Data mining approaches to identify predictors of frequent malpractice claims against dentists

Joseph Finkelstein; Sinan Zhu

doi:10.1109/UEMCON.2017.8249086

Data mining approaches to identify predictors of frequent malpractice claims against dentists

Joseph Finkelstein
, Sinan Zhu

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

2 Scopus citations

Abstract

We separated all malpractice records for US dentists into two groups according to the total number of malpractice records (0: less than 5 records, 1: more than 4 records), extracted the first malpractice record of all dental practitioners' and used malpractice allegation group, payment and years between graduation and year of the first record in logistic regression to identify crucial factors for predicting dentists who made more than four malpractice records. Bivariate statistics, cross-correlation and principal component analysis were used to identify predictive features. Resulting model allowed prediction of dentists with frequent malpractice records based on the following characteristics of the first malpractice record: allegation type, payment amount and number of years from graduation to the first malpractice claim. Time between provider graduation year and the first malpractice record as well higher malpractice payment for the first claim were negatively correlated with the total number of malpractice records in individual providers.

Original language	English (US)
Title of host publication	2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017
Editors	Satyajit Chakrabarti, Himadri Nath Saha
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	463-468
Number of pages	6
ISBN (Electronic)	9781538611043
DOIs	https://doi.org/10.1109/UEMCON.2017.8249086
State	Published - Jul 1 2017
Externally published	Yes
Event	8th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017 - New York City, United States Duration: Oct 19 2017 → Oct 21 2017

Publication series

Name	2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017
Volume	2018-January

Conference

Conference	8th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017
Country/Territory	United States
City	New York City
Period	10/19/17 → 10/21/17

Keywords

Big data
Data mining
Dentistry
Malpractice claims
Medical errors
Patient safety
Predictive analytics

ASJC Scopus subject areas

Signal Processing
Hardware and Architecture
Computer Networks and Communications
Biotechnology

Access to Document

10.1109/UEMCON.2017.8249086

Cite this

Finkelstein, J., & Zhu, S. (2017). Data mining approaches to identify predictors of frequent malpractice claims against dentists. In S. Chakrabarti, & H. N. Saha (Eds.), 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017 (pp. 463-468). (2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/UEMCON.2017.8249086

Data mining approaches to identify predictors of frequent malpractice claims against dentists. / Finkelstein, Joseph; Zhu, Sinan.
2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017. ed. / Satyajit Chakrabarti; Himadri Nath Saha. Institute of Electrical and Electronics Engineers Inc., 2017. p. 463-468 (2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017; Vol. 2018-January).

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

Finkelstein, J & Zhu, S 2017, Data mining approaches to identify predictors of frequent malpractice claims against dentists. in S Chakrabarti & HN Saha (eds), 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017. 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 463-468, 8th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017, New York City, United States, 10/19/17. https://doi.org/10.1109/UEMCON.2017.8249086

Finkelstein J, Zhu S. Data mining approaches to identify predictors of frequent malpractice claims against dentists. In Chakrabarti S, Saha HN, editors, 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 463-468. (2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017). doi: 10.1109/UEMCON.2017.8249086

Finkelstein, Joseph ; Zhu, Sinan. / Data mining approaches to identify predictors of frequent malpractice claims against dentists. 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017. editor / Satyajit Chakrabarti ; Himadri Nath Saha. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 463-468 (2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2017).

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abstract = "We separated all malpractice records for US dentists into two groups according to the total number of malpractice records (0: less than 5 records, 1: more than 4 records), extracted the first malpractice record of all dental practitioners' and used malpractice allegation group, payment and years between graduation and year of the first record in logistic regression to identify crucial factors for predicting dentists who made more than four malpractice records. Bivariate statistics, cross-correlation and principal component analysis were used to identify predictive features. Resulting model allowed prediction of dentists with frequent malpractice records based on the following characteristics of the first malpractice record: allegation type, payment amount and number of years from graduation to the first malpractice claim. Time between provider graduation year and the first malpractice record as well higher malpractice payment for the first claim were negatively correlated with the total number of malpractice records in individual providers.",

keywords = "Big data, Data mining, Dentistry, Malpractice claims, Medical errors, Patient safety, Predictive analytics",

author = "Joseph Finkelstein and Sinan Zhu",

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Data mining approaches to identify predictors of frequent malpractice claims against dentists

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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