ArticleBMJ open2020
Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore.
Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 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
- Care Coordination for High-Need, High-Cost Commercially Insured Patients: A Randomized Clinical Trial.JAMA network open · 2025Trial
- 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
- FishKP-YOLOv11: An Automatic Estimation Model for Fish Size and Mass in Complex Underwater Environments.Animals : an open access journal from MDPI · 2025Article
- Predicting high-need high-cost pediatric hospitalized patients in China based on machine learning methods.Scientific reports · 2025Article
- Relationship between handgrip strength and timed up-and-go test on hospitalization costs in older adults: a population-based study.BMC public health · 2025Article
- Identifying persistent high-cost patients in the hospital for care management: development and validation of prediction models.BMC health services research · 2024Article
- Assessing differences among persistent, episodic, and non- high-need high-cost hospitalized children in China after categorization by an unsupervised learning algorithm.Archives of public health = Archives belges de sante publique · 2024Article
- High-cost users still came to hospitals during the COVID-19 pandemic during first wave data in Thailand: secondary data analysis.BMC public health · 2024Article
- Characteristics and Health Care Spending of Persistently and Transiently High-cost Older Adults in Korea.Journal of preventive medicine and public health = Yebang Uihakhoe chi · 2023Article
- Identifying patterns of clinical conditions among high-cost older adult health care users using claims data: a latent class approach.International journal for equity in health · 2022Article
- Improving the Prediction of Persistent High Health Care Utilizers: Retrospective Analysis Using Ensemble Methodology.JMIR medical informatics · 2022Article
- Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature.Applied clinical informatics · 2022Review
- A Personalized Mobile Health Program for Type 2 Diabetes During the COVID-19 Pandemic: Single-Group Pre-Post Study.JMIR diabetes · 2021Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe aim to characterise persistent high utilisers (PHUs) of healthcare services, and correspondingly, transient high utilisers (THUs) and non-high utilisers (non-HUs) for comparison, to facilitate stratifying HUs for targeted intervention. Subsequently we apply machine learning algorithms to predict which HUs will persist as PHUs, to inform future trials testing the effectiveness of interventions in reducing healthcare utilisation in PHUs. DESIGN AND
settingThis is a retrospective cohort study using administrative data from an Academic Medical Centre (AMC) in Singapore.
participantsPatients who had at least one inpatient admission to the AMC between 2005 and 2013 were included in this study. HUs incurred Singapore Dollar 8150 or more within a year. PHUs were defined as HUs for three consecutive years, while THUs were HUs for 1 or 2 years. Non-HUs did not incur high healthcare costs at any point during the study period. OUTCOME MEASURES: PHU status at the end of the third year was the outcome of interest. Socio-demographic profiles, clinical complexity and utilisation metrics of each group were reported. Area under curve (AUC) was used to identify the best model to predict persistence.
resultsPHUs were older and had higher comorbidity and mortality. Over the three observed years, PHUs' expenditure generally increased, while THUs and non-HUs' spending and inpatient utilisation decreased. The predictive model exhibited good performance during both internal (AUC: 83.2%, 95% CI: 82.2% to 84.2%) and external validation (AUC: 79.8%, 95% CI: 78.8% to 80.8%).
conclusionsThe HU population could be stratified into PHUs and THUs, with distinctly different utilisation trajectories. We developed a model that could predict at the end of 1 year, whether a patient in our population will continue to be a HU in the next 2 years. This knowledge would allow healthcare providers to target PHUs in our health system with interventions in a cost-effective manner.
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