Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Aug 1, 2023
Date Accepted: Nov 13, 2023
Geospatial and Predictive Models for Healthcare Data Breaches
ABSTRACT
Background:
Healthcare data breaches are the most rapidly increasing type of cybercrime; however, the predictors of healthcare data breaches are uncertain.
Objective:
This quantitative study aimed to develop a predictive model to explain the number of hospital data breaches at the county level.
Methods:
This study evaluated 1032 hospitals on the association of demographic, socioeconomic, hospital workload, hospital financial, hospital type, and county political party leadership predictors and data breach occurrences at the county level.
Results:
General linear modeling with logistic regression revealed significance for several predictors. Five predictors were statistically significant in the full model which has the best accuracy (0.83) and precision (0.58), p = 0.05. The significant predictors were Accounts Receivable (OR=155269.27), population density (OR=150.54), Asian (OR=49.65), poverty (OR=3.10), and medical center (OR=2.85).
Conclusions:
The results of this study provide a predictive model for healthcare care data breaches that may provide guidance for healthcare managers to reduce the risk of data breaches. Clinical Trial: n/a
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Copyright
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