Suboptimal exchange of information can have tragic consequences to patient's safety and survival. To this end, the Joint Commission lists communication error among the most common attributable causes of sentinel events. The risk management literature further supports this finding, ascribing communication error as a major factor (70%) in adverse events. Despite numerous strategies to improve patient safety, which are rooted in other high reliability industries (e.g., commercial aviation and naval aviation), communication remains an adaptive challenge that has proven difficult to overcome in the sociotechnical landscape that defines healthcare. Attributing a breakdown in information exchange to simply a generic "communication error" without further specification is ineffective and a gross oversimplification of a complex phenomenon. Further dissection of the communication error using root cause analysis, a failure modes and effects analysis, or through an event reporting system is needed. Generalizing rather than categorizing is an oversimplification that clouds clear pattern recognition and thereby prevents focused interventions to improve process reliability. We propose that being more precise when describing communication error is a valid mechanism to learn from these errors. We assert that by deconstructing communication in healthcare into its elemental parts, a more effective organizational learning strategy emerges to enable more focused patient safety improvement efforts. After defining the barriers to effective communication, we then map evidence-based recovery strategies and tools specific to each barrier as a tactic to enhance the reliability and validity of information exchange within healthcare.

Key Points Question Can a real-time, targeted, patient-centered education bundle reduce nonadministration of venous thromboembolism prophylaxis in hospitalized patients? Findings In this controlled preintervention-postintervention comparison trial of 19 652 adult patients on medical and surgical units, nonadministration of venous thromboembolism prophylaxis significantly declined on units that received an intervention that combined an alert to a health educator about a missed dose of venous thromboembolism prophylaxis with patient education compared with control units. Meaning Timely, targeted education significantly reduces nonadministration of VTE prophylaxis in hospitalized patients and improves health care quality by leveraging real-time data to target interventions for at-risk patients.

BackgroundRacial disparities are common in healthcare. Venous thromboembolism (VTE) is a leading cause of preventable harm, and disparities observed in prevention practices. We examined the impact of a patient-centered VTE education bundle on the non-administration of preventive prophylaxis by race. MethodsA post-hoc, subset analysis (stratified by race) of a larger nonrandomized trial. Pre-post comparisons analysis were conducted on 16 inpatient units; study periods were October 2014 through March 2015 (baseline) and April through December 2015 (post-intervention). Patients on 4 intervention units received the patient-centered, nurse educator-led

Objective: To review common qualitative and quantitative methods of measuring shared mental models appropriate for use in the healthcare setting. Background: Shared mental models are the overlap of individuals' set of knowledge and/or assumptions that act as the basis for understanding and decision making between individuals. Within healthcare, shared mental models facilitate effective teamwork and theorized to influence clinical decision making and performance. With the current rapid growth and expansion of healthcare teams, it is critical that we understand and correctly use shared mental model measurement methods assess optimal team performance. Unfortunately, agreement on the proper measurement of shared mental models within healthcare remains diffuse. Method: This paper presents methods appropriate to measure shared mental models within healthcare. Results: Multiple shared mental model measurement methods are discussed with regard to their utility within this setting, ease of use, and difficulties in deploying within the healthcare operational environment. For rigorous analysis of shared mental models, it is recommended that a combination of qualitative and quantitative analyses be employed. Conclusion: There are multitude of shared mental model measurement methods that can be used in the healthcare domain; although there is no perfect solution for every situation. Researchers can utilize this article to determine the best approach for their needs.