TY - JOUR
T1 - Characterising and predicting persistent high-cost utilisers in healthcare
T2 - A retrospective cohort study in Singapore
AU - Ng, Sheryl Hui Xian
AU - Rahman, Nabilah
AU - Ang, Ian Yi Han
AU - Sridharan, Srinath
AU - Ramachandran, Sravan
AU - Wang, Debby Dan
AU - Khoo, Astrid
AU - Tan, Chuen Seng
AU - Feng, Mengling
AU - Toh, Sue Anne Ee Shiow
AU - Tan, Xin Quan
N1 - Publisher Copyright:
© 2020 Author(s) (or their employer(s)).
PY - 2020/1/6
Y1 - 2020/1/6
N2 - 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.
AB - 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.
KW - healthcare costs
KW - high utiliser
KW - machine learning
KW - persistence
UR - http://www.scopus.com/inward/record.url?scp=85077676925&partnerID=8YFLogxK
U2 - 10.1136/bmjopen-2019-031622
DO - 10.1136/bmjopen-2019-031622
M3 - Article
C2 - 31911514
AN - SCOPUS:85077676925
VL - 10
JO - BMJ Open
JF - BMJ Open
IS - 1
M1 - e031622
ER -