Understanding the characteristics of high users of hospital services in Singapore and their associations with healthcare utilisation and mortality: A cluster analysis

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Abstract

Introduction

High users of hospital services require targeted healthcare services planning for effective resource allocation due to their high costs. This study aims to segmentize the population in the “Ageing In Place-Community Care Team” (AIP-CCT), a programme for complex patients with high inpatient service use, and examine the association of segment membership and healthcare utilisation and mortality.

Methods

We analysed 1,012 patients enrolled between June 2016 and February 2017. To identify patient segments, a cluster analysis was performed based on medical complexity and psychosocial needs. Next, multivariable negative binomial regression was performed using patient segments as the predictor, with healthcare and programme utilisation over the 180-day follow-up as outcomes. Multivariate cox proportional hazard regression was applied to assess the time to first hospital admission and mortality between segments within the 180-day follow-up. All models were adjusted for age, gender, ethnicity, ward class, and baseline healthcare utilisation.

Results

Three distinct segments were identified (Segment 1 (n = 236), Segment 2 (n = 331), and Segment 3 (n = 445)). Medical, functional, and psychosocial needs of individuals were significantly different between segments ( p-value<0.001). The rates of hospitalisation in Segments 1 (IRR = 1.63, 95%CI:1.3–2.1) and 2 (IRR = 2.11, 95%CI:1.7–2.6) were significantly higher than in Segment 3 on follow-up. Similarly, both Segments 1 (IRR = 1.76, 95%CI:1.6–2.0) and 2 (IRR = 1.25, 95%CI:1.1–1.4) had higher rates of programme utilisation compared to Segment 3. Patients in Segments 1 (HR = 2.48, 95%CI:1.5–4.1) and 2 (HR = 2.25, 95%CI:1.3–3.6) also had higher mortality on follow-up.

Conclusions

This study provided a data-based approach to understanding healthcare needs among complex patients with high inpatient services utilisation. Resources and interventions can be tailored according to the differences in needs among segments, to facilitate better allocation.

Related collections

Most cited references 29

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Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

Hude Quan, Vijaya Sundararajan, Patricia Halfon … (2005)

Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms. ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms. Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm. These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.

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The triple aim: care, health, and cost.

Donald M Berwick, Thomas W Nolan, John Whittington (2008)

Improving the U.S. health care system requires simultaneous pursuit of three aims: improving the experience of care, improving the health of populations, and reducing per capita costs of health care. Preconditions for this include the enrollment of an identified population, a commitment to universality for its members, and the existence of an organization (an "integrator") that accepts responsibility for all three aims for that population. The integrator's role includes at least five components: partnership with individuals and families, redesign of primary care, population health management, financial management, and macro system integration.

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Is Open Access

Charlson Comorbidity Index: A Critical Review of Clinimetric Properties

Mary E Charlson, Danilo Carrozzino, Jenny Guidi … (2022)

The present critical review was conducted to evaluate the clinimetric properties of the Charlson Comorbidity Index (CCI), an assessment tool designed specifically to predict long-term mortality, with regard to its reliability, concurrent validity, sensitivity, incremental and predictive validity. The original version of the CCI has been adapted for use with different sources of data, ICD-9 and ICD-10 codes. The inter-rater reliability of the CCI was found to be excellent, with extremely high agreement between self-report and medical charts. The CCI has also been shown either to have concurrent validity with a number of other prognostic scales or to result in concordant predictions. Importantly, the clinimetric sensitivity of the CCI has been demonstrated in a variety of medical conditions, with stepwise increases in the CCI associated with stepwise increases in mortality. The CCI is also characterized by the clinimetric property of incremental validity, whereby adding the CCI to other measures increases the overall predictive accuracy. It has been shown to predict long-term mortality in different clinical populations, including medical, surgical, intensive care unit (ICU), trauma, and cancer patients. It may also predict in-hospital mortality, although in some instances, such as ICU or trauma patients, the CCI did not perform as well as other instruments designed specifically for that purpose. The CCI thus appears to be clinically useful not only to provide a valid assessment of the patient’s unique clinical situation, but also to demarcate major diagnostic and prognostic differences among subgroups of patients sharing the same medical diagnosis.

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Author and article information

Contributors

Mimaika Luluina Ginting:

ORCID: https://orcid.org/0000-0002-0319-8097

Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: Writing – original draftRole: Writing – review & editing

Yan Hoon Ang: Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: Writing – review & editing

Soon Hoe Ho: Role: Data curationRole: Formal analysisRole: Writing – review & editing

Grace Sum: Role: Writing – review & editing

Chek Hooi Wong: Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing – review & editing

Chandan Kumar: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS One

Journal ID (publisher-id): plos

Title: PLOS ONE

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Electronic): 1932-6203

Publication date (Electronic): 11 July 2023

Publication date Collection: 2023

Volume: 18

Issue: 7

Electronic Location Identifier: e0288441

Affiliations

[1 ] Geriatric Education and Research Institute Singapore, Singapore, Singapore

[2 ] Department of Geriatric Medicine, Khoo Teck Puat Hospital, Singapore, Singapore

[3 ] Health Services & Systems Research, Duke-NUS, Singapore, Singapore

TERI School of Advanced Studies, INDIA

Author notes

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: ginting.mimaika.luluina@ 123456geri.com.sg

Author information

Mimaika Luluina Ginting https://orcid.org/0000-0002-0319-8097

Article

Publisher ID: PONE-D-22-27084

DOI: 10.1371/journal.pone.0288441

PMC ID: 10335687

SO-VID: 523112c6-5b86-4f23-80c5-97cc628e8928

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 30 September 2022

Date accepted : 27 June 2023

Page count

Figures: 1, Tables: 4, Pages: 15

Funding

Funded by: Geriatric Education and Research Institute Singapore

Award ID: GERI1614

Award Recipient :

ORCID: https://orcid.org/0000-0002-0319-8097

Mimaika Luluina Ginting

This research received grant funding from Geriatric Education and Research Institute (GERI) intramural grant, GERI1614. URL: https://www.geri.com.sg/. The funder did not have any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of the authors are articulated in the ‘author contributions’ section.

Custom metadata

Data Availability Data cannot be shared publicly because of the restrictions in the data access and sharing policy imposed by the organizations that owned the data i.e., Yishun Health and National Healthcare Group, Singapore. The conditions for the data access is restricted to the authorized data users requested under this study, and data could not be shared to any third party without prior approval from the data owner. Data request could be raised through Geriatric Education and Research Institute, Research Admin office (contact via Ms Shijia Qiu, email: qiu.shijia@ 123456geri.com.sg ).

Understanding the characteristics of high users of hospital services in Singapore and their associations with healthcare utilisation and mortality: A cluster analysis

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Abstract

Introduction

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Most cited references 29

Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.

The triple aim: care, health, and cost.

Charlson Comorbidity Index: A Critical Review of Clinimetric Properties

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