On September 30th, 2014, the Centers for Disease Control and Prevention (CDC) confirmed the first travel-associated case of US Ebola in Dallas, TX. This case exposed two of the greatest concerns in patient safety in the US outpatient health care system: misdiagnosis and ineffective use of electronic health records (EHRs). The case received widespread media attention highlighting failures in disaster management, infectious disease control, national security, and emergency department (ED) care. In addition, an error in making a correct and timely Ebola diagnosis on initial ED presentation brought diagnostic decision-making vulnerabilities in the EHR era into the public eye. In this paper, we use this defining “teachable moment” to highlight the public health challenge of diagnostic errors and discuss the effective use of EHRs in the diagnostic process. We analyze the case to discuss several missed opportunities and outline key challenges and opportunities facing diagnostic decision-making in EHR-enabled health care. It is important to recognize the reality that EHRs suffer from major usability and inter-operability issues, but also to acknowledge that they are only tools and not a replacement for basic history-taking, examination skills, and critical thinking. While physicians and health care organizations ultimately need to own the responsibility for addressing diagnostic errors, several national-level initiatives can help, including working with software developers to improve EHR usability. Multifaceted approaches that account for both technical and non-technical factors will be needed. Ebola US Patient Zero reminds us that in certain cases, a single misdiagnosis can have widespread and costly implications for public health.

No abstract

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.

Reducing errors in diagnosis is the next big challenge for patient safety. Diagnostic safety improvement efforts should become a priority for health care organizations, payers, and accrediting bodies; however, external incentives, policies, and practical guidance to develop these efforts are largely absent. In this Perspective, the authors highlight ways in which health care organizations can pursue learning and exploration of diagnostic excellence (LEDE). Building on current evidence and their recent experiences in developing such a learning organization at Geisinger, the authors propose a 5-point action plan and corresponding policy levers to support development of LEDE organizations. These recommendations, which are applicable to many health care organizations, include (1) implementing a virtual hub to coordinate organizational activities for improving diagnosis, such as identifying risks and prioritizing interventions that cross intra-institutional silos while promoting a culture of learning and safety; (2) participating in novel scientific initiatives to generate and translate evidence, given the rapidly evolving “basic science” of diagnostic excellence; (3) avoiding the “tyranny of metrics” by focusing on measurement for improvement rather than using measures to reward or punish; (4) engaging clinicians in activities for improving diagnosis and framing missed opportunities positively as learning opportunities rather than negatively as errors; and (5) developing an accountable culture of engaging and learning from patients, who are often underexplored sources of information. The authors also outline specific policy actions to support organizations in implementing these recommendations. They suggest this action plan can stimulate scientific, practice, and policy progress needed for achieving diagnostic excellence and reducing preventable patient harm.