Designing risk prediction models for ambulatory no-shows across different specialties and clinics.
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Objective:As available data increases, so does the opportunity to develop risk scores on more refined patient populations. In this paper we assessed the ability to derive a risk score for a patient no-showing to a clinic visit. Methods:Using data from 2 264 235 outpatient appointments we assessed the performance of models built across 14 different specialties and 55 clinics. We used regularized logistic regression models to fit and assess models built on the health system, specialty, and clinic levels. We evaluated fits based on their discrimination and calibration. Results:Overall, the results suggest that a relatively robust risk score for patient no-shows could be derived with an average C-statistic of 0.83 across clinic level models and strong calibration. Moreover, the clinic specific models, even with lower training set sizes, often performed better than the more general models. Examination of the individual models showed that risk factors had different degrees of predictability across the different specialties. Implementation of optimal modeling strategies would lead to capturing an additional 4819 no-shows per-year. Conclusion:Overall, this work highlights both the opportunity for and the importance of leveraging the available electronic health record data to develop more refined risk models.
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Published Version (Please cite this version)10.1093/jamia/ocy002
Publication InfoDing, Xiruo; Gellad, Ziad F; Mather, Chad; Barth, Pamela; Poon, Eric G; Newman, Mark; & Goldstein, Benjamin A (2018). Designing risk prediction models for ambulatory no-shows across different specialties and clinics. Journal of the American Medical Informatics Association : JAMIA, 25(8). pp. 924-930. 10.1093/jamia/ocy002. Retrieved from https://hdl.handle.net/10161/21421.
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Associate Professor of Medicine
Dr. Gellad is an associate professor of medicine in the Division of Gastroenterology at Duke University Medical Center and a faculty member of the Duke Clinical Research Institute. He is also a VA Career Development Awardee and holds an appointment in the Health Services Research and Development Center of Innovation at the Durham VA Medical Center. His research focuses on the implementation of systems engineering methods to improve the quality and value of health care delivery wit
Associate Professor of Biostatistics & Bioinformatics
I study the meaningful use of Electronic Health Records data. My research interests sit at the intersection of biostatistics, biomedical informatics, machine learning and epidemiology. I collaborate with researchers both locally at Duke as well as nationally. I am interested in speaking with any students, methodologistis or collaborators interested in EHR data.Please find more information at: https://sites.duke.edu/bgoldstein/
Professor of Medicine
I currently serve as the Chief Health Information Officer for Duke Medicine. I also practice primary care internal medicine at the Durham Medical Center as part of Duke Primary Care. In my capacity as CHIO, I am responsible for the visioning and strategic planning of clinical and analytic information systems that impact patient care, research and education. I work with the Duke Medicine leadership to ensure technology solutions are well aligned with our overall organizational objectives
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