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Tytuł pozycji:

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

Tytuł:
Characterising and predicting persistent high-cost utilisers in healthcare: a retrospective cohort study in Singapore.
Autorzy:
Ng SHX; Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.; Regional Health System Office, National University Health System, Singapore, Singapore.
Rahman N; Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.; Regional Health System Office, National University Health System, Singapore, Singapore.
Ang IYH; Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.; Regional Health System Office, National University Health System, Singapore, Singapore.
Sridharan S; Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
Ramachandran S; Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
Wang DD; Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
Khoo A; Regional Health System Office, National University Health System, Singapore, Singapore.
Tan CS; Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
Feng M; Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.
Toh SES; Regional Health System Office, National University Health System, Singapore, Singapore.; Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.; Singapore Population Health Improvement Centre (SPHERiC), National University Health System, Singapore, Singapore.
Tan XQ; Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore .; Regional Health System Office, National University Health System, Singapore, Singapore.
Źródło:
BMJ open [BMJ Open] 2020 Jan 06; Vol. 10 (1), pp. e031622. Date of Electronic Publication: 2020 Jan 06.
Typ publikacji:
Journal Article; Research Support, Non-U.S. Gov't
Język:
English
Imprint Name(s):
Original Publication: [London] : BMJ Publishing Group Ltd, 2011-
MeSH Terms:
Machine Learning*
Health Care Costs/*statistics & numerical data
Health Services/*economics
Patient Acceptance of Health Care/*statistics & numerical data
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Retrospective Studies ; Singapore
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Contributed Indexing:
Keywords: healthcare costs; high utiliser; machine learning; persistence
Entry Date(s):
Date Created: 20200109 Date Completed: 20210210 Latest Revision: 20210210
Update Code:
20240105
PubMed Central ID:
PMC6955475
DOI:
10.1136/bmjopen-2019-031622
PMID:
31911514
Czasopismo naukowe
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.
Competing Interests: Competing interests: None declared.
(© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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