BACKGROUND Learning from medical errors and near-misses based on retrospective, single-case outcomes is an ubiquitous part of medical training, so much so that morbidity and mortality (M&M) conferences are a required component of graduate medical education in the United States and have been since 1983. 1 Despite widespread use of the M&M conference, its format remains heterogenous with significant variation between programs. 1,2 The origin of the M&M conference can be traced to the early 20th century when Ernest Codman, a surgeon and outspoken reformer at Massachusetts General Hospital, introduced the end-results system, which employed end-result cards to publicly document individual surgeon's outcomes. 2 While this system of blame assignment was met with intense opposition at the time, it largely informed the initial iteration of the M&M conference. 2 Despite over a century of shared experience with M&M conferences among medical centers, many of the limitations of the primitive M&M conference still exist today. These include haphazard retrospective collection of data, focus on isolated and anecdotal events without consideration of previous similar events, recall bias, lack of meaningful audit, narrow focus on individual performance, lack of systems-based thinking, and lack of

Objectives: 1) To assess temporal patterns in historical patient arrival rates in an emergency department (ED) to determine the appropriate number of shift schedules in an acute care area and a fast-track clinic and 2) to determine whether physician scheduling can be improved by aligning physician productivity with patient arrivals using an optimization planning model. Methods: Historical data were statistically analyzed to determine whether the number of patients arriving at the ED varied by weekday, weekend, or holiday weekend. Poisson-based generalized additive models were used to develop models of patient arrival rate throughout the day. A mathematical programming model was used to produce an optimal ED shift schedule for the estimated patient arrival rates. We compared the current physician schedule to three other scheduling scenarios: 1) a revised schedule produced by the planning model, 2) the revised schedule with an additional acute care physician, and 3) the revised schedule with an additional fast-track clinic physician. Results: Statistical modelling found that patient arrival rates were different for acute care versus fast-track clinics; the patterns in arrivals followed essentially the same daily pattern in the acute care area; and arrival patterns differed on weekdays versus weekends in the fast-track clinic. The planning model reduced the unmet patient demand (i.e., the average number of patients arriving at the ED beyond the average physician productivity) by 19%, 39%, and 69% for the three scenarios examined. Conclusions: The planning model improved the shift schedules by aligning physician productivity with patient arrivals at the ED.