The overall goal of this application is to assess levels of metal workload and performance of providers during abnormal test results workflows within the Epic environment. Rationale: Despite extensive efforts to automate the alert systems in EMRs, 7% of abnormal outpatient laboratory results and 8% of abnormal imaging results lacked follow-up within 30 days; 18% of diagnostic imaging alerts and 10% of diagnostic lab alerts were not acknowledged by clinicians; with most cases occurring during 'cross-coverage' (hand-offs) while subjected to heavy volume of automated alerts (e.g., > 50 per day) leading to cognitive overload, fatigue, and thus suboptimal performance. Therefore, automated/electronic alerts do not eliminate the problem of missed abnormal test results. Efforts must be made to enhance providers' ability to acknowledge abnormal result alerts and forcing documentation of follow-up plans before an alert can be closed out while ensuring proper levels of mental (or cognitive) workload. Methods: Providers, randomized to regular- vs. cross-coverage and to 'current' (no forcing function) vs. 'enhanced' (with forcing faction) Epic environment, will be assessed to quantify the impact of these conditions on providers' performance and mental workload. Using a 2 x 2 balanced randomization design, we will use analysis of variance (ANOVA) to evaluate main and interaction effects from the four groups (+/-regular- vs. cross-coverage, and 'current' vs. 'enhanced' user interface and cognitive workflow). Research areas: Patient safety, abnormal test results, mental workload, individual performance. Scientific disciplines: Cognitive and Behavioral Sciences; Human Factors.
Missed abnormal test results in electronic health record (EHR) are a significant patient safety concern. Efforts must be made to enhance providers' ability to acknowledge and follow-up on critically abnormal result alerts before an alert can be closed out. The overall goal of this application is assess levels of metal workload and performance of providers during abnormal test results workflows within the current vs. enhanced Epic environment under condition of regular- vs. cross-coverage.
| Mazur, Lukasz M; Mosaly, Prithima R; Moore, Carlton et al. (2016) Toward a better understanding of task demands, workload, and performance during physician-computer interactions. J Am Med Inform Assoc 23:1113-1120 |
