OBJECTIVES We sought to evaluate methodologies to compare physician-related long-term patient outcomes appropriately. BACKGROUND Evaluation of physicians on the basis of short-term patient outcome is becoming widely p...
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OBJECTIVES We sought to evaluate methodologies to compare physician-related long-term patient outcomes appropriately. BACKGROUND Evaluation of physicians on the basis of short-term patient outcome is becoming widely practiced. These analyses fail to consider the importance of long-term outcome, and methods appropriate to such an analysis are poorly defined. METHODS All patients undergoing coronary angiography between 1992 and 1994 who received all of their cardiac care at our institution were followed for 27 +/- 13 months (mean +/- SD). Patients (n = 754) were cared for by one or more of 17 staff physicians. Risk-adjusted models were developed for four candidate clinical end points and cost. Physicians were then evaluated for each outcome measure. RESULTS Of the clinical end points, death could be modeled most accurately (c-statistic = 0.83), The c-statistics for other end points ranged from 0.63 to 0.70. Physicians with outcomes statistically different (p < 0.05) from other physicians were identified more commonly than would be expected from the play of chance (p = 0.005). However, improvement in the c-statistics by the addition of physician identifiers was very modest. Physician's evaluations by the four measures of clinical outcome were variably correlated (r = .00 to .85). Graphic display of clinical and cost results for each physician did identify certain physicians who might be judged to provide more cost-effective care than others. CONCLUSIONS Although comparisons of groups of physicians on the basis of long-term patient outcomes may have merit, individual physician-to-physician comparisons will be more difficult, owing to 1) multiple physicians contributing care to individual patients;2) the poor predictive capacity of models other than that for survival;and 3) the modest apparent impact of differences in physician providers on long-term patient outcome. With these caveats in mind, modeling to compare patient outcomes of individual physicians with homogeneou
Background it has been nearly a decade since Goldman's computer-driven algorithm to predict myocardial infarction was validated. Despite the potential to avoid admission of patients without acute myocardial infarc...
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Background it has been nearly a decade since Goldman's computer-driven algorithm to predict myocardial infarction was validated. Despite the potential to avoid admission of patients without acute myocardial infarction (AMI) to the coronary care unit (CCU), the routine use of computer-generated protocols has not been widely adopted. Methods Two hundred consecutive patients admitted to a university-affiliate community hospital with the suspected diagnosis of AMI as determined by physicians without the aid of the Goldman protocol underwent a blinded prospective evaluation to assess the performance of the Goldman algorithm in predicting the presence of AMI. Over the same time period, the Goldman algorithm was applied by retrospective chart review in 762 patients with non-AMI admitting diagnoses. Prospective history, physical examination, and electrocardiographic data were obtained within 24 hours of admission to the CCU by a physician blinded to each patient's clinical course. Retrospective chart reviews were conducted for 762 patients with chest pain given with non-AMI diagnoses. Results The diagnosis of AMI was confirmed in 68.5% (137/200) of patients with suspected AM I admitted to the CCU. In prospective parallel evaluations the Goldman algorithm predicted the presence of AMI in 167 (83.5%) of these 200 patients. All 137 confirmed patients with AMI were correctly identified by the Goldman algorithm. All major in-hospital complications occurred in the 137 patients who were diagnosed as having AMI. Of the 762 patients with chest pain with non-AMI diagnoses, only 27 (3.5%) sustained an AMI. The Goldman algorithm predicted the presence of AMI in 85% (23/27) of these patients. Adherence to the use of Goldman's algorithm in the triage of chest pain could have prevented 16.5% of CCU admissions for AMI. Conclusions Routine adherence to the Goldman algorithm for the evaluation of patients with acute chest pain could have decreased the number of CCU admissions for suspected A
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