Translating electronic health record-based patient safety algorithms from research to clinical practice at multiple sites

Introduction Researchers are increasingly developing algorithms that impact patient care, but algorithms must also be implemented in practice to improve quality and safety.Objective We worked with clinical operations personnel at two US health systems to implement algorithms to proactively identify patients without timely follow-up of abnormal test results that warrant diagnostic evaluation for colorectal or lung cancer. We summarise the steps involved and lessons learned.Methods Twelve sites were involved across two health systems. Implementation involved extensive software documentation, frequent communication with sites and local validation of results. Additionally, we used automated edits of existing code to adapt it to sites’ local contexts.Results All sites successfully implemented the algorithms. Automated edits saved sites significant work in direct code modification. Documentation and communication of changes further aided sites in implementation.Conclusion Patient safety algorithms developed in research projects were implemented at multiple sites to monitor for missed diagnostic opportunities. Automated algorithm translation procedures can produce more consistent results across sites

Improving diagnostic performance through feedback: the Diagnosis Learning Cycle

BACKGROUND: Errors 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. METHODS: We 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. RESULTS: We 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. CONCLUSION: A 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