Population Segmentation Based on Healthcare Needs: Validation of a Brief Clinician-Administered Tool

Segmenting the Population and Estimating Transition Probabilities Using Data on Health and Health-Related Social Service Needs from the US Health and Retirement Study

BackgroundSimulation modeling is a promising tool to help policy makers and providers make evidence-based decisions when evaluating integrated care programs. The functionality of such models, however, depends on 2 prerequisites: 1) the analytical segmentation of populations to capture both health and health-related social service (HASS) needs and 2) the precise estimation of transition probabilities among the various states of need.MethodsWe took a validated instrument for segmenting the population by HASS needs and adapted it to the Health and Retirement Study, a nationally representative survey dataset from the US population older than 50 y. We then estimated the transition probabilities across all 10 need states and death using multistate modeling. A need state was defined as a combination of any of the 5 ordinal global impression segments and a complicating factor status.ResultsKaplan-Meier survival curves, log-rank tests, and c-indices were used to assess predictive validity in relation to mortality. The Markov traces, using the estimated transition probability to replicate 2 closed cohorts, resembled the proportion of individuals per health state across subsequent waves well enough to indicate adequate fit of the estimated transition probabilities.ConclusionsThis article provides a population segmentation approach that incorporates HASS needs for the US population and 1-y transition probabilities across HASS need states and death. This is the first application of HASS segmentation that can estimate transitions between all 10 HASS need states, facilitating novel analysis of policy decisions related to integrated care.ImplicationsOur results will be used as input for a simulation model that performs scenario analysis on the long-term effects of various integrated care policies on population health.HighlightsWe took a validated tool for segmenting the population according to health and health-related social service (HASS) needs and adapted it to the Health and Retirement Study, a nationally representative survey dataset from the US population over the age of 50 y.We estimated the 1-y transition probabilities across all 10 HASS segments and death.This is the first application of a version of this HASS segmentation tool that includes HASSs in the various need states when estimating transition probabilities.Our results will be used as input for a simulation model that performs scenario analysis on the long-term effects of various integrated care policies on population health.

Development and validation of a brief assessment of normative health and health-related social needs using the Simple Segmentation Tool

OBJECTIVES

Population segmentation provides a promising solution to address patients' complex needs to provide "whole person" care. The primary objective of this study is to create an expert-based algorithm based on combinations of medical and social characteristics derived from the Simple Segmentation Tool (SST), that are indicative of high value health and health-related social service (HASS) needs for an elderly population. The secondary objective was to examine the association between failing to meet the HASS needs 3-months post hospital discharge suggested by the algorithm and adverse outcomes over the ensuing year.

DESIGN & SETTING

Based on a parsimonious set of 10 patient characteristics identified in the SST, a representative expert panel was engaged using the Modified Appropriateness Methodology (MAM). A prospective study was then performed on patients admitted to the Singapore General Hospital, using HASS needs identified at discharge and met needs at 3 months post-discharge follow-up of services received, to assess whether unmet needs were associated with higher adverse outcomes in the year following discharge. The primary outcome of interest was time to all-cause mortality over 12-months post-discharge and was assessed with Cox regression analysis.

RESULTS

The MAM exercise resulted in 12 normatively defined high value services, using a combination of patients' medical and social characteristics based on the SST, as well as a list of means of providing those service needs. The all-cause mortality hazard ratio of having at least one unmet need versus having all needs met for individuals deemed to be chronically symptomatic at discharge was 1.949, (95% CI: 0.99 - 3.84, and p = 0.05), while for those who were either healthy or only had asymptomatic chronic conditions the all-cause mortality ratio of having at least one unmet need versus having all needs met was 0.28 (95% CI = 0.06-1.27 and p-value = 0.10). The hazard ratio for ED visits and hospital readmission were above one but did not reach level of 95% confidence level.

CONCLUSION

The SST methodology provides a practical way to assess HASS needs that are predictive of mortality when needs are not met. It could serve as a screening tool to identify individuals who are likely to benefit from detailed care planning and follow-up.

Impact of family doctor system on diabetic patients with distinct service utilisation patterns: a difference-in-differences analysis based on group-based trajectory modelling

INTRODUCTION

This study examines the impact of China's family doctor system (FDS) on healthcare utilisation and costs among diabetic patients with distinct long-term service utilisation patterns.

METHODS

Conducted in City A, eastern China, this retrospective cohort study used data from the Health Information System and Health Insurance Claim Databases, covering diabetic patients from 1 January 2014 to 31 December 2019.Patients were categorised into service utilisation trajectories based on quarterly outpatient visits to community health centres (CHCs) and secondary/tertiary hospitals from 2014 to 2017 using group-based trajectory models. Propensity score matching within each trajectory group matched FDS-enrolled patients (intervention) with non-enrolled patients (control). Difference-in-differences analysis compared outcomes between groups, with a SUEST test for cross-model comparison. Outcomes included outpatient visits indicator, costs indicator and out-of-pocket (OOP) expenses.

RESULTS

Among 17 232 diabetic patients (55.21% female, mean age 62.85 years), 13 094 were enrolled in the FDS (intervention group) and 4138 were not (control group). Patients were classified into four trajectory groups based on service utilisation from 2014 to 2017: (1) low overall outpatient utilisation, (2) high CHC visits, (3) high secondary/tertiary hospital visits and (4) high overall outpatient utilisation. After enrolled in FDS From 2018 to 2019, the group with high secondary/tertiary hospital visits saw a 6.265 increase in CHC visits (225.4% cost increase) and a 3.345 decrease in hospital visits (55.5% cost reduction). The high overall utilisation group experienced a 4.642 increase in CHC visits (109.5% cost increase) and a 1.493 decrease in hospital visits. OOP expenses were significantly reduced across all groups.

CONCLUSION

The FDS in China significantly increases primary care utilisation and cost, while reducing hospital visits and costs among diabetic patients, particularly among patients with historically high hospital usage. Policymakers should focus on enhancing the FDS to further encourage primary care usage and improve chronic disease management.

Validation and application of a needs-based segmentation tool for cross-country comparisons

OBJECTIVE

To compare countries' health care needs by segmenting populations into a set of needs-based health states.

DATA SOURCES

We used seven waves of the Survey of Health, Aging and Retirement in Europe (SHARE) panel survey data.

STUDY DESIGN

We developed the Cross-Country Simple Segmentation Tool (CCSST), a validated clinician-administered instrument for categorizing older individuals by distinct, homogeneous health and related social service needs. Using clinical indicators, self-reported physician diagnosis of chronic disease, and performance-based tests conducted during the survey interview, individuals were assigned to 1-5 global impressions (GI) segments and assessed for having any of the four identifiable complicating factors (CFs). We used Cox proportional hazard models to estimate the risk of mortality by segment. First, we show the segmentation cross-sectionally to assess cross-country differences in the fraction of individuals with different levels of medical needs. Second, we compare the differences in the rate at which individuals transition between those levels and death.

DATA COLLECTION/EXTRACTION METHODS

We segmented 270,208 observations (from Austria, Belgium, Czech Republic, Denmark, France, Germany, Greece, Israel, Italy, the Netherlands, Poland, Spain, Sweden, and Switzerland) from 96,396 individuals into GI and CF categories.

PRINCIPAL FINDINGS

The CCSST is a valid tool for segmenting populations into needs-based states, showing Switzerland with the lowest fraction of individuals in high medical needs segments, followed by Denmark and Sweden, and Poland with the highest fraction, followed by Italy and Israel. Comparing hazard ratios of transitioning between health states may help identify country-specific areas for analysis of ecological and cultural risk factors.

CONCLUSIONS

The CCSST is an innovative tool for aggregate cross-country comparisons of both health needs and transitions between them. A cross-country comparison gives policy makers an effective means of comparing national health system performance and provides targeted guidance on how to identify strategies for curbing the rise of high-need, high-cost patients.

Early Implementation of a Patient-Centered Medical Home in Singapore: A Qualitative Study Using Theory on Diffusion of Innovations

Patient-Centered Medical Home (PCMH) has been found to improve care for complex needs patients in some countries but has not yet been widely adopted in Singapore. This study explored the ground-up implementation of a PCMH in Singapore by describing change strategies and unpacking initial experience and perception. In-depth interviews were conducted for twenty-two key informants from three groups: the implementers, their implementation partners, and other providers. “Diffusion of innovations” emerged as an overarching theory to contextualize PCMH in its early implementation. Three core “innovations” differentiated the PCMH from usual primary care: (i) team-based and integrated care; (ii) empanelment; and (iii) shared care with other general practitioners. Change strategies employed to implement these innovations included repurposing pre-existing resources, building a partnership to create supporting infrastructure and pathways in the delivery system, and doing targeted outreach to introduce the PCMH. Initial experience and perception were characterized by processes to “adopt” and “assimilate” the innovations, which were identified as challenging due to less predictable, self-organizing behaviors by multiple players. To work with the inherent complexity and novelty of the innovations, time, leadership, standardized methods, direct communication, and awareness-building efforts are needed. This study was retrospectively registered (Protocol ID: NCT04594967).

Web-Based Patient Segmentation in Finnish Primary Care: Protocol for Clinical Validation of the Navigator Service in Patients With Diabetes

BACKGROUND

An aging population and increasing multimorbidity challenge health care systems worldwide. Patient segmentation aims to recognize groups of patients with similar needs, offer targeted services to these groups, and reduce the burden of health care. In this study, the unique Finnish innovation Navigator, a web-based service for patient segmentation, is presented. Both patients and health care professionals complete the electronic questionnaire concerning patients' coping in everyday life and health state. Thus, it considers the patient perspective on self-care. One of four customership-strategy (CS) groups (self-acting, community, cooperating, and network) is then proposed in response to the answers given. This resulting strategy helps both professionals to coordinate patient health care and patients to utilize appropriate health services.

OBJECTIVE

This study aims to determine the feasibility, validity, and reliability of the Navigator service in the segmentation of patients with diabetes into four CS groups in a primary care setting. Patient characteristics concerning demographic status, chronic conditions, disabilities, health-related quality of life, and well-being in different CS groups will be described. We hypothesize that patients in the network group will be older, have more illnesses, chronic conditions or disabilities, and require more health care services than patients in the self-acting group.

METHODS

In this mixed methods study, data collection was based on questionnaires (user experience of Navigator, demographic and health status, World Health Organization Disability Assessment Schedule 2.0, EuroQol 5D, Wellbeing Questionnaire 12, and the Diabetes Treatment Satisfaction Questionnaire) issued to 300 patients with diabetes and on user-experience questionnaires for and semistructured focus-group interviews with 12 nurses. Navigator-database reports and diabetes-care values (blood pressure, BMI, HbA1c, low-density lipoprotein, albumin-creatinine, smoking status) were collected. Qualitative and descriptive analyses were used to study the feasibility, content, concurrent, and face validity of Navigator. While criterion and concurrent validity were examined with correlations, reliability was examined by calculating Cohen kappa and Cronbach alpha. Construct validity is studied by performing exploratory-factor analysis on Navigator data reports and by hypothesis testing. The values, demographics, and health status of patients in different groups were described, and differences between groups were studied by comparing means. Linear regression analysis was performed to assess which variables affect CS group variation.

RESULTS

Data collection was completed in September 2019, and the first feasibility results are expected by the end of 2020. Further results and publications are expected in 2021 and 2022.

CONCLUSIONS

This is the first scientific study concerning Navigator's psychometric properties. The study will examine the segregation of patients with diabetes into four CS groups in a primary care setting and the differences between patients in groups. This study will assist in Navigator's further development as a patient segmentation method considering patients' perspectives on self-care. This study will not prove the effectiveness or efficacy of Navigator; therefore, it is essential to study these outcomes of separate care pathways.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/20570.