Result notification systems Failure to communicate test results has been repeatedly noted as a contributing factor to delayed diagnosis and treatment of patients in both ambulatory and inpatient settings. Due to the negative impact on patients of missed communication of results, The Joint Commission made timely reporting of critical results of tests and diagnostic procedures a National Patient Safety Goal for their Critical Access Hospital and Hospital Programs. 11 The laboratory and radiographic testing process has three distinct phases: the pre-analytic phase, during which the test is ordered and that order is implemented; the analytic phase, when the test is performed; and the post-analytic phase, in which results are relayed to the ordering clinician, who acts upon the results, and notifies and follows up with the patient (Figure 1). The post-analytic phase, specifically the step where results, clinically significant test results (CSTR) in particular, are relayed back to the ordering clinician, is a source of diagnostic error. To reduce errors that occur during this step, experts have advocated for the use of automated alert notification systemsto ensure timely communication of CSTR. Result notification systems (RNS) can be completely automated, where an abnormal result generates an alert to the ordering clinician; or the RNS may require manual activation by the clinician. There are also a variety of modalities that can be used to alert the practitioner of actionable test results, including short messages relayed via mobile phones; emails; and results (with or without accompanying alerts) in the EHR. Some have raised a hypothetical concern about alert fatigue, a potential unintended consequence of implementing alerting RNSs. Etchells et al. (2010) noted that critical results, such as those from repeated troponin tests, were viewed as nuisances by receiving clinicians during a pilot of the system. 12 They also noted that because physician schedules were not fully automated, it was not possible to consistently route critical results to a responsible and available physician to take action. To compensate for this, physicians handed off “critical value pagers” so that the physician-on- call carried several pagers. Although this could reduce the number of missed alerts, it also created confusion when the on-call physician often could not discern which pager was alerting.
Dalal et al. (2014) attributed the successful implementation of their TPAD email-generating RNS to the existing institutional culture that supports the use of email as a routine part of clinical care. 13 The RNS was integrated into their current practice, which facilitated uptake. Several authors mentioned the need for clear policies and procedures for the RNS such as the need to have clear policies about who is responsible for acknowledging an alert and taking action, so that there is no ambiguity. One institution, after much deliberation, established the policy that the responsibility for following up a test rested on the “ordering” clinician, and that this responsibility could be discharged only after a handoff where the “new owner” recipient acknowledged receipt and agreed to take over the follow-up. Automated physician scheduling is important for optimal performance of automated critical value alerting systems. For example, when physician schedules are not fully automated, it is impossible to route alerts to the responsible (e.g., on-call) physician who can take action. Although studies of this topic are generally of high quality and some findings are significant, studies in other settings are needed to test and demonstrate generalizability, as well as to engage research in this field more widely. Diagnostic errors due to lapses in communication occur during care transitions, but only three studies (all in the same healthcare system) evaluated RNS to improve delivery of results finalized after the transition from the inpatient to the outpatient setting. It is challenging when many providers are taking care of a patient, as the RNS needs to discern who is responsible for which patient at any given time. Institutions are establishing policies aimed at addressing this challenge, but how the policies perform needs to be investigated. Education and training In the 2015 National Academies of Sciences, Engineering, and Medicine (NASEM) report Improving Diagnosis in Health Care, one of the recommended strategies for improving diagnosis is to enhance healthcare professional education and training in the diagnostic process. 4 The content of this education can be guided by an understanding of the root causes of diagnostic errors. Studies have uncovered two broad categories of underlying root causes: cognitive-based factors, such as failed heuristics; and systems-based factors, such as lack of provider-to-provider communication and coordination. In the realm of cognitive-based
errors, there are also two main streams of thought about causes: heuristics failures and shortcomings in disease-specific knowledge and experience. These sets of broad conceptual factors are by no means mutually exclusive, and ideally system redesign and educational efforts can leverage overlaps and synergies. How to best provide education and training to change these underlying factors and thereby improve diagnostic accuracy and reduce diagnostic errors leads to a more fundamental question of whether education and training lead to improved diagnostic performance. General training in clinical reasoning Clinical reasoning is the process by which clinicians collect data, process the information, and develop a problem representation, leading to the generation and testing of a hypothesis to eventually arrive at adiagnosis. Cook et al. (2010) conducted a meta-analysis and systematic review of the effects on training outcomes of using virtual patients, including the effects on clinical reasoning. 14 The learners interact with a computer program that simulates real-life clinical scenarios to obtain a history, conduct a physical exam, and make diagnostic and treatment decisions. The main takeaway from this meta-analysis and review was that the use of virtual patients is associated with large positive effects on clinical reasoning and other learning outcomes when compared with no intervention and is associated with small effects in comparison with noncomputer instruction. BEFORE MOVING ONTO THE NEXT SECTION, PLEASE COMPLETE CASE STUDY 1 ON THE NEXT PAGE. Training in metacognitive skills to reduce biases Cognitive biases can affect clinical reasoning and influence the diagnostic process, contributing to a large proportion of misdiagnoses. Metacognition, the understanding, control, and monitoring of one’s cognitive processes, can be used to gain better insight and counteract these biases. A review of studies focused on techniques to enhance metacognitive skills found mixed results, but overall they suggest the use of training metacognitive strategies to improve diagnostic performance.
Figure 1: Conceptual Framework of the Testing Process
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