Dataset Information

Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore.

ABSTRACT:

Objective

We 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 setting

This is a retrospective cohort study using administrative data from an Academic Medical Centre (AMC) in Singapore.

Participants

Patients 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.

Results

PHUs 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%).

Conclusions

The 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.

SUBMITTER: Ng SHX

PROVIDER: S-EPMC6955475 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore.

Ng Sheryl Hui Xian SHX Rahman Nabilah N Ang Ian Yi Han IYH Sridharan Srinath S Ramachandran Sravan S Wang Debby Dan DD Khoo Astrid A Tan Chuen Seng CS Feng Mengling M Toh Sue-Anne Ee Shiow SES Tan Xin Quan XQ

BMJ open 20200106 1

<h4>Objective</h4>We 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.<h4>Design and setting</h4>This is a ret ...[more]

PMID: 31911514

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Dataset Information

Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore.

Objective

Design and setting

Participants

Outcome measures

Results

Conclusions

Publications

Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore.

Similar Datasets

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