Implementing electronic health care predictive analytics: Considerations and challenges

Ruben Amarasingham, Rachel E. Patzer, Marco Huesch, Nam Q. Nguyen, Bin Xie

Research output: Contribution to journalArticle

56 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1148-1154
Number of pages7
JournalHealth Affairs
Volume33
Issue number7
DOIs
StatePublished - 2014

ASJC Scopus subject areas

  • Health Policy

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    Amarasingham, R., Patzer, R. E., Huesch, M., Nguyen, N. Q., & Xie, B. (2014). Implementing electronic health care predictive analytics: Considerations and challenges. Health Affairs, 33(7), 1148-1154. https://doi.org/10.1377/hlthaff.2014.0352