Can Care Coordination for Multiple Long-Term Conditions Be Meaningfully Measured Using Routine Data? A Multi-Stage Consultation.

چکیده اصلی

BACKGROUND: Care coordination is essential for improving healthcare experiences and outcomes for people living with multiple long-term conditions (MLTC), yet it remains challenging to measure care coordination in ways that are both meaningful and feasible at scale. Routine electronic health record (EHR) data offer potential for system-wide measurement, but it is unclear whether such measures reflect what matters to patients, carers, and those delivering care. METHODS: We undertook a multi-stage, collaborative consultation process to examine whether care coordination in MLTC can be meaningfully measured using routine data. Public contributors (n = 18), professional contributors (n = 23), researchers (n = 15) and data experts (n = 10) participated in structured discussions, co-developed appraisal criteria (including relevance to lived experience and feasibility in EHR data), and assessed measures identified through a linked systematic review. A consensus survey evaluated the appropriateness of shortlisted measures. RESULTS: Participants identified priorities including teamwork, communication, continuity, care management, and accountability. Appraisal criteria balanced patient relevance with feasibility in routine data. While a feasible shortlist of measures was identified, findings showed only qualified support for routine data-derived indicators. Continuity measures were most strongly endorsed but widely recognised as proxies capturing observable activity rather than the relational, experiential, and cross-sector dimensions central to coordinated care. Concerns were raised regarding data quality, conceptual validity, and the risk of incomplete or misleading representations. CONCLUSIONS: Routine data-derived measures of care coordination in MLTC hold clear potential but are inherently limited. The findings support a conditional position: such measures can provide useful system-level signals, but only if interpreted cautiously, supported by robust data infrastructure, and complemented by patient-reported insights. Advancing measurement will require integrated approaches that better capture what matters to patients and carers.

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