ArticleQuality in primary care
Identifying Future High Cost Individuals within an Intermediate Cost Population.
Article in Quality in primary care. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Characterization of high healthcare utilizer groups using administrative data from an electronic medical record database.BMC health services research · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundImproving health and controlling healthcare costs requires better tools for predicting future health needs across populations. We sought to identify factors associated with transitioning of enrollees in an indigent care program from an intermediate cost segment to a high cost segment of this population.
methodsWe analyzed data from 9,624 enrollees of the Virginia Coordinated Care program between 2010 and 2013. Each fiscal year included all enrollees who were classified in intermediate cost segment in the preceding year and also enrolled in the program in the following year. Using information from the preceding year, we built logistic regression models to identify the individuals in the top 10% of expenditures in the following year. The effect of demographics, count of chronic conditions, presence of the prevalent chronic conditions, and utilization indicators were evaluated and compared. Models were compared via the Bayesian information criterion and c-statistic.
resultsThe count of chronic conditions, diagnosis of congestive heart failure, and numbers of total hospital visits and prescriptions were significantly and independently associated with being in the future high cost segment. Overall, the model that included demographics and utilization indicators had a reasonable discrimination (c=0.67).
conclusionsA simple model including demographics and health utilization indicators predicted high future costs. The count of chronic conditions and certain medical diagnoses added additional predictive value. With further validation, the approach could be used to identify high-risk individuals and target interventions that decrease utilization and improve health.
Indexed as
Identifiers
27212892PMC4874657What 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.