Consensus Statement on Electronic Health Predictive Analytics: A Guiding Framework image

eGEMs (Generating Evidence & Methods to improve patient outcomes), 2016, Vol. 4, Issue 1
I. Glenn Cohen (Faculty Director) et al.

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Abstract

Context: 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.
bioethics health information technology health law policy i. glenn cohen privacy regulation