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.
Copyright: © 2023 Ginting et al. 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.