Learning from incident reports in the Australian medical imaging setting: handover and communication errors

Objective: To determine the type and nature of incidents occurring within medical imaging settings in Australia and identify strategies that could be engaged to reduce the risk of their re-occurrence. Methods: 71 search terms, related to clinical handover and communication, were applied to 3976 incidents in the Radiology Events Register. Detailed classification and thematic analysis of a subset of incidents that involved handover or communication (n=298) were undertaken to identify the most prevalent types of error and to make recommendations about patient safety initiatives in medical imaging. Results: Incidents occurred most frequently during patient preparation (34%), when requesting imaging (27%) and when communicating a diagnosis (23%). Frequent problems within each of these stages of the imaging cycle included: inadequate handover of patients (41%) or unsafe or inappropriate transfer of the patient to or from medical imaging (35%); incorrect information on the request form (52%); and delayed communication of a diagnosis (36%) or communication of a wrong diagnosis (36%). Conclusion: The handover of patients and clinical information to and from medical imaging is fraught with error, often compromising patient safety and resulting in communication of delayed or wrong diagnoses, unnecessary radiation exposure and a waste of limited resources. Corrective strategies to address safety concerns related to new information technologies, patient transfer and inadequate test result notification policies are relevant to all healthcare settings.N Hannaford, C Mandel, C Crock, K Buckley, F Magrabi, M Ong, S Allen, and T Schult

Cognitive and motivational biases in decision and risk analysis

Behavioral decision research has demonstrated that judgments and decisions of ordinary people and experts are subject to numerous biases. Decision and risk analysis were designed to improve judgments and decisions and to overcome many of these biases. However, when eliciting model components and parameters from decisionmakers or experts, analysts often face the very biases they are trying to help overcome. When these inputs are biased they can seriously reduce the quality of the model and resulting analysis. Some of these biases are due to faulty cognitive processes; some are due to motivations for preferred analysis outcomes. This article identifies the cognitive and motivational biases that are relevant for decision and risk analysis because they can distort analysis inputs and are difficult to correct. We also review and provide guidance about the existing debiasing techniques to overcome these biases. In addition, we describe some biases that are less relevant because they can be corrected by using logic or decomposing the elicitation task. We conclude the article with an agenda for future research