Electronic Health Record Logs Indicate That Physicians Split Time Evenly Between Seeing Patients And Desktop Medicine

The impact of compassion-based interventions on perioperative anxiety and depression: A systematic review and meta-analysis

BACKGROUND

The perioperative period can be a stressful time for many patients. Concerns for the procedure or fearing potential complications contribute to perioperative anxiety and depression, which significantly impact patient wellbeing and recovery. Understanding the psychological impact of the perioperative period can inform individualized care focused on each patient's unique stressors. Compassion-based interventions are limited but have shown benefits in non-surgical healthcare settings, and can provide support by prioritizing empathy and understanding in the perioperative period. This review evaluates the impact of compassion-based interventions on anxiety and depression among adult surgical patients.

METHODS

A systematic review of 25 randomized controlled trials was conducted with a meta-analysis of 14 studies for anxiety and 9 studies for depression that provided sufficient information.

RESULTS

The included studies tested compassion-based interventions that focused on enhanced communication, emotional support, and individualized attention from healthcare professionals. In 72 % of the studies, the interventions decreased anxiety and depression, compared to control groups. These interventions improved health-related outcomes such patient satisfaction and postoperative complications. The meta-analysis indicated a large effect of the compassion-based interventions for anxiety (g = -0.95) and depressive symptoms (g = -0.82). The findings were consistent among various surgeries and patient populations.

LIMITATIONS

Many of the included studies lacked clarity in their methods and only 14 studies provided sufficient information for the meta-analysis.

CONCLUSIONS

Given the growing evidence suggesting that compassion-based psychological interventions are feasible and applicable in the perioperative setting, their inclusion in routine care could reduce depression and anxiety around surgery and improve patient outcomes and experiences.

Prompt engineering on leveraging large language models in generating response to InBasket messages

OBJECTIVES

Large Language Models (LLMs) have been proposed as a solution to address high volumes of Patient Medical Advice Requests (PMARs). This study addresses whether LLMs can generate high quality draft responses to PMARs that satisfies both patients and clinicians with prompt engineering.

MATERIALS AND METHODS

We designed a novel human-involved iterative processes to train and validate prompts to LLM in creating appropriate responses to PMARs. GPT-4 was used to generate response to the messages. We updated the prompts, and evaluated both clinician and patient acceptance of LLM-generated draft responses at each iteration, and tested the optimized prompt on independent validation data sets. The optimized prompt was implemented in the electronic health record production environment and tested by 69 primary care clinicians.

RESULTS

After 3 iterations of prompt engineering, physician acceptance of draft suitability increased from 62% to 84% (P <.001) in the validation dataset (N = 200), and 74% of drafts in the test dataset were rated as "helpful." Patients also noted significantly increased favorability of message tone (78%) and overall quality (80%) for the optimized prompt compared to the original prompt in the training dataset, patients were unable to differentiate human and LLM-generated draft PMAR responses for 76% of the messages, in contrast to the earlier preference for human-generated responses. Majority (72%) of clinicians believed it can reduce cognitive load in dealing with InBasket messages.

DISCUSSION AND CONCLUSION

Informed by clinician and patient feedback synergistically, tuning in LLM prompt alone can be effective in creating clinically relevant and useful draft responses to PMARs.

Impact of a Digital Scribe System on Clinical Documentation Time and Quality: Usability Study

BACKGROUND

Physicians spend approximately half of their time on administrative tasks, which is one of the leading causes of physician burnout and decreased work satisfaction. The implementation of natural language processing-assisted clinical documentation tools may provide a solution.

OBJECTIVE

This study investigates the impact of a commercially available Dutch digital scribe system on clinical documentation efficiency and quality.

METHODS

Medical students with experience in clinical practice and documentation (n=22) created a total of 430 summaries of mock consultations and recorded the time they spent on this task. The consultations were summarized using 3 methods: manual summaries, fully automated summaries, and automated summaries with manual editing. We then randomly reassigned the summaries and evaluated their quality using a modified version of the Physician Documentation Quality Instrument (PDQI-9). We compared the differences between the 3 methods in descriptive statistics, quantitative text metrics (word count and lexical diversity), the PDQI-9, Recall-Oriented Understudy for Gisting Evaluation scores, and BERTScore.

RESULTS

The median time for manual summarization was 202 seconds against 186 seconds for editing an automatic summary. Without editing, the automatic summaries attained a poorer PDQI-9 score than manual summaries (median PDQI-9 score 25 vs 31, P<.001, ANOVA test). Automatic summaries were found to have higher word counts but lower lexical diversity than manual summaries (P<.001, independent t test). The study revealed variable impacts on PDQI-9 scores and summarization time across individuals. Generally, students viewed the digital scribe system as a potentially useful tool, noting its ease of use and time-saving potential, though some criticized the summaries for their greater length and rigid structure.

CONCLUSIONS

This study highlights the potential of digital scribes in improving clinical documentation processes by offering a first summary draft for physicians to edit, thereby reducing documentation time without compromising the quality of patient records. Furthermore, digital scribes may be more beneficial to some physicians than to others and could play a role in improving the reusability of clinical documentation. Future studies should focus on the impact and quality of such a system when used by physicians in clinical practice.

Predicting Primary Care Physician Burnout From Electronic Health Record Use Measures

OBJECTIVE

To evaluate the ability of routinely collected electronic health record (EHR) use measures to predict clinical work units at increased risk of burnout and potentially most in need of targeted interventions.

METHODS

In this observational study of primary care physicians, we compiled clinical workload and EHR efficiency measures, then linked these measures to 2 years of well-being surveys (using the Stanford Professional Fulfillment Index) conducted from April 1, 2019, through October 16, 2020. Physicians were grouped into training and confirmation data sets to develop predictive models for burnout. We used gradient boosting classifier and other prediction modeling algorithms to quantify the predictive performance by the area under the receiver operating characteristics curve (AUC).

RESULTS

Of 278 invited physicians from across 60 clinics, 233 (84%) completed 396 surveys. Physicians were 67% women with a median age category of 45 to 49 years. Aggregate burnout score was in the high range (≥3.325/10) on 111 of 396 (28%) surveys. Gradient boosting classifier of EHR use measures to predict burnout achieved an AUC of 0.59 (95% CI, 0.48 to 0.77) and an area under the precision-recall curve of 0.29 (95% CI, 0.20 to 0.66). Other models' confirmation set AUCs ranged from 0.56 (random forest) to 0.66 (penalized linear regression followed by dichotomization). Among the most predictive features were physician age, team member contributions to notes, and orders placed with user-defined preferences. Clinic-level aggregate measures identified the top quartile of clinics with 56% sensitivity and 85% specificity.

CONCLUSION

In a sample of primary care physicians, routinely collected EHR use measures demonstrated limited ability to predict individual burnout and moderate ability to identify high-risk clinics.

Physician EHR Time and Visit Volume Following Adoption of Team-Based Documentation Support

Importance

Physicians spend the plurality of active electronic health record (EHR) time on documentation. Excessive documentation limits time spent with patients and is associated with burnout. Organizations need effective strategies to reduce physician documentation burden; however, evidence on team-based documentation (eg, medical scribes) has been limited to small, single-institution studies lacking rigorous estimates of how documentation support changes EHR time and visit volume.

Objectives

To analyze how EHR documentation time and visit volume change following the adoption of team-based documentation approaches.

Design, Setting, and Participants

This national longitudinal cohort study analyzed physician-week EHR metadata from September 2020 through April 2021. A 2-way fixed-effects difference-in-differences regression approach was used to analyze changes in the main outcomes after team-based documentation support adoption. Event study regression models were used to examine variation in changes over time and stratified models to analyze the moderating role of support intensity. The sample included US ambulatory physicians using the EHR. Data were analyzed between October 2022 and September 2023.

Exposure

Team-based documentation support, defined as new onset and consistent use of coauthored documentation with another clinical team member.

Main Outcomes and Measures

The main outcomes included weekly visit volume, EHR documentation time, total EHR time, and EHR time outside clinic hours.

Results

Of 18 265 physicians, 1024 physicians adopted team-based documentation support, with 17 241 comparison physicians who did not adopt such support. The sample included 57.2% primary care physicians, 31.6% medical specialists, and 11.2% surgical specialists; 40.0% practiced in academic settings and 18.4% in outpatient safety-net settings. For adopter physicians, visit volume increased by 6.0% (2.5 visits/wk [95% CI, 1.9-3.0]; P < .001), and documentation time decreased by 9.1% (23.3 min/wk [95% CI, -30.3 to -16.2]; P < .001). Following a 20-week postadoption learning period, visits per week increased by 10.8% and documentation time decreased by 16.2%. Only high-intensity adopters (>40% of note text authored by others) realized reductions in documentation time, both for the full postadoption period (-53.9 min/wk [95% CI, -65.3 to -42.4]; 21.0% decrease; P < .001) and following the learning period (-72.2 min/wk; 28.1% decrease). Low adopters saw no meaningful change in EHR time but realized a similar increase in visit volume.

Conclusions and Relevance

In this national longitudinal cohort study, physicians who adopted team-based documentation experienced increased visit volume and reduced documentation and EHR time, especially after a learning period.

Electronic Health Record Usability, Satisfaction, and Burnout for Family Physicians

Importance

Electronic health record (EHR) work has been associated with decreased physician well-being. Understanding the association between EHR usability and physician satisfaction and burnout, and whether team and technology strategies moderate this association, is critical to informing efforts to address EHR-associated physician burnout.

Objectives

To measure family physician satisfaction with their EHR and EHR usability across functions and evaluate the association of EHR usability with satisfaction and burnout, as well as the moderating association of 4 team and technology EHR efficiency strategies.

Design, Setting, and Participants

This study uses data from a cross-sectional survey conducted from December 12, 2021, to October 17, 2022, of all family physicians seeking American Board of Family Medicine recertification in 2022.

Exposure

Physicians perceived EHR usability across 6 domains, as well as adoption of 4 EHR efficiency strategies: scribes, support from other staff, templated text, and voice recognition or transcription.

Main Outcomes and Measures

Physician EHR satisfaction and frequency of experiencing burnout measured with a single survey item ("I feel burned out from my work"), with answers ranging from "never" to "every day."

Results

Of the 2067 physicians (1246 [60.3%] younger than 50 years; 1051 men [50.9%]; and 1729 [86.0%] practicing in an urban area) who responded to the survey, 562 (27.2%) were very satisfied and 775 (37.5%) were somewhat satisfied, while 346 (16.7%) were somewhat dissatisfied and 198 (9.6%) were very dissatisfied with their EHR. Readability of information had the highest usability, with 543 physicians (26.3%) rating it as excellent, while usefulness of alerts had the lowest usability, with 262 physicians (12.7%) rating it as excellent. In multivariable models, good or excellent usability for entering data (β = 0.09 [95% CI, 0.05-0.14]; P < .001), alignment with workflow processes (β = 0.11 [95% CI, 0.06-0.16]; P < .001), ease of finding information (β = 0.14 [95% CI, 0.09-0.19]; P < .001), and usefulness of alerts (β = 0.11 [95% CI, 0.06-0.16]; P < .001) were associated with physicians being very satisfied with their EHR. In addition, being very satisfied with the EHR was associated with reduced frequency of burnout (β = -0.64 [95% CI, -1.06 to -0.22]; P < .001). In moderation analysis, only physicians with highly usable EHRs saw improvements in satisfaction from adopting efficiency strategies.

Conclusions and Relevance

In this survey study of physician EHR usability and satisfaction, approximately one-fourth of family physicians reported being very satisfied with their EHR, while another one-fourth reported being somewhat or very dissatisfied, a concerning finding amplified by the inverse association between EHR satisfaction and burnout. Electronic health record-based alerts had the lowest reported usability, suggesting EHR vendors should focus their efforts on improving alerts. Electronic health record efficiency strategies were broadly adopted, but only physicians with highly usable EHRs realized gains in EHR satisfaction from using these strategies, suggesting that EHR burden-reduction interventions are likely to have heterogenous associations across physicians with different EHRs.

Measuring Documentation Burden in Healthcare

BACKGROUND

The enactment of the Health Information Technology for Economic and Clinical Health Act and the wide adoption of electronic health record (EHR) systems have ushered in increasing documentation burden, frequently cited as a key factor affecting the work experience of healthcare professionals and a contributor to burnout. This systematic review aims to identify and characterize measures of documentation burden.

METHODS

We integrated discussions with Key Informants and a comprehensive search of the literature, including MEDLINE, Embase, Scopus, and gray literature published between 2010 and 2023. Data were narratively and thematically synthesized.

RESULTS

We identified 135 articles about measuring documentation burden. We classified measures into 11 categories: overall time spent in EHR, activities related to clinical documentation, inbox management, time spent in clinical review, time spent in orders, work outside work/after hours, administrative tasks (billing and insurance related), fragmentation of workflow, measures of efficiency, EHR activity rate, and usability. The most common source of data for most measures was EHR usage logs. Direct tracking such as through time-motion analysis was fairly uncommon. Measures were developed and applied across various settings and populations, with physicians and nurses in the USA being the most frequently represented healthcare professionals. Evidence of validity of these measures was limited and incomplete. Data on the appropriateness of measures in terms of scalability, feasibility, or equity across various contexts were limited. The physician perspective was the most robustly captured and prominently focused on increased stress and burnout.

DISCUSSION

Numerous measures for documentation burden are available and have been tested in a variety of settings and contexts. However, most are one-dimensional, do not capture various domains of this construct, and lack robust validity evidence. This report serves as a call to action highlighting an urgent need for measure development that represents diverse clinical contexts and support future interventions.

Differences in Patient Secure Message Volume Among Otolaryngologists: A Retrospective Cohort Study

OBJECTIVE

To identify differences in inbox and secure message burden among otolaryngologists based on demographics and subspecialty over 4 years.

METHODS

Inbox data were queried from January 2019 until December 2022. Otolaryngologists were categorized into cohorts by area of practice and gender. All inbox tasks, secure messages, and clinical encounters were collected and compared by gender, practice type, and years in practice. Means were compared using t-tests and chi-squared tests.

RESULTS

Of the 128 physicians, 45.7% were comprehensive otolaryngologists and 61.3% were male. The most common subspecialties were facial plastics (15.6%), oncology (8.6%), and otology (7.8%). Otolaryngologists had an average of 143.5 inbox tasks per month, with 97.2 (67.7%) of them being secure messages, resulting in an average of 1.14 inbox tasks and 0.80 secure messages per clinical encounter. The ratio of secure messages per clinical encounter was consistent across all specialties except oncology (1.10, P = .003). Otology (0.86, P = .032) and facial plastics (0.95, P = .028) had significantly lower ratios of inbox tasks to clinical encounters when compared to their colleagues, while oncology had a higher ratio (1.70, P < .001). No significant differences in inbox burden were observed between genders, years in practice, or languages spoken. Secure messages steadily increased over the study period.

CONCLUSION

Inbox burden for otolaryngologists primarily stems from patient secure messages and varies across subspecialties. Considerations should be made to the inbox burden of head and neck oncologists. The implementation of support systems for inbox management could improve the imbalance between clinical and non-clinical responsibilities in otolaryngology.

LEVEL OF EVIDENCE

Level III, Retrospective Cohort Study.

An Electronic Health Record-Integrated Application for Standardizing Care and Monitoring Patients With Autosomal Dominant Polycystic Kidney Disease Enrolled in a Tolvaptan Clinic: Design and Implementation Study

Background

Tolvaptan is the only US Food and Drug Administration-approved drug to slow the progression of autosomal dominant polycystic kidney disease (ADPKD), but it requires strict clinical monitoring due to potential serious adverse events.

Objective

We aimed to share our experience in developing and implementing an electronic health record (EHR)-based application to monitor patients with ADPKD who were initiated on tolvaptan.

Methods

The application was developed in collaboration with clinical informatics professionals based on our clinical protocol with frequent laboratory test monitoring to detect early drug-related toxicity. The application streamlined the clinical workflow and enabled our nursing team to take appropriate actions in real time to prevent drug-related serious adverse events. We retrospectively analyzed the characteristics of the enrolled patients.

Results

As of September 2022, a total of 214 patients were enrolled in the tolvaptan program across all Mayo Clinic sites. Of these, 126 were enrolled in the Tolvaptan Monitoring Registry application and 88 in the Past Tolvaptan Patients application. The mean age at enrollment was 43.1 (SD 9.9) years. A total of 20 (9.3%) patients developed liver toxicity, but only 5 (2.3%) had to discontinue the drug. The 2 EHR-based applications allowed consolidation of all necessary patient information and real-time data management at the individual or population level. This approach facilitated efficient staff workflow, monitoring of drug-related adverse events, and timely prescription renewal.

Conclusions

Our study highlights the feasibility of integrating digital applications into the EHR workflow to facilitate efficient and safe care delivery for patients enrolled in a tolvaptan program. This workflow needs further validation but could be extended to other health care systems managing chronic diseases requiring drug monitoring.

Exploring the Impact of In Basket Metrics on the Adoption of a New Electronic Health Record System Among Specialists in a Tertiary Hospital in Alberta: Descriptive Study

BACKGROUND

Health care organizations implement electronic health record (EHR) systems with the expectation of improved patient care and enhanced provider performance. However, while these technologies hold the potential to create improved care and system efficiencies, they can also lead to unintended negative consequences, such as patient safety issues, communication problems, and provider burnout.

OBJECTIVE

This study aims to document metrics related to the In Basket communication hub (time in In Basket per day, time in In Basket per appointment, In Basket messages received per day, and turnaround time) of the EHR system implemented by Alberta Health Services, the province-wide health delivery system called Connect Care (Epic Systems). The objective was to identify how a newly implemented EHR system was used, the timing of its use, and the duration of use specifically related to In Basket activities.

METHODS

A descriptive study was conducted. Due to the diversity of specialties, the providers were grouped into medical and surgical based on previous similar studies. The participants were further subgrouped based on their self-reported clinical full-time equivalent (FTE ) measure. This resulted in 3 subgroups for analysis: medical FTE <0.5, medical FTE >0.5, and surgical (all of whom reported FTE >0.5). The analysis was limited to outpatient clinical interactions and explicitly excluded inpatient activities.

RESULTS

A total of 72 participants from 19 different specialties enrolled in this study. The providers had, on average, 8.31 appointments per day during the reporting periods. The providers received, on average, 21.93 messages per day, and they spent 7.61 minutes on average in the time in In Basket per day metric and 1.84 minutes on average in the time in In Basket per appointment metric. The time for the providers to mark messages as done (turnaround time) was on average 11.45 days during the reporting period. Although the surgical group had, on average, approximately twice as many appointments per scheduled day, they spent considerably less connected time (based on almost all time metrics) than the medical group. However, the surgical group took much longer than the medical group to mark messages as done (turnaround time).

CONCLUSIONS

We observed a range of patterns with no consistent direction. There does not seem to be evidence of a "learning curve," which would have shown a consistent reduction in time spent on the system over time due to familiarity and experience. While this study does not show how the included metrics could be used as predictors of providers' satisfaction or feelings of burnout, the use trends could be used to start discussions about future Canadian studies needed in this area.

AI-Generated Draft Replies Integrated Into Health Records and Physicians' Electronic Communication

Importance

Timely tests are warranted to assess the association between generative artificial intelligence (GenAI) use and physicians' work efforts.

Objective

To investigate the association between GenAI-drafted replies for patient messages and physician time spent on answering messages and the length of replies.

Design, Setting, and Participants

Randomized waiting list quality improvement (QI) study from June to August 2023 in an academic health system. Primary care physicians were randomized to an immediate activation group and a delayed activation group. Data were analyzed from August to November 2023.

Exposure

Access to GenAI-drafted replies for patient messages.

Main Outcomes and Measures

Time spent (1) reading messages, (2) replying to messages, (3) length of replies, and (4) physician likelihood to recommend GenAI drafts. The a priori hypothesis was that GenAI drafts would be associated with less physician time spent reading and replying to messages. A mixed-effects model was used.

Results

Fifty-two physicians participated in this QI study, with 25 randomized to the immediate activation group and 27 randomized to the delayed activation group. A contemporary control group included 70 physicians. There were 18 female participants (72.0%) in the immediate group and 17 female participants (63.0%) in the delayed group; the median age range was 35-44 years in the immediate group and 45-54 years in the delayed group. The median (IQR) time spent reading messages in the immediate group was 26 (11-69) seconds at baseline, 31 (15-70) seconds 3 weeks after entry to the intervention, and 31 (14-70) seconds 6 weeks after entry. The delayed group's median (IQR) read time was 25 (10-67) seconds at baseline, 29 (11-77) seconds during the 3-week waiting period, and 32 (15-72) seconds 3 weeks after entry to the intervention. The contemporary control group's median (IQR) read times were 21 (9-54), 22 (9-63), and 23 (9-60) seconds in corresponding periods. The estimated association of GenAI was a 21.8% increase in read time (95% CI, 5.2% to 41.0%; P = .008), a -5.9% change in reply time (95% CI, -16.6% to 6.2%; P = .33), and a 17.9% increase in reply length (95% CI, 10.1% to 26.2%; P < .001). Participants recognized GenAI's value and suggested areas for improvement.

Conclusions and Relevance

In this QI study, GenAI-drafted replies were associated with significantly increased read time, no change in reply time, significantly increased reply length, and some perceived benefits. Rigorous empirical tests are necessary to further examine GenAI's performance. Future studies should examine patient experience and compare multiple GenAIs, including those with medical training.

Generative AI in healthcare: an implementation science informed translational path on application, integration and governance

BACKGROUND

Artificial intelligence (AI), particularly generative AI, has emerged as a transformative tool in healthcare, with the potential to revolutionize clinical decision-making and improve health outcomes. Generative AI, capable of generating new data such as text and images, holds promise in enhancing patient care, revolutionizing disease diagnosis and expanding treatment options. However, the utility and impact of generative AI in healthcare remain poorly understood, with concerns around ethical and medico-legal implications, integration into healthcare service delivery and workforce utilisation. Also, there is not a clear pathway to implement and integrate generative AI in healthcare delivery.

METHODS

This article aims to provide a comprehensive overview of the use of generative AI in healthcare, focusing on the utility of the technology in healthcare and its translational application highlighting the need for careful planning, execution and management of expectations in adopting generative AI in clinical medicine. Key considerations include factors such as data privacy, security and the irreplaceable role of clinicians' expertise. Frameworks like the technology acceptance model (TAM) and the Non-Adoption, Abandonment, Scale-up, Spread and Sustainability (NASSS) model are considered to promote responsible integration. These frameworks allow anticipating and proactively addressing barriers to adoption, facilitating stakeholder participation and responsibly transitioning care systems to harness generative AI's potential.

RESULTS

Generative AI has the potential to transform healthcare through automated systems, enhanced clinical decision-making and democratization of expertise with diagnostic support tools providing timely, personalized suggestions. Generative AI applications across billing, diagnosis, treatment and research can also make healthcare delivery more efficient, equitable and effective. However, integration of generative AI necessitates meticulous change management and risk mitigation strategies. Technological capabilities alone cannot shift complex care ecosystems overnight; rather, structured adoption programs grounded in implementation science are imperative.

CONCLUSIONS

It is strongly argued in this article that generative AI can usher in tremendous healthcare progress, if introduced responsibly. Strategic adoption based on implementation science, incremental deployment and balanced messaging around opportunities versus limitations helps promote safe, ethical generative AI integration. Extensive real-world piloting and iteration aligned to clinical priorities should drive development. With conscientious governance centred on human wellbeing over technological novelty, generative AI can enhance accessibility, affordability and quality of care. As these models continue advancing rapidly, ongoing reassessment and transparent communication around their strengths and weaknesses remain vital to restoring trust, realizing positive potential and, most importantly, improving patient outcomes.

Explaining Variability in Electronic Health Record Effort in Primary Care Ambulatory Encounters

BACKGROUND

 Electronic health record (EHR) user interface event logs are fast providing another perspective on the value and efficiency EHR technology brings to health care. Analysis of these detailed usage data has demonstrated their potential to identify EHR and clinical process design factors related to user efficiency, satisfaction, and burnout.

OBJECTIVE

 This study aimed to analyze the event log data across 26 different health systems to determine the variability of use of a single vendor's EHR based on four event log metrics, at the individual, practice group, and health system levels.

METHODS

 We obtained de-identified event log data recorded from June 1, 2018, to May 31, 2019, from 26 health systems' primary care physicians. We estimated the variability in total Active EHR Time, Documentation Time, Chart Review Time, and Ordering Time across health systems, practice groups, and individual physicians.

RESULTS

 In total, 5,444 physicians (Family Medicine: 3,042 and Internal Medicine: 2,422) provided care in a total of 2,285 different practices nested in 26 health systems. Health systems explain 1.29, 3.55, 3.45, and 3.30% of the total variability in Active Time, Documentation Time, Chart Review Time, and Ordering Time, respectively. Practice-level variability was estimated to be 7.96, 13.52, 8.39, and 5.57%, respectively, and individual physicians explained the largest proportion of the variability for those same outcomes 17.09, 27.49, 17.51, and 19.75%, respectively.

CONCLUSION

 The most variable physician EHR usage patterns occurs at the individual physician level and decreases as you move up to the practice and health system levels. This suggests that interventions to improve individual users' EHR usage efficiency may have the most potential impact compared with those directed at health system or practice levels.

Demographics and clinical features associated with rates of electronic message utilization in the primary care setting

INTRODUCTION

Electronic messages are growing as an important form of patient-provider communication, particularly in the primary care setting. However, adoption of healthcare technology has been under-utilized by underserved patient populations. The purpose of this study was to describe how adoption and utilization of electronic messaging occurred within a large primary care urban-based patient population.

METHODS

In this retrospective study, the frequency of electronic messages initiated by adult outpatient primary care patients was observed. Patients were classified as either non-portal adopters, non-message utilizers, low message utilizers, and high message utilizers. Logistic regression modeling was used to compare factors associated with message utilization rates to determine disparities in access.

RESULTS

Among a sample of 27,453 ethnically diverse adult patients from the Houston, Texas Metropolitan area, 33,497 unique messages were sent (1.22 messages/patient). Message burden was predominantly derived by a small number of high utilizers (individuals who sent 3 or more messages), who comprised 15.7 % of the study population (n = 4302) but accounted for 77 % of the message volume (n = 25,776). These high utilizers were typically older, White, English speaking, from middle to upper income zip codes, had higher number of comorbidities, and a higher number of clinical visits.

CONCLUSIONS

Most inbox messages were generated by a small number of patients. While it was reassuring to see older and sicker individuals utilizing electronic messaging, patients from minority and/or lower income background utilized electronic messaging much less. This may propagate systematic bias and decrease the level of care for traditionally underserved patients.

Large language models: a primer and gastroenterology applications

Over the past year, the emergence of state-of-the-art large language models (LLMs) in tools like ChatGPT has ushered in a rapid acceleration in artificial intelligence (AI) innovation. These powerful AI models can generate tailored and high-quality text responses to instructions and questions without the need for labor-intensive task-specific training data or complex software engineering. As the technology continues to mature, LLMs hold immense potential for transforming clinical workflows, enhancing patient outcomes, improving medical education, and optimizing medical research. In this review, we provide a practical discussion of LLMs, tailored to gastroenterologists. We highlight the technical foundations of LLMs, emphasizing their key strengths and limitations as well as how to interact with them safely and effectively. We discuss some potential LLM use cases for clinical gastroenterology practice, education, and research. Finally, we review critical barriers to implementation and ongoing work to address these issues. This review aims to equip gastroenterologists with a foundational understanding of LLMs to facilitate a more active clinician role in the development and implementation of this rapidly emerging technology.

Quantifying the impact of telemedicine and patient medical advice request messages on physicians' work-outside-work

The COVID-19 pandemic has boosted digital health utilization, raising concerns about increased physicians' after-hours clinical work ("work-outside-work"). The surge in patients' digital messages and additional time spent on work-outside-work by telemedicine providers underscores the need to evaluate the connection between digital health utilization and physicians' after-hours commitments. We examined the impact on physicians' workload from two types of digital demands - patients' messages requesting medical advice (PMARs) sent to physicians' inbox (inbasket), and telemedicine. Our study included 1716 ambulatory-care physicians in New York City regularly practicing between November 2022 and March 2023. Regression analyses assessed primary and interaction effects of (PMARs) and telemedicine on work-outside-work. The study revealed a significant effect of PMARs on physicians' work-outside-work and that this relationship is moderated by physicians' specialties. Non-primary care physicians or specialists experienced a more pronounced effect than their primary care peers. Analysis of their telemedicine load revealed that primary care physicians received fewer PMARs and spent less time in work-outside-work with more telemedicine. Specialists faced increased PMARs and did more work-outside-work as telemedicine visits increased which could be due to the difference in patient panels. Reducing PMAR volumes and efficient inbasket management strategies needed to reduce physicians' work-outside-work. Policymakers need to be cognizant of potential disruptions in physicians carefully balanced workload caused by the digital health services.

We Shape Our Tools and Then Our Tools Shape Us: OPAT and the EHR

A commentary on Canterino et al. (2024) and Munsiff et al. (2024), articles where clinicians from two large OPAT programs.

Navigating the doctor-patient-AI relationship - a mixed-methods study of physician attitudes toward artificial intelligence in primary care

BACKGROUND

Artificial intelligence (AI) is a rapidly advancing field that is beginning to enter the practice of medicine. Primary care is a cornerstone of medicine and deals with challenges such as physician shortage and burnout which impact patient care. AI and its application via digital health is increasingly presented as a possible solution. However, there is a scarcity of research focusing on primary care physician (PCP) attitudes toward AI. This study examines PCP views on AI in primary care. We explore its potential impact on topics pertinent to primary care such as the doctor-patient relationship and clinical workflow. By doing so, we aim to inform primary care stakeholders to encourage successful, equitable uptake of future AI tools. Our study is the first to our knowledge to explore PCP attitudes using specific primary care AI use cases rather than discussing AI in medicine in general terms.

METHODS

From June to August 2023, we conducted a survey among 47 primary care physicians affiliated with a large academic health system in Southern California. The survey quantified attitudes toward AI in general as well as concerning two specific AI use cases. Additionally, we conducted interviews with 15 survey respondents.

RESULTS

Our findings suggest that PCPs have largely positive views of AI. However, attitudes often hinged on the context of adoption. While some concerns reported by PCPs regarding AI in primary care focused on technology (accuracy, safety, bias), many focused on people-and-process factors (workflow, equity, reimbursement, doctor-patient relationship).

CONCLUSION

Our study offers nuanced insights into PCP attitudes towards AI in primary care and highlights the need for primary care stakeholder alignment on key issues raised by PCPs. AI initiatives that fail to address both the technological and people-and-process concerns raised by PCPs may struggle to make an impact.

Will Generative Artificial Intelligence Deliver on Its Promise in Health Care?

Importance

Since the introduction of ChatGPT in late 2022, generative artificial intelligence (genAI) has elicited enormous enthusiasm and serious concerns.

Observations

History has shown that general purpose technologies often fail to deliver their promised benefits for many years ("the productivity paradox of information technology"). Health care has several attributes that make the successful deployment of new technologies even more difficult than in other industries; these have challenged prior efforts to implement AI and electronic health records. However, genAI has unique properties that may shorten the usual lag between implementation and productivity and/or quality gains in health care. Moreover, the health care ecosystem has evolved to make it more receptive to genAI, and many health care organizations are poised to implement the complementary innovations in culture, leadership, workforce, and workflow often needed for digital innovations to flourish.

Conclusions and Relevance

The ability of genAI to rapidly improve and the capacity of organizations to implement complementary innovations that allow IT tools to reach their potential are more advanced than in the past; thus, genAI is capable of delivering meaningful improvements in health care more rapidly than was the case with previous technologies.

Changes in Physician Electronic Health Record Use With the Expansion of Telemedicine

Importance

Understanding the drivers of electronic health record (EHR) burden, including EHR time and patient messaging, may directly inform strategies to address physician burnout. Given the COVID-19-induced expansion of telemedicine-now used for a substantial proportion of ambulatory encounters-its association with EHR burden should be evaluated.

Objective

To measure the association of the telemedicine expansion with time spent working in the EHR and with patient messaging among ambulatory physicians before and after the onset of the COVID-19 pandemic.

Design, Setting, and Participants

This longitudinal cohort study analyzed weekly EHR metadata of ambulatory physicians at UCSF Health, a large academic medical center. The same EHR measures were compared for 1 year before the COVID-19 pandemic (August 2018-September 2019) with the same period 1 year after its onset (August 2020-September 2021). Multivariable regression models evaluating the association between level of telemedicine use and EHR use were then assessed after the onset of the pandemic. The sample included all physician-weeks with at least 1 scheduled half-day clinic in the 11 largest ambulatory specialties at UCSF Health. Data analyses were performed from March 1, 2022, through July 1, 2023.

Exposures

Physicians' weekly modality mix of either entirely face-to-face visits, mixed modalities, or entirely telemedicine.

Main Outcomes and Measures

The EHR time during and outside of patient scheduled hours (PSHs), time spent documenting (normalized per 8 PSHs), and electronic messages sent to and received from patients.

Results

The study sample included 1052 physicians (437 [41.5%] men and 615 [58.5%] women) during 115 weeks, which provided 35 697 physician-week observations. Comparing the period before to the period after pandemic onset showed that physician time spent working in the EHR during PSHs increased from 4.53 to 5.46 hours per 8 PSH (difference, 0.93; 95% CI, 0.87-0.98; P < 0.001); outside of PSHs, increased from 4.29 to 5.34 hours (difference, 1.04; 95% CI, 0.95-1.14; P < 0.001); and time documenting during and outside of PSHs increased from 6.35 to 8.18 hours (difference, 1.83; 95% CI, 1.72-1.94; P < 0.001). Mean weekly messages received from patients increased from 16.76 to 30.33, and messages sent to patients increased from 13.82 to 29.83. In multivariable models, weeks with a mix of face-to-face and telemedicine (β, 0.43; 95% CI, 0.31-0.55; P < .001) visits or entirely telemedicine (β, 0.91; 95% CI, 0.74-1.09; P < .001) had more EHR time during PSHs than all face-to-face weeks, with similar results for EHR time outside of PSHs. There was no association between telemedicine use and messages received from patients, whereas mixed modalities (β, -0.90; 95% CI, -1.73 to -0.08; P = .03) and all telemedicine (β, -4.06; 95% CI, -5.19 to -2.93; P < .001) were associated with fewer messages sent to patients compared with entirely face-to-face weeks.

Conclusions and Relevance

The findings of this longitudinal cohort study suggest that telemedicine is associated with greater physician time spent working in the EHR, both during and outside of scheduled hours, mostly documenting visits and not messaging patients. Health systems may need to adjust productivity expectations for physicians and develop strategies to address EHR documentation burden for physicians.

Differences in Health Professionals' Engagement With Electronic Health Records Based on Inpatient Race and Ethnicity

Importance

US health professionals devote a large amount of effort to engaging with patients' electronic health records (EHRs) to deliver care. It is unknown whether patients with different racial and ethnic backgrounds receive equal EHR engagement.

Objective

To investigate whether there are differences in the level of health professionals' EHR engagement for hospitalized patients according to race or ethnicity during inpatient care.

Design, Setting, and Participants

This cross-sectional study analyzed EHR access log data from 2 major medical institutions, Vanderbilt University Medical Center (VUMC) and Northwestern Medicine (NW Medicine), over a 3-year period from January 1, 2018, to December 31, 2020. The study included all adult patients (aged ≥18 years) who were discharged alive after hospitalization for at least 24 hours. The data were analyzed between August 15, 2022, and March 15, 2023.

Exposures

The actions of health professionals in each patient's EHR were based on EHR access log data. Covariates included patients' demographic information, socioeconomic characteristics, and comorbidities.

Main Outcomes and Measures

The primary outcome was the quantity of EHR engagement, as defined by the average number of EHR actions performed by health professionals within a patient's EHR per hour during the patient's hospital stay. Proportional odds logistic regression was applied based on outcome quartiles.

Results

A total of 243 416 adult patients were included from VUMC (mean [SD] age, 51.7 [19.2] years; 54.9% female and 45.1% male; 14.8% Black, 4.9% Hispanic, 77.7% White, and 2.6% other races and ethnicities) and NW Medicine (mean [SD] age, 52.8 [20.6] years; 65.2% female and 34.8% male; 11.7% Black, 12.1% Hispanic, 69.2% White, and 7.0% other races and ethnicities). When combining Black, Hispanic, or other race and ethnicity patients into 1 group, these patients were significantly less likely to receive a higher amount of EHR engagement compared with White patients (adjusted odds ratios, 0.86 [95% CI, 0.83-0.88; P < .001] for VUMC and 0.90 [95% CI, 0.88-0.92; P < .001] for NW Medicine). However, a reduction in this difference was observed from 2018 to 2020.

Conclusions and Relevance

In this cross-sectional study of inpatient EHR engagement, the findings highlight differences in how health professionals distribute their efforts to patients' EHRs, as well as a method to measure these differences. Further investigations are needed to determine whether and how EHR engagement differences are correlated with health care outcomes.

Association of physician burnout with perceived EHR work stress and potentially actionable factors

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.

COVID exacerbated the gender disparity in physician electronic health record inbox burden

The COVID-19 pandemic was associated with significant changes to the delivery of ambulatory care, including a dramatic increase in patient messages to physicians. While asynchronous messaging is a valuable communication modality for patients, a greater volume of patient messages is associated with burnout and decreased well-being for physicians. Given that women physicians experienced greater electronic health record (EHR) burden and received more patient messages pre-pandemic, there is concern that COVID may have exacerbated this disparity. Using EHR audit log data of ambulatory physicians at an academic medical center, we used a difference-in-differences framework to evaluate the impact of the pandemic on patient message volume and compare differences between men and women physicians. We found patient message volume increased post-COVID for all physicians, and women physicians saw an additional increase compared to men. Our results contribute to the growing evidence of different communication expectations for women physicians that contribute to the gender disparity in EHR burden.

Physician Turnover in the United States

BACKGROUND

Medical groups, health systems, and professional associations are concerned about potential increases in physician turnover, which may affect patient access and quality of care.

OBJECTIVE

To examine whether turnover has changed over time and whether it is higher for certain types of physicians or practice settings.

DESIGN

The authors developed a novel method using 100% of traditional Medicare billing to create national estimates of turnover. Standardized turnover rates were compared by physician, practice, and patient characteristics.

SETTING

Traditional Medicare, 2010 to 2020.

PARTICIPANTS

Physicians billing traditional Medicare.

MEASUREMENTS

Indicators of physician turnover-physicians who stopped practicing and those who moved from one practice to another-and their sum.

RESULTS

The annual rate of turnover increased from 5.3% to 7.2% between 2010 and 2014, was stable through 2017, and increased modestly in 2018 to 7.6%. Most of the increase from 2010 to 2014 came from physicians who stopped practicing increasing from 1.6% to 3.1%; physicians moving increased modestly from 3.7% to 4.2%. Modest but statistically significant (P < 0.001) differences existed across rurality, physician sex, specialty, and patient characteristics. In the second and third quarters of 2020, quarterly turnover was slightly lower than in the corresponding quarters of 2019.

LIMITATION

Measurement was based on traditional Medicare claims.

CONCLUSION

Over the past decade, physician turnover rates have had periods of increase and stability. These early data, covering the first 3 quarters of 2020, give no indication yet of the COVID-19 pandemic increasing turnover, although continued tracking of turnover is warranted. This novel method will enable future monitoring and further investigations into turnover.

PRIMARY FUNDING SOURCE

The Physicians Foundation Center for the Study of Physician Practice and Leadership.

A narrative review of the physiology and health effects of burnout associated with veterinarian-pertinent occupational stressors

Chronic workplace stress and burnout are serious problems in veterinary medicine. Although not classified as a medical condition, burnout can affect sleep patterns and contributes to chronic low grade systemic inflammation, autonomic imbalance, hormonal imbalances and immunodeficiencies, thereby increasing the risks of physical and psychological ill health in affected individuals. Cultural misconceptions in the profession often lead to perceptions of burnout as a personal failure, ideas that healthcare professionals are somehow at lower risk for suffering, and beliefs that affected individuals can or should somehow heal themselves. However, these concepts are antiquated, harmful and incorrect, preventing the design of appropriate solutions for this serious and growing challenge to the veterinary profession. Veterinarians must first correctly identify the nature of the problem and understand its causes and impacts before rational solutions can be implemented. In this first part of two companion reviews, burnout will be defined, pathophysiology discussed, and healthcare and veterinary-relevant occupational stressors that lead to burnout identified.

Documentation dynamics: Note composition, burden, and physician efficiency

OBJECTIVE

To analyze how physician clinical note length and composition relate to electronic health record (EHR)-based measures of burden and efficiency that have been tied to burnout.

DATA SOURCES AND STUDY SETTING

Secondary EHR use metadata capturing physician-level measures from 203,728 US-based ambulatory physicians using the Epic Systems EHR between September 2020 and May 2021.

STUDY DESIGN

In this cross-sectional study, we analyzed physician clinical note length and note composition (e.g., content from manual or templated text). Our primary outcomes were three time-based measures of EHR burden (time writing EHR notes, time in the EHR after-hours, and EHR time on unscheduled days), and one measure of efficiency (percent of visits closed in the same day). We used multivariate regression to estimate the relationship between our outcomes and note length and composition.

DATA EXTRACTION

Physician-week measures of EHR usage were extracted from Epic's Signal platform used for measuring provider EHR efficiency. We calculated physician-level averages for our measures of interest and assigned physicians to overall note length deciles and note composition deciles from six sources, including templated text, manual text, and copy/paste text.

PRINCIPAL FINDINGS

Physicians in the top decile of note length demonstrated greater burden and lower efficiency than the median physician, spending 39% more time in the EHR after hours (p < 0.001) and closing 5.6 percentage points fewer visits on the same day (p < 0.001). Copy/paste demonstrated a similar dose/response relationship, with top-decile copy/paste users closing 6.8 percentage points fewer visits on the same day (p < 0.001) and spending more time in the EHR after hours and on days off (both p < 0.001). Templated text (e.g., Epic's SmartTools) demonstrated a non-linear relationship with burden and efficiency, with very low and very high levels of use associated with increased EHR burden and decreased efficiency.

CONCLUSIONS

"Efficiency tools" like copy/paste and templated text meant to reduce documentation burden and increase provider efficiency may have limited efficacy.

Best Practices for Technology in Clinical Social Work and Mental Health Professions to Promote Well-being and Prevent Fatigue

The shift to communication technologies during the pandemic has had positive and negative effects on clinical social worker practice. Best practices are identified for clinical social workers to maintain emotional well-being, prevent fatigue, and avoid burnout when using technology. A scoping review from 2000 to 21 of 15 databases focused on communication technologies for mental health care within four areas: (1) behavioral, cognitive, emotional, and physical impact; (2) individual, clinic, hospital, and system/organizational levels; (3) well-being, burnout, and stress; and (4) clinician technology perceptions. Out of 4795 potential literature references, full text review of 201 papers revealed 37 were related to technology impact on engagement, therapeutic alliance, fatigue and well-being. Studies assessed behavioral (67.5%), emotional (43.2%), cognitive (57.8%), and physical (10.8%) impact at the individual (78.4%), clinic (54.1%), hospital (37.8%) and system/organizational (45.9%) levels. Participants were clinicians, social workers, psychologists, and other providers. Clinicians can build a therapeutic alliance via video, but this requires additional skill, effort, and monitoring. Use of video and electronic health records were associated with clinician physical and emotional problems due to barriers, effort, cognitive demands, and additional workflow steps. Studies also found high user ratings on data quality, accuracy, and processing, but low satisfaction with clerical tasks, effort required and interruptions. Studies have overlooked the impact of justice, equity, diversity and inclusion related to technology, fatigue and well-being, for the populations served and the clinicians providing care. Clinical social workers and health care systems must evaluate the impact of technology in order to support well-being and prevent workload burden, fatigue, and burnout. Multi-level evaluation and clinical, human factor, training/professional development and administrative best practices are suggested.

Perspectives on the Intersection of Electronic Health Records and Health Care Team Communication, Function, and Well-being

Importance

Understanding of the interplay between the electronic health record (EHR), health care team relations, and physician well-being is currently lacking. Approaches to cultivate interpersonal interactions may be necessary to complement advancements in health information technology with high-quality team function.

Objective

To examine ways in which the EHR, health care team functioning, and physician well-being intersect and interact.

Design, Setting, and Participants

Secondary qualitative analysis of semistructured interview data from 2 studies used keyword-in-context approaches to identify excerpts related to teams. Thematic analysis was conducted using pattern coding, then organized using the relationship-centered organization model. Two health care organizations in California from March 16 to October 13, 2017, and February 28 to April 21, 2022, participated, with respondents including attending and resident physicians.

Main Outcome and Measures

Across data sets, themes centered around the interactions between the EHR, health care team functioning, and physician well-being. The first study data focused on EHR-related distressing events and their role in attending physician and resident physician emotions and actions. The second study focused on EHR use and daily EHR irritants.

Results

The 73 respondents included attending physicians (53 [73%]) and resident physicians (20 [27%]). Demographic data were not collected. Participants worked in ambulatory specialties (33 [45%]), hospital medicine (10 [14%]), and surgery (10 [14%]). The EHR was reported to be the dominant communication modality among all teams. Interviewees indicated that the EHR facilitates task-related communication and is well suited to completing simple, uncomplicated tasks. However, EHR-based communication limited the rich communication and social connection required for building relationships and navigating conflict. The EHR was found to negatively impact team function by promoting disagreement and introducing areas of conflict into team relationships related to medical-legal pressures, role confusion, and undefined norms around EHR-related communication. In addition, interviewees expressed that physician EHR-related distress affects interactions within the team, eroding team well-being.

Conclusions and Relevance

In this study, the EHR supported task-oriented and efficient communication among team members to get work done and care for patients; however, participants felt that the technology shifts attention away from the human needs of the care team that are necessary for developing relationships, building trust, and resolving conflicts. Interventions to cultivate interpersonal interactions and team function are necessary to complement the efficiency benefits of health information technology.

Occupational Characteristics Associated With Professional Fulfillment and Burnout Among US Physiatrists

INTRODUCTION

Multiple national studies suggest that among physicians, physiatrists are at increased risk for occupational burnout.

OBJECTIVE

The aim of the study is to identify characteristics of the work environment associated with professional fulfillment and burnout among US physiatrists.

DESIGN

Between May and December 2021, a mixed qualitative and quantitative approach was used to identify factors contributing to professional fulfillment and burnout in physiatrists.

SETTING

Online interviews, focus groups, and survey were conducted.

PARTICIPANTS

The participants are physiatrists in the American Academy of Physical Medicine and Rehabilitation Membership Masterfile.

MAIN OUTCOME MEASURES

Burnout and professional fulfillment were assessed using the Stanford Professional Fulfillment Index.

RESULTS

Individual interviews with 21 physiatrists were conducted to identify domains that contributed to professional fulfillment followed by focus groups for further definition. Based on themes identified, scales were identified or developed to evaluate: control over schedule (6 items, Cronbach α = 0.86); integration of physiatry into patient care (3 items, Cronbach α = 0.71); personal-organizational values alignment (3 items, Cronbach α = 0.90); meaningfulness of physiatrist clinical work (6 items, Cronbach α = 0.90); teamwork and collaboration (3 items, Cronbach α = 0.89). Of 5760 physiatrists contacted in the subsequent national survey, 882 (15.4%) returned surveys (median age, 52 yrs; 46.1% women). Overall, 42.6% (336 of 788) experienced burnout and 30.6% (244 of 798) had high levels of professional fulfillment. In multivariable analysis, each one-point improvement in control over schedule (odds ratio = 1.96; 95% confidence interval = 1.45-2.69), integration of physiatry into patient care (odds ratio = 1.77; 95% confidence interval = 1.32-2.38), personal-organizational values alignment (odds ratio = 1.92; 95% confidence interval = 1.48-2.52), meaningfulness of physiatrist clinical work (odds ratio = 2.79; 95% confidence interval = 1.71-4.71), and teamwork and collaboration score (odds ratio = 2.11; 95% confidence interval = 1.48-3.03) was independently associated with higher likelihood of professional fulfillment.

CONCLUSIONS

Control over schedule, optimal integration of physiatry into clinical care, personal-organizational values alignment, teamwork, and meaningfulness of physiatrist clinical work are strong and independent drivers of occupational well-being in US physiatrists. Variation in these domains by practice setting and subspecialty suggests that tailored approaches are needed to promote professional fulfillment and reduce burnout among US physiatrists.

Physician Note Composition Patterns and Time on the EHR Across Specialty Types: a National, Cross-sectional Study

BACKGROUND

The burden of clinical documentation in electronic health records (EHRs) has been associated with physician burnout. Numerous tools (e.g., note templates and dictation services) exist to ease documentation burden, but little evidence exists regarding how physicians use these tools in combination and the degree to which these strategies correlate with reduced time spent on documentation.

OBJECTIVE

To characterize EHR note composition strategies, how these strategies differ in time spent on notes and the EHR, and their distribution across specialty types.

DESIGN

Secondary analysis of physician-level measures of note composition and EHR use derived from Epic Systems' Signal data warehouse. We used k-means clustering to identify documentation strategies, and ordinary least squares regression to analyze the relationship between documentation strategies and physician time spent in the EHR, on notes, and outside scheduled hours.

PARTICIPANTS

A total of 215,207 US-based ambulatory physicians using the Epic EHR between September 2020 and May 2021.

MAIN MEASURES

Percent of note text derived from each of five documentation tools: SmartTools, copy/paste, manual text, NoteWriter, and voice recognition and transcription; average total and after-hours EHR time per visit; average time on notes per visit.

KEY RESULTS

Six distinct note composition strategies emerged in cluster analyses. The most common strategy was predominant SmartTools use (n=89,718). In adjusted analyses, physicians using primarily transcription and dictation (n=15,928) spent less time on notes than physicians with predominant Smart Tool use. (b=-1.30, 95% CI=-1.62, -0.99, p<0.001; average 4.8 min per visit), while those using mostly copy/paste (n=23,426) spent more time on notes (b=2.38, 95% CI=1.92, 2.84, p<0.001; average 13.1 min per visit).

CONCLUSIONS

Physicians' note composition strategies have implications for both time in notes and after-hours EHR use, suggesting that how physicians use EHR-based documentation tools can be a key lever for institutions investing in EHR tools and training to reduce documentation time and alleviate EHR-associated burden.

"Mm-hm," "Uh-uh": are non-lexical conversational sounds deal breakers for the ambient clinical documentation technology?

OBJECTIVES

Ambient clinical documentation technology uses automatic speech recognition (ASR) and natural language processing (NLP) to turn patient-clinician conversations into clinical documentation. It is a promising approach to reducing clinician burden and improving documentation quality. However, the performance of current-generation ASR remains inadequately validated. In this study, we investigated the impact of non-lexical conversational sounds (NLCS) on ASR performance. NLCS, such as Mm-hm and Uh-uh, are commonly used to convey important information in clinical conversations, for example, Mm-hm as a "yes" response from the patient to the clinician question "are you allergic to antibiotics?"

MATERIALS AND METHODS

In this study, we evaluated 2 contemporary ASR engines, Google Speech-to-Text Clinical Conversation ("Google ASR"), and Amazon Transcribe Medical ("Amazon ASR"), both of which have their language models specifically tailored to clinical conversations. The empirical data used were from 36 primary care encounters. We conducted a series of quantitative and qualitative analyses to examine the word error rate (WER) and the potential impact of misrecognized NLCS on the quality of clinical documentation.

RESULTS

Out of a total of 135 647 spoken words contained in the evaluation data, 3284 (2.4%) were NLCS. Among these NLCS, 76 (0.06% of total words, 2.3% of all NLCS) were used to convey clinically relevant information. The overall WER, of all spoken words, was 11.8% for Google ASR and 12.8% for Amazon ASR. However, both ASR engines demonstrated poor performance in recognizing NLCS: the WERs across frequently used NLCS were 40.8% (Google) and 57.2% (Amazon), respectively; and among the NLCS that conveyed clinically relevant information, 94.7% and 98.7%, respectively.

DISCUSSION AND CONCLUSION

Current ASR solutions are not capable of properly recognizing NLCS, particularly those that convey clinically relevant information. Although the volume of NLCS in our evaluation data was very small (2.4% of the total corpus; and for NLCS that conveyed clinically relevant information: 0.06%), incorrect recognition of them could result in inaccuracies in clinical documentation and introduce new patient safety risks.

Healthcare professionals' knowledge, attitude and its associated factors toward electronic personal health record system in a resource-limited setting: A cross-sectional study

Introduction

Electronic personal health record (e-PHR) system enables individuals to access their health information and manage it themselves. It helps patient engagement management of health information that is accessed and shared with their healthcare providers using the platform. This improves individual healthcare through the exchange of health information between patients and healthcare providers. However, less is known about e-PHRs among healthcare professionals.

Objective

Therefore, this study aimed to assess Health professionals' Knowledge and attitude and its associated factors toward e-PHR at the teaching hospital in northwest Ethiopia.

Methods

An institution-based cross-sectional study design was used to determine healthcare professionals' knowledge and attitude and their associated factors toward e-PHR systems in teaching hospitals of Amhara regional state, Ethiopia, from 20 July to 20 August 2022. Pretested structured self-administered questionnaires were used to collect the data. Descriptive statistic was computed based on sociodemographic and other variables presented in the form of table graphs and texts. Bivariable and multivariable logistic analyses were performed with an adjusted odds ratio (AOR) and 95% CI to identify predictor variables.

Result

Of the total study participants, 57% were males and nearly half of the respondents had a bachelor's degree. Out of 402 participants, ~65.7% [61–70%] and 55.5% [50–60%] had good knowledge and favorable attitude toward e-PHR systems, respectively. Having a social media account 4.3 [AOR = 4.3, 95% CI (2.3–7.9)], having a smartphone 4.4 [AOR = 4.4, 95% CI (2.2–8.6)], digital literacy 8.8 [(AOR = 8.8, 95% CI (4.6–15.9)], being male 2.7 [AOR = 2.7, 95% CI (1.4–5.0)], and perceived usefulness 4.5 [(AOR = 4.5, 95% CI (2.5–8.5)] were positively associated with knowledge toward e-PHR systems. Similarly, having a personal computer 1.9 [AOR = 1.9, 95% CI (1.1–3.5)], computer training 3.9 [AOR = 3.9, 95% CI (1.8–8.3)], computer skill 19.8 [AOR = 19.8, 95% CI (10.7–36.9)], and Internet access 6.0 [AOR = 6.0, 95% CI (3.0–12.0)] were predictors for attitude toward e-PHR systems.

Conclusion

The findings from the study showed that healthcare professionals have good knowledge and a favorable attitude toward e-PHRs. Providing comprehensive basic computer training to improve healthcare professionals' expectation on the usefulness of e-PHR systems has a paramount contribution to the advancement of their knowledge and attitude toward successfully implementing e-PHRs.

Merit-based incentive payment system participation and after-hours documentation among US office-based physicians: Findings from the 2021 National Electronic Health Records Survey

BACKGROUND

After-hours documentation burden among US clinicians is often uncompensated work and has been associated with burnout, leading health systems to identify root causes and seek interventions to reduce this. A few studies have suggested quality programme participation (e.g., Merit-Based Incentive Payment System [MIPS]) was associated with a higher administrative burden. However, the association between MIPS participation and after-hours documentation has not been fully explored. Thus, this study aims to assess whether participation in the MIPS programme was independently associated with after-hours documentation burden.

METHODS

We used 2021 data from the National Electronic Health Records Survey. We used a multivariable ordinal logistic regression model to assess whether MIPS participation was associated with the amount of after-hours documentation burden when controlling for other factors. We controlled for physician age, specialty, sex, number of practice locations, number of physicians, practice ownership, whether team support (e.g., scribes) is used for documentation tasks, and whether the practice accepts Medicaid patients.

RESULTS

We included 1801 office-based US physician respondents with complete data for variables of interest. After controlling for other factors, MIPS participation was associated with greater odds of spending a greater number of hours on after-hours documentation (odds ratio = 1.44, 95% confidence interval 1.06-1.95).

CONCLUSIONS

MIPS participation may increase after-hours documentation burden among US office-based physicians, suggesting that physicians may require additional resources to more efficiently report data.

Dynamic reaction picklist for improving allergy reaction documentation: A usability study

OBJECTIVE

To assess novel dynamic reaction picklists for improving allergy reaction documentation compared to a static reaction picklist.

MATERIALS AND METHODS

We developed three web-based user interfaces (UIs) mimicking the Mass General Brigham's EHR allergy module: the first and second UIs (i.e., UI-1D, UI-2D) implemented two dynamic reaction picklists with different ranking algorithms and the third UI (UI-3S) implemented a static reaction picklist like the one used in the current EHR. We recruited 18 clinicians to perform allergy entry for 10 test cases each via UI-1D and UI-3S, and another 18 clinicians via UI-2D and UI-3S. Primary measures were the number of free-text entries and time to complete the allergy entry. Clinicians were also interviewed using 30 questions before and after the data entry.

RESULTS AND DISCUSSIONS

Among 36 clinicians, less than half were satisfied with the current EHR reaction picklists, due to their incomprehensiveness, inefficiency, and lack of intuitiveness. The clinicians used significantly fewer free-text entries when using UI-1D or UI-2D compared to UI-3S (p < 0.05). The clinicians used on average 51 s (15 %) less time via UI-1D and 50 s (16 %) less time via UI-2D in completing the allergy entries versus UI-3S, and there was not a statistically significant difference in documentation time for either group between the dynamic and static UIs. Overall, 15-17 (83-94 %) clinicians rated UI-1D and 13-15 (72-83 %) clinicians rated UI-2D as efficient, easy to use, and useful, while less than half rated the same for UI-3S. Most clinicians reported that the dynamic reaction picklists always or often suggested appropriate reactions (n = 30, 83 %) and would decrease the free-text entries (n = 26, 72 %); nearly all preferred the dynamic picklist over the static picklist (n = 32, 89 %).

CONCLUSION

We found that dynamic reaction picklists significantly reduced the number of free-text entries and could reduce the time for allergy documentation by 15%. Clinicians preferred the dynamic reaction picklist over the static picklist.

Predicting physician departure with machine learning on EHR use patterns: A longitudinal cohort from a large multi-specialty ambulatory practice

Physician turnover places a heavy burden on the healthcare industry, patients, physicians, and their families. Having a mechanism in place to identify physicians at risk for departure could help target appropriate interventions that prevent departure. We have collected physician characteristics, electronic health record (EHR) use patterns, and clinical productivity data from a large ambulatory based practice of non-teaching physicians to build a predictive model. We use several techniques to identify possible intervenable variables. Specifically, we used gradient boosted trees to predict the probability of a physician departing within an interval of 6 months. Several variables significantly contributed to predicting physician departure including tenure (time since hiring date), panel complexity, physician demand, physician age, inbox, and documentation time. These variables were identified by training, validating, and testing the model followed by computing SHAP (SHapley Additive exPlanation) values to investigate which variables influence the model's prediction the most. We found these top variables to have large interactions with other variables indicating their importance. Since these variables may be predictive of physician departure, they could prove useful to identify at risk physicians such who would benefit from targeted interventions.

I had not time to make it shorter: an exploratory analysis of how physicians reduce note length and time in notes

OBJECTIVE

We analyze observed reductions in physician note length and documentation time, 2 contributors to electronic health record (EHR) burden and burnout.

MATERIALS AND METHODS

We used EHR metadata from January to May, 2021 for 130 079 ambulatory physician Epic users. We identified cohorts of physicians who decreased note length and/or documentation time and analyzed changes in their note composition.

RESULTS

37 857 physicians decreased either note length (n = 15 647), time in notes (n = 15 417), or both (n = 6793). Note length decreases were primarily attributable to reductions in copy/paste text (average relative change of -18.9%) and templated text (-17.2%). Note time decreases were primarily attributable to reductions in manual text (-27.3%) and increases in note content from other care team members (+21.1%).

DISCUSSION

Organizations must consider priorities and tradeoffs in the distinct approaches needed to address different contributors to EHR burden.

CONCLUSION

Future research should explore scalable burden-reduction initiatives responsive to both note bloat and documentation time.

Revisiting the Time Needed to Provide Adult Primary Care

BACKGROUND

Many patients do not receive guideline-recommended preventive, chronic disease, and acute care. One potential explanation is insufficient time for primary care providers (PCPs) to provide care.

OBJECTIVE

To quantify the time needed to provide 2020 preventive care, chronic disease care, and acute care for a nationally representative adult patient panel by a PCP alone, and by a PCP as part of a team-based care model.

DESIGN

Simulation study applying preventive and chronic disease care guidelines to hypothetical patient panels.

PARTICIPANTS

Hypothetical panels of 2500 patients, representative of the adult US population based on the 2017-2018 National Health and Nutrition Examination Survey.

MAIN MEASURES

The mean time required for a PCP to provide guideline-recommended preventive, chronic disease and acute care to the hypothetical patient panels. Estimates were also calculated for visit documentation time and electronic inbox management time. Times were re-estimated in the setting of team-based care.

KEY RESULTS

PCPs were estimated to require 26.7 h/day, comprising of 14.1 h/day for preventive care, 7.2 h/day for chronic disease care, 2.2 h/day for acute care, and 3.2 h/day for documentation and inbox management. With team-based care, PCPs were estimated to require 9.3 h per day (2.0 h/day for preventive care and 3.6 h/day for chronic disease care, 1.1 h/day for acute care, and 2.6 h/day for documentation and inbox management).

CONCLUSIONS

PCPs do not have enough time to provide the guideline-recommended primary care. With team-based care the time requirements would decrease by over half, but still be excessive.

Characterizing the macrostructure of electronic health record work using raw audit logs: an unsupervised action embeddings approach

Raw audit logs provide a comprehensive record of clinicians' activities on an electronic health record (EHR) and have considerable potential for studying clinician behaviors. However, research using raw audit logs is limited because they lack context for clinical tasks, leading to difficulties in interpretation. We describe a novel unsupervised approach using the comparison and visualization of EHR action embeddings to learn context and structure from raw audit log activities. Using a dataset of 15 767 634 raw audit log actions performed by 88 intern physicians over 6 months of EHR use across inpatient and outpatient settings, we demonstrated that embeddings can be used to learn the situated context for EHR-based work activities, identify discrete clinical workflows, and discern activities typically performed across diverse contexts. Our approach represents an important methodological advance in raw audit log research, facilitating the future development of metrics and predictive models to measure clinician behaviors at the macroscale.

Optimising the educational value of indirect patient care

INTRODUCTION

Indirect patient care activities (IPCA) such as documentation, reviewing investigations and filling out forms require an increasing amount of physician time. While an essential part of patient care, rising rates of IPCA work correspond with increases in physician burnout and job dissatisfaction. It is not known how best to prepare residents in IPCA-heavy specialties (e.g. family medicine) for this aspect of their career. This study investigates how educators and residency programmes can optimise IPCA work during residency to best prepare residents for future practice.

METHODS

Using Constructivist Grounded Theory, we conducted focus groups and individual interviews with 42 clinicians (19 family medicine residents, 16 family physicians in the first 5 years of practice and 7 family physician educators). All participants were connected to one family medicine residency programme. We analysed interview data iteratively, using a staged approach to constant comparative analysis.

RESULTS

While residents, early career physicians and educators perceived the educational value of IPCAs differently, they all reported IPCAs as a necessary weight that family physicians carry throughout their career. Some residents described IPCAs as a burden, creating inequities in workload and interfering with other learning and personal opportunities. In contrast, educators conceptualised IPCAs as an opportunity to build and develop the skills required to carry the weight of IPCAs throughout their career. We make specific recommendations for helping residents recognise this educational opportunity, such as clarifying expectations, navigating equity, understanding purpose and maintaining consistency when teaching IPCAs.

CONCLUSION

IPCAs are a key competency for many medical residents but require explicit pedagogical attention. If the educational opportunities are not made explicit, residents may miss the opportunity to develop strategies for practice management, professional boundaries, and administrative efficiencies.

Medication non-adherence and therapeutic inertia independently contribute to poor disease control for cardiometabolic diseases

Poorly controlled cardiometabolic biometric health gap measures [e.g.,uncontrolled blood pressure (BP), HbA1c, and low-density lipoprotein cholesterol (LDL-C)] are mediated by medication adherence and clinician-level therapeutic inertia (TI). The study of comparing relative contribution of these two factors to disease control is lacking. We conducted a retrospective cohort study using 7 years of longitudinal electronic health records (EHR) from primary care cardiometabolic patients who were 35 years or older. Cox-regression modeling was applied to estimate how baseline proportion of days covered (PDC) and TI were associated with cardiometabolic related health gap closure. 92,766 patients were included in the analysis, among which 89.9%, 85.8%, and 73.3% closed a BP, HbA1c, or LDL-C gap, respectively, with median days to gap closure ranging from 223 to 408 days. Patients who did not retrieve a medication were the least likely to achieve biometric control, particularly for LDL-C (HR = 0.58, 95% CI: 0.55-0.60). TI or uncertainty of TI was associated with a high risk of health gap persistence, particularly for LDL-C (HR ranges 0.46-0.48). Both poor medication adherence and TI are independently associated with persistent health gaps, and TI has a much higher impact on disease control compared to medication adherence, implying disease management strategies should prioritize reducing TI.

Association of Electronic Health Record Inbasket Message Characteristics With Physician Burnout

IMPORTANCE

Physician 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.

OBJECTIVE

To evaluate associations between EHR inbasket message characteristics and physician burnout.

DESIGN, SETTING, AND PARTICIPANTS

Cross-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.

EXPOSURES

Physicians responded to a survey including the validated Mini-Z 5-point burnout scale.

MAIN OUTCOMES AND MEASURES

Physician 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.

RESULTS

Of 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 RELEVANCE

In 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.

Physician Health and Wellness

Physician health and wellness is a complex topic relevant to all pediatricians. Survey studies have established that pediatricians experience burnout at comparable rates to colleagues across medical specialties. Prevalence of burnout increased for all pediatric disciplines from 2011 to 2014. During that time, general pediatricians experienced a more than 10% increase in burnout, from 35.3% to 46.3%. Pediatric medical subspecialists and pediatric surgical specialists experienced slightly higher baseline rates of burnout in 2011 and similarly increased to just under 50%. Women currently constitute a majority of pediatricians, and surveys report a 20% to 60% higher prevalence of burnout in women physicians compared with their male counterparts. The purpose of this report is to update the reader and explore approaches to pediatrician well-being and reduction of occupational burnout risk throughout the stages of training and practice. Topics covered include burnout prevalence and diagnosis; overview of national progress in physician wellness; update on physician wellness initiatives at the American Academy of Pediatrics; an update on pediatric-specific burnout and well-being; recognized drivers of burnout (organizational and individual); a review of the intersection of race, ethnicity, gender, and burnout; protective factors; and components of wellness (organizational and individual). The development of this clinical report has inevitably been shaped by the social, cultural, public health, and economic factors currently affecting our communities. The coronavirus disease 2019 (COVID-19) pandemic has layered new and significant stressors onto medical practice with physical, mental, and logistical challenges and effects that cannot be ignored.

Understanding Physician Work and Well-being Through Social Network Modeling Using Electronic Health Record Data: a Cohort Study

BACKGROUND

Understanding association between factors related to clinical work environment and well-being can inform strategies to improve physicians' work experience.

OBJECTIVE

To model and quantify what drivers of work composition, team structure, and dynamics are associated with well-being.

DESIGN

Utilizing social network modeling, this cohort study of physicians in an academic health center examined inbasket messaging data from 2018 to 2019 to identify work composition, team structure, and dynamics features. Indicators from a survey in 2019 were used as dependent variables to identify factors predictive of well-being.

PARTICIPANTS

EHR data available for 188 physicians and their care teams from 18 primary care practices; survey data available for 163/188 physicians.

MAIN MEASURES

Area under the receiver operating characteristic curve (AUC) of logistic regression models to predict well-being dependent variables was assessed out-of-sample.

KEY RESULTS

The mean AUC of the model for the dependent variables of emotional exhaustion, vigor, and professional fulfillment was, respectively, 0.665 (SD 0.085), 0.700 (SD 0.082), and 0.669 (SD 0.082). Predictors associated with decreased well-being included physician centrality within support team (OR 3.90, 95% CI 1.28-11.97, P=0.01) and share of messages related to scheduling (OR 1.10, 95% CI 1.03-1.17, P=0.003). Predictors associated with increased well-being included higher number of medical assistants within close support team (OR 0.91, 95% CI 0.83-0.99, P=0.05), nurse-centered message writing practices (OR 0.89, 95% CI 0.83-0.95, P=0.001), and share of messages related to ambiguous diagnosis (OR 0.92, 95% CI 0.87-0.98, P=0.01).

CONCLUSIONS

Through integration of EHR data with social network modeling, the analysis highlights new characteristics of care team structure and dynamics that are associated with physician well-being. This quantitative methodology can be utilized to assess in a refined data-driven way the impact of organizational changes to improve well-being through optimizing team dynamics and work composition.

Accuracy of Physician Electronic Health Record Usage Analytics using Clinical Test Cases

Usage log data are an important data source for characterizing the potential burden related to use of the electronic health record (EHR) system. However, the utility of this data source has been hindered by concerns related to the real-world validity and accuracy of the data. While time-motion studies have historically been used to address this concern, the restrictions caused by the pandemic have made it difficult to carry out these studies in-person. In this regard, we introduce a practical approach for conducting validation studies for usage log data in a controlled environment. By developing test runs based on clinical workflows and conducting them within a test EHR environment, it allows for both comparison of the recorded timings and retrospective investigation of any discrepancies. In this case report, we describe the utility of this approach for validating our physician EHR usage logs at a large academic teaching mental health hospital in Canada. A total of 10 test runs were conducted across 3 days to validate 8 EHR usage log metrics, finding differences between recorded measurements and the usage analytics platform ranging from 9 to 60%.

A Medical and Moral Imperative: Testimony for the U.S. Senate Budget Committee "Medicare for All" Hearing

On May 12, 2022, Senator Bernie Sanders held a hearing in the U.S. Senate Budget Committee on Medicare for All legislation. These were the first such hearings in the U.S. Senate. In testimony presented to the Budget Committee, I argued that the achievement of Medicare for All was a medical and moral imperative. I explored the problem of uninsurance, noting that 30 million Americans remain uninsured at a cost of more than 30,000 deaths annually. I contended that improving the quality of coverage was equally crucial, describing how some 41 million Americans remain underinsured at a grave cost to their health and financial wellbeing. Finally, I examined the economics of Medicare for All reform, and showed how the reduction of the enormous administrative waste in American healthcare could save hundreds of billions of dollars a year. Medicare for All, I concluded, is the one health reform that could expand and improve coverage for all while simultaneously controlling costs.