ImportancePhysician burnout is an ongoing epidemic; electronic health record (EHR) use has been associated with burnout, and the burden of EHR inbasket messages has grown in the context of the COVID-19 pandemic. Understanding how EHR inbasket messages are associated with physician burnout may uncover new insights for intervention strategies.ObjectiveTo evaluate associations between EHR inbasket message characteristics and physician burnout.Design, Setting, and ParticipantsCross-sectional study in a single academic medical center involving physicians from multiple specialties. Data collection took place April to September 2020, and data were analyzed September to December 2020.ExposuresPhysicians responded to a survey including the validated Mini-Z 5-point burnout scale.Main Outcomes and MeasuresPhysician burnout according to the self-reported burnout scale. A sentiment analysis model was used to calculate sentiment scores for EHR inbasket messages extracted for participating physicians. Multivariable modeling was used to model risk of physician burnout using factors such as message characteristics, physician demographics, and clinical practice characteristics.ResultsOf 609 physicians who responded to the survey, 297 (48.8%) were women, 343 (56.3%) were White, 391 (64.2%) practiced in outpatient settings, and 428 (70.28%) had been in medical practice for 15 years or less. Half (307 [50.4%]) reported burnout (score of 3 or higher). A total of 1 453 245 inbasket messages were extracted, of which 630 828 (43.4%) were patient messages. Among negative messages, common words included medical conditions, expletives and/or profanity, and words related to violence. There were no significant associations between message characteristics (including sentiment scores) and burnout. Odds of burnout were significantly higher among Hispanic/Latino physicians (odds ratio [OR], 3.44; 95% CI, 1.18-10.61; P = .03) and women (OR, 1.60; 95% CI, 1.13-2.27; P = .01), and significantly lower among physicians in clinical practice for more than 15 years (OR, 0.46; 95% CI, 0.30-0.68; P < .001).Conclusions and RelevanceIn this cross-sectional study, message characteristics were not associated with physician burnout, but the presence of expletives and violent words represents an opportunity for improving patient engagement, EHR portal design, or filters. Natural language processing represents a novel approach to understanding potential associations between EHR inbasket messages and physician burnout and may also help inform quality improvement initiatives aimed at improving patient experience.

Objective Physicians of all specialties experienced unprecedented stressors during the COVID-19 pandemic, exacerbating preexisting burnout. We examine burnout’s association with perceived and actionable electronic health record (EHR) workload factors and personal, professional, and organizational characteristics with the goal of identifying levers that can be targeted to address burnout. Materials and Methods Survey of physicians of all specialties in an academic health center, using a standard measure of burnout, self-reported EHR work stress, and EHR-based work assessed by the number of messages regarding prescription reauthorization and use of a staff pool to triage messages. Descriptive and multivariable regression analyses examined the relationship among burnout, perceived EHR work stress, and actionable EHR work factors. Results Of 1038 eligible physicians, 627 responded (60% response rate), 49.8% reported burnout symptoms. Logistic regression analysis suggests that higher odds of burnout are associated with physicians feeling higher level of EHR stress (odds ratio [OR], 1.15; 95% confidence interval [CI], 1.07–1.25), having more prescription reauthorization messages (OR, 1.23; 95% CI, 1.04–1.47), not feeling valued (OR, 3.38; 95% CI, 1.69–7.22) or aligned in values with clinic leaders (OR, 2.81; 95% CI, 1.87–4.27), in medical practice for ≤15 years (OR, 2.57; 95% CI, 1.63–4.12), and sleeping for <6 h/night (OR, 1.73; 95% CI, 1.12–2.67). Discussion Perceived EHR stress and prescription reauthorization messages are significantly associated with burnout, as are non-EHR factors such as not feeling valued or aligned in values with clinic leaders. Younger physicians need more support. Conclusion A multipronged approach targeting actionable levers and supporting young physicians is needed to implement sustainable improvements in physician well-being.

Objective To examine sociodemographic factors associated with having unmet needs in medications, mental health, and food security among older adults during the COVID‐19 pandemic. Data Sources and Study Setting Primary data and secondary data from the electronic health records (EHR) in an age‐friendly academic health system in 2020 were used. Study Design Observational study examining factors associated with having unmet needs in medications, food, and mental health. Data Collecting/Extraction Methods Data from a computer‐assisted telephone interview and EHR on community‐dwelling older patients were analyzed. Principle Findings Among 3400 eligible patients, 1921 (53.3%) (average age 76, SD 11) responded, with 857 (45%) of respondents having at least one unmet need. Unmet needs for medications were present in 595 (31.0%), for food in 196 (10.2%), and for mental health services in 292 (15.2%). Racial minorities had significantly higher probabilities of having unmet needs for medicine and food, and of being referred for services related to medications, food, and mental health. Patients living in more resource‐limited neighborhoods had a higher probability of being referred for mental health services. Conclusions Age‐friendly health systems (AFHS) and their recognition should include assessing and addressing social risk factors among older adults. Proactive efforts to address unmet needs should be integral to AFHS.

A primary concern of public health researchers involves identifying and quantifying heterogeneous exposure effects across population subgroups. Understanding the magnitude and direction of these effects on a given scale provides researchers the ability to recommend policy prescriptions and assess the external validity of findings. Furthermore, increasing popularity in fields such as precision medicine that rely on accurate estimation of high-dimensional interaction effects has highlighted the importance of understanding effect modification. Traditional methods for effect measure modification analyses include parametric regression modeling with either stratified analyses and corresponding heterogeneity tests or including an interaction term in a multivariable model. However, these methods require manual model specification and are often impractical or not feasible to conduct by hand in high-dimensional settings. Recent developments in machine learning aim to solve this issue by automating heterogeneous subgroup identification and effect estimation. In this paper, we summarize and provide the intuition behind modern machine learning methods for effect measure modification analyses to serve as a reference for public health researchers. We discuss their implementation in R, provide annotated syntax and review available supplemental analysis tools by assessing the heterogeneous effects of drought on stunting among children in the Demographic and Health Survey data set as a case study.