Implementing electronic health care predictive analytics: considerations and challenges

Health Aff (Millwood). 2014 Jul;33(7):1148-54. doi: 10.1377/hlthaff.2014.0352.


The use of predictive modeling for real-time clinical decision making is increasingly recognized as a way to achieve the Triple Aim of improving outcomes, enhancing patients' experiences, and reducing health care costs. The development and validation of predictive models for clinical practice is only the initial step in the journey toward mainstream implementation of real-time point-of-care predictions. Integrating electronic health care predictive analytics (e-HPA) into the clinical work flow, testing e-HPA in a patient population, and subsequently disseminating e-HPA across US health care systems on a broad scale require thoughtful planning. Input is needed from policy makers, health care executives, researchers, and practitioners as the field evolves. This article describes some of the considerations and challenges of implementing e-HPA, including the need to ensure patients' privacy, establish a health system monitoring team to oversee implementation, incorporate predictive analytics into medical education, and make sure that electronic systems do not replace or crowd out decision making by physicians and patients.

Keywords: Information Technology; Medicine/Clinical Issues; Public Health; Research And Technology.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Comparative Effectiveness Research
  • Confidentiality
  • Data Interpretation, Statistical*
  • Datasets as Topic*
  • Decision Support Systems, Clinical
  • Delivery of Health Care / economics
  • Delivery of Health Care / statistics & numerical data*
  • Electronic Health Records*
  • Humans
  • Medical Informatics
  • Models, Statistical
  • Quality Control