精准:医疗保健领域保护隐私的云辅助质量改进服务。

Feng Chen, Shuang Wang, Noman Mohammed, Samuel Cheng, Xiaoqian Jiang
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引用次数: 10

摘要

质量改进(QI)需要系统和持续的努力来提高医疗保健服务。医疗保健提供者可能希望将当地统计数据与其他机构的统计数据进行比较,以便确定问题并制定干预措施以提高护理质量。然而,机构信息的共享可能会受到机构隐私的阻碍,因为公开这些统计数据可能会导致尴尬甚至经济损失。在本文中,我们提出了一种保护隐私的云辅助医疗保健质量改进服务(PRECISE),旨在实现医疗保健统计数据的跨机构比较,同时保护隐私。提出的框架依赖于一组最先进的加密协议,包括同态加密和Yao的乱码电路方案。通过安全地汇集来自不同机构的数据,PRECISE可以对加密统计数据进行排名,以促进参与机构之间的QI。我们使用MIMIC II数据库进行了实验,并证明了所提出的PRECISE框架的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

PRECISE:PRivacy-prEserving Cloud-assisted quality Improvement Service in hEalthcare.

PRECISE:PRivacy-prEserving Cloud-assisted quality Improvement Service in hEalthcare.

PRECISE:PRivacy-prEserving Cloud-assisted quality Improvement Service in hEalthcare.

PRECISE:PRivacy-prEserving Cloud-assisted quality Improvement Service in hEalthcare.

Quality improvement (QI) requires systematic and continuous efforts to enhance healthcare services. A healthcare provider might wish to compare local statistics with those from other institutions in order to identify problems and develop intervention to improve the quality of care. However, the sharing of institution information may be deterred by institutional privacy as publicizing such statistics could lead to embarrassment and even financial damage. In this article, we propose a PRivacy-prEserving Cloud-assisted quality Improvement Service in hEalthcare (PRECISE), which aims at enabling cross-institution comparison of healthcare statistics while protecting privacy. The proposed framework relies on a set of state-of-the-art cryptographic protocols including homomorphic encryption and Yao's garbled circuit schemes. By securely pooling data from different institutions, PRECISE can rank the encrypted statistics to facilitate QI among participating institutes. We conducted experiments using MIMIC II database and demonstrated the feasibility of the proposed PRECISE framework.

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