To standardise clinical practices and inform targeted policy decisions, major health systems segment their populations into case-mix groups (CMGs). With expert input and analytical methods, CMGs are designed with optimal granularity—balancing individual-level clinical care decisions and population-level acute care resource allocation1—and judicious parsimony, selecting indicators from the wealth of information extracted from patient electronic health records (see online supplementary Table 1 for a comparison of major healthcare systems’ case-mix frameworks).

However, clinical case-mix systems often provide imperfect estimations of their populations’ acute care utilisation.2 3 4 It has been suggested that critical drivers of acute care admissions and 28-day rehospitalisations, such as clinical complexity,5 6 are not often included as indicators for stratifying patients. Also, the linkage between case mixes of populations and their respective post–acute care (PAC) needs has not been established, although PAC can reduce rehospitalisations and mitigate the rehospitalisation risk associated with clinical complexity.7 In fact, not only have the PAC needs of patients discharged under various case-mix classifications remained unexplored, but studies examining the effects of PAC on acute care utilisation often fail to consider the diversity of PAC service types8 9 and their differential effects on patients with distinct clinical profiles.10 11 12

Therefore, this study aimed to identify the factors contributing to the discrepancy between the objectives of case-mix systems—optimising the efficient allocation of acute care resources—and the observed heterogeneity in acute care utilisation among patients within the same CMGs. Specifically, although clinical complexity and PAC utilisation influence the rehospitalisation risk of discharged patients—which in turn affects the accuracy of population-level acute care resource planning—they are not typically included in case-mix systems for patient stratification. Thus, we examined the heterogeneity and relationships among clinical complexity, PAC utilisation, and rehospitalisation risk within homogeneous patient segments. These segments were partitioned from the study population using conventional case-mix parameters and acute care utilisation metrics. Given this context, we hypothesised that among patients within the same homogeneous segments, those who did not receive effective PAC would exhibit the highest rates of 28-day rehospitalisation. Additionally, we hypothesised that greater clinical complexity would increase the likelihood of rehospitalisation occurring before receipt of any effective PAC.

In this study of an inpatient population of 197 805 individuals (aged >50 years) discharged into the community, a combination of unsupervised and supervised learning algorithms was deployed (Fig 1). First, unsupervised learning algorithms were applied to identify typical patients (ie, medoids) using a comprehensive set of clinical parameters (including discharged patients’ CMGs) and acute care utilisation data.13 Patients similar to typical patients in terms of these parameters were clustered into the same segments. Each resulting segment was labelled according to the Major Clinical Category (MCC)13 assigned to its medoid. According to case-mix methodologies adopted by major healthcare systems (eg, CMG+ of Canada13), the MCC reflects the primary body system or medical specialty involved and provides a high-level overview of the patient’s condition. Within each MCC, patients are further classified into more specific CMGs based on detailed clinical and resource utilisation characteristics. We therefore expected that patients within the same segment would share the same MCC as the medoid, although their CMGs might differ. Consequently, each segment was labelled with the medoid’s MCC. The International Classification of Diseases codes constituting each CMG and the corresponding MCC for each are shown in online supplementary Table 2.

Second, with additional features representing the types and timing of PAC service utilisation, 28-day rehospitalisation outcome–supervised machine learning algorithms (Unbiased Recursive Partitioning with Surrogate Splitting [URPSS]14) were applied to recursively partition clinically homogeneous segments into subpopulations, each characterised by homogeneous PAC utilisation. The URPSS has previously been used to compare the effects of clinical profiles and acute care utilisation on 28-day rehospitalisations with those of different PAC service types, isolating the unique contribution of patients’ clinical and acute care factors.15 In this study, we adopted a complementary approach by isolating each PAC service type’s unique contribution to 28-day rehospitalisation while adjusting for the influence of the end user’s clinical profile and acute care utilisation. To achieve this approach, we first partitioned the population into segments with homogeneous clinical and acute care utilisation profiles. Within each segment, the URPSS algorithm was then applied to infer the effects of PAC on 28-day rehospitalisation, contingent on patients’ clinical and acute care characteristics. A detailed description of the hybrid machine learning approach used to disentangle post-acute from acute influences is provided in the online Appendix.14 15 16

Among the different subpopulations partitioned from each segment, one inevitably remained unpartitioned by any feature representing the PAC services for which the algorithm found significant conditional inferences on 28-day rehospitalisation. We hypothesised that this unpartitioned subpopulation—representing patients whose acute care needs (as reflected by the comprehensive segmenting features of clinical and acute care utilisation parameters) were homogeneous with others in the same segment but who lacked any 28-day rehospitalisation–mitigating PAC services—would exhibit the highest clinical complexity and 28-day rehospitalisation rates. These groups of discharged patients, whose rehospitalisation risk was high but who lacked algorithm-selected PAC services, are hereafter referred to as the No Service groups (NS groups).

In conjunction with the 28-day rehospitalisation rate, the prevalence of clinical complexity—reflected by the presence of two or more chronic illnesses diagnosed prior to the index hospitalisation and by acute care resource-intensifying co-morbid diagnoses at index hospitalisation5 6—was also compared between the NS groups and their corresponding segments. We hypothesised that greater clinical complexity would be associated with an increased likelihood of patients being rehospitalised before receiving any effective PAC. Comparisons were also made between populations aged 50-64 years and 65 years or above. Research has shown that adults aged 50 to 64 years face unique health challenges and experience care gaps not observed among those aged 65 years or above.16 In particular, care gaps predominantly affecting the 50-64 age-group have been linked to inaccuracies in predicting patients’ acute care needs using case-mix models,17 which were primarily developed from inpatient populations aged 65 years and older.18 19 20 21

Although many comparisons could be made between the NS groups and their corresponding segments across all segments partitioned from the 50-64–year-old or ≥65–year-old populations—and between the NS groups or segments of the two populations—comparisons were restricted to the NS groups and their corresponding segments that shared the same medoid CMGs, to ensure homogeneity in clinical and acute care utilisation profiles between the subgroups being compared. Similarly, comparisons between the 50-64–year-old and ≥65–year-old NS groups or between the 50-64–year-old and ≥65–year-old segments were confined to pairs with the same medoid CMGs. The odds ratios (ORs), 95% confidence intervals (95% CIs), and P values resulting from comparisons between each same-CMG pair for clinical complexity and 28-day rehospitalisation were calculated from a subset of the descriptive statistics reported in online supplementary Tables 3 (for the 50-64–year-old age-group) and 4 (for the ≥65–year-old age-group). In addition to the presence of data regarding the prevalence of clinical complexity and 28-day rehospitalisations, these supplementary tables include the comprehensive set of features that: (1) constitute the CMGs adopted in this study, (2) segment the 50-64–year-old and ≥65–year-old populations, and (3) partition each segment to identify its corresponding NS groups. These features encompass diagnoses, age, sex, resource-intensive interventions received at index acute care hospitalisation, and resource-intensifying co-morbidities diagnosed at index acute care hospitalisation. Given that the contributions of these features to clinical profile variability had already been adjusted for through multiple iterations, they were unlikely to be selected by the URPSS algorithm to split a segment into subpopulations. Our focus therefore remained on demonstrating the high prevalence of clinical complexity and 28-day rehospitalisation among the NS groups, rather than on features not selected by the URPSS.

We tested our hypotheses regarding the elevated risks of the NS groups compared with their parent segments (particularly for the 50-64–year-old population) through selected paired comparisons and omnibus testing. By aggregating results across different same-CMG pairs, we followed the standard epidemiological practice of utilising all available evidence from various subgroups within a single sample to maximise the robustness and generalisability of estimates while adjusting for inherent sample stratification.22 23 Indeed, whereas analysis of an entire sample may overlook underlying confounding factors, a strong focus on stratified subgroup analyses can lead to misinterpretations that inflate the effects of confounding variables on outcomes and distort the relationships between risk factors and outcomes.24 25 To quantify the likelihood of clinical complexity and 28-day rehospitalisation rates in the NS groups versus their parent segments, we pooled ORs using the Mantel-Haenszel formula26 across same-CMG pairs within each age population and between the 50-64–year-old and ≥65–year-old populations (calculated from the ORs and associated 95% CIs and P values reported in Table 1). This approach allowed us to evaluate overall differences in co-morbidity, chronic illnesses, and 28-day rehospitalisations between age-groups and between the NS groups and their corresponding segments. The Mantel-Haenszel formula has been applied in diverse clinical contexts involving a single patient sample or population, including a targeted patient group with traumatic brain injury,27 a regional population admitted from multiple hospitals with different major diagnoses,28 and a case-control study combining matched and unmatched control groups.29 Results reported below include pooled ORs, 95% CIs, P values, and, where applicable, Q statistics with corresponding P values to indicate significant heterogeneity among pooled ORs.

Below, we describe the clinical profiles of typical patients (medoids) in the 50-64–year-old and ≥65–year-old populations and their corresponding population segments. We then report the order in which the URPSS algorithm selected PAC services based on their unique statistical importance in classifying 28-day rehospitalisation. We also characterise the clinical profiles of patients who received none of the URPSS-selected PAC services (ie, the NS groups). Finally, we compare the rates of resource-intensifying co-morbidities, the presence of two or more chronic diseases, and 28-day rehospitalisations between the NS groups and their corresponding segments, as well as between the 50-64–year-old and ≥65–year-old populations.

The Calinski–Harabasz index indicated that the optimal number of segments was seven for the 50-64–year-old population and eight for the ≥65–year-old population.³⁰ Our analyses revealed that the seven typical patients identified in the 50-64–year-old population belonged to the same MCCs as their counterparts in the ≥65–year-old population: Circulatory, Digestive, Nephrology and urology, Musculoskeletal, Respiratory, Multiple systems of diseases and disorders, and Other reasons for hospitalisation. Additionally, four MCCs shared between the two age-groups were characterised by identical CMGs: Symptom or sign of digestive system (Digestive), Malignant neoplasm of urinary system (Nephrology and urology), Chronic obstructive pulmonary disease (Respiratory), and General symptom or sign (Other reasons for hospitalisation). In the ≥65–year-old population, we identified an eighth segment, whose typical patient’s CMG was dementia, belonging to the MCC of Diseases and disorders of the mental system.

Tables 2 and 3 report the type, sequence (reflecting the descending rank order of marginal contribution feature importance), and associated 28-day rehospitalisation rates of each PAC service selected by the URPSS algorithm. With areas under the receiver operating characteristic curve ranging from 0.85 to 0.93, the URPSS algorithms classified 28-day rehospitalisation outcomes in every segment partitioned from the two populations using features selected for their unique contributions to outcomes. Among all features in the pool to which the URPSSs were applied (online supplementary Table 5), only PAC-related features were selected to split segments that had previously been partitioned from the population using other features (eg, sex) that were unrelated to PAC.

Our analyses revealed that, compared with all other PAC services, specialist outpatient clinics (SOPCs) had the greatest marginal contribution to 28-day rehospitalisation outcomes among patients with similar clinical profiles and acute care utilisation patterns, even after adjusting for the effects of the segments’ patient clinical profiles and acute care utilisation patterns on 28-day rehospitalisations through conditional inference. Additionally, SOPCs’ contribution to 28-day rehospitalisation was not conditional on the effects of other features. Consequently, the lowest 28-day rehospitalisation rates were observed among SOPC attendees across all homogeneous population segments. Nevertheless, although SOPCs had the highest marginal contribution feature importance—and were associated with the lowest 28-day rehospitalisation rates—in all segments across both populations, the 28-day rehospitalisation rates among SOPC attendees were higher in every segment of the 50-64–year-old population compared with the corresponding segments of the ≥65–year-old population (mean difference between segments with the same MCC profiles: 9.5%).

As shown in Tables 2 and 3, the 28-day rehospitalisation rates were consistently the highest among subpopulations within each segment that remained unsplit after the sequential selection and partitioning by features representing PAC services that the URPSS identified as highly important to 28-day rehospitalisation outcomes (ie, the NS groups). For example, among the 50-64–year-old population, the mean difference in 28-day rehospitalisation rates between the NS groups and those in the same segments who received SOPC care (the PAC service with the greatest feature importance) was 70.01%; the mean difference between the NS groups and their corresponding full segments was 66.69%. Similarly, among the ≥65–year-old population, the mean difference between the NS groups and patients in the same segments who received SOPC care was 76.28%; the mean difference between the NS groups and their corresponding full segments was 62.26%. Notably, whereas the NS groups consistently showed the highest 28-day rehospitalisation rates among all subpopulations, the NS groups of the 50-64–year-old population exhibited a greater mean difference in 28-day rehospitalisation rates compared with their ≥65–year-old counterparts (by a mean difference of 2.99%).

The above analyses identified a subpopulation (ie, the NS groups) within each segment that exhibited high 28-day rehospitalisation rates but lacked effective PAC services. To provide a more in-depth understanding of the NS groups, we compared 28-day rehospitalisation rates, the prevalence of resource-intensifying co-morbidities, and the presence of two or more chronic illnesses between the NS groups and their corresponding segments, as well as between the 50-64–year-old and ≥65–year-old populations. Not all NS groups’ typical patients shared the same CMGs as the medoids of their corresponding segments, nor were the same CMGs shared between the medoids of the 50-64–year-old and ≥65–year-old populations. Chronic obstructive pulmonary disease (COPD) was the only CMG consistently identified as a medoid CMG in both populations and their corresponding subpopulations. Therefore, a more detailed analysis was conducted on the segment and subpopulation with COPD CMGs to illustrate factors contributing to the differences between NS groups and their corresponding segments, and between the 50-64–year-old and ≥65–year-old populations.

Table 1 reports the ORs (and their associated 95% CIs and P values) for resource-intensifying co-morbidities, the presence of two or more chronic illnesses, and 28-day rehospitalisations of NS groups relative to their corresponding 50-64–year-old or ≥65–year-old population segments sharing the same medoid CMGs. As shown in the table, even when diseases of different systems were considered across both populations, the NS groups exhibited significantly higher rates of 28-day rehospitalisation compared with their same-medoid-CMG segments (pooled OR=19.27, 95% CI=17.86-20.79; P<0.001); they also showed a greater prevalence of having two or more chronic illnesses (pooled OR=1.84, 95% CI=1.64-2.07; P<0.001).

Although resource-intensifying co-morbidity is also a measure of clinical complexity, it was not more likely to be found among NS groups than among their same–medoid-CMG segments. Follow-up analyses revealed that the pooled OR for the ≥65–year-old population was heterogeneous (Q statistic=39.97, P<0.001), whereas the Q statistic for pooled ORs in the 50-64–year-old population was not statistically significant. Upon closer examination, the rate of resource-intensifying co-morbidity was indeed higher in NS groups of the 50-64–year-old population than in their same–medoid-CMG segments (pooled OR=1.23, 95% CI=1.00-1.52; P=0.05); it was lower in the NS group population aged ≥65 years than in their corresponding segments (pooled OR=0.76, 95% CI=0.68-0.85; P<0.001).

The observation that the 50-64–year-old population exhibits higher clinical complexity and 28-day rehospitalisation rates compared with their ≥65–year-old counterparts was directly examined among same–medoid-CMG pairs of the 50-64–year-old and ≥65–year-old population segments, as well as among pairs of NS group populations aged 50-64 years and ≥65 years (Table 1). Whereas the 50-64–year-old population showed higher rates of resource-intensifying co-morbidity and 28-day rehospitalisation compared with the ≥65–year-old population at both the segment and NS-group levels, these differences were not statistically significant (pooled ORs=1.27, 95% CI=0.55-2.93, and 1.18, 95% CI=0.84-1.65, respectively). Follow-up analysis revealed substantial heterogeneity in the pooled statistics, attributable to significant variation among the pooled ORs of NS-group pairs (Q statistics=7.81-9.43; all P<0.05). Follow-up segment-level analyses also showed significantly lower prevalence of all study parameters in the 50-64–year-old population compared with the ≥65–year-old population: OR=0.56 (95% CI=0.52-0.59; P<0.001), OR=0.22 (95% CI=0.20-0.24; P<0.001), and OR=0.93 (95% CI=0.89-0.96; P<0.001) for rates of resource-intensifying co-morbidity, the presence of two or more chronic illnesses, and 28-day rehospitalisation, respectively.

Given the high heterogeneity of pooled ORs for the NS-group CMG pairs, differences in the prevalence of study parameters between the 50-64–year-old and ≥65–year-old populations were examined within individual NS-group pairs. Follow-up analyses revealed that, although not all NS-group CMG pairs showed higher rates of resource-intensifying co-morbidity or 28-day rehospitalisation in the 50-64–year-old population, those that did—such as when the medoid CMG was renal failure or COPD—also showed significantly higher 28-day rehospitalisation rates compared with their ≥65–year-old counterparts sharing the same medoid CMGs. For example, in the case of renal failure, the ORs were 63.11 (95% CI=50.26-79.38; P<0.001) and 1.35 (95% CI=1.06-1.70; P=0.01) for resource-intensifying co-morbidity and 28-day rehospitalisation rates, respectively (Table 1).

Finally, to consider differences in study parameter prevalence between the NS group and its corresponding segment when comparing clinical complexity and 28-day rehospitalisation outcomes between the 50-64–year-old and ≥65–year-old populations, we examined cases in which the CMG was COPD. Chronic obstructive pulmonary disease was the only CMG that served as the medoid of both the population segment and the corresponding NS group for the 50-64–year-old and ≥65–year-old populations, allowing us to adjust for differences in study parameter prevalence between the NS group and its full segment when comparing the two age-groups. Our analyses revealed that, relative to the statistics of the full segments, the ORs for resource-intensifying co-morbidity, two or more chronic illnesses, and 28-day rehospitalisation rates were significantly greater in the 50-64–year-old NS group than in the ≥65–year-old counterparts [ratios of ORs=1.50 (95% CI=1.06-2.11; P=0.02), 1.17 (95% CI=1.01-1.37; P=0.04), and 2.34 (95% CI=1.84-2.96; P<0.001), respectively].

Patients aged 50 to 64 years who were discharged without receiving algorithm-selected PAC services (ie, the NS groups) were generally more likely to be rehospitalised within 28 days of discharge than their counterparts who shared similar clinical and acute care utilisation profiles but received such services. In some cases, the 50-64–year-old NS groups were rehospitalised at even higher rates than the ≥65–year-old NS groups. Under these circumstances, the 50-64–year-old NS groups also exhibited higher rates of resource-intensifying co-morbidity. This elevated co-morbidity among patients aged 50-64 years who experienced more frequent rehospitalisation than their ≥65–year-old counterparts was exemplified by NS groups whose clinical and acute care utilisation profiles resembled the CMGs of typical patients with renal failure and COPD—the same CMGs characterising typical patients in the ≥65–year-old NS groups. In the case of COPD, the rates of co-morbidity, chronic illnesses, and rehospitalisation within the full segment could be directly considered when comparing the 50-64–year-old and ≥65–year-old NS groups.

Similar to COPD, the majority of typical patients’ CMGs in the full segments and NS groups identified in the present study were considered ambulatory care–sensitive conditions (ACSCs),31 for which hospitalisations are potentially avoidable through timely and effective ambulatory care. Because avoidable hospitalisations among ACSC patients could be prevented with better access to ambulatory and primary care services, it has been argued that resources should be redistributed from acute care to these services.32 Our findings provide rare empirical support for this argument. By comparing rehospitalisation rates among subpopulations of patients with homogeneous clinical profiles and acute care utilisation patterns but differing PAC assignments, we demonstrated, at a population level, the benefits of ambulatory care (eg, specialist follow-up and in-home nursing transitional care) and primary care in reducing rehospitalisation rates among typical patient profiles whose CMGs were ACSCs.

Notably, even ACSCs may progress into more acute diagnoses, with a higher likelihood of co-morbidity and elevated 28-day rehospitalisation rates. For instance, whereas Angina or Arrhythmia were the CMGs of typical patient profiles in the full patient segments of the 50-64–year-old and ≥65–year-old populations, respectively, the CMG of their NS groups’ typical patient profile was Heart Failure; these patients exhibited higher rates of co-morbidities and 28-day rehospitalisation. Similarly, Digestive Malignancy was the CMG of the typical patient profile in a 50-64–year-old NS group, which showed higher rates of co-morbidities and 28-day rehospitalisation than its corresponding full patient segment, whose typical CMG was Symptom or Sign of the Digestive System.

Despite such evidence, these services remain largely unavailable for individuals in the studied populations. For example, the average wait time for SOPC appointments ranges from 9 to 111 weeks,33 in sharp contrast to the median interval between discharge and rehospitalisation among NS patients, which is 14 days. Given the constraints on healthcare professional availability in the public sector, reducing SOPC wait times may be challenging. Therefore, by quantifying the benefits of different PAC services for various patient profiles, the findings presented here suggest the need for the following policy actions: (1) procure specialist follow-up services from the private sector and ensure effective public–private service coordination within the parallel public and private tracks of the healthcare system studied; and (2) enhance the provision of less scarce, near-equivalent alternatives available in the community, rather than relying solely on medical specialists.

In addition to the higher rehospitalisation rates identified in the present study, typical patient profiles with ACSC CMGs that lacked effective PAC services also exhibited a high prevalence of co-morbidities. The rates of co-morbidities and 28-day rehospitalisations were particularly high among individuals aged 50 to 64 years who fit these patient profiles. This finding aligns with recent studies showing that younger patients with diabetes—also a chronic ACSC—have significantly greater co-morbidities and worse outcomes than their older counterparts.34 Furthermore, we found that younger patients not only have more complex health needs but also benefit less from conventional PAC services and are more likely to be rehospitalised before receiving ambulatory or primary care. This finding is consistent with current literature, which indicates that effective rehospitalisation prevention programmes for chronically ill patients with multiple health problems,35 especially younger patients, require a multidisciplinary approach to address diverse needs such as smoking cessation,36 rather than the conventional ‘assess-and-advise’ primary care model of rehospitalisation prevention.37

Indeed, most evidence supporting the benefits of multidisciplinary primary care for chronic conditions is derived from intervention studies targeting diseases that also represented the CMGs of typical patients identified in our study populations—particularly those who did not receive PAC services deemed effective in reducing 28-day rehospitalisation. For example, multidisciplinary pulmonary rehabilitation programmes, which are most effective in preventing rehospitalisation among patients with COPD, include not only clinician-led physical rehabilitation but also health-related education, advice regarding exercise programmes, targeted interventions addressing cognitive and behavioural issues, and personalised care plans tailored to individual needs.38 39 Similarly, community-based cardiac rehabilitation programmes that integrate cardioprotective therapeutics with psychosocial care and lifestyle management are most effective in preventing rehospitalisation among patients with angina and arrhythmia—conditions that are often underdiagnosed in acute care settings yet associated with high rehospitalisation rates and natural progression to heart failure if left untreated.40 Furthermore, effective pain management programmes for patients with pain-related musculoskeletal conditions—such as the inflammatory and reactive arthropathy CMGs assigned to our typical patient profiles—are multidisciplinary in nature and combine physiotherapy with approaches that promote active coping and self-management.41

Patients with multiple chronic health conditions benefit most from multidisciplinary care but often require treatment from numerous healthcare professionals across both primary and secondary care settings. To mitigate the risk of care fragmentation redundant patient assessments, a case management approach has been advocated as a holistic means of addressing the complex needs of such patients (Fig 2). For example, patients with COPD have diverse and evolving care needs throughout their care journey,39 requiring care that is not only multidisciplinary but also integrated through case management. Effective case management for patients with COPD involves healthcare professionals who address the most pressing needs at the initial stage of the care journey assuming the role of case manager, supported by community health practitioners who coordinate other professional services as required.42

Given the complexity of multidisciplinary care needs in patients with multiple chronic conditions, and the challenge of delivering the right intervention from the right healthcare professionals to the right patients at the right time, the training and provision of case management can be enhanced through a precision-driven approach. By leveraging advanced data analytics and machine learning, such an approach can accurately identify care needs and service gaps to improve the integration of multidisciplinary care.43 44 The approach used in the present study—segmenting patient populations based on diagnostic profiles and patterns of acute and PAC service utilisation through iterative applications of unsupervised and 28-day rehospitalisation outcome–supervised machine learning algorithms—can profile unmet needs and service gaps among patient populations discharged into the community. Thus, our study adds value to a body of literature largely focused on identifying homogeneous inpatient segments solely based on diagnoses45 46 47 48 49 50 51 or cost,52 aimed at improving acute care management.

This study has several limitations. First, the data were solely derived from public hospitals as information from private hospitals and other healthcare providers outside the public system was not accessible. However, it is worth noting that public hospitals account for over 90% of inpatient services. Second, the coding system may not capture all patient health conditions because it mainly focuses on chief complaints. Finally, the lack of socio-demographic data limits the ability to generate more precise predictions.

This hybrid machine learning analysis of electronic health records of discharged patient population showed that patients aged 50 to 64 years with typical ambulatory care—sensitive case-mix profiles who did not receive algorithm-selected PAC services had substantially higher levels of multimorbidity and increased risk of 28-day rehospitalisation compared with clinically similar peers receiving such care. Integrating PAC utilisation and clinical complexity indicators into case-mix stratification can enable precision tertiary prevention and guide the development of targeted, multidisciplinary, case-managed services in the community.

Concept or design: E Leung, A Lee, J Guan.
Acquisition of data: E Leung, J Guan, SCC Ching.
Analysis or interpretation of data: E Leung, J Guan, SCC Ching.
Drafting of the manuscript: E Leung, A Lee, FY Chen.
Critical revision of the manuscript for important intellectual content: All authors.

All authors had full access to the data, contributed to the study, approved the final version for publication, and take responsibility for its accuracy and integrity.

As the Chief Editor of the journal, MCS Wong was not involved in the peer review process. Other authors declared no conflicts of interest.

This research was supported by the Strategic Public Policy Research Funding Scheme of the Hong Kong SAR Government (Project No.: S2019.A4.015.19S) awarded to A Lee and E Leung; the Community Involvement Fund of the Home Affairs Department, Hong Kong SAR Government, through Sham Shui Po District Council (Project Nos.: 220179 and 220180) awarded to E Leung and A Lee; and the General Research Fund of the Research Grants Council of Hong Kong (Project No.: 9043763) awarded to FY Chen. The funders had no role in the study design, data collection/analysis/interpretation, or manuscript preparation.

This research was approved by the Joint Chinese University of Hong Kong–New Territories East Cluster Clinical Research Ethics Committee, Hong Kong (Ref No.: SBRE-22-0386). The requirement for patient consent was waived by the Committee due to the use of unidentifiable information of participants in the research.

The supplementary material was provided by the authors and some information may not have been peer reviewed. Accepted supplementary material will be published as submitted by the authors, without any editing or formatting. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by the Hong Kong Academy of Medicine and the Hong Kong Medical Association. The Hong Kong Academy of Medicine and the Hong Kong Medical Association disclaim all liability and responsibility arising from any reliance placed on the content.

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