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

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.