内部和外部因素对医院数据泄露的影响:定量研究
Diane Dolezel1, Brad Beauvais2, Paula Stigler Granados3
1Health Informatics & Information Management Department, Texas State University, Round Rock, TX, United States.
Journal of medical Internet research
|December 21, 2023
概括
医疗保健数据泄露事件正在增加,但其预测因素尚不清楚. 本研究确定了关键的风险因素,如患者的工作量和设施类型,以帮助减少违规行为.
科学领域:
- 医疗信息学 医疗信息学
- 网络安全 网络安全
- 医疗保健管理的管理
背景情况:
- 医疗保健数据泄露是一个快速增长的网络犯罪类别.
- 医疗数据泄露的预测因素仍然不确定,需要进一步研究.
研究的目的:
- 在县级开发医院数据泄露的预测模型.
- 识别与数据泄露事件相关的人口,社会经济和运营因素.
主要方法:
- 从1032个短期急性护理医院的数据进行定量分析.
- 使用后勤回归,感知子和支持矢量机器模型.
- 评估预测因素包括县人口统计,社会经济,医院工作量,设施类型和财务指标.
主要成果:
- 支持矢量机器模型在分类数据泄露方面表现出卓越的性能.
- 住院工作量,医疗中心状态和应收账款是违规风险增加的重要预测因素.
- 某些人口组合,包括黑人,亚洲人和西班牙裔人口的特定百分比,与更低的违规率有关.
结论:
- 已经开发了一种针对医疗数据泄露的预测模型.
- 通过了解关键因素,这些发现可以指导医疗保健管理人员减轻数据泄露风险.
- 提高对已识别的风险因素的认识,可以提高医疗机构的数据安全性.
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