TY - JOUR
T1 - Risk prediction and segmentation models used in the United States for assessing risk in whole populations
T2 - A critical literature review with implications for nurses' role in population health management
AU - Jeffery, Alvin D.
AU - Hewner, Sharon
AU - Pruinelli, Lisiane
AU - Lekan, Deborah
AU - Lee, Mikyoung
AU - Gao, Grace
AU - Holbrook, Laura
AU - Sylvia, Martha
N1 - Publisher Copyright:
© 2019 Published by Oxford University Press on behalf of the American Medical Informatics Association 2019.
PY - 2019/1/4
Y1 - 2019/1/4
N2 - 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.
AB - 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.
KW - community health planning
KW - decision support techniques
KW - population health
KW - risk assessment
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U2 - 10.1093/jamiaopen/ooy053
DO - 10.1093/jamiaopen/ooy053
M3 - Review article
AN - SCOPUS:85089122921
SN - 2574-2531
VL - 2
SP - 205
EP - 214
JO - JAMIA Open
JF - JAMIA Open
IS - 1
M1 - ooy053
ER -