Start Submission Become a Reviewer

Reading: Consensus Statement on Electronic Health Predictive Analytics: A Guiding Framework to Addres...

Download

A- A+
dyslexia friendly

Model / Framework

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

Authors:

Ruben Amarasingham ,

PCCI
X close

Anne-Marie J. Audet,

The Commonwealth Fund
X close

David W. Bates,

Brigham and Women’s Hospital
X close

I. Glenn Cohen,

Harvard Law School
X close

Martin Entwistle,

Palo Alto Medical Foundation
X close

G. J. Escobar,

Vincent Liu,

Kaiser Permanente
X close

Lynn Etheredge,

Bernard Lo,

The Greenwall Foundation
X close

Lucila Ohno-Machado,

University of California, San Diego
X close

Sudha Ram,

Management Information Systems, University of Arizona, Tucson, AZ, United States.
X close

Suchi Saria,

Johns Hopkins University
X close

Lisa M. Schilling,

University of Colorado, School of Medicine
X close

Anand Shah,

PCCI
X close

Walter F. Stewart,

Sutter Health
X close

Ewout W. Steyerberg,

Erasmus MC
X close

Bin Xie

PCCI
X close

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:

1. Data Barriers: Establish mechanisms within the scientific community to support data sharing for predictive model development and testing.
2. Transparency: Set standards around e-HPA validation based on principles of scientific transparency and reproducibility.
3. Ethics: Develop both individual-centered and society-centered risk-benefit approaches to evaluate e-HPA.
4. Regulation and Certification: Construct a self-regulation and certification framework within e-HPA.
5. Education and Training: Make significant changes to medical, nursing, and paraprofessional curricula by including training for understanding, evaluating, and utilizing predictive models.
How to Cite: Amarasingham R, Audet A-MJ, Bates DW, Cohen IG, Entwistle M, Escobar GJ, et al.. Consensus Statement on Electronic Health Predictive Analytics: A Guiding Framework to Address Challenges. eGEMs (Generating Evidence & Methods to improve patient outcomes). 2016;4(1):3. DOI: http://doi.org/10.13063/2327-9214.1163
2
Views
2
Downloads
Published on 07 Mar 2016.
Peer Reviewed

Downloads

  • PDF (EN)