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© 2023. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Background:Health care data breaches are the most rapidly increasing type of cybercrime; however, the predictors of health care data breaches are uncertain.

Objective:This quantitative study aims to develop a predictive model to explain the number of hospital data breaches at the county level.

Methods:This study evaluated data consolidated at the county level from 1032 short-term acute care hospitals. We considered the association between data breach occurrence (a dichotomous variable), predictors based on county demographics, and socioeconomics, average hospital workload, facility type, and average performance on several hospital financial metrics using 3 model types: logistic regression, perceptron, and support vector machine.

Results:The model coefficient performance metrics indicated convergent validity across the 3 model types for all variables except bad debt and the factor level accounting for counties with >20% and up to 40% Hispanic populations, both of which had mixed coefficient directionality. The support vector machine model performed the classification task best based on all metrics (accuracy, precision, recall, F1-score). All the 3 models performed the classification task well with directional congruence of weights. From the logistic regression model, the top 5 odds ratios (indicating a higher risk of breach) included inpatient workload, medical center status, pediatric trauma center status, accounts receivable, and the number of outpatient visits, in high to low order. The bottom 5 odds ratios (indicating the lowest odds of experiencing a data breach) occurred for counties with Black populations of >20% and <40%, >80% and <100%, and >40% but <60%, as well as counties with ≤20% Asian or between 80% and 100% Hispanic individuals. Our results are in line with those of other studies that determined that patient workload, facility type, and financial outcomes were associated with the likelihood of health care data breach occurrence.

Conclusions:The results of this study provide a predictive model for health care data breaches that may guide health care managers to reduce the risk of data breaches by raising awareness of the risk factors.

Details

Title
Effects of Internal and External Factors on Hospital Data Breaches: Quantitative Study
Author
Dolezel, Diane  VIAFID ORCID Logo  ; Beauvais, Brad  VIAFID ORCID Logo  ; Paula Stigler Granados  VIAFID ORCID Logo  ; Fulton, Lawrence  VIAFID ORCID Logo  ; Kruse, Clemens Scott  VIAFID ORCID Logo 
First page
e51471
Section
Clinical Informatics
Publication year
2023
Publication date
2023
Publisher
Gunther Eysenbach MD MPH, Associate Professor
e-ISSN
1438-8871
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2917629718
Copyright
© 2023. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.