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Journal of the American Statistical Association Volume 86, 1991 - Issue 414
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Applications and Case Studies

Using Medical Malpractice Data to Predict the Frequency of Claims: A Study of Poisson Process Models with Random Effects

Bruce Cooil Owen Graduate School of Management, Vanderbilt University, Nashville, TN, 37203
Pages 285-295 | Received 01 Apr 1989, Published online: 27 Feb 2012

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MarjorieA. Rosenberg, EdwardW. Frees, Jiafeng Sun, PaulH. Johnson$suffix/text()$suffix/text() & Jim Robinson. (2007) Predictive Modeling with Longitudinal Data. North American Actuarial Journal 11:3, pages 54-69.
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RobertD. Gibbons, Donald Hedeker, SaraC. Charles & Paul Frisch. (1994) A Random-Effects Probit Model for Predicting Medical Malpractice Claims. Journal of the American Statistical Association 89:427, pages 760-767.
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Articles from other publishers (13)

Katherine Morris & Matthew J. Spittal. (2023) Grading pharmacists’ risk of complaints to a regulator: A retrospective cohort study. Journal of Pharmacy & Pharmaceutical Sciences 26.
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Byeong Min Mun, Paul H. Kvam & Suk Joo Bae. (2021) Mixed-Effects Nonhomogeneous Poisson Process Model for Multiple Repairable Systems. IEEE Access 9, pages 71900-71908.
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Javier A. Caballero & Steve P. Brown. (2019) Engagement, not personal characteristics, was associated with the seriousness of regulatory adjudication decisions about physicians: a cross-sectional study. BMC Medicine 17:1.
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Matthew J. Spittal, Marie M. Bismark & David M. Studdert. (2019) Identification of practitioners at high risk of complaints to health profession regulators. BMC Health Services Research 19:1.
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Bernard Black, David A. Hyman & Joshua Y. Lerner. (2019) Physicians with multiple paid medical malpractice claims: Are they outliers or just unlucky?. International Review of Law and Economics 58, pages 146-157.
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Timothy J. Vogus & Dawn Iacobucci. (2016) Creating Highly Reliable Health Care. ILR Review 69:4, pages 911-938.
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Marco Bonetti, Pasquale Cirillo, Paola Musile Tanzi & Elisabetta Trinchero. (2016) An Analysis of the Number of Medical Malpractice Claims and Their Amounts. PLOS ONE 11:4, pages e0153362.
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David M. Studdert, Marie M. Bismark, Michelle M. Mello, Harnam Singh & Matthew J. Spittal. (2016) Prevalence and Characteristics of Physicians Prone to Malpractice Claims. New England Journal of Medicine 374:4, pages 354-362.
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Matthew J Spittal, Marie M Bismark & David M Studdert. (2015) The PRONE score: an algorithm for predicting doctors’ risks of formal patient complaints using routinely collected administrative data. BMJ Quality & Safety 24:6, pages 360-368.
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Jing-Zhi Huang, Zhan Shi & Wei Zhong. 2015. Handbook of Financial Econometrics and Statistics. Handbook of Financial Econometrics and Statistics 2093 2118 .
Marie M Bismark, Matthew J Spittal, Lyle C Gurrin, Michael Ward & David M Studdert. (2013) Identification of doctors at risk of recurrent complaints: a national study of healthcare complaints in Australia. BMJ Quality & Safety 22:7, pages 532-540.
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Evan A. Saltzman, John H. Drew, Lawrence M. Leemis & Shane G. Henderson. (2012) Simulating Multivariate Nonhomogeneous Poisson Processes Using Projections. ACM Transactions on Modeling and Computer Simulation 22:3, pages 1-13.
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Timothy J. Vogus & Kathleen M. Sutcliffe. (2007) The Impact of Safety Organizing, Trusted Leadership, and Care Pathways on Reported Medication Errors in Hospital Nursing Units. Medical Care 45:10, pages 997-1002.
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