Stakeholder-informed mapping of alternative care service pathways for prehospital care redesign in Singapore: a descriptive qualitative focus group study

Singapore’s aging population and rising chronic disease burden have intensified pressure on emergency departments (EDs), revealing care fragmentation and insufficient community-based alternatives. This has prompted increasing efforts to redesign Prehospital Emergency Care (PEC) within the wider shift toward population health management. To support this transformation, the study engaged key stakeholders to (1) Validate earlier findings (2), Identify service gaps across distinct patient segments, and (3) Prioritize and refine prehospital care strategies. This study used a descriptive qualitative design through a series of Focus Group Discussions (FGD). Participants recruited were PEC stakeholders, including emergency consultants, paramedics and administrative leaders of various government organizations involved in PEC. Data were analyzed using inductive thematic analysis. Twenty-two participants took part in the FGD, with the findings organized around four main themes: (1) Validating Barriers, Mapping Services and Needs in Community Care (2) Aligning Interventions to Patient Needs (3) Refining, Prioritizing and Identifying key partners for interventions (4) Identifying System-level and Data implementation Enablers for PEC interventions. Some key findings include the significance of a Health Navigator Portal to optimize patient flow, strengthening system integration to facilitate information flow across regional clusters and enhancing affordability and capabilities of community-anchored services. This study offers valuable insights and suggestions into the challenges and strategies for the PEC landscape, while highlighting the priorities of implementation strategies, assigning roles of key stakeholders and critical enablers for PEC policy redesign. Findings for this study serve to facilitate the development of more efficient, integrated, community-anchored solutions and co-development of future PEC models that are responsive, scalable, and contextually relevant.