Automated problem list generation and physicians perspective from a pilot study
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An accurate, comprehensive and up-to-date problem list can help clinicians provide patient-centered care. Unfortunately, problem lists created and maintained in electronic health records by providers tend to be inaccurate, duplicative and out of date. With advances in machine learning and natural language processing, it is possible to automatically generate a problem list from the data in the EHR and keep it current. In this paper, we describe an automated problem list generation method and report on insights from a pilot study of physicians’ assessment of the generated problem lists compared to existing providers-curated problem lists in an institution's EHR system. Materials and methods The natural language processing and machine learning-based Watson 1
Published Version (Please cite this version)10.1016/j.ijmedinf.2017.05.015
Publication InfoDevarakonda, MV; Mehta, N; Tsou, CH; Liang, JJ; Nowacki, AS; & Jelovsek, John E (2017). Automated problem list generation and physicians perspective from a pilot study. International Journal of Medical Informatics, 105. pp. 121-129. 10.1016/j.ijmedinf.2017.05.015. Retrieved from https://hdl.handle.net/10161/15105.
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Associate Professor of Obstetrics and Gynecology
Dr. Jelovsek is the Vice Chair of Education and the Director of Data Science for Women’s Health in Department of Obstetrics & Gynecology (OBGYN) at Duke University. He is Board Certified in OBGYN by the American Board of OBGYN and Board Certified in Female Pelvic Medicine & Reconstructive Surgery by the American Board of OBGYN and American Board of Urology. He currently practices Female Pelvic Medicine and Reconstructive Surgery (FPMRS). He has expertise in the development and v