Though an important component of high-quality healthcare, the routine collection of patient experience data is limited in primary care, as is the evidence for how this data is being used for quality improvement. This study used a learning health system (LHS) framework to describe how a university-affiliated community general practice is integrating patient experience data into service and quality improvement efforts, and to identify barriers and facilitators. A co-designed qualitative case study was conducted with academic researchers and staff from a university-affiliated general practice in Australia. Semi-structured interviews were conducted in April 2024 with practice staff, and transcripts were deductively coded according to a five-domain learning health systems framework, and with additional codes capturing barriers and facilitators. Eighteen (53%) practice staff were interviewed, including general practitioners (n=11), a practice nurse (n=1), and administrative staff (n=6). Participants identified multiple methods through which the practice captured the patient experience that spanned all domains of the LHS framework. However, there was less evidence of a coherent quality improvement strategy being employed, with associated barriers identified around staff workloads, training, and existing government funding policies. Key facilitators to the use of patient experience data included: membership of a larger health organisation and university; key dedicated administrative and clinical roles; and effective leadership, governance structures and policies to support continuous learning and drive service improvement. This study presents a case example of how patient experience data is being integrated into general practice and identifies key barriers and facilitators to initiating and translating this data for continuous healthcare improvement. By mapping the use of patient experience data to a LHS framework, this study shows how LHS principles can be applied to primary care to facilitate the capture and use of patient experience data on an ongoing basis.
Read full abstract