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



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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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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

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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. 


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.


Joel Boggan

Associate Professor of Medicine

I am a hospital medicine physician interested in quality improvement, patient safety, and medical education across the UME, GME, and CME environments. My current QI and research projects include work on readmissions, inpatient ORYX and patient experience measures, clinical documentation improvement, medication reconciliation, and appropriate utilization of inpatient resources. Alongside this work, I serve as the lead mentor for our Durham VA Chief Resident in Quality and Safety within the Department of Medicine and the Program Director for the Duke University Hospital CRQS.

As Associate Program Director for Quality Improvement and Patient Safety in the Duke Internal Medicine Residency Program, I oversee QI and safety education and projects for our residents and help co-lead our Residency Patient Safety and Quality Council. Additionally, I supervise housestaff and students on our general medicine wards, precept housestaff evidence-based medicine resident reports, and serve as a small group leader for our second-year medical student Clinical Skills Course. Finally, I lead our Innovation Sciences committee as part of the ongoing School of Medicine Curriculum Innovation Initiative.


Samantha Thomas

Biostatistician, Principal

Samantha is the manager of the Duke Cancer Institute (DCI) Biostatistics Shared Resource. Collaboratively, she primarily works with physicians in DCI, specifically in research of Endocrine Neoplasia and Breast Cancer. She is also the director of the Biostatistics, Epidemiology, Research, and Design Methods (BERD) Core Training and Internship Program (BCTIP). Her professional experience involves study design, analysis, and reporting of clinical trials and observational studies. Her specific areas of interest include training of collaborative biostatisticians, modeling of non-linear associations, and application of partitioning analyses to identify homogeneous patient groups.


Jonathan Gregory Bae

Associate Professor of Medicine

Patient safety and quality improvement, hospital based performance improvement, care transitions and hospital readmissions, general internal medicine hospital care, resident and medical student education.

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