Evaluation of a practical expert defined approach to patient population segmentation: a case study in Singapore

Patterns of Morbidity Across the Lifespan: A Population Segmentation Framework for Classifying Health Care Needs for All Ages

BACKGROUND

Classification systems to segment such patients into subgroups for purposes of care management and population analytics should balance administrative simplicity with clinical meaning and measurement precision.

OBJECTIVE

To describe and empirically apply a new clinically relevant population segmentation framework applicable to all payers and all ages across the lifespan.

RESEARCH DESIGN AND SUBJECTS

Cross-sectional analyses using insurance claims database for 3.31 Million commercially insured and 1.05 Million Medicaid enrollees under 65 years old; and 5.27 Million Medicare fee-for-service beneficiaries aged 65 and older.

MEASURES

The "Patient Need Groups" (PNGs) framework, we developed, classifies each person within the entire 0-100+ aged population into one of 11 mutually exclusive need-based categories. For each PNG segment, we documented a range of clinical and resource endpoints, including health care resource use, avoidable emergency department visits, hospitalizations, behavioral health conditions, and social need factors.

RESULTS

The PNG categories included: (1) nonuser, (2) low-need child, (3) low-need adult, (4) low-complexity multimorbidity, (5) medium-complexity multimorbidity, (6) low-complexity pregnancy, (7) high-complexity pregnancy, (8) dominant psychiatric/behavioral condition, (9) dominant major chronic condition, (10) high-complexity multimorbidity, and (11) frailty. Each PNG evidenced a characteristic age-related trajectory across the full lifespan. In addition to offering clinically cogent groupings, large percentages (29%-62%) of patients in two pregnancy and high-complexity multimorbidity and frailty PNGs were in a high-risk subgroup (upper 10%) of potential future health care utilization.

CONCLUSIONS

The PNG population segmentation approach represents a comprehensive measurement framework that captures and categorizes available electronic health care data to characterize individuals of all ages based on their needs.

Segmentation and shielding of the most vulnerable members of the population as elements of an exit strategy from COVID-19 lockdown

This study demonstrates that an adoption of a segmenting and shielding strategy could increase the scope to partially exit COVID-19 lockdown while limiting the risk of an overwhelming second wave of infection. We illustrate this using a mathematical model that segments the vulnerable population and their closest contacts, the ‘shielders’. Effects of extending the duration of lockdown and faster or slower transition to post-lockdown conditions and, most importantly, the trade-off between increased protection of the vulnerable segment and fewer restrictions on the general population are explored. Our study shows that the most important determinants of outcome are: (i) post-lockdown transmission rates within the general and between the general and vulnerable segments; (ii) fractions of the population in the vulnerable and shielder segments; (iii) adherence to protective measures; and (iv) build-up of population immunity. Additionally, we found that effective measures in the shielder segment, e.g. intensive routine screening, allow further relaxations in the general population. We find that the outcome of any future policy is strongly influenced by the contact matrix between segments and the relationships between physical distancing measures and transmission rates. This strategy has potential applications for any infectious disease for which there are defined proportions of the population who cannot be treated or who are at risk of severe outcomes.

This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK’.

Integration of health services for the elderly in Asia: A scoping review of Hong Kong, Singapore, Malaysia, Indonesia

Against the backdrop of rapid ageing populations, there is an increasing recognition of the need to integrate various health services for the elderly, not only to provide more coordinated care, but also to contain the rapid cost inflation driven primarily by the curative sector. Funded by the Asia-Pacific Observatory on Health Systems and Policies, this scoping review seeks to synthesize the received knowledge on care integration for the elderly in four Asian societies representing varying socioeconomic and health-system characteristics: Singapore, Hong Kong, Malaysia, and Indonesia. The search for English-language literature published between 2009 and 2019 yielded 67 publications in the final sample. The review finds that both research and practice regarding health service integration are at a preliminary stage of development. It notes a marked trend in seeking to integrate long-term elderly care with curative and preventive care, especially in community settings. Many distinctive models proliferated. Integration is demonstrated not only horizontally but also vertically, transcending public-private boundaries. The central role of primary care is highly prominent in almost all the integration models. However, these models are associated with a variety of drawbacks in relation to capacity, perception, and operation that necessitate further scholarly and policy scrutiny, indicating the robustness and persistence of siloed healthcare practices.

Can cluster analyses of linked healthcare data identify unique population segments in a general practice-registered population?

BACKGROUND

Population segmentation is useful for understanding the health needs of populations. Expert-driven segmentation is a traditional approach which involves subjective decisions on how to segment data, with no agreed best practice. The limitations of this approach are theoretically overcome by more data-driven approaches such as utilisation-based cluster analysis. Previous explorations of using utilisation-based cluster analysis for segmentation have demonstrated feasibility but were limited in potential usefulness for local service planning. This study explores the potential for practical application of using utilisation-based cluster analyses to segment a local General Practice-registered population in the South Wales Valleys.

METHODS

Primary and secondary care datasets were linked to create a database of 79,607 patients including socio-demographic variables, morbidities, care utilisation, cost and risk factor information. We undertook utilisation-based cluster analysis, using k-means methodology to group the population into segments with distinct healthcare utilisation patterns based on seven utilisation variables: elective inpatient admissions, non-elective inpatient admissions, outpatient first & follow-up attendances, Emergency Department visits, GP practice visits and prescriptions. We analysed segments post-hoc to understand their morbidity, risk and demographic profiles.

RESULTS

Ten population segments were identified which had distinct profiles of healthcare use, morbidity, demographic characteristics and risk attributes. Although half of the study population were in segments characterised as 'low need' populations, there was heterogeneity in this group with respect to variables relevant to service planning - e.g. settings in which care was mostly consumed. Significant and complex healthcare need was a feature across age groups and was driven more by deprivation and behavioural risk factors than by age and functional limitation.

CONCLUSIONS

This analysis shows that utilisation-based cluster analysis of linked primary and secondary healthcare use data for a local GP-registered population can segment the population into distinct groups with unique health and care needs, providing useful intelligence to inform local population health service planning and care delivery. This segmentation approach can offer a detailed understanding of the health and care priorities of population groups, potentially supporting the integration of health and care, reducing fragmentation of healthcare and reducing healthcare costs in the population.

Differential Health Care Use, Diabetes-Related Complications, and Mortality Among Five Unique Classes of Patients With Type 2 Diabetes in Singapore: A Latent Class Analysis of 71,125 Patients

OBJECTIVE

With rising health care costs and finite health care resources, understanding the population needs of different type 2 diabetes mellitus (T2DM) patient subgroups is important. Sparse data exist for the application of population segmentation on health care needs among Asian T2DM patients. We aimed to segment T2DM patients into distinct classes and evaluate their differential health care use, diabetes-related complications, and mortality patterns.

RESEARCH DESIGN AND METHODS

Latent class analysis was conducted on a retrospective cohort of 71,125 T2DM patients. Latent class indicators included patient's age, ethnicity, comorbidities, and duration of T2DM. Outcomes evaluated included health care use, diabetes-related complications, and 4-year all-cause mortality. The relationship between class membership and outcomes was evaluated with the appropriate regression models.

RESULTS

Five classes of T2DM patients were identified. The prevalence of depression was high among patients in class 3 (younger females with short-to-moderate T2DM duration and high psychiatric and neurological disease burden) and class 5 (older patients with moderate-to-long T2DM duration and high disease burden with end-organ complications). They were the highest tertiary health care users. Class 5 patients had the highest risk of myocardial infarction (hazard ratio [HR] 12.05, 95% CI 10.82-13.42]), end-stage renal disease requiring dialysis initiation (HR 25.81, 95% CI 21.75-30.63), stroke (HR 19.37, 95% CI 16.92-22.17), lower-extremity amputation (HR 12.94, 95% CI 10.90-15.36), and mortality (HR 3.47, 95% CI 3.17-3.80).

CONCLUSIONS

T2DM patients can be segmented into classes with differential health care use and outcomes. Depression screening should be considered for the two identified classes of patients.

Identifying optimal indicators and purposes of population segmentation through engagement of key stakeholders: a qualitative study

BACKGROUND

Various population segmentation tools have been developed to inform the design of interventions that improve population health. However, there has been little consensus on the core indicators and purposes of population segmentation. The existing frameworks were further limited by their applicability in different practice settings involving stakeholders at all levels. The aim of this study was to generate a comprehensive set of indicators and purposes of population segmentation based on the experience and perspectives of key stakeholders involved in population health.

METHODS

We conducted in-depth semi-structured interviews using purposive sampling with key stakeholders (e.g. government officials, healthcare professionals, social service providers, researchers) involved in population health at three distinct levels (micro, meso, macro) in Singapore. The interviews were audio-recorded and transcribed verbatim. Thematic content analysis was undertaken using NVivo 12.

RESULTS

A total of 25 interviews were conducted. Eight core indicators (demographic characteristics, economic characteristics, behavioural characteristics, disease state, functional status, organisation of care, psychosocial factors and service needs of patients) and 21 sub-indicators were identified. Age and financial status were commonly stated as important indicators that could potentially be used for population segmentation across three levels of participants. Six intended purposes for population segmentation included improving health outcomes, planning for resource allocation, optimising healthcare utilisation, enhancing psychosocial and behavioural outcomes, strengthening preventive efforts and driving policy changes. There was consensus that planning for resource allocation and improving health outcomes were considered two of the most important purposes for population segmentation.

CONCLUSIONS

Our findings shed light on the need for a more person-centric population segmentation framework that incorporates upstream and holistic indicators to be able to measure population health outcomes and to plan for appropriate resource allocation. Core elements of the framework may apply to other healthcare settings and systems responsible for improving population health.

TRIAL REGISTRATION

The study was approved by the SingHealth Institutional Review Board (CIRB Reference number: 2017/2597).

Identifying subgroups of high-need, high-cost, chronically ill patients in primary care: A latent class analysis

Introduction

Segmentation of the high-need, high-cost (HNHC) population is required for reorganizing care to accommodate person-centered, integrated care delivery. Therefore, we aimed to identify and characterize relevant subgroups of the HNHC population in primary care by using demographic, biomedical, and socioeconomic patient characteristics.

Methods

This was a retrospective cohort study within a Dutch primary care group, with a follow-up period from September 1, 2014 to August 31, 2017. Chronically ill patients were included in the HNHC population if they belonged to the top 10% of care utilizers and/or suffered from multimorbidity and had an above-average care utilization. In a latent class analysis, forty-one patient characteristics were initially used as potential indicators of heterogeneity in HNHC patients’ needs.

Results

Patient data from 12 602 HNHC patients was used. A 4-class model was considered statistically and clinically superior. The classes were named according to the characteristics that were most dominantly present and distinctive between the classes (i.e. mainly age, household position, and source of income). Class 1 (‘older adults living with partner’) included 39.3% of patients, class 2 (‘older adults living alone’) included 25.5% of patients, class 3 (‘middle-aged, employed adults with family’) included 23.3% of patients, and class 4 (‘middle-aged adults with social welfare dependency’) included 11.9% of patients. Diabetes was the most common condition in all classes; the second most prevalent condition differed between osteoarthritis in class 1 (21.7%) and 2 (23.8%), asthma in class 3 (25.3%), and mood disorders in class 4 (23.1%). Furthermore, while general practitioner (GP) care utilization increased during the follow-up period in the classes of older adults, it remained relatively stable in the middle-aged classes.

Conclusions

Although the HNHC population is heterogeneous, distinct subgroups with relatively homogeneous patterns of mainly demographic and socioeconomic characteristics can be identified. This calls for tailoring care and increased attention for social determinants of health.

Epidemiologic Characteristics of Multimorbidity and Sociodemographic Factors Associated With Multimorbidity in a Rapidly Aging Asian Country

IMPORTANCE

Multimorbidity is a growing health care problem in aging societies and is strongly associated with epidemiologic characteristics and sociodemographic factors. Knowledge of these associations is important for the design of effective preventive and management strategies.

OBJECTIVES

To determine the association between multimorbidity and sociodemographic factors (age, socioeconomic status [SES], sex, and race/ethnicity) and the association between mental health diseases and physical diseases, as well as their implications for the types and costs of health care use.

DESIGN, SETTING, AND PARTICIPANTS

This population-based cross-sectional study used deidentified Singapore Eastern Regional Health System data collected between January 1, 2012, and December 31, 2016. Patients who were alive as of January 1, 2016, and residing in the Regional Health System region in 2016 (N = 1 181 024) were included. Patients who had no year of birth records (n = 573), were born in 2017 (n = 93), or died before January 1, 2016 (n = 47 322), were excluded.

MAIN OUTCOMES AND MEASURES

Multimorbidity, age, sex, SES, mental health, race/ethnicity, and health care use.

RESULTS

In the study population of 1 181 024 individuals, the mean (SD) age was 39.6 (22.1) years, 51.2% were women, 70.1% were Chinese, 7.1% were Indian, 13.5% were Malayan, and 9.3% were other races/ethnicities. Multimorbidity, present in 26.2% of the population, was more prevalent in female (26.8%; 95% CI, 26.7%-26.9%) than in male (25.6%; 95% CI, 25.5%-25.7%) patients and among patients with low SES (41.6%) than those with high SES (20.1%). Mental health diseases were significantly more prevalent among individuals with low SES (5.2%; 95% CI, 5.1%-5.2%) than high SES (2.1%; 95% CI, 2.0%-2.1%; P < .001). The 3 most prevalent disease combinations were chronic kidney disease and hypertension, chronic kidney disease and lipid disorders, and hypertension and lipid disorders. Although chronic kidney disease, hypertension, lipid disorders, and type 1 and/or type 2 diabetes-related diseases had a low cost per capita, the large number of patients with these conditions caused the overall proportion of the cost incurred by health care use to be more than twice that incurred in other diseases.

CONCLUSIONS AND RELEVANCE

These findings emphasize the association between multimorbidity and sociodemographic factors such as increasing age, lower SES, female sex, and increasing number of mental disorders. Health care policies need to take sociodemographic factors into account when tackling multimorbidity in a population.

Population segmentation based on healthcare needs: a systematic review

BACKGROUND

Healthcare needs-based population segmentation is a promising approach for enabling the development and evaluation of integrated healthcare service models that meet healthcare needs. However, healthcare policymakers interested in understanding adult population healthcare needs may not be aware of suitable population segmentation tools available for use in the literature and barring better-known alternatives, may reinvent the wheel by creating and validating their own tools rather than adapting available tools in the literature. Therefore, we undertook a systematic review to identify all available tools which operationalize healthcare need-based population segmentation, to help inform policymakers developing population-level health service programmes.

METHODS

Using search terms reflecting concepts of population, healthcare need and segmentation, we systematically reviewed and included articles containing healthcare need-based adult population segmentation tools in PubMed, CINAHL and Web of Science databases. We included tools comprising mutually exclusive segments with prognostic value for clinically relevant outcomes. An updated secondary search on the PubMed database was also conducted as the last search was conducted 2 years ago. All identified tools were characterized in terms of segment formulation, segmentation base, whether they received peer-reviewed validation, requirement for comprehensive electronic medical records, proprietary status and number of segments.

RESULTS

A total of 16 unique tools were identified from systematically reviewing 9970 articles. Peer-reviewed validation studies were found for 9 of these tools.

DISCUSSION AND CONCLUSIONS

The underlying segmentation basis of most identified tools was found to be conceptually comparable to each other which suggests a broad recognition of archetypical patient overall healthcare need profiles. While many tools operate based on administrative record data, it is noted that healthcare systems without comprehensive electronic medical records would benefit from tools which segment populations through primary data collection. Future work could therefore include development and validation of such primary data collection-based tools. While this study is limited by exclusion of non-English literature, the identified and characterized tools will nonetheless facilitate efforts by policymakers to improve patient-centred care through development and evaluation of services tailored for specific populations segmented by these tools.

Utilisation of Electronic Health Records for Public Health in Asia: A Review of Success Factors and Potential Challenges

INTRODUCTION

Electronic health records offer a valuable resource to improve health surveillance and evaluation as well as informing clinical decision making. They have been introduced in many different settings, including low- and middle-income countries, yet little is known of the progress and effectiveness of similar information systems within Asia. This study examines the implementation of EHR systems for use at a population health level in Asia and to identify their current role within public health, key success factors, and potential barriers in implementation.

MATERIAL AND METHODS

A systematic search process was implemented. Five databases were searched with MeSH key terms and Boolean phrases. Articles selected for this review were based on hospital provider electronic records with a component of implementation, utilisation, or evaluation for health systems or at least beyond direct patient care. A proposed analytic framework considered three interactive components: the content, the process, and the context.

RESULTS

Thirty-two articles were included in the review. Evidence suggests that benefits are significant but identifying and addressing potential challenges are critical for success. A comprehensive preparation process is necessary to implement an effective and flexible system.

DISCUSSION

Electronic health records implemented for public health can allow the identification of disease patterns, seasonality, and global trends as well as risks to vulnerable populations. Addressing implementation challenges will facilitate the development and efficacy of public health initiatives in Asia to identify current health needs and mitigate future risks.

Predictive ability of an expert-defined population segmentation framework for healthcare utilization and mortality - a retrospective cohort study

Background

Population segmentation of patients into parsimonious and relatively homogenous subgroups or segments based on healthcare requirements can aid healthcare resource planning and the development of targeted intervention programs. In this study, we evaluated the predictive ability of a previously described expert-defined segmentation approach on 3-year hospital utilization and mortality.

Methods

We segmented all adult patients who had a healthcare encounter with Singapore Health Services (SingHealth) in 2012 using the SingHealth Electronic Health Records (SingHealth EHRs). Patients were divided into non-overlapping segments defined as Mostly Healthy, Stable Chronic, Serious Acute, Complex Chronic without Frequent Hospital Admissions, Complex Chronic with Frequent Hospital Admissions, and End of Life, using a previously described expert-defined segmentation approach. Hospital admissions, emergency department attendances (ED), specialist outpatient clinic attendances (SOC) and mortality in different patient subgroups were analyzed from 2013 to 2015.

Results

819,993 patients were included in this study. Patients in Complex Chronic with Frequent Hospital Admissions segment were most likely to have a hospital admission (IRR 22.7; p < 0.001) and ED visit (IRR 14.5; p < 0.001) in the follow-on 3 years compared to other segments. Patients in the End of Life and Complex Chronic with Frequent Hospital Admissions segments had the lowest three-year survival rates of 58.2 and 62.6% respectively whereas other segments had survival rates of above 90% after 3 years.

Conclusion

In this study, we demonstrated the predictive ability of an expert-driven segmentation framework on longitudinal healthcare utilization and mortality.

Electronic supplementary material

The online version of this article (10.1186/s12913-019-4251-6) contains supplementary material, which is available to authorized users.

Identifying heterogeneous health profiles of primary care utilizers and their differential healthcare utilization and mortality - a retrospective cohort study

BACKGROUND

Heterogeneity of population health needs and the resultant difficulty in health care resources planning are challenges faced by primary care systems globally. To address this challenge in population health management, it is critical to have a better understanding of primary care utilizers' heterogeneous health profiles. We aimed to segment a population of primary care utilizers into classes with unique disease patterns, and to report the 1 year follow up healthcare utilizations and all-cause mortality across the classes.

METHODS

Using de-identified administrative data, we included all adult Singapore citizens or permanent residents who utilized Singapore Health Services (SingHealth) primary care services in 2012. Latent class analysis was used to identify patient subgroups having unique disease patterns in the population. The models were assessed by Bayesian Information Criterion and clinical interpretability. We compared healthcare utilizations in 2013 and one-year all-cause mortality across classes and performed regression analysis to assess predictive ability of class membership on healthcare utilizations and mortality.

RESULTS

We included 100,747 patients in total. The best model (k = 6) revealed the following classes of patients: Class 1 "Relatively healthy" (n = 58,213), Class 2 "Stable metabolic disease" (n = 26,309), Class 3 "Metabolic disease with vascular complications" (n = 2964), Class 4 "High respiratory disease burden" (n = 1104), Class 5 "High metabolic disease without complication" (n = 11,122), and Class 6 "Metabolic disease with multi-organ complication" (n = 1035). The six derived classes had different disease patterns in 2012 and 1 year follow up healthcare utilizations and mortality in 2013. "Metabolic disease with multiple organ complications" class had the highest healthcare utilization (e.g. incidence rate ratio = 19.68 for hospital admissions) and highest one-year all-cause mortality (hazard ratio = 27.97).

CONCLUSIONS

Primary care utilizers are heterogeneous and can be segmented by latent class analysis into classes with unique disease patterns, healthcare utilizations and all-cause mortality. This information is critical to population level health resource planning and population health policy formulation.

Outpatient primary and tertiary healthcare utilisation among public rental housing residents in Singapore

Background

Globally, public housing is utilized to provide affordable housing for low-income households. Studies have shown an association between public housing and negative health outcomes. There is paucity of data pertaining to outpatient primary and tertiary healthcare resources utilization among public rental housing residents in Singapore.

Methods

A retrospective cohort study was performed, involving patients under the care of SingHealth Regional Health System (SHRS) in Year 2012. Healthcare utilization outcomes evaluated included number of outpatient primary and specialist care clinic visits, emergency department visits and hospitalization in Year 2011. Multivariate logistical analyses were used to examine the association between public rental housing and healthcare utilization.

Results

Of 147,105 patients, 10,400 (7.1%) patients stayed in public rental housing. There were more elderly (54.8 ± 18.0 vs 49.8 ± 17.1, p < 0.001) and male patients [5279 (50.8%) vs 56,892 (41.6%), p < 0.001] residing in public rental housing. Co-morbidities such as hypertension and hyperlipidemia were more prevalent among public rental housing patients. (p < 0.05).

After adjustment for covariates, public rental housing was not associated with frequent outpatient primary care clinic or specialist outpatient clinic attendances (p > 0.05). However, it was associated with increased number of emergency department visits (OR: 2.41, 95% CI: 2.12–2.74) and frequent hospitalization (OR: 1.56, 95% CI: 1.33–1.83).

Conclusion

Residing in public rental housing was not associated with increased utilization of outpatient healthcare resources despite patients’ higher disease burden and frequency of emergency department visits and hospitalizations. Further research is required to elucidate their health seeking behaviours.

Electronic supplementary material

The online version of this article (10.1186/s12913-019-4047-8) contains supplementary material, which is available to authorized users.