medicine (FM) practicing between June 2011 and September 2012 at the University of Pennsylvania. All of these clinics were teaching practices where attending physicians practiced independently and served as preceptors for residents. The practices were located within the same ZIP code, and all providers used the same EHR. We classified providers by training level as attending physician or resident using publicly available listings of faculty and resident rosters (1114), along with special request from the Internal Medicine Residency Program, Family Medicine Residency Program, Division of General Internal Medicine, and Department of Family Medicine. Intervention During the preintervention period (June to December 2011), both IM and FM providers used the same EHR medication prescriber and were shown the same resulting options. If a provider searched for a brand-name medication, the dosing options for prescribing brand names were listed at the top, with the dosing options for generic-equivalent medications listed below (Figure 1). During the postintervention period (January to September 2012), this remained unchanged for FM providers. However, if IM providers searched for a brand-name medication, the results listed only dosing options for generic-equivalent medications (Figure 2). The IM providers had the ability to opt out (by clicking on Database Lookup or pressing F7 on the keyboard) and would then be shown the dosing options for brand names at the top, with the generic-equivalent medications listed below. Figure 1. Example of medication prescriber results before the intervention. Resulting medication dosing options before the intervention for internal medicine and family medicine providers when a search for Coreg was conducted. Brand-name options are listed first, followed by generic-equivalent options. Figure 2. Example of medication prescriber results after the intervention. Resulting medication dosing options after the intervention for internal medicine providers when a search for Coreg was conducted. Only the generic-equivalent options are listed; users have the ability to opt out and choose the brand-name if warranted. Data Provider prescription data were obtained by using EPICs analytical reporting database, Clarity (EPIC Systems). These data reported the number of prescriptions per month by each provider that were sent electronically to the pharmacy for each medication included in the sample. When an electronic prescription is sent to the pharmacy, a patient can arrive without a paper copy and pick up the medications after presenting appropriate identification. Our reports capture all electronic prescribing from June 2011 to September 2012. Handwritten or faxed prescriptions were not captured by the reports produced for this study. Reports were produced as Excel files (Microsoft), coded, and then analyzed with Stata software, version 12 (StataCorp). Selection of Medications We evaluated 3 classes of medications: -blockers, statins, and proton-pump inhibitors (PPIs). Because we did not have access to data that allowed adjustment for patient characteristics, we chose these 3 medication classes because patients with indications for these medications are similar between the 2 specialties in our study, thus providing a better comparison between the intervention and control groups. Among these 3 classes of medications, we excluded prescriptions for which a generic equivalent did not exist (e.g., Crestor [AstraZeneca]) to remove bias due to scenarios in which it was unclear whether prescribing a nonequivalent generic medication of the same class was not feasible or had already been done but was ineffective. We excluded combination medications to remove any bias related to scenarios in which the provider was attempting to decrease pill burden for the patient (for example, niacinsimvastatin extended-release) and an equivalent generic combination did not exist. We excluded prescriptions for Lipitor (Pfizer) and atorvastatin from the analysis because the generic equivalent had become available just 1 month before the intervention (30 November 2011) and there may have been alternative motives for switching between the brand and generic other than that related to the objective of our study. While this represented a large proportion of statins in the original sample, prescribing trends in the preintervention period differed because of the lack of a generic equivalent (see Data Supplement). Data Supplement. Additional Information The following medications and their associated brand-name equivalents were included in our sample: -blockers (atenolol, carvedilol, labetalol, metoprolol, nadolol, and propranolol), statins (lovastatin, pravastatin, and simvastatin), and PPIs (lansoprazole, omeprazole, and pantoprazole). Outcome Measures To evaluate the effect of the intervention for IM providers compared with FM providers, we examined monthly prescribing trends of generic medication equivalents in the pre- and postintervention periods. Outcomes were evaluated for all providers (attending physicians and residents) for all medications in the sample and by medication class (-blockers, statins, and PPIs). Subset analyses were conducted to separately examine trends for attending physicians and residents because evidence suggests that these 2 groups may have differing practice patterns (1517). Residents are closer than attending physicians to medical school training, where generic medication names are more commonly taught and tested. Attending physicians are likely to be exposed to longer periods of industry detailing than are residents. In addition, interventions to change provider behavior may differ in effect among attending physicians, who have had longer to form practicing habits, than residents, who may be newly learning their practice style. Statistical Analysis For each measure, a multivariate logistic regression model was fit by using each prescription as the unit of analysis and clustering on provider. The effect of time was modeled by a dummy variable for each month. We evaluated the effect of the intervention by using an interaction term for provider specialty (IM vs. FM) and time to compare the proportion of generic medication prescriptions for each medication with itself before and after the intervention, contrasting the changes in IM to changes in FM. By using FM (not exposed to the intervention) as the control group in this study design, we reduce potential biases from unmeasured variables, such as general trends in prescribing behavior over time. Standard errors in the models were adjusted to account for clustering provider (18, 19). We estimated the odds of prescribing a generic medication for IM providers compared with FM providers in the postintervention period relative to the preintervention period. To assess the mean effect of the intervention in the postintervention period, we exponentiated the mean of the monthly log odds ratios (ORs) and calculated a 95% CI for this quantity (20, 21). We report the estimated probabilities of generic medic

The validity of an essay test of clinical judgment

General Internal Medicine (GIM) faces a burgeoning crisis in the United States, while patients with chronic illness confront a disintegrating health care system. Reimbursement that rewards using procedures and devices rather than thoughtful examination and management, plus onerous administrative burdens, are prompting physicians to pursue specialties other than GIM. This monograph promotes 9 principles supporting the concept of Coordinated Care—a strategy to sustain quality and enhance the attractiveness and viability of care delivered by highly trained General Internists who specialize in the longitudinal care of adult patients with acute and chronic illness. This approach supplements and extends the concept of the Advanced Medical Home set forth by the American College of Physicians. Specific components of Coordinated Care include clinical support, information management, and access and scheduling. Success of the model will require changes in the payment system that fairly reimburse physicians who provide leadership to teams that deliver high quality, coordinated care.General Internal Medicine (GIM) faces a burgeoning crisis in the United States, while patients with chronic illness confront a disintegrating health care system. Reimbursement that rewards using procedures and devices rather than thoughtful examination and management, plus onerous administrative burdens, are prompting physicians to pursue specialties other than GIM. This monograph promotes 9 principles supporting the concept of Coordinated Care—a strategy to sustain quality and enhance the attractiveness and viability of care delivered by highly trained General Internists who specialize in the longitudinal care of adult patients with acute and chronic illness. This approach supplements and extends the concept of the Advanced Medical Home set forth by the American College of Physicians. Specific components of Coordinated Care include clinical support, information management, and access and scheduling. Success of the model will require changes in the payment system that fairly reimburse physicians who provide leadership to teams that deliver high quality, coordinated care.

Generic Medication Prescription Rates After Health System–Wide Redesign of Default Options Within the Electronic Health Record

No abstract available.