An evaluation of physician predictions of discharge on a general medicine service.

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2015-12

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Abstract

The goal of this study was to evaluate general medicine physicians' ability to predict hospital discharge. We prospectively asked study subjects to predict whether each patient under their care would be discharged on the next day, on the same day, or neither. Discharge predictions were recorded at 3 time points: mornings (7-9 am), midday (12-2 pm), or afternoons (5-7 pm), for a total of 2641 predictions. For predictions of next-day discharge, the sensitivity (SN) and positive predictive value (PPV) were lowest in the morning (27% and 33%, respectively), but increased by the afternoon (SN 67%, PPV 69%). Similarly, for same-day discharge predictions, SN and PPV were highest at midday (88% and 79%, respectively). We found that although physicians have difficulty predicting next-day discharges in the morning prior to the day of expected discharge, their ability to correctly predict discharges continually improved as the time to actual discharge decreased. Journal of Hospital Medicine 2015;10:808-810. © 2015 Society of Hospital Medicine.

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Published Version (Please cite this version)

10.1002/jhm.2439

Publication Info

Sullivan, B, D Ming, JC Boggan, RD Schulteis, S Thomas, J Choi and J Bae (2015). An evaluation of physician predictions of discharge on a general medicine service. J Hosp Med, 10(12). pp. 808–810. 10.1002/jhm.2439 Retrieved from https://hdl.handle.net/10161/12041.

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Scholars@Duke

Sullivan

Brian Sullivan

Assistant Professor of Medicine

I am a Physician Scientist in Gastroenterology, with a research focus in optimizing colorectal cancer (CRC) screening and surveillance recommendations. This includes evaluating current and evolving CRC screening strategies and identifying people at high risk for underlying hereditary/genetic CRC syndromes. 

Ming

David Yung Ming

Associate Professor of Pediatrics

I am a med-peds hospitalist and researcher with interests in improving systems of care of patients with complex health needs. My research focus areas include implementation science, population health sciences, community-engaged research, and digital health. My vision is to design, implement, evaluate, and scale programs and interventions that will simplify the delivery of complex care. By doing so, we can equitably improve the health outcomes that matter most to children and adults with complex health needs and their families.


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