Risk prediction and segmentation models used in the United States for assessing risk in whole populations: a critical literature review with implications for nurses' role in population health management

Alvin D Jeffery; Sharon Hewner; Lisiane Pruinelli; Deborah Lekan; Mikyoung Lee; Grace Gao; Laura Holbrook; Martha Sylvia

doi:10.1093/jamiaopen/ooy053

Risk prediction and segmentation models used in the United States for assessing risk in whole populations: a critical literature review with implications for nurses' role in population health management

JAMIA Open. 2019 Jan 4;2(1):205-214. doi: 10.1093/jamiaopen/ooy053. eCollection 2019 Apr.

Authors

Alvin D Jeffery¹, Sharon Hewner², Lisiane Pruinelli³, Deborah Lekan⁴, Mikyoung Lee⁵, Grace Gao⁶, Laura Holbrook⁷, Martha Sylvia⁸

Affiliations

¹ Department of Veterans Affairs and Vanderbilt University Department of Biomedical Informatics, Nashville, Tennessee, USA.
² Family, Community and Health Systems Science Department, University at Buffalo School of Nursing, Buffalo, New York, USA.
³ School of Nursing, University of Minnesota, Minneapolis, Minnesota, USA.
⁴ School of Nursing, University of North Carolina, Greensboro, North Carolina, USA.
⁵ College of Nursing, Texas Woman's University, Denton, Texas, USA.
⁶ Department of Nursing, St. Catherine University, St. Paul, Minnesota, USA.
⁷ American Sentinel University, Aurora, California, USA.
⁸ College of Nursing, Medical University of South Carolina, Charleston, South Carolina, USA.

Abstract

Objective: We sought to assess the current state of risk prediction and segmentation models (RPSM) that focus on whole populations.

Materials: Academic literature databases (ie MEDLINE, Embase, Cochrane Library, PROSPERO, and CINAHL), environmental scan, and Google search engine.

Methods: We conducted a critical review of the literature focused on RPSMs predicting hospitalizations, emergency department visits, or health care costs.

Results: We identified 35 distinct RPSMs among 37 different journal articles (n = 31), websites (n = 4), and abstracts (n = 2). Most RPSMs (57%) defined their population as health plan enrollees while fewer RPSMs (26%) included an age-defined population (26%) and/or geographic boundary (26%). Most RPSMs (51%) focused on predicting hospital admissions, followed by costs (43%) and emergency department visits (31%), with some models predicting more than one outcome. The most common predictors were age, gender, and diagnostic codes included in 82%, 77%, and 69% of models, respectively.

Discussion: Our critical review of existing RPSMs has identified a lack of comprehensive models that integrate data from multiple sources for application to whole populations. Highly depending on diagnostic codes to define high-risk populations overlooks the functional, social, and behavioral factors that are of great significance to health.

Conclusion: More emphasis on including nonbilling data and providing holistic perspectives of individuals is needed in RPSMs. Nursing-generated data could be beneficial in addressing this gap, as they are structured, frequently generated, and tend to focus on key health status elements like functional status and social/behavioral determinants of health.

Keywords: community health planning; decision support techniques; population health; risk assessment.

Published by Oxford University Press on behalf of the American Medical Informatics Association 2019.

Publication types

Review