title: From clinical requirement to personalized wellness decision support: a data-driven framework for computer-supported guideline refinement.
abstract: 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.
authors.affiliation: IBM T.J. Watson Research Center, USA.
authors.forename: Pei-Yun
authors.initials: PY
authors.lastname: Hsueh
authors.forename: Ci-Wei
authors.initials: CW
authors.lastname: Lan
authors.forename: Vincent
authors.initials: V
authors.lastname: Deng
authors.forename: Xinxin
authors.initials: X
authors.lastname: Zhu
mesh_terms: D016208:Databases, Factual / Q000592:standards*; D020000:Decision Support Systems, Clinical / Q000592:standards*; D003661:Decision Support Techniques; D016347:Medical Records Systems, Computerized / Q000592:standards*; D017410:Practice Guidelines as Topic; D014481:United States