Phenotyping physicians with frequent malpractice claims

Joseph Finkelstein; Sinan Zhu

doi:10.1109/BIBM.2017.8217893

Phenotyping physicians with frequent malpractice claims

Joseph Finkelstein, Sinan Zhu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Editors	Illhoi Yoo, Jane Huiru Zheng, Yang Gong, Xiaohua Tony Hu, Chi-Ren Shyu, Yana Bromberg, Jean Gao, Dmitry Korkin
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1567-1570
Number of pages	4
ISBN (Electronic)	9781509030491
DOIs	https://doi.org/10.1109/BIBM.2017.8217893
State	Published - 15 Dec 2017
Externally published	Yes
Event	2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States Duration: 13 Nov 2017 → 16 Nov 2017

Publication series

Name	Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Volume	2017-January

Conference

Conference	2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Country/Territory	United States
City	Kansas City
Period	13/11/17 → 16/11/17

Keywords

big data
data mining
malpractice claims
medical errors
patient safety
predictive analytics

Access to Document

10.1109/BIBM.2017.8217893

Cite this

Finkelstein, J., & Zhu, S. (2017). Phenotyping physicians with frequent malpractice claims. In I. Yoo, J. H. Zheng, Y. Gong, X. T. Hu, C.-R. Shyu, Y. Bromberg, J. Gao, & D. Korkin (Eds.), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 (pp. 1567-1570). (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2017.8217893

Finkelstein, Joseph ; Zhu, Sinan. / Phenotyping physicians with frequent malpractice claims. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. editor / Illhoi Yoo ; Jane Huiru Zheng ; Yang Gong ; Xiaohua Tony Hu ; Chi-Ren Shyu ; Yana Bromberg ; Jean Gao ; Dmitry Korkin. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1567-1570 (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017).

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title = "Phenotyping physicians with frequent malpractice claims",

abstract = "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.",

keywords = "big data, data mining, malpractice claims, medical errors, patient safety, predictive analytics",

author = "Joseph Finkelstein and Sinan Zhu",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 ; Conference date: 13-11-2017 Through 16-11-2017",

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month = dec,

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doi = "10.1109/BIBM.2017.8217893",

language = "English",

series = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",

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Finkelstein, J & Zhu, S 2017, Phenotyping physicians with frequent malpractice claims. in I Yoo, JH Zheng, Y Gong, XT Hu, C-R Shyu, Y Bromberg, J Gao & D Korkin (eds), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 1567-1570, 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, Kansas City, United States, 13/11/17. https://doi.org/10.1109/BIBM.2017.8217893

Phenotyping physicians with frequent malpractice claims. / Finkelstein, Joseph; Zhu, Sinan.
Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. ed. / Illhoi Yoo; Jane Huiru Zheng; Yang Gong; Xiaohua Tony Hu; Chi-Ren Shyu; Yana Bromberg; Jean Gao; Dmitry Korkin. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1567-1570 (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Zhu, Sinan

PY - 2017/12/15

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N2 - 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.

AB - 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.

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A2 - Shyu, Chi-Ren

A2 - Bromberg, Yana

A2 - Gao, Jean

A2 - Korkin, Dmitry

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

Y2 - 13 November 2017 through 16 November 2017

ER -

Finkelstein J, Zhu S. Phenotyping physicians with frequent malpractice claims. In Yoo I, Zheng JH, Gong Y, Hu XT, Shyu CR, Bromberg Y, Gao J, Korkin D, editors, Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1567-1570. (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017). doi: 10.1109/BIBM.2017.8217893

Phenotyping physicians with frequent malpractice claims

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Keywords

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