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. 2020 Jan 6;10(1):e031622.
doi: 10.1136/bmjopen-2019-031622.

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

Sheryl Hui Xian Ng  1   2 , Nabilah Rahman  1   2 , Ian Yi Han Ang  1   2 , Srinath Sridharan  1 , Sravan Ramachandran  1 , Debby Dan Wang  1 , Astrid Khoo  2 , Chuen Seng Tan  1 , Mengling Feng  1 , Sue-Anne Ee Shiow Toh  2   3   4 , Xin Quan Tan  5   2
Affiliations

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

Sheryl Hui Xian Ng et al. BMJ Open. .

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.

Keywords: healthcare costs; high utiliser; machine learning; persistence.

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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
Illustration of persistent high utiliser (PHU)/transient high utiliser (THU) cohort generation using three hypothetical patients.
Figure 2
Figure 2
Inclusion and exclusion criteria for persistent high utiliser (PHU)/transient high utiliser (THU) cohort.
Figure 3
Figure 3
Yearly utilisation for three observation years, with yearly medians labelled. Non-HU, non-high utiliser; PHU, persistent high utiliser; THU, transient high utiliser.
Figure 4
Figure 4
Variable importance for 51 features in final model.
See this image and copyright information in PMC

References

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