Background: UK primary care accounts for 90% of patient contacts in the NHS, and over 300 million consultations every year. Consequently, when primary is suboptimal it has important impacts on population health. At the same time, virtually all general practices use electronic health records (EHR) to capture patient data. Clinical Decision Support (CDS) systems use it to highlight when individual patients do not receive care consistent with clinical guidelines, though ignore the wider population. Electronic Audit and Feedback (e-A&F) systems address the wider population, but their results are difficult to interpret. EHR data has the richness to suggest ways in which care quality could be improved, though this is currently not exploited. The aim of this thesis was to make progress towards better use of primary care EHR data for the purposes of quality improvement (QI) by focusing on e-A&F as a vehicle. Research Objectives were: 1) Develop a model and recommendations to guide EHR data analysis and its communication to health professionals; 2) Use these models and recommendations to develop a system for UK primary care; 3) Implement and evaluate the system to test the models and recommendations, and derive generalisable knowledge. Methods: The overall approach of this thesis was informed by guidance from the Medical Research Council on the development of complex interventions, and Boyrcki et al.â€™s evidence-based framework for the development of health information technologies (Chapter 2). Theory was first identified through a critical examination of the empirical and theoretical literature regarding CDS and e-A&F systems (Chapter 3), then built upon in a systematic literature search and metasynthesis of qualitative studies of A&F (and e-A&F) interventions (Chapter 4). This resulted in the development a new theory of A&F (Clinical Performance Feedback Intervention Theory; CP-FIT), which was used to inform the development of an e-A&F system for UK primary care â€“ the Performance Improvement plaN GeneratoR (PINGR; version 1). PINGR was then iteratively optimised through a series of three empirical studies. First, its usability was evaluated by software experts using Heuristic Evaluation and Cognitive Walkthrough methodologies (Chapter 5). GPs then performed structured tasks using the system in a laboratory whilst their on-screen interactions and eye movements were recorded (Chapter 6). Finally, PINGR was implemented in 15 GP practices, and CP-FIT used to guide the mixed methods evaluation including examinations of usage records, and interviews with 38 health professionals. Results: There are both empirical and theoretical arguments for combining features from CDS and e-A&F systems to increase their effectiveness; a key recommendation is that e-A&F systems should suggest clinical actions to health professionals (Chapter 3). This is supported by CP-FIT, which has three core propositions: 1) A&F interventions exert their effects through health professionals taking action; 2) Health care organisations have limited capacity to engage with A&F; and 3) Health care professionals and organisations have a strong set of beliefs and behaviours regarding how they provide patient care (Chapter 4). Based on these findings, the unique feature of PINGR is that it suggests improvement actions to users based on EHR data analysis (â€˜decision-supported feedbackâ€™). Key findings from PINGRâ€™s usability evaluation with software experts translated into a set of design guidelines for e-A&F interfaces regarding: summarising clinical performance, patient lists, patient-level information, and suggested actions (Chapter 5). When tested with GPs, these guidelines were found to impact: user engagement; actionability; and information prioritisation (Chapter 6). Following its implementation in practice, PINGR was used on 227 occasions to facilitate the care of 725 patients. These patients were 1.8 (95% CI 1.6-1.9) times more likely to receive improved care according to at least one clinical guideline. Barriers and facilitators to its success included: the resources available to use it; its perceived relative advantages; how compatible it was with pre-existing beliefs and ways of working; the credibility of its data; the complexity of the clinical problems it highlighted; and the ability to act on its recommendations (Chapter 7). Conclusion: It is both feasible and acceptable to health professionals to make better use of EHR data for QI by enabling e-A&F systems to suggest actions for them to take. When designing e-A&F interfaces, attention should be paid to how they summarise clinical performance, and present patient lists and detailed patient-level information. Implementation of e-A&F interventions is influenced by availability of resources, compatibility with existing workflows, and ability to take action based on their feedback results. Unresolved tensions exist regarding how they may deal with patient complexity. Policymakers should consider the relevance of these findings for National Clinical Audits and pay-for-performance initiatives.