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
| Autor: | Laura Holbrook, Martha Sylvia, Mikyoung Lee, Alvin D. Jeffery, Grace Gao, Lisiane Pruinelli, Sharon Hewner, Deborah Lekan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
medicine.medical_specialty
education.field_of_study business.industry 030503 health policy & services Population MEDLINE Health Informatics Review Population health CINAHL 03 medical and health sciences 0302 clinical medicine Family medicine Health care Medicine 030212 general & internal medicine Social determinants of health Diagnosis code 0305 other medical science business Risk assessment education |
| Zdroj: | JAMIA Open. 2:205-214 |
| ISSN: | 2574-2531 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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