Data-informed healthcare service design for multiple long-term conditions using online patient stories

TL;DR

This paper demonstrates how analyzing a large database of patient stories can inform healthcare service redesign for multiple long-term conditions, highlighting key areas for improvement.

Contribution

It introduces a data-informed approach using online patient stories to identify priority areas for healthcare service redesign in MLTC.

Findings

Identified key redesign opportunities: continuity of care, care coordination, access to services.

Combined qualitative insights with large-scale data for comprehensive analysis.

Showed real-life experiences can effectively inform healthcare improvements.

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.