Each year 1.5 million patients are admitted to coronary-care units (CCUs) for suspected acute ischemic heart disease; for half of these, the diagnosis is ultimately "ruled out." In this study, conducted in the emergency rooms of six New England hospitals ranging in type from urban teaching centers to rural nonteaching hospitals, we sought to develop a diagnostic aid to help emergency room physicians reduce the number of their CCU admissions of patients without acute cardiac ischemia. From data on 2801 patients, we developed a predictive instrument for use in a hand-held programmable calculator, which requires only 20 seconds to compute a patient's probability of having acute cardiac ischemia. In a prospective trial that included 2320 patients in the six hospitals, physicians' diagnostic specificity for acute ischemia increased when the probability value determined by the instrument was made available to them. Rates of false-positive diagnosis decreased without any increase in rates of false-negative diagnosis. Among study patients with a final diagnosis of "not acute ischemia," the number of CCU admissions decreased 30 per cent, without any increase in missed diagnoses of ischemia. The proportion of CCU admissions that represented patients without acute ischemia dropped from 44 to 33 per cent. Widespread use of this predictive instrument could reduce the number of CCU admissions in this country by more than 250,000 per year.
A mathematical instrument was developed to supplement the diagnostic information available to physicians in the emergency room to improve physicians' diagnostic accuracy in managing patients with acute ischemic heart disease and thereby reduce inappropriate coronary care unit admissions. The instrument was empirically derived and is based on nine clinical, historical, and electrocardiographic predictive variables. Probabilities of acute ischemic heart disease generated by the instrument were given to the house staff in an emergency room during alternate months. Comparison of the control months (455 patients) with the experimental months (401 patients) showed the following: The overall diagnostic accuracy increased from 83% to 91% (P less than 0.005), the overdiagnostic accuracy increased from 51% to 33% (P less than 0.01), and the admission rate to the coronary care unit fell from 26% to 14% (P less than 0.001), while the inappropriate discharge rate from the emergency room did not change, 3% versus 3% (not significant).
This study examines the popular belief that increased educational supervision and increased administrative support in university outpatient clinics will improve physician performance, which in turn will improve the process and outcome of patient care. Positive effects on house officers' attitudes and better func-
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