基于数据的FMEA方法用于数字健康中的危险识别和风险评估
Hafiz Muhammad Waseem1, Saif Ul Islam2, Stuart Harrison3
1Warwick Manufacturing Group, University of Warwick, Coventry, CV4 7AL, UK. hm.waseem@warwick.ac.uk.
Scientific reports
|July 23, 2025
概括
数字医疗保健系统提供了好处,但也带来了数据泄露等风险. 这项研究使用故障模式和影响分析 (FMEA) 来识别和优先考虑这些风险,确保更安全的数字健康技术.
科学领域:
- 医疗信息学 医疗信息学
- 风险管理 风险管理
- 数字健康系统安全 数字健康系统安全
背景情况:
- 数字医疗保健系统提高了患者护理和效率,但引入了诸如未经授权访问和隐私侵犯等漏洞.
- 系统的风险评估对于在医疗保健中安全采用新兴技术至关重要.
- 现有的研究缺乏对各种数字健康数据进行全面的故障模式和影响分析 (FMEA).
研究的目的:
- 使用FMEA系统地识别,评估和优先考虑数字医疗保健系统中的潜在危险.
- 为各种医疗保健数据类别和模式提供关于脆弱性的新视角.
- 为增强数据完整性,安全性和互操作性提供可行的建议.
主要方法:
- 使用故障模式和影响分析 (FMEA) 进行系统的风险评估.
- 在临床,操作和患者报告的数据类别中调查的风险.
- 分析了文本,图像,表格,音频和视频数据模式中的风险.
- 通过专家咨询和文献审查评估故障模式,计算风险优先级号码 (RPN).
主要成果:
- 确定了包括未经授权访问,数据腐败,传输错误和隐私侵犯在内的重大风险.
- 突出了对患者安全和数字医疗系统可靠性的威胁.
- 基于严重程度,发生率和可检测性而定量的优先级危险.
结论:
- 制定了可行的建议,以加强数字医疗保健中的数据完整性,安全性和互操作性.
- 该研究支持人工智能 (AI),区块链和其他先进技术的安全整合.
- 这些发现有助于建立更安全,更有弹性的数字医疗生态系统.
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