Amarasingham, RubenAudet, Anne-Marie J.Bates, DavidGlenn Cohen, I.Entwistle, MartinEscobar, G. J.Liu, VincentEtheredge, LynnLo, BernardOhno-Machado, LucilaRam, SudhaSaria, SuchiSchilling, Lisa M.Shahi, AnandStewart, Walter F.Steyerberg, Ewout W.Xie, Bin2016-06-142016Amarasingham, R., A. J. Audet, D. W. Bates, I. Glenn Cohen, M. Entwistle, G. J. Escobar, V. Liu, et al. 2016. “Consensus Statement on Electronic Health Predictive Analytics: A Guiding Framework to Address Challenges.” eGEMs 4 (1): 1163. doi:10.13063/2327-9214.1163. http://dx.doi.org/10.13063/2327-9214.1163.2327-9214http://nrs.harvard.edu/urn-3:HUL.InstRepos:27320442Context: The recent explosion in available electronic health record (EHR) data is motivating a rapid expansion of electronic health care predictive analytic (e-HPA) applications, defined as the use of electronic algorithms that forecast clinical events in real time with the intent to improve patient outcomes and reduce costs. There is an urgent need for a systematic framework to guide the development and application of e-HPA to ensure that the field develops in a scientifically sound, ethical, and efficient manner. Objectives: Building upon earlier frameworks of model development and utilization, we identify the emerging opportunities and challenges of e-HPA, propose a framework that enables us to realize these opportunities, address these challenges, and motivate e-HPA stakeholders to both adopt and continuously refine the framework as the applications of e-HPA emerge. Methods: To achieve these objectives, 17 experts with diverse expertise including methodology, ethics, legal, regulation, and health care delivery systems were assembled to identify emerging opportunities and challenges of e-HPA and to propose a framework to guide the development and application of e-HPA. Findings: The framework proposed by the panel includes three key domains where e-HPA differs qualitatively from earlier generations of models and algorithms (Data Barriers, Transparency, and Ethics) and areas where current frameworks are insufficient to address the emerging opportunities and challenges of e-HPA (Regulation and Certification; and Education and Training). The following list of recommendations summarizes the key points of the framework: Data Barriers: Establish mechanisms within the scientific community to support data sharing for predictive model development and testing.Transparency: Set standards around e-HPA validation based on principles of scientific transparency and reproducibility.Ethics: Develop both individual-centered and society-centered risk-benefit approaches to evaluate e-HPA.Regulation and Certification: Construct a self-regulation and certification framework within e-HPA.Education and Training: Make significant changes to medical, nursing, and paraprofessional curricula by including training for understanding, evaluating, and utilizing predictive models.en-USInformaticsHealth Information TechnologyEthicsClinical decision support systemselectronic predictive analyticspredictive modelsbig dataJournal Article2016-06-1410.13063/2327-9214.1163