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

Abstract

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).

Construction and validation of an aging-related gene signature predicting the prognosis of pancreatic cancer

“…GSEA is a computing methodology used to evaluate whether a given set of genes shows statistically significant between two biological states ( Yoon et al, 2020 ). To study the role of aging in pancreatic cancer, GSEA analysis was completed using ClusterProfiler R package with parameters: nPerm = 1,000, p -value Cutoff = 0.05 and pAdjustMethod = “BH”.…”

Patterns of healthcare utilisation in children and young people: a retrospective cohort study using routinely collected healthcare data in Northwest London

“…20 21 Models with low D-B scores and high C-H scores were examined further: (a) confusion matrices were constructed to compare cluster assignment in the model with k clusters against the model with k+1 clusters and (b) mean values for each utilisation variable were calculated for each cluster in each model. Further details of the clustering approach are given in online supplemental file 1 (see pages [5][6][7][8][9][10][11][12][13][14][15][16][17].…”

Supporting professionals to implement integrated, person-centered care for people with chronic conditions: the TARGET pilot study

“…As advocated internationally, this study underlines the value of a comprehensive, biopsychosocial approach to patients, enabled by the TARGET segmentation tool and PCNA [ 46 , 47 ]. To design the TARGET segmentation tool as truly holistic, it should be expanded with social data, as acknowledged by other studies into segmentation tools as well [ 48 ]. However, these data are often lacking as the internationally classified codes for registering social issues, Z-codes, are systematically underutilized by healthcare professionals and exchange of patient information between domains is complicated [ 17 , 49–52 ].…”