OBJECTIVE: The aim of this article is to compare the aims, measures, methods, limitations, and scope of studies that employ vendor-derived and investigator-derived measures of electronic health record (EHR) use, and to assess measure consistency across studies. MATERIALS AND METHODS: We searched PubMed for articles published between July 2019 and December 2021 that employed measures of EHR use derived from EHR event logs. We coded the aims, measures, methods, limitations, and scope of each article and compared articles employing vendor-derived and investigator-derived measures. RESULTS: One hundred and two articles met inclusion criteria; 40 employed vendor-derived measures, 61 employed investigator-derived measures, and 1 employed both. Studies employing vendor-derived measures were more likely than those employing investigator-derived measures to observe EHR use only in ambulatory settings (83% vs 48%, P = .002) and only by physicians or advanced practice providers (100% vs 54% of studies, P < .001). Studies employing vendor-derived measures were also more likely to measure durations of EHR use (P < .001 for 6 different activities), but definitions of measures such as time outside scheduled hours varied widely. Eight articles reported measure validation. The reported limitations of vendor-derived measures included measure transparency and availability for certain clinical settings and roles. DISCUSSION: Vendor-derived measures are increasingly used to study EHR use, but only by certain clinical roles. Although poorly validated and variously defined, both vendor- and investigator-derived measures of EHR time are widely reported. CONCLUSION: The number of studies using event logs to observe EHR use continues to grow, but with inconsistent measure definitions and significant differences between studies that employ vendor-derived and investigator-derived measures.

Despite basic federal requirements promoting a user-centered design approach to electronic health record (EHR) development and usability testing there have been usability and safety risks with EHR technology. Four EHR vendors were asked to provide written descriptions of their usability practices, and we reviewed these descriptions to identify areas where there has been advancement and areas for improvement. All 4 vendors described user-centered design processes and usability testing methods that demonstrate advancement from previous studies of vendor practices. Importantly, vendors are also beginning to address aspects of EHR implementation that play a critical role in shaping EHR usability. There are important areas for improvement in vendor practices including a greater focus on safety and on measurement and benchmarking. Vendors sharing their current usability practices demonstrates a step toward greater transparency which has typically been lacking.

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.

OBJECTIVES: To measure nurse-perceived electronic health records (EHR) usability with a standardized metric of technology usability and evaluate its association with professional burnout. METHODS: A cross-sectional survey of a random sample of US nurses was conducted in November 2017. EHR usability was measured with the System Usability Scale (SUS; range 0-100) and burnout with the Maslach Burnout Inventory. RESULTS: Among the 86 858 nurses who were invited, 8638 (9.9%) completed the survey. The mean nurse-rated EHR SUS score was 57.6 (SD 16.3). A score of 57.6 is in the bottom 24% of scores across previous studies and categorized with a grade of "F." On multivariable analysis adjusting for age, gender, race, ethnicity, relationship status, children, highest nursing-related degree, mean hours worked per week, years of nursing experience, advanced certification, and practice setting, nurse-rated EHR usability was associated with burnout with each 1 point more favorable SUS score and associated with a 2% lower odds of burnout (OR 0.98; 95% CI, 0.97-0.99; P < .001). CONCLUSIONS: Nurses rated the usability of their current EHR in the low marginal range of acceptability using a standardized metric of technology usability. EHR usability and the odds of burnout were strongly associated with a dose-response relationship.

Healthcare information technologies are now a routine component of patient-clinician interactions. Originally designed for operational functions including billing and regulatory compliance, these systems have had unintended consequences including increased exam room documentation, divided attention during the visit, and use of scribes to alleviate documentation burdens. In an age in which technology is ubiquitous in everyday life, we must re-envision healthcare technology to support both clinical operations and, above all, the patient-clinician relationship. We present 6 habits for designing user-centered health technologies: (1) put patient care first, (2) assemble a team with the right skills, (3) relentlessly ask WHY, (4) keep it simple, (5) be Darwinian, and (6) don't lose the forest for the trees. These habits should open dialogues between developers, implementers, end users, and stakeholders, as well as outline a path for better, more usable technology that puts patients and their clinicians back at the center of care.

Objective: Clinical decision support (CDS) hard-stop alerts-those in which the user is either prevented from taking an action altogether or allowed to proceed only with the external override of a third party-are increasingly common but can be problematic. To understand their appropriate application, we asked 3 key questions: (1) To what extent are hard-stop alerts effective in improving patient health and healthcare delivery outcomes? (2) What are the adverse events and unintended consequences of hard-stop alerts? (3) How do hard-stop alerts compare to soft-stop alerts? Methods and Materials: Studies evaluating computerized hard-stop alerts in healthcare settings were identified from biomedical and computer science databases, gray literature sites, reference lists, and reviews. Articles were extracted for process outcomes, health outcomes, unintended consequences, user experience, and technical details. Results: Of 32 studies, 15 evaluated health outcomes, 16 process outcomes only, 10 user experience, and 4 compared hard and soft stops. Seventy-nine percent showed improvement in health outcomes and 88% in process outcomes. Studies reporting good user experience cited heavy user involvement and iterative design. Eleven studies reported on unintended consequences including avoidance of hard-stopped workflow, increased alert frequency, and delay to care. Hard stops were superior to soft stops in 3 of 4 studies. Conclusions: Hard stops can be effective and powerful tools in the CDS armamentarium, but they must be implemented judiciously with continuous user feedback informing rapid, iterative design. Investigators must report on associated health outcomes and unintended consequences when implementing IT solutions to clinical problems.