Problem
Research questions and friction points this paper is trying to address.
multiple long-term conditions
healthcare service design
patient stories
continuity of care
care coordination
Data-informed healthcare service design for multiple long-term conditions using online patient stories
🤖 AI Summary
Traditional healthcare service designs struggle to meet the complex needs of patients with multiple long-term conditions (MLTC). This study addresses this gap by systematically integrating 2,320 online narratives from patients and caregivers, combining qualitative content analysis with large-scale text mining to construct a structured story database that precisely identifies service pain points. Innovatively bridging qualitative depth with quantitative breadth, the research distills three critical dimensions for service improvement: continuity of care, care coordination, and service accessibility. These findings provide empirical grounding and strategic priorities for redesigning healthcare systems to better support individuals living with MLTC.
📝 Abstract
Conventional service design methods are valuable for improving healthcare experience, but are limited in scale and information capture. Based on a constructed database of 2,320 stories from patients and carers with multiple long-term conditions (MLTC), this paper shows how real-life experiences can be used to inform healthcare service redesign. By combining the richness of qualitative insight with the breadth and representativeness of large-scale data, it identifies "Continuity of care", "Care coordination", and "Temporal - Access to services" as the priority redesign opportunities for MLTC.
Innovation
Methods, ideas, or system contributions that make the work stand out.
data-informed design
multiple long-term conditions
patient stories
care coordination
continuity of care
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