The use of data and dashboards to improve quality of neonatal hospital care in Kenya

The Sustainable Development Goals (SDGs) aim to reduce neonatal mortality to at least 12 deaths per 1,000 live births by 2030, yet significant newborn care challenges persist in low- and middle-income countries (LMICs), casting doubt on whether this target will be met. The World Health Organization’s Every Newborn Action Plan highlights the urgent need to reduce newborn mortality and morbidity and use data to improve quality of care. However, research has focused on data availability, systems and technology for tracking these goals. While data can provide evidence-based insights, improving neonatal care requires translating data into actionable insights that enable users to act based on the feedback they receive. Dashboards have become popular tools in healthcare to translate data into condensed information. In high-income settings, dashboards are used as Audit and Feedback (A&F) interventions to provide performance feedback to clinicians and managers. Although extensive literature on performance feedback and dashboards in healthcare exists, little is known about the effectiveness of dashboards for quality of newborn care improvements in LMICs. This study explores how dashboards may enable the use of data for performance feedback at public hospitals and improve the quality of neonatal hospital care in Kenya. Reflecting on two neonatal care initiatives that have implemented feedback on neonatal indicators, the study examines how data, visually presented with dashboards, can be used by hospital staff in newborn care. A mixed methods approach explored the design and implementation of dashboards, assessing their alignment with user needs and context to make newborn data actionable for quality improvements. The study drew on the Clinical Performance Feedback Intervention Theory (CP-FIT) and user-centred design literature. A scoping review included 18 articles to learn from newborn hospital dashboard developments globally. A quantitative survey with potential users (n=103) measured dashboard usability, in particular, data visualisation interpretability. Implementers (n=22) from Kenya, Malawi, Nigeria, Tanzania, the UK, and the USA, were interviewed about dashboard development experiences. Users (n=20), including paediatricians, nurses, data and hospital managers at Kenyan hospitals, were interviewed to understand their engagement with dashboards, the impact on daily clinical practices, and contextual factors. The background literature and scoping review confirmed that the usability of newborn dashboards is not regularly tested, and user experience studies are missing for LMIC settings. Empirical study findings indicate that dashboards fall short in supporting the complex efforts to improve newborn care due to design and contextual challenges. Around 50% of users struggled to interpret key dashboard visuals. Usability scores and user perceptions revealed challenges in using dashboards even when described as ‘very easy to use’ and ‘useful’. End-user interviews found diverse challenges, including the need for more intuitive dashboards, effective training to use them, and improved data quality and systems to ensure trust. Staff shortages exacerbate these challenges, increasing workloads and reducing available time to use feedback for quality improvements. Implementers, on the other hand, reported challenges to integrate dashboards into existing systems and provided additional insights into wider implementation challenges. This study underscores the importance and need for an iterative, user-centred design strategy that incorporates user and implementer knowledge and contextual influences. Findings demonstrate that thorough user testing and active user engagement are crucial when developing hospital dashboards for quality improvement. Understanding diverse user perspectives and the context should be the foundation for building, tailoring and implementing dashboards. Dashboards adapted to user needs and institutional contexts that provide reassurance in data quality, data analysis, and the underlying information systems can help to build trust in the feedback presented in dashboards and thus promote data-driven quality improvement.