ArticleHealth services research2010
Using information on clinical conditions to predict high-cost patients.
Article in Health services research, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- The Charlson comorbidity index can be used prospectively to identify patients who will incur high future costs.PloS one · 2014Trial
- Network Analysis-Driven Machine Learning Model for Identifying High-Cost Stroke Inpatients Using Hospital Discharge Data: Retrospective Study.JMIR medical informatics · 2026Article
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- Association between multimorbidity and informal long-term care use in China: a nationwide cohort study.BMC geriatrics · 2023Article
- Does intergenerational support affect older People's social participation? An Empirical study of an older Chinese population.SSM - population health · 2023Article
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- Predicting Readmission Charges Billed by Hospitals: Machine Learning Approach.JMIR medical informatics · 2022Article
- Defining Complex Patient Populations: Implications for Population Size, Composition, Utilization, and Costs.Journal of general internal medicine · 2022Article
- Comparison of deep learning with traditional models to predict preventable acute care use and spending among heart failure patients.Scientific reports · 2021Article
- [Predictive validity of a functional classification method in older adultsValidação preditiva de método de classificação funcional em idosos].Revista panamericana de salud publica = Pan American journal of public health · 2021Article
- Identifying and understanding determinants of high healthcare costs for breast cancer: a quantile regression machine learning approach.BMC health services research · 2020Article
- Machine-learning-based prediction models for high-need high-cost patients using nationwide clinical and claims data.NPJ digital medicine · 2020Article
- Real-World Costs of Adverse Events in First-Line Treatment of Metastatic Non-Small Cell Lung Cancer.Journal of managed care & specialty pharmacy · 2020Article
- A population-based measure of chronic disease severity for health planning and evaluation in the United States.AIMS public health · 2020Article
- Who Will be the Costliest Patients? Using Recent Claims to Predict Expensive Surgical Episodes.Medical care · 2019Article
- Article
- The associations of multimorbidity with the sum of annual medical and long-term care expenditures in Japan.BMC geriatrics · 2019Article
- Comparison of the Financial Burden of Survivors of Head and Neck Cancer With Other Cancer Survivors.JAMA otolaryngology-- head & neck surgery · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo compare the ability of different models to predict prospectively whether someone will incur high medical expenditures. DATA SOURCE: Using nationally representative data from the Medical Expenditure Panel Survey (MEPS), prediction models were developed using cohorts initiated in 1996-1999 (N=52,918), and validated using cohorts initiated in 2000-2003 (N=61,155). STUDY
designWe estimated logistic regression models to predict being in the upper expenditure decile in Year 2 of a cohort, based on data from Year 1. We compared a summary risk score based on diagnostic cost group (DCG) prospective risk scores to a count of chronic conditions and indicators for 10 specific high-prevalence chronic conditions. We examined whether self-rated health and functional limitations enhanced prediction, controlling for clinical conditions. Models were evaluated using the Bayesian information criterion and the c-statistic. PRINCIPAL
findingsMedical condition information substantially improved prediction of high expenditures beyond gender and age, with the DCG risk score providing the greatest improvement in prediction. The count of chronic conditions, self-reported health status, and functional limitations were significantly associated with future high expenditures, controlling for DCG score. A model including these variables had good discrimination (c=0.836).
conclusionsThe number of chronic conditions merits consideration in future efforts to develop expenditure prediction models. While significant, self-rated health and indicators of functioning improved prediction only slightly.
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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.