Journal of general internal medicine
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To determine the extent of variability in the administration of advanced cardiac life support (ACLS) and to determine if age is associated with variability. ⋯ Wide variability exists in the administration of ACLS at the studied site. The finding that some patients receive no ACLS suggests that physicians at this site may be making bedside determinations of the likelihood of its benefit based on individual patient characteristics. The association between older age and short ACLS trials among all nonsurvivors suggests that age is most important of these characteristics.
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Comparative Study
Evaluation of internists' spirometric interpretations.
Correct interpretation of screening spirometry results is essential in making accurate clinical diagnoses and directing subsequent pulmonary evaluation. The general internist is largely responsible for interpreting screening spirometric tests at community hospitals. However, reports of new guidelines for screening spirometry are infrequently published in the general internal medicine literature. This can lead to incorrect interpretations. We sought to evaluate whether spirometric interpretations by a group of practicing general internists differed from those of two board-certified pulmonologists using guidelines published by the American Thoracic Society (ATS). ⋯ The spirometric interpretations of a group of general internists differed significantly from those of two board-certified pulmonologists using published guidelines in approximately one third of cases. This may be because subspecialty guidelines are infrequently published in the general internal medicine literature. We believe that wider dissemination of these interpretative guidelines and ongoing physician education would improve general internists' ability to identify patients who require further pulmonary evaluation.
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We studied the association between calendar and weather variables and daily unscheduled patient volume in a walk-in clinic and emergency department. Calendar variables (season, week of month, day of week, holidays, and federal check-delivery days) and weather variables (high temperature and snowfall) forecasted clinic volume, explaining 84% of daily variance and 44% of weekday variance. Staffing according to predicted volume could have decreased overstaffing from 59% to 15% of days, but would have increased understaffing from 2% to 18% of days. Models using calendar and weather data that forecast local utilization may help to schedule staffing for walk-in clinics and emergency departments more efficiently.