PP40 Co-design and refinement of a logic model describing case management for people who frequently call ambulance services – the stretched evaluation
Year: 2024
Type: conference-abstract
Abstract: Background Multi-agency case management across the UK addresses the needs of those who call 999 ambulance services frequently (≥5 times in a month, or ≥12 times in a three-month period). However, there is little evidence about what works and how. STRETCHED (Strategies to Manage Emergency Ambulance Telephone Callers with Sustained High needs – an Evaluation using Linked Data) evaluated case management using mixed methods. One objective was to develop and refine a logic model to describe cross-sectoral case management and its expected impacts, as a basis for evaluation. Methods Stage 1: We conducted a stakeholder event with 37 people from Wales, England and Northern Ireland including public partners (with lived experience) and relevant staff to develop an initial logic model. Stage 2: Following the main data collection and analysis phases of the study, we reviewed and refined the logic model in the light of findings. Results The logic model summarises in graphic form: Inputs: resources and activities Mechanisms of change: at organisational and individual level Impacts: on service users, ambulance services, individual staff, and wider service networks. Study findings showed that: case management initiatives varied considerably between sites; patients had a high level of need with high mortality and rates of hospital admission; the conditions of many patients are not amenable to individual behaviour change. We then revised the logic model to show: the range of contextual/mediating factors shaping local initiatives variation in case management activities between sites a reduced emphasis on individual behaviour change. Conclusion In this challenging and complex area of health care, a logic model can be an essential tool to support evaluation, and refined on the basis of study insights.
Source: Poster presentations
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4405154977