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EGEMS (Wash DC). 2016 Mar 7;4(1):1163. doi: 10.13063/2327-9214.1163. eCollection 2016.

Consensus Statement on Electronic Health Predictive Analytics: A Guiding Framework to Address Challenges.

Author information

1
PCCI.
2
The Commonwealth Fund.
3
Brigham and Women's Hospital.
4
Harvard Law School.
5
Palo Alto Medical Foundation.
6
Kaiser Permanente.
7
The Greenwall Foundation.
8
University of California, San Diego.
9
Management Information Systems, University of Arizona.
10
Johns Hopkins University.
11
University of Colorado, School of Medicine.
12
Sutter Health.
13
Erasmus Medical Center.

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.

KEYWORDS:

Clinical decision support systems; Ethics; Health Information Technology; Informatics; big data; electronic predictive analytics; predictive models

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