Hsueh Pei-Yun, Lan Ci-Wei, Deng Vincent, Zhu Xinxin
IBM T.J. Watson Research Center, USA.
Stud Health Technol Inform. 2012;180:457-61.
Personalized wellness decision support has gained significant attention, owing to the shift to a patient-centric paradigm in healthcare domains, and the consequent availability of a wealth of patient-related data. Despite the success of data-driven analytics in improving practice outcome, there is a gap towards their deployment in guideline-based practice. In this paper we report on findings related to computer-supported guideline refinement, which maps a patient's guideline requirements to personalized recommendations that suit the patient's current context. In particular, we present a novel data-driven personalization framework, casting the mapping task as a statistical decision problem in search of a solution to maximize expected utility. The proposed framework is well suited to produce personalized recommendations based on not only clinical factors but contextual factors that reflect individual differences in non-clinical settings. We then describe its implementation within the guideline-based clinical decision support system and discuss opportunities and challenges looking forward.
由于医疗领域向以患者为中心的模式转变，以及随之而来的大量患者相关数据的可用性，个性化健康决策支持受到了广泛关注。尽管数据驱动的分析在改善实践结果方面取得了成功，但在基于指南的实践中部署这些分析仍存在差距。在本文中，我们报告了与计算机支持的指南细化相关的研究结果，该细化将患者的指南要求映射到适合患者当前情况的个性化建议。特别是，我们提出了一个新颖的数据驱动个性化框架，将映射任务视为一个统计决策问题，以寻找最大化预期效用的解决方案。所提出的框架不仅非常适合基于临床因素，而且适合基于反映非临床环境中个体差异的上下文因素来生成个性化建议。然后，我们描述了其在基于指南的临床决策支持系统中的实现，并展望了未来的机遇和挑战。
