ArticleMedical care2018
Predicting High Health Care Resource Utilization in a Single-payer Public Health Care System: Development and Validation of the High Resource User Population Risk Tool.
Article in Medical care, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prediction Models for Future High-Need High-Cost Healthcare Use: a Systematic Review.Journal of general internal medicine · 2022Pooled it
- Trial
- 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
- Predicting high health care use in patients with spinal disorder in secondary care: model development and validation.Pain reports · 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
- Development and validation of a population-based risk algorithm for premature mortality in Canada: the Premature Mortality Population Risk Tool (PreMPoRT).BMJ public health · 2024Article
- Identifying persistent high-cost patients in the hospital for care management: development and validation of prediction models.BMC health services research · 2024Article
- High-cost users after sepsis: a population-based observational cohort study.Critical care (London, England) · 2024Observational
- Article
- A study protocol for a predictive model to assess population-based avoidable hospitalization risk: Avoidable Hospitalization Population Risk Prediction Tool (AvHPoRT).Diagnostic and prognostic research · 2024Article
- Predictive risk modelling of high resource users under different prescription drug coverage policies in Ontario and Manitoba, Canada.BMC health services research · 2023Article
- Article
- Association of Function, Symptoms, and Social Support Reported in Standardized Outpatient Clinic Questionnaires With Subsequent Hospital Discharge Disposition and 30-Day Readmissions.Archives of physical medicine and rehabilitation · 2022Article
- Can diverse population characteristics be leveraged in a machine learning pipeline to predict resource intensive healthcare utilization among hospital service areas?BMC health services research · 2022Article
- Medical Complexity Mediates Healthcare Resource Use in the Socially Vulnerable.Journal of healthcare management / American College of Healthcare Executives · 2022Observational
- Looking beyond Administrative Health Care Data: The Role of Socioeconomic Status in Predicting Future High-cost Patients with Mental Health and Addiction.Canadian journal of psychiatry. Revue canadienne de psychiatrie · 2022Article
- Downstream health impacts of employment losses during the COVID-19 pandemic.Canadian journal of public health = Revue canadienne de sante publique · 2022Article
- Predicting Future Service Use in Dutch Mental Healthcare: A Machine Learning Approach.Administration and policy in mental health · 2022Article
- Agreement between self-report and administrative health data on occurrence of non-cancer chronic disease among participants of the BC generations project.Frontiers in epidemiology · 2022Article
- Characteristics and resource utilization of high-cost users in the intensive care unit: a population-based cohort study.BMC health services research · 2021Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundA large proportion of health care spending is incurred by a small proportion of the population. Population-based health planning tools that consider both the clinical and upstream determinants of high resource users (HRU) of the health system are lacking.
objectiveTo develop and validate the High Resource User Population Risk Tool (HRUPoRT), a predictive model of adults that will become the top 5% of health care users over a 5-year period, based on self-reported clinical, sociodemographic, and health behavioral predictors in population survey data. RESEARCH
designThe HRUPoRT model was developed in a prospective cohort design using the combined 2005 and 2007/2008 Canadian Community Health Surveys (CCHS) (N=58,617), and validated using the external 2009/2010 CCHS cohort (N=28,721). Health care utilization for each of the 5 years following CCHS interview date were determined by applying a person-centered costing algorithm to the linked health administrative databases. Discrimination and calibration of the model were assessed using c-statistic and Hosmer-Lemeshow (HL) χ statistic.
resultsThe best prediction model for 5-year transition to HRU status included 12 predictors and had good discrimination (c-statistic=0.8213) and calibration (HL χ=18.71) in the development cohort. The model performed similarly in the validation cohort (c-statistic=0.8171; HL χ=19.95). The strongest predictors in the HRUPoRT model were age, perceived general health, and body mass index.
conclusionsHRUPoRT can accurately project the proportion of individuals in the population that will become a HRU over 5 years. HRUPoRT can be applied to inform health resource planning and prevention strategies at the community level.
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