Perceived Roles and Barriers in Delivering Community-Based Care: A Qualitative Study of Health and Social Care Professionals

Introduction: As healthcare systems increasingly embrace population health management, the integration of health and social care to improve the health and well-being of individuals is crucial. Thus, we conducted a qualitative study in Singapore to understand health and social care professionals’ (HCPs and SCPs) perception of the roles they played in delivering community-based care. Methods: A descriptive phenomenological research design was adopted. HCPs and SCPs (n = 53) providing services in community settings were recruited purposefully and interviewed through eleven focus group discussions. Each session was recorded and transcribed. Thematic analysis was applied. Results: Our results revealed eight themes in three main categories describing the roles played by HCPs and SCPs, including: (1) delivering needs-based care in community settings; (2) activating and empowering clients in health care, and (3) fostering community-based sustainable support networks. Six barriers encountered while performing these roles were also identified. Discussion and Conclusion: Our results highlight that the roles of HCPs and SCPs go beyond the provision of direct medical and social care. They were involved in activating and empowering clients to take care of their health, and importantly, fostering community-based sustainable support networks to better empower individuals in coping with health challenges. The identified barriers shed light on areas for potential improvements for integrated community care

Validation of the care transition measure in multi-ethnic South-East Asia in Singapore

10.1186/1472-6963-12-256BMC Health Services Research121

Using the Johns Hopkins ACG Case-Mix System for population segmentation in a hospital-based adult patient population in Singapore

Objective Population health management involves risk characterisation and patient segmentation. Almost all population segmentation tools require comprehensive health information spanning the full care continuum. We assessed the utility of applying the ACG System as a population risk segmentation tool using only hospital data.Design Retrospective cohort study.Setting Tertiary hospital in central Singapore.Participants 100 000 randomly selected adult patients from 1 January to 31 December 2017.Intervention Hospital encounters, diagnoses codes and medications prescribed to the participants were used as input data to the ACG System.Primary and Secondary Outcome Measures Hospital costs, admission episodes and mortality of these patients in the subsequent year (2018) were used to assess the utility of ACG System outputs such as resource utilisation bands (RUBs) in stratifying patients and identifying high hospital care users.Results Patients placed in higher RUBs had higher prospective (2018) healthcare costs, and were more likely to have healthcare costs in the top five percentile, to have three or more hospital admissions, and to die in the subsequent year. A combination of RUBs and ACG System generated rank probability of high healthcare costs, age and gender that had good discriminatory ability for all three outcomes, with area under the receiver-operator characteristic curve (AUC) values of 0.827, 0.889 and 0.876, respectively. Application of machine learning methods improved AUCs marginally by about 0.02 in predicting the top five percentile of healthcare costs and death in the subsequent year.Conclusion A population stratification and risk prediction tool can be used to appropriately segment populations in a hospital patient population even with incomplete clinical data