ObjectiveIn this study, we used human factors (HF) methods and principles to design a clinical decision support (CDS) that provides cognitive support to the pulmonary embolism (PE) diagnostic decision-making process in the emergency department. We hypothesised that the application of HF methods and principles will produce a more usable CDS that improves PE diagnostic decision-making, in particular decision about appropriate clinical pathway.Materials and methodsWe conducted a scenario-based simulation study to compare a HF-based CDS (the so-called CDS for PE diagnosis (PE-Dx CDS)) with a web-based CDS (MDCalc); 32 emergency physicians performed various tasks using both CDS. PE-Dx integrated HF design principles such as automating information acquisition and analysis, and minimising workload. We assessed all three dimensions of usability using both objective and subjective measures: effectiveness (eg, appropriate decision regarding the PE diagnostic pathway), efficiency (eg, time spent, perceived workload) and satisfaction (perceived usability of CDS).ResultsEmergency physicians made more appropriate diagnostic decisions (94% with PE-Dx; 84% with web-based CDS; p<0.01) and performed experimental tasks faster with the PE-Dx CDS (on average 96 s per scenario with PE-Dx; 117 s with web-based CDS; p<0.001). They also reported lower workload (p<0.001) and higher satisfaction (p<0.001) with PE-Dx.ConclusionsThis simulation study shows that HF methods and principles can improve usability of CDS and diagnostic decision-making. Aspects of the HF-based CDS that provided cognitive support to emergency physicians and improved diagnostic performance included automation of information acquisition (eg, auto-populating risk scoring algorithms), minimisation of workload and support of decision selection (eg, recommending a clinical pathway). These HF design principles can be applied to the design of other CDS technologies to improve diagnostic safety.

Objective Medication list discrepancies between outpatient clinics and pharmacies can lead to medication errors. Within the last decade, a new health information technology (IT), CancelRx, emerged to send a medication cancellation message from the clinic’s electronic health record (EHR) to the outpatient pharmacy’s software. The objective of this study was to measure the impact of CancelRx on reducing medication discrepancies between the EHR and pharmacy dispensing software. Materials and Methods CancelRx was implemented in October 2017 at an academic health system. For 12 months prior, and 12 months after CancelRx implementation, data were collected on discontinued medications in the health system’s EHR and whether those prescriptions were successfully discontinued in the pharmacy’s dispensing software. An interrupted time series analysis was conducted to model the occurrence of prescriptions successfully discontinued over time. Results There was an immediate (lag = 0), significant (P &lt; 0.001), and sustained (post-implementation slope 0.02) increase in the proportion of successful medication discontinuations after CancelRx implementation (from 34% to 93%). CancelRx had variable impact based on whether the clinic was primary care (71.4% change prepost) or specialty care (53.9% change prepost). CancelRx reduced the time between when a medication was discontinued in the clinic EHR and pharmacy dispensing software. Conclusion CancelRx automated a manual process and illustrated the role for health IT in communicating medication discontinuations between clinics and pharmacies. Overall, CancelRx had a marked benefit on medication list discrepancies and illustrated how health IT can be used across different settings to improve patient care.

Objective The electronic health record (EHR) data deluge makes data retrieval more difficult, escalating cognitive load and exacerbating clinician burnout. New auto-summarization techniques are needed. The study goal was to determine if problem-oriented view (POV) auto-summaries improve data retrieval workflows. We hypothesized that POV users would perform tasks faster, make fewer errors, be more satisfied with EHR use, and experience less cognitive load as compared with users of the standard view (SV). Methods Simple data retrieval tasks were performed in an EHR simulation environment. A randomized block design was used. In the control group (SV), subjects retrieved lab results and medications by navigating to corresponding sections of the electronic record. In the intervention group (POV), subjects clicked on the name of the problem and immediately saw lab results and medications relevant to that problem. Results With POV, mean completion time was faster (173 seconds for POV vs 205 seconds for SV; P &lt; .0001), the error rate was lower (3.4% for POV vs 7.7% for SV; P = .0010), user satisfaction was greater (System Usability Scale score 58.5 for POV vs 41.3 for SV; P &lt; .0001), and cognitive task load was less (NASA Task Load Index score 0.72 for POV vs 0.99 for SV; P &lt; .0001). Discussion The study demonstrates that using a problem-based auto-summary has a positive impact on 4 aspects of EHR data retrieval, including cognitive load. Conclusion EHRs have brought on a data deluge, with increased cognitive load and physician burnout. To mitigate these increases, further development and implementation of auto-summarization functionality and the requisite knowledge base are needed.

Hospitals are complex environments that rely on clinicians working together to provide appropriate care to patients. These clinical teams adapt their interactions to meet changing situational needs. Venous thromboembolism (VTE) prophylaxis is a complex process that occurs throughout a patient's hospitalisation, presenting five stages with different levels of complexity: admission, interruption, re-initiation, initiation, and transfer. The objective of our study is to understand how the VTE prophylaxis team adapts as the complexity in the process changes; we do this by using social network analysis (SNA) measures. We interviewed forty-five clinicians representing 9 different cases, creating 43 role networks. The role networks were analysed using SNA measures to understand team changes between low and high complexity stages. When comparing low and high complexity stages, we found two team adaptation mechanisms: (1) relative increase in the number of people, team activities, and interactions within the team, or (2) relative increase in discussion among the team, reflected by an increase in reciprocity.

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