Abstract / Summary
Abstract Background High-quality stroke data are essential for evidence-based clinical care, surveillance, research, and health-system planning. However, the organizational readiness of health facilities to generate, manage, and utilize stroke data remains poorly understood in many low- and middle-income countries. This study assessed the readiness of stroke data ecosystems for evidence-based care in Uganda using a data capability maturity framework. Methods We conducted a multicenter cross-sectional assessment in six health facilities providing stroke care in Uganda. Using the Higher Education Statistics Agency (HESA) data capability toolkit, we assessed four domains of stroke data capability maturity: people and culture, data activities, business processes, and technology. Responses were converted into standardized maturity scores (0-100%). An overall unweighted stroke data maturity score was calculated as the arithmetic mean of the four domain scores, while a weighted composite assigned 30%, 25%, 30%, and 15% to people and culture, data activities, business processes, and technology, respectively. Results Unweighted overall maturity scores ranged from 60.8% to 70.0%, with four facilities classified as proactive and two as stable. Weighted maturity scores ranged from 62.8% to 72.1%. Substantial variation was observed across domains, with business processes scoring 44.4–100.0%, people and culture 61.5–76.9%, data activities 30.0–80.0%, and technology 36.4–81.8%. Strengths included governance structures, leadership support for evidence-informed decision-making, standardized data collection tools, and integration of stroke data into routine clinical workflows. Important gaps remained in advanced analytical capacity, routine data quality assurance, interoperability, digitization, workforce capacity, and secure data backup. Hybrid paper-electronic workflows predominated, and only two facilities reported fully interoperable stroke information systems. Five of 24 items showed no between-facility variation, and their exclusion resulted in reclassification of two facilities from proactive to stable maturity. Conclusions Participating health facilities demonstrated moderate to high stroke data capability, with relatively strong organizational and governance foundations but important gaps in technical, analytical, and operational capacity. Variation across domains indicates that overall maturity scores may mask specific facility-level weaknesses that constrain the effective generation and use of stroke data. Strengthening analytical capacity, data quality processes, interoperability, workforce capacity, and secure data infrastructure should complement existing organizational strengths.