Phenotyping physicians with frequent malpractice claims

Medical errors and patient safety have been receiving significant attention since the landmark publication by Institute of Medicine in 2000. However, characteristics of physicians implicated in frequent medical errors were not studied systematically. We used National Practitioner Data Bank (NPDB) containing malpractice claims since 1990 to identify characteristics predictive of physicians with frequent malpractice claims. We separated all malpractice records for US physicians into two groups according to the total number of malpractice records (0: less than 5 records, 1: more than 4 records) and compared characteristics of the first malpractice record in each group. Overall, 137,590 unique records were analyzed. Four percent of physicians (5371) had 5 or more malpractice claims. Bivariate statistics, cross-correlation and principal component analysis were used to identify predictive features. Logistic regression was used for predictive modeling. Resulting model allowed prediction of physicians with frequent malpractice records based on the following characteristics of the first malpractice record: allegation type, practitioner age, number of years from graduation to the first malpractice claim.

[1]  F. Mullan,et al.  The National Practitioner Data Bank. Report from the first year. , 1992, JAMA.

[2]  J A Lovitky.  The National Practitioner Data Bank: coping with the uncertainties. , 2000, Journal of health law.

[3]  L. Kohn,et al.  To Err Is Human : Building a Safer Health System , 2007 .

[4]  M. Roizen,et al.  Paid Malpractice Claims for Adverse Events in Inpatient and Outpatient Settings , 2012 .

[5]  Joseph Finkelstein,et al.  Feasibility of a Palmtop-Based Interactive Education to Promote Patient Safety , 2003, AMIA.

[6]  Lovitky Ja.  The National Practitioner Data Bank: coping with the uncertainties. , 2000 .

[7]  Joseph Finkelstein,et al.  Analysis of Maryland Poisoning Deaths Using Classification And Regression Tree (CART) Analysis , 2008, AMIA.

[8]  McKenzie Bedra,et al.  Computerized Insulin Order Sets Can Lead to Unanticipated Consequences , 2015, ICIMTH.

[9]  Joseph Finkelstein,et al.  Machine learning approaches to personalize early prediction of asthma exacerbations , 2017, Annals of the New York Academy of Sciences.

[10]  Jeffrey Wood,et al.  Introducing a Comprehensive Informatics Framework to Promote Breast Cancer Risk Assessment and Chemoprevention in the Primary Care Setting , 2017, AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science.

[11]  Joseph Finkelstein,et al.  Patient Subjective Assessment of Drug Side Effects in Inflammatory Bowel Disease , 2008, Journal of clinical gastroenterology.

[12]  Joseph Finkelstein,et al.  Use of provider-level dashboards and pay-for-performance in venous thromboembolism prophylaxis. , 2015, Journal of hospital medicine.