OBJECTIVE: To derive 7 proposed core electronic health record (EHR) use metrics across 2 healthcare systems with different EHR vendor product installations and examine factors associated with EHR time. MATERIALS AND METHODS: A cross-sectional analysis of ambulatory physicians EHR use across the Yale-New Haven and MedStar Health systems was performed for August 2019 using 7 proposed core EHR use metrics normalized to 8 hours of patient scheduled time. RESULTS: Five out of 7 proposed metrics could be measured in a population of nonteaching, exclusively ambulatory physicians. Among 573 physicians (Yale-New Haven N = 290, MedStar N = 283) in the analysis, median EHR-Time8 was 5.23 hours. Gender, additional clinical hours scheduled, and certain medical specialties were associated with EHR-Time8 after adjusting for age and health system on multivariable analysis. For every 8 hours of scheduled patient time, the model predicted these differences in EHR time (P < .001, unless otherwise indicated): female physicians +0.58 hours; each additional clinical hour scheduled per month -0.01 hours; practicing cardiology -1.30 hours; medical subspecialties -0.89 hours (except gastroenterology, P = .002); neurology/psychiatry -2.60 hours; obstetrics/gynecology -1.88 hours; pediatrics -1.05 hours (P = .001); sports/physical medicine and rehabilitation -3.25 hours; and surgical specialties -3.65 hours. CONCLUSIONS: For every 8 hours of scheduled patient time, ambulatory physicians spend more than 5 hours on the EHR. Physician gender, specialty, and number of clinical hours practicing are associated with differences in EHR time. While audit logs remain a powerful tool for understanding physician EHR use, additional transparency, granularity, and standardization of vendor-derived EHR use data definitions are still necessary to standardize EHR use measurement.

OBJECTIVE: We sought to determine rates of computerized provider order entry (CPOE) patient identity verification and when and where in the ordering process verification occurred. MATERIALS AND METHODS: Fifty-five physicians from 4 healthcare systems completed simulated patient scenarios using their respective CPOE system (Epic or Cerner). Eye movements were recorded and analyzed. RESULTS: Across all participants patient id was verified significantly more often than not (62.4% vs 37.6%). Vendor A had significantly higher verification rates than not; vendor B had no difference. Participants using vendor A verified information significantly more often before signing the order than after (88.4% vs 11.6%); there was no difference in vendor B. The banner bar was the most frequent verification location. DISCUSSION: Factors such as CPOE design, physician training, and the use of a simulated methodology may be impacting verification rates. CONCLUSIONS: Verification rates vary by CPOE product, and this can have patient safety consequences.

Objective: We sought to understand the types of clinical processes, such as image and medication ordering, that are disrupted during electronic health record (EHR) downtime periods by analyzing the narratives of patient safety event report data. Materials and Methods: From a database of 80 381 event reports, 76 reports were identified as explicitly describing a safety event associated with an EHR downtime period. These reports were analyzed and categorized based on a developed code book to identify the clinical processes that were impacted by downtime. We also examined whether downtime procedures were in place and followed. Results: The reports were coded into categories related to their reported clinical process: Laboratory, Medication, Imaging, Registration, Patient Handoff, Documentation, History Viewing, Delay of Procedure, and General. A majority of reports (48.7%, n = 37) were associated with lab orders and results, followed by medication ordering and administration (14.5%, n = 11). Incidents commonly involved patient identification and communication of clinical information. A majority of reports (46%, n = 35) indicated that downtime procedures either were not followed or were not in place. Only 27.6% of incidents (n = 21) indicated that downtime procedures were successfully executed. Discussion: Patient safety report data offer a lens into EHR downtime-related safety hazards. Important areas of risk during EHR downtime periods were patient identification and communication of clinical information; these should be a focus of downtime procedure planning to reduce safety hazards. Conclusion: EHR downtime events pose patient safety hazards, and we highlight critical areas for downtime procedure improvement.

Interruptions in the emergency department (ED) can have serious patient safety consequences, and few solutions exist to mitigate the disruptiveness of interruptions. We developed a theoretically motivated model to predict the likelihood of emergency physicians returning to an interrupted task. Eighteen emergency physicians were observed individually for two-hour blocks of time, resulting in a total of 2160 minutes of observation and 231 interruptions. We used a mixed effects logistic regression model to predict the likelihood of primary task resumption after interruptions. The likelihood of primary task resumption was predicted by memory decay, measured by the duration of the interruption, workload, measured by the patient volume during the shift, and whether shift was day or night. With a better understanding of these interruptions, we can help design interventions to manage interruptions, minimize medical errors, and improve patient safety.

IMPORTANCE AND OBJECTIVES: As health information technologies become more prevalent in physician workflow, it is increasingly important to understand how physicians are using and interacting with these systems. This includes understanding how physicians search for information presented through health information technology systems. Eye tracking technologies provide a useful technique to understand how physicians visually search for information. However, analyzing eye tracking data can be challenging and is often done by measuring summative metrics, such as total time looking at a specific area and first-order transitions. METHODS: In this paper, we propose an algorithmic approach to identify different visual search patterns. We demonstrate this approach by identifying common visual search patterns from physicians using a simulated prototype emergency department patient tracking system. RESULTS AND CONCLUSIONS: We evaluate and compare the visual search pattern results to first-order transition results. We discuss the benefits and limitations of this approach and insights from this initial evaluation.

An increasing number of healthcare providers are adopting patient safety event reporting systems, yet leveraging these data to improve safety remains a challenge, particularly with large datasets composed of thousands of event reports. A MedStar Health research team, with expertise in data analytics and human factors, developed intuitive visualization dashboards to facilitate data exploration and trend analysis. Dashboards were developed using an iterative design and development process that was end-user focused. A system level dashboard, representing data from multiple hospitals, and a hospital level dashboard were developed. The dashboards allowed users to directly manipulate the data, provided coordinated displays in different formats, and allowed users to quickly zoom in on specific variables of interest. Overall feedback was incredibly positive with nearly all users wanting to adopt the system immediately. Several improvements were suggested and are discussed. The success of this approach highlights the need for more intuitive data analysis tools.