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 (ß = 0.09 [95% CI, 0.05-0.14]; P < .001), alignment with workflow processes (ß = 0.11 [95% CI, 0.06-0.16]; P < .001), ease of finding information (ß = 0.14 [95% CI, 0.09-0.19]; P < .001), and usefulness of alerts (ß = 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 (ß = -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.

A machine learning technology for addressing medication-related risk in older, multimorbid patients.

OBJECTIVES: To evaluate the FeelBetter machine learning system's ability to accurately identify older patients with multimorbidity at Brigham and Women's Hospital at highest risk of medication-associated emergency department (ED) visits and hospitalizations, and to assess the system's ability to provide accurate medication recommendations for these patients. STUDY DESIGN: Retrospective cohort study. METHODS: The system uses medications, demographics, diagnoses, laboratory results, health care utilization patterns, and costs to stratify patients' risk of ED visits and hospitalizations. Patients were assigned 1 of 22 risk levels based on their system-generated risk percentile of either ED visits or hospitalizations. Logistic regression models were used to estimate the odds of ED visits and hospitalizations associated with each successive risk level compared with the 45th to 50th percentiles. After stratification, 100 high-risk (95th-100th percentiles) and 100 medium-risk (45th-55th percentiles) patients were randomly selected for generation of medication recommendations. Two clinical pharmacists reviewed the system-generated medication recommendations for these patients. RESULTS: Logistic regression models predicting 3-month utilization showed that compared with the 45th to 50th percentiles, patients in the top 1% risk percentile had ORs of 7.9 and 17.3 for ED visits and hospitalizations, respectively. The first 5 high-priority medications on each patient's medication list were associated with a mean (SD) of 6.65 (4.09) warnings. Of 1290 warnings reviewed, 1151 (89.2%) were assessed as correct. CONCLUSIONS: The FeelBetter system effectively stratifies older patients with multimorbidity at risk of ED use and hospitalizations. Medication recommendations provided by the system are largely accurate and can potentially be beneficial for patient care.

Impact of COVID-19 on Characteristics and Funding of U.S. Healthcare Startups: Retrospective Review.

BACKGROUND: The rise of telehealth and telemedicine during the pandemic allowed patients and providers to develop a sense of comfort with telehealth, which may have increased the demand for virtual-first care solutions with spillover effects into venture capital funding. OBJECTIVE: We aimed to understand the size and type of digital health investments occurring in the prepandemic and pandemic periods. METHODS: We examined health care companies founded from March 14, 2019, to March 14, 2020 (prepandemic) versus those founded from March 15, 2020, to March 14, 2022, after pandemic onset. Data were obtained from Crunchbase, a publicly available database that catalogs information about venture capital investments for companies. We also compared companies founded prepandemic to those founded after the first year of the pandemic (pandemic steady-state). We performed a Wilcoxon rank sum test to compare median funding amounts. We compared the 2 groups of companies according to the type of funding round raised, geography, health care subcategory, total amount of funding per year since founding, and number of founders. RESULTS: There were 2714 and 2218 companies founded prepandemic and during the pandemic, respectively. The companies were similarly distributed across geographies in the prepandemic and pandemic periods (P=.46) with no significant differences in the number of founders (P=.32). There was a significant difference in total funding per year since founding between prepandemic and pandemic companies (US $10.8 million vs US $20.9 million; P<.001). The distribution of funding rounds differed significantly for companies founded in prepandemic and pandemic periods (P<.001). On excluding data from the first year of the pandemic, there were 581 companies founded in the pandemic steady-state period from March 14, 2021, to March 14, 2022. Companies founded prepandemic had a significantly greater mean number of founders than those founded during the pandemic (P=.02). There was no significant difference in total funding per year since founding between prepandemic and steady-state pandemic companies (US $10.8 million vs US $14.4 million; P=.34). The most common types of health care companies included wellness, biotech/biopharma, and software companies. Distributions of companies across health care subcategories were not significantly different before and during the pandemic. However, significant differences were identified when data from the first year of the pandemic were excluded (P<.001). Companies founded during the steady-state pandemic period were significantly more likely to be classified as artificial intelligence (7.3% vs 4.7%; P=.005), software (17.3% vs 12.7%; P=.002), and insurance (3.3% vs 1.7%; P=.003), and were significantly less likely to be classified as health care diagnostics (2.4% vs 5.1%; P=.002). CONCLUSIONS: We demonstrate no significant changes in the types of health care companies founded before versus during the pandemic, although significant differences emerge when comparing prepandemic companies to those founded after the first year of the pandemic.

Impact of response bias in three surveys on primary care providers' experiences with electronic health records.

OBJECTIVE: To identify impacts of different survey methodologies assessing primary care physicians' (PCPs') experiences with electronic health records (EHRs), we compared three surveys: the 2022 Continuous Certification Questionnaire (CCQ) from the American Board of Family Medicine, the 2022 University of California San Francisco (UCSF) Physician Health IT Survey, and the 2021 National Electronic Health Records Survey (NEHRS). MATERIALS AND METHODS: We evaluated differences between survey pairs using Rao-Scott corrected chi-square tests, which account for weighting. RESULTS: CCQ received 3991 responses from PCPs (100% response rate), UCSF received 1375 (3.6% response rate), and NEHRS received 858 (18.2% response rate). Substantial, statistically significant differences in demographics were detected across the surveys. CCQ respondents were younger and more likely to work in a health system; NEHRS respondents were more likely to work in private practice; and UCSF respondents disproportionately practiced in larger academic settings. Many EHR experience indicators were similar between CCQ and NEHRS, but CCQ respondents reported higher documentation burden. DISCUSSION: The UCSF approach is unlikely to supply reliable data. Significant demographic differences between CCQ and NEHRS raise response bias concerns, and while there were similarities in some reported EHR experiences, there were important, significant differences. CONCLUSION: Federal EHR policy monitoring and maintenance require reliable data. This test of existing and alternative sources suggest that diversified data sources are necessary to understand physicians' experiences with EHRs and interoperability. Comprehensive surveys administered by specialty boards have the potential to contribute to these efforts, since they are likely to be free of response bias.

Virtual Scribes and Physician Time Spent on Electronic Health Records.

Importance: Time on the electronic health record (EHR) is associated with burnout among physicians. Newer virtual scribe models, which enable support from either a real-time or asynchronous scribe, have the potential to reduce the burden of the EHR and EHR-related documentation. Objective: To characterize the association of use of virtual scribes with changes in physicians' EHR time and note and order composition and to identify the physician, scribe, and scribe response factors associated with changes in EHR time upon virtual scribe use. Design, Setting, and Participants: Retrospective, pre-post quality improvement study of 144 physicians across specialties who had used a scribe for at least 3 months from January 2020 to September 2022, were affiliated with Brigham and Women's Hospital and Massachusetts General Hospital, and cared for patients in the outpatient setting. Data were analyzed from November 2022 to January 2024. Exposure: Use of either a real-time or asynchronous virtual scribe. Main Outcomes: Total EHR time, time on notes, and pajama time (5:30 pm to 7:00 am on weekdays and nonscheduled weekends and holidays), all per appointment; proportion of the note written by the physician and team contribution to orders. Results: The main study sample included 144 unique physicians who had used a virtual scribe for at least 3 months in 152 unique scribe participation episodes (134 [88.2%] had used an asynchronous scribe service). Nearly two-thirds of the physicians (91 physicians [63.2%]) were female and more than half (86 physicians [59.7%]) were in primary care specialties. Use of a virtual scribe was associated with significant decreases in total EHR time per appointment (mean [SD] of 5.6 [16.4] minutes; P < .001) in the 3 months after vs the 3 months prior to scribe use. Scribe use was also associated with significant decreases in note time per appointment and pajama time per appointment (mean [SD] of 1.3 [3.3] minutes; P < .001 and 1.1 [4.0] minutes; P = .004). In a multivariable linear regression model, the following factors were associated with significant decreases in total EHR time per appointment with a scribe use at 3 months: practicing in a medical specialty (-7.8; 95% CI, -13.4 to -2.2 minutes), greater baseline EHR time per appointment (-0.3; 95% CI, -0.4 to -0.2 minutes per additional minute of baseline EHR time), and decrease in the percentage of the note contributed by the physician (-9.1; 95% CI, -17.3 to -0.8 minutes for every percentage point decrease). Conclusions and Relevance: In 2 academic medical centers, use of virtual scribes was associated with significant decreases in total EHR time, time spent on notes, and pajama time, all per appointment. Virtual scribes may be particularly effective among medical specialists and those physicians with greater baseline EHR time.

Comparison of Hospital Mortality and Readmission Rates by Physician and Patient Sex.

BACKGROUND: Little is known as to whether the effects of physician sex on patients' clinical outcomes vary by patient sex. OBJECTIVE: To examine whether the association between physician sex and hospital outcomes varied between female and male patients hospitalized with medical conditions. DESIGN: Retrospective observational study. SETTING: Medicare claims data. PATIENTS: 20% random sample of Medicare fee-for-service beneficiaries hospitalized with medical conditions during 2016 to 2019 and treated by hospitalists. MEASUREMENTS: The primary outcomes were patients' 30-day mortality and readmission rates, adjusted for patient and physician characteristics and hospital-level averages of exposures (effectively comparing physicians within the same hospital). RESULTS: Of 458 108 female and 318 819 male patients, 142 465 (31.1%) and 97 500 (30.6%) were treated by female physicians, respectively. Both female and male patients had a lower patient mortality when treated by female physicians; however, the benefit of receiving care from female physicians was larger for female patients than for male patients (difference-in-differences, -0.16 percentage points [pp] [95% CI, -0.42 to 0.10 pp]). For female patients, the difference between female and male physicians was large and clinically meaningful (adjusted mortality rates, 8.15% vs. 8.38%; average marginal effect [AME], -0.24 pp [CI, -0.41 to -0.07 pp]). For male patients, an important difference between female and male physicians could be ruled out (10.15% vs. 10.23%; AME, -0.08 pp [CI, -0.29 to 0.14 pp]). The pattern was similar for patients' readmission rates. LIMITATION: The findings may not be generalizable to younger populations. CONCLUSION: The findings indicate that patients have lower mortality and readmission rates when treated by female physicians, and the benefit of receiving treatments from female physicians is larger for female patients than for male patients. PRIMARY FUNDING SOURCE: Gregory Annenberg Weingarten, GRoW @ Annenberg.

Looking Beyond Mortality Prediction: Primary Care Physician Views of Patients' Palliative Care Needs Predicted by a Machine Learning Tool.

OBJECTIVES: To assess primary care physicians' (PCPs) perception of the need for serious illness conversations (SIC) or other palliative care interventions in patients flagged by a machine learning tool for high 1-year mortality risk. METHODS: We surveyed PCPs from four Brigham and Women's Hospital primary care practice sites. Multiple mortality prediction algorithms were ensembled to assess adult patients of these PCPs who were either enrolled in the hospital's integrated care management program or had one of several chronic conditions. The patients were classified as high or low risk of 1-year mortality. A blinded survey had PCPs evaluate these patients for palliative care needs. We measured PCP and machine learning tool agreement regarding patients' need for an SIC/elevated risk of mortality. RESULTS: Of 66 PCPs, 20 (30.3%) participated in the survey. Out of 312 patients evaluated, 60.6% were female, with a mean (standard deviation [SD]) age of 69.3 (17.5) years, and a mean (SD) Charlson Comorbidity Index of 2.80 (2.89). The machine learning tool identified 162 (51.9%) patients as high risk. Excluding deceased or unfamiliar patients, PCPs felt that an SIC was appropriate for 179 patients; the machine learning tool flagged 123 of these patients as high risk (68.7% concordance). For 105 patients whom PCPs deemed SIC unnecessary, the tool classified 83 as low risk (79.1% concordance). There was substantial agreement between PCPs and the tool (Gwet's agreement coefficient of 0.640). CONCLUSIONS: A machine learning mortality prediction tool offers promise as a clinical decision aid, helping clinicians pinpoint patients needing palliative care interventions.

Customised knowledge-sharing platform to foster resident quality improvement activities, tracking and scholarship.

INTRODUCTION: Meeting accreditation requirements to train resident physicians in quality improvement (QI) may require more than education. Barriers to resident QI engagement underscore the need to demonstrate the impact and value of resident QI work. It is not known whether a platform to track and publicise resident QI projects and scholarship is feasible or acceptable to implement within a residency programme. We aimed to create a searchable online platform and associated programming to promote resident QI work. METHODS: This intervention targeted resident physicians in an internal medicine residency training programme at a tertiary, academic medical centre. We designed an intervention to track resident QI and related scholarship in a searchable online platform, including practical details of implementing each project. Newsletters and events were used to publicise these project profiles. RESULTS: During the 2020-2021 academic year, 104 projects were profiled from 238 sourced projects. Average readership was 31.5% across 11 newsletters sent to residents and key faculty. DISCUSSION: A platform to track and share resident QI work and scholarship can be feasibly and acceptably implemented within a residency programme, serving as a novel way to engage residents around QI.

Trends in Scientific Collaboration Between Men and Women Following the #MeToo Movement.

BACKGROUND: The #MeToo movement raised global awareness about harassment in the workplace. Concerns were raised, however, that the movement may have unintendedly harmed women in academia by decreasing collaboration invitations from men in senior positions, who might be more reluctant to collaborate. OBJECTIVE: To analyze whether collaborations between first author women and last author men decreased after the #MeToo movement. DESIGN: Retrospective observational study. PARTICIPANTS: Names of first and last authors of 122,450 US review articles from the PubMed database published between 2014 and 2020. MAIN MEASURES: Change in the proportion of review articles with a first author woman and a last author man following the peak of the #MeToo movement in October 2017. Additionally, among review articles with a last author man, trends of women first authorship in the USA and Europe (control group) were compared. KEY RESULTS: We analyzed 122,450 review articles with first and last authors from US institutions. Of 85,015 articles by a man last author, 37.5% (31,902) had a woman first author. In contrast, when the last author was a woman, the first author was also a woman in 53.6% of articles (20,078) (p<0.001 for difference). Among review articles with a last author man, there was no change in the proportion of articles with a woman first author before versus after the peak of the #MeToo movement (e.g., p=0.13 for difference between the 12 months following October 2017 compared to the pre-#Me-too period). Among European institutions, of 72,036 articles by a man last author, 43.4% (31,294) had a woman first author, higher than the proportion observed in the USA. Trends in collaboration between first author women and last author men were similar in the USA and Europe after the peak of the #MeToo movement (p=0.65). CONCLUSIONS: The #MeToo movement was not associated with a reduction in the rate of scientific review article authorship collaborations between first author women and last author men in the life sciences. These findings, if generalizable, suggest it is possible to promote accountability for harassment in the workplace without limiting decreases in collaboration.

Quantifying EHR and Policy Factors Associated with the Gender Productivity Gap in Ambulatory, General Internal Medicine.

BACKGROUND: The gender gap in physician compensation has persisted for decades. Little is known about how differences in use of the electronic health record (EHR) may contribute. OBJECTIVE: To characterize how time on clinical activities, time on the EHR, and clinical productivity vary by physician gender and to identify factors associated with physician productivity. DESIGN, SETTING, AND PARTICIPANTS: This longitudinal study included general internal medicine physicians employed by a large ambulatory practice network in the Northeastern United States from August 2018 to June 2021. MAIN MEASURES: Monthly data on physician work relative value units (wRVUs), physician and practice characteristics, metrics of EHR use and note content, and temporal trend variables. KEY RESULTS: The analysis included 3227 physician-months of data for 108 physicians (44% women). Compared with men physicians, women physicians generated 23.8% fewer wRVUs per month, completed 22.1% fewer visits per month, spent 4.0 more minutes/visit and 8.72 more minutes on the EHR per hour worked (all p < 0.001), and typed or dictated 36.4% more note characters per note (p = 0.006). With multivariable adjustment for physician age, practice characteristics, EHR use, and temporal trends, physician gender was no longer associated with productivity (men 4.20 vs. women 3.88 wRVUs/hour, p = 0.31). Typing/dictating fewer characters per note, relying on greater teamwork to manage orders, and spending less time on documentation were associated with higher wRVUs/hour. The 2021 E/M code change was associated with higher wRVUs/hour for all physicians: 10% higher for men physicians and 18% higher for women physicians (p < 0.001 and p = 0.009, respectively). CONCLUSIONS: Increased team support, briefer documentation, and the 2021 E/M code change were associated with higher physician productivity. The E/M code change may have preferentially benefited women physicians by incentivizing time-intensive activities such as medical decision-making, preventive care discussion, and patient counseling that women physicians have historically spent more time performing.

System-Level Factors and Time Spent on Electronic Health Records by Primary Care Physicians.

Importance: Primary care physicians (PCPs) spend the most time on the electronic health record (EHR) of any specialty. Thus, it is critical to understand what factors contribute to varying levels of PCP time spent on EHRs. Objective: To characterize variation in EHR time across PCPs and primary care clinics, and to describe how specific PCP, patient panel, clinic, and team collaboration factors are associated with PCPs' time spent on EHRs. Design, Setting, and Participants: This cross-sectional study included 307 PCPs practicing across 31 primary care clinics at Massachusetts General Hospital and Brigham and Women's Hospital during 2021. Data were analyzed from October 2022 to October 2023. Main Outcomes and Measures: Total per-visit EHR time, total per-visit pajama time (ie, time spent on the EHR between 5:30 pm to 7:00 am and on weekends), and total per-visit time on the electronic inbox as measured by activity log data derived from an EHR database. Results: The sample included 307 PCPs (183 [59.6%] female). On a per-visit basis, PCPs spent a median (IQR) of 36.2 (28.9-45.7) total minutes on the EHR, 6.2 (3.1-11.5) minutes of pajama time, and 7.8 (5.5-10.7) minutes on the electronic inbox. When comparing PCP time expenditure by clinic, median (IQR) total EHR time, median (IQR) pajama time, and median (IQR) electronic inbox time ranged from 23.5 (20.7-53.1) to 47.9 (30.6-70.7) minutes per visit, 1.7 (0.7-10.5) to 13.1 (7.7-28.2) minutes per visit, and 4.7 (4.1-5.2) to 10.8 (8.9-15.2) minutes per visit, respectively. In a multivariable model with an outcome of total per-visit EHR time per visit, an above median percentage of teamwork on orders was associated with 3.81 (95% CI, 0.49-7.13) minutes per visit fewer and having a clinic pharmacy technician was associated with 7.87 (95% CI, 2.03-13.72) minutes per visit fewer. Practicing in a community health center was associated with fewer minutes of total EHR time per visit (5.40 [95% CI, 0.06-10.74] minutes). Conclusions and Relevance: There is substantial variation in EHR time among individual PCPs and PCPs within clinics. Organization-level factors, such as team collaboration on orders, support for medication refill functions, and practicing in a community health center, are associated with lower EHR time for PCPs. These findings highlight the importance of addressing EHR burden at a systems level.

Proportion Of Preventive Primary Care Visits Nearly Doubled, Especially Among Medicare Beneficiaries, 2001-19.

There is debate about the value of preventive visits in primary care, and multiple policy trends during the past fifteen years may have influenced the likelihood of US adults undergoing preventive primary care visits. Using nationally representative, serial cross-sectional data on adult visits to primary care physicians from the 2001-19 National Ambulatory Medical Care Survey, we characterized temporal trends in the proportion of primary care visits with a preventive focus and the differential characteristics of these visits. Based on a sample of 139,783 unweighted (5,902,144,258 weighted) US primary care visits, we found that the proportion of primary care visits with a preventive focus increased between 2001 and 2019 (12.8 percent of visits in 2001-02 versus 24.6 percent in 2018-19; [Formula: see text]), with the greatest rate of increase seen for people with Medicare. Primary care visits with a preventive focus involved more time spent with the physician and addressed fewer reasons for the visit compared with problem-based visits. At least one of the following was significantly more likely to occur during a preventive visit than a problem-based visit: counseling provision, ordering of preventive labs, or ordering of a preventive image or procedure. Our findings demonstrate a relative increase in preventive versus problem-based visits in primary care and suggest the importance of enhanced insurance coverage in influencing preventive care delivery trends.

Identifying and Addressing Barriers to Implementing Core Electronic Health Record Use Metrics for Ambulatory Care: Virtual Consensus Conference Proceedings.

Precise, reliable, valid metrics that are cost-effective and require reasonable implementation time and effort are needed to drive electronic health record (EHR) improvements and decrease EHR burden. Differences exist between research and vendor definitions of metrics. PROCESS: We convened three stakeholder groups (health system informatics leaders, EHR vendor representatives, and researchers) in a virtual workshop series to achieve consensus on barriers, solutions, and next steps to implementing the core EHR use metrics in ambulatory care. CONCLUSION: Actionable solutions identified to address core categories of EHR metric implementation challenges include: (1) maintaining broad stakeholder engagement, (2) reaching agreement on standardized measure definitions across vendors, (3) integrating clinician perspectives, and (4) addressing cognitive and EHR burden. Building upon the momentum of this workshop's outputs offers promise for overcoming barriers to implementing EHR use metrics.