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To give providers a better understanding of how to use the electronic health record (EHR), improve efficiency, and reduce burnout.

Burnout is a long-term stress reaction marked by emotional exhaustion, depersonalization, and a lack of sense of personal accomplishment. Burnout in clinicians is receiving significant attention. Some have proposed that clinicians are experiencing symptoms of moral injury, defined as "perpetrating, failing to prevent, bearing witness to, or learning about acts that transgress deeply held moral beliefs and expectations." Current efforts to improve the electronic health record (EHR) have focused on [...]

Electronic health record systems are increasingly used to send messages to physicians, but research on physicians' inbox use patterns is limited. This study's aims were to (1) quantify the time primary care physicians (PCPs) spend managing inboxes; (2) describe daily patterns of inbox use; (3) investigate which types of messages consume the most time; and (4) identify factors associated with inbox work duration.

In 2017, 43.9% of US physicians reported symptoms of burnout. Poor electronic health record (EHR) usability and time-consuming data entry contribute to burnout. However, less is known about how modifiable dimensions of EHR use relate to burnout and how these associations vary by medical specialty. Using the KLAS Arch Collaborative's large-scale nationwide physician (MD/DO) data, we used ordinal logistic regression to analyze associations between self-reported burnout and after-hours charting and [...]

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 [...]

Electronic health records (EHRs) are linked with documentation burden resulting in clinician burnout. While clear classifications and validated measures of burnout exist, documentation burden remains ill-defined and inconsistently measured. We aim to conduct a scoping review focused on identifying approaches to documentation burden measurement and their characteristics.

Electronic health records (EHR) use is often considered a significant contributor to clinician burnout. Informatics researchers often measure clinical workload using EHR-derived audit logs and use it for quantifying the contribution of EHR use to clinician burnout. However, translating clinician workload measured using EHR-based audit logs into a meaningful burnout metric requires an alignment with the conceptual and theoretical principles of burnout. In this perspective, we describe a systems-oriented conceptual [...]

Clinicians face competing pressures of being clinically productive while using imperfect electronic health record (EHR) systems and maximizing face-to-face time with patients. EHR use is increasingly associated with clinician burnout and underscores the need for interventions to improve clinicians' experiences. With an aim of addressing this need, we share evidence-based informatics approaches, pragmatic next steps, and future research directions to improve 3 of the highest contributors to EHR burden: (1) [...]

Case-control study designs are commonly used in retrospective analyses of real-world evidence (RWE). Due to the increasingly wide availability of RWE, it can be difficult to determine whether findings are robust or the result of testing multiple hypotheses.

Feature engineering is a major bottleneck in phenotyping. Properly learned medical concept embeddings (MCEs) capture the semantics of medical concepts, thus are useful for retrieving relevant medical features in phenotyping tasks. We compared the effectiveness of MCEs learned from knowledge graphs and electronic healthcare records (EHR) data in retrieving relevant medical features for phenotyping tasks.