No abstract

Evaluation of the system in a randomised controlled trial in breast, gynaecological and colorectal cancer patients undergoing systemic therapy is currently underway. To adapt eRAPID for different treatment groups, pilot studies are being undertaken with patients receiving pelvic radiotherapy and upper gastrointestinal surgery. ISRCTN88520246.

Healthcare organizations are confronted with challenges including the contention between tightening budgets and increased care needs. In the light of these challenges, they are becoming increasingly aware of the need to improve their processes to ensure quality of care for patients. To identify process improvement opportunities, a thorough process analysis is required, which can be based on real-life process execution data captured by health information systems. Process mining is a research field that focuses on the development of techniques to extract process-related insights from process execution data, providing valuable and previously unknown information to instigate evidence-based process improvement in healthcare. However, despite the potential of process mining, its uptake in healthcare organizations outside case studies in a research context is rather limited. This observation was the starting point for an international brainstorm seminar. Based on the seminar's outcomes and with the ambition to stimulate a more widespread use of process mining in healthcare, this paper formulates recommendations to enhance the usability and understandability of process mining in healthcare. These recommendations are mainly targeted towards process mining researchers and the community to consider when developing a new research agenda for process mining in healthcare. Moreover, a limited number of recommendations are directed towards healthcare organizations and health information systems vendors, when shaping an environment to enable the continuous use of process mining.

BackgroundThere are many proposed benefits of using learning health systems (LHSs), including improved patient outcomes. There has been little adoption of LHS in practice due to challenges and barriers that limit adoption of new data-driven technologies in healthcare. We have identified a more fundamental explanation: the majority of developments in LHS are not identified as LHS. The absence of a unifying namespace and framework brings a lack of consistency in how LHS is identified and classified. As a result, the LHS ‘community’ is fragmented, with groups working on similar systems being unaware of each other’s work. This leads to duplication and the lack of a critical mass of researchers necessary to address barriers to adoption.ObjectiveTo find a way to support easy identification and classification of research works within the domain of LHS.MethodA qualitative meta-narrative study focusing on works that self-identified as LHS was used for two purposes. First, to find existing standard definitions and frameworks using these to create a new unifying framework. Second, seeking whether it was possible to classify those LHS solutions within the new framework.ResultsThe study found that with apparently limited awareness, all current LHS works fall within nine primary archetypes. These findings were used to develop a unifying framework for LHS to classify works as LHS, and reduce diversity and fragmentation within the domain.ConclusionsOur finding brings clarification where there has been limited awareness for LHS among researchers. We believe our framework is simple and may help researchers to classify works in the LHS domain. This framework may enable realisation of the critical mass necessary to bring more substantial collaboration and funding to LHS. Ongoing research will seek to establish the framework’s effect on the LHS domain.