BACKGROUND: Patient outcomes with hospitalist care have been studied in many settings, yet little is known about how hospitalist care interacts with trainee care to affect patient outcomes in teaching hospitals. OBJECTIVES: The aim of this study was to compare patient outcomes between hospitalist-preceptors and hospitalists working alone (isolating the effect of housestaff involvement), and between hospitalistpreceptors and academician-preceptors (isolating the effect of attending type, given housestaff involvement). CONCLUSIONS: Preceptor-led medicine services were associated with more readmissions within 30 days, shorter lengths of stay, and lower index admission-associated costs. However, when considering cumulative hospitalization costs, patients discharged by academicianpreceptors incurred the highest cost and hospitalistpreceptors incurred the lowest cost.KEY WORDS: quality of health care; hospitalists; academic medical centers; patient readmission; hospital costs; length of stay.

We aimed to develop and validate an instrument to detect hospital medication prescribing errors using repurposed clinical decision support system data. Despite significant efforts to eliminate medication prescribing errors, these events remain common in hospitals. Data from clinical decision support systems have not been used to identify prescribing errors as an instrument for physician-level performance. We evaluated medication order alerts generated by a knowledge-based electronic prescribing system occurring in one large academic medical center's acute care facilities for patient encounters between 2009 and 2012. We developed and validated an instrument to detect medication prescribing errors through a clinical expert panel consensus process to assess physician quality of care. Six medication prescribing alert categories were evaluated for inclusion, one of which dosewas included in the algorithm to detect prescribing errors. The instrument was 93% sensitive (recall), 51% specific, 40% precise, 62% accurate, with an F1 score of 55%, positive predictive value of 96%, and a negative predictive value of 32%. Using repurposed electronic prescribing system data, dose alert overrides can be used to systematically detect medication prescribing errors occurring in an inpatient setting with high sensitivity. Keywords Decision support systems, clinical. Quality of health care. Outcome and process assessment (health care). Medication errors. Medical informatics applications. Electronic health records