An analytical solution for consent management in patient privacy preservation

With the growing awareness and enforcement of patient rights, patients are empowered with increasing control on their medical information. In many situations, laws and regulation rules require the acquisition of patients' consent before one can access the patients' health data. However, in practice, patients oftentimes have difficulties determining whether they should permit or deny a certain access request. In this article, we propose an analytical approach to assist patients in the consent management of their medical information. Our consent management system employs a statistical learning method that evaluates the benefits and risks associated with access requests, so as to make personalized recommendation on consent decisions. Multiple factors are considered in the assessment process, including the importance of the request, the sensitivity of the requested information, and correlation information. We have implemented a prototype of our solution and performed evaluation with large-scale medical records. Copyright © 2012 ACM.