ReviewJAMIA open2019
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.
Review in JAMIA open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- The use of individual and multilevel data in the development of a risk prediction model to predict patients' likelihood of completing colorectal cancer screening.Preventive medicine reports · 2023Article
- Comparing and validating medication complexity from insurance claims against electronic health records.Journal of managed care & specialty pharmacy · 2022Article
- Moving Beyond Simple Risk Prediction: Segmenting Patient Populations Using Consumer Data.Frontiers in public health · 2021Article
- A Performance Management System in Healthcare for All Seasons?International journal of environmental research and public health · 2020Article
- Using Personas as a Tool for People-Centred Segmentation and Health Systems Planning in Ontario, Canada.International journal of integrated careArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe 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.
methodsWe conducted a critical review of the literature focused on RPSMs predicting hospitalizations, emergency department visits, or health care costs.
resultsWe identified 35 distinct RPSMs among 37 different journal articles ( 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.
conclusionMore 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.