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 hospitalist-preceptors and academician-preceptors (isolating the effect of attending type, given housestaff involvement).

Design

A four-year retrospective cohort study of patients (n = 13,313) admitted to all internal medicine services at an academic medical center from July 2008 to June 2012.

Main measures

Using generalized estimating equations, we measured readmission within 30 days, hospital length of stay, cost of the index hospitalization, and cumulative cost including readmissions within 30 days.

Key results

In the adjusted models, 30-day readmission odds were higher for academic-preceptors (OR, 1.14 [95% CI, 1.03 - 1.26]) and hospitalist-preceptors (OR, 1.10 [95% CI, 1.002 - 1.21]) than for hospitalists working alone. Compared with hospitalists working alone, academic-preceptors were associated with shorter length of stay (mean difference, 0.27 days [95% CI, 0.18 - 0.38]), lower index hospitalization costs (mean difference, $386 [95% CI, $192 - $576]), but similar cumulative inpatient costs within 30 days of discharge. Compared with hospitalists working alone, hospitalist-preceptors were associated with shorter length of stay (mean difference, 0.34 days [95% CI, 0.26 - 0.42]), lower index hospitalization cost (mean difference, $570 [95% CI, $378 - $760]), and a trend toward lower cumulative cost (mean difference, $1347 [95% CI, $254 - $2,816]).

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 academician-preceptors incurred the highest cost and hospitalist-preceptors incurred the lowest cost.

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 - dose - was 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.