Teamwork is fundamental for high-quality clinical reasoning and diagnosis, and many different individuals are involved in the diagnostic process. However, there are substantial gaps in how these individuals work as members of teams and, often, work is done in parallel, rather than in an integrated, collaborative fashion. In order to understand how individuals work together to create knowledge in the clinical context, it is important to consider social cognitive theories, including situated cognition and distributed cognition. In this article, the authors describe existing gaps and then describe these theories as well as common structures of teams in health care and then provide ideas for future study and improvement.

BackgroundErrors in reasoning are a common cause of diagnostic error. However, it is difficult to improve performance partly because providers receive little feedback on diagnostic performance. Examining means of providing consistent feedback and enabling continuous improvement may provide novel insights for diagnostic performance.MethodsWe developed a model for improving diagnostic performance through feedback using a six-step qualitative research process, including a review of existing models from within and outside of medicine, a survey, semistructured interviews with individuals working in and outside of medicine, the development of the new model, an interdisciplinary consensus meeting, and a refinement of the model.ResultsWe applied theory and knowledge from other fields to help us conceptualise learning and comparison and translate that knowledge into an applied diagnostic context. This helped us develop a model, the Diagnosis Learning Cycle, which illustrates the need for clinicians to be given feedback about both their confidence and reasoning in a diagnosis and to be able to seamlessly compare diagnostic hypotheses and outcomes. This information would be stored in a repository to allow accessibility. Such a process would standardise diagnostic feedback and help providers learn from their practice and improve diagnostic performance. This model adds to existing models in diagnosis by including a detailed picture of diagnostic reasoning and the elements required to improve outcomes and calibration.ConclusionA consistent, standard programme of feedback that includes representations of clinicians’ confidence and reasoning is a common element in non-medical fields that could be applied to medicine. Adapting this approach to diagnosis in healthcare is a promising next step. This information must be stored reliably and accessed consistently. The next steps include testing the Diagnosis Learning Cycle in clinical settings.

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