SynthesisJournal of general internal medicine2022
Prediction Models for Future High-Need High-Cost Healthcare Use: a Systematic Review.
Synthesis in Journal of general internal medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Care Coordination for High-Need, High-Cost Commercially Insured Patients: A Randomized Clinical Trial.JAMA network open · 2025Trial
- Prediction of Medical Disputes Between Health Care Workers and Patients in Terms of Hospital Legal Construction Using Machine Learning Techniques: Externally Validated Cross-Sectional Study.Journal of medical Internet research · 2023Trial
- A Multi-Schedule Machine Learning Pipeline for Medicare Reimbursement Change Prediction and Operational Risk Stratification.medRxiv : the preprint server for health sciences · 2026Article
- Nonlinear modeling of health checkup factors and future claims-based healthcare costs in Japan: a retrospective cohort study.Health economics review · 2026Article
- Machine learning-based prediction of high hospital charges after lung transplantation in chronic obstructive pulmonary disease patients.BMC health services research · 2026Article
- Predicting high-cost healthcare users: development and external validation of multivariable models using the HUNT and Tromsø studies linked to Norwegian health registries.BMC health services research · 2026Article
- Machine learning framework for early identification of high-spending Crohn's disease patients using administrative claims.BMJ health & care informatics · 2026Article
- Ensemble Machine Learning Models for Predicting Patients With High Usage: Model Validation and Economic Impact Analysis.JMIR medical informatics · 2026Article
- Knowledge, attitudes, and practices of resident physicians and intern physicians on narrative medicine.Scientific reports · 2026Article
- Risk stratification and determinant identification of high-need, high-cost ICU patients using machine learning: a large-scale retrospective study from a multi-specialty ICU in a tertiary hospital.Frontiers in public health · 2026Article
- Can patient-reported data improve predictions about who will be a high-need, high-cost patient in British Columbia?Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Article
- Predicting high-need high-cost pediatric hospitalized patients in China based on machine learning methods.Scientific reports · 2025Article
- Identifying persistent high-cost patients in the hospital for care management: development and validation of prediction models.BMC health services research · 2024Article
- Article
- Development and validation of a tool to predict high-need, high-cost patients hospitalised with ischaemic heart disease.BMJ open · 2023Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn an effort to improve both quality of care and cost-effectiveness, various care-management programmes have been developed for high-need high-cost (HNHC) patients. Early identification of patients at risk of becoming HNHC (i.e. case finding) is crucial to a programme's success. We aim to systematically identify prediction models predicting future HNHC healthcare use in adults, to describe their predictive performance and to assess their applicability.
methodsOvid MEDLINE® All, EMBASE, CINAHL, Web of Science and Google Scholar were systematically searched from inception through January 31, 2021. Risk of bias and methodological quality assessment was performed through the Prediction model Risk Of Bias Assessment Tool (PROBAST).
resultsOf 5890 studies, 60 studies met inclusion criteria. Within these studies, 313 unique models were presented using a median development cohort size of 20,248 patients (IQR 5601-174,242). Predictors were derived from a combination of data sources, most often claims data (n = 37; 62%) and patient survey data (n = 29; 48%). Most studies (n = 36; 60%) estimated patients' risk to become part of some top percentage of the cost distribution (top-1-20%) within a mean time horizon of 16 months (range 12-60). Five studies (8%) predicted HNHC persistence over multiple years. Model validation was performed in 45 studies (76%). Model performance in terms of both calibration and discrimination was reported in 14 studies (23%). Overall risk of bias was rated as 'high' in 40 studies (67%), mostly due to a 'high' risk of bias in the subdomain 'Analysis' (n = 37; 62%). DISCUSSION: This is the first systematic review (PROSPERO CRD42020164734) of non-proprietary prognostic models predicting HNHC healthcare use. Meta-analysis was not possible due to heterogeneity. Most identified models estimated a patient's risk to incur high healthcare expenditure during the subsequent year. However, case-finding strategies for HNHC care-management programmes are best informed by a model predicting HNHC persistence. Therefore, future studies should not only focus on validating and extending existing models, but also concentrate on clinical usefulness.
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