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. Issue 1 (4th January 2019)

• Record Type: Journal Article
• Authors: Jeffery, Alvin D; Hewner, Sharon; Pruinelli, Lisiane; Lekan, Deborah; Lee, Mikyoung; Gao, Grace; Holbrook, Laura; Sylvia, Martha
• Is Part Of: JAMIA open. Volume 2:Issue 1(2019)
• Page Start: 205
• Page End: 214
• Publication Date: 2019-01-04
• Subjects: population health -- risk assessment -- decision support techniques -- community health planning; Medical informatics -- Periodicals; 610.285
• DOI: 10.1093/jamiaopen/ooy053
• Languages: English
• ISSNs: 2574-2531

Abstract

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.