The Organization of Academic General Internal Medicine Practice at the Top Primary Care Schools.

BACKGROUND: While prior studies have explored staffing infrastructure for primary care practices in general, little is known about the range of academic primary care practice models and supports available for academic general internists. OBJECTIVE: To characterize the range of practice arrangements and expectations for attending academic physicians in general internal medicine (GIM) practices at the top 22 medical schools across the USA. DESIGN: Cross-sectional survey administered electronically between October 30, 2022, and December 28, 2022. PARTICIPANTS: Clinical leaders in GIM at the top 22 primary care medical schools, as identified by the 2023 US News and World Report Rankings. MAIN MEASURES: Clinical load, productivity expectations, cross-coverage, and team-based care models. KEY RESULTS: Twenty-two leaders responded, representing 68% (15/22) of medical schools surveyed. The practices were mostly in urban locations (18/22, 82%) and 86% (19/22) included residents. Practices ranged from 7 to 200 PCPs and from 3 to 112 clinical FTEs. A full-time (1.0 FTE) clinical role for academic attending GIM physicians entailed a median of 9 (IQR 8, 10) weekly half-day clinic sessions, with a median panel size expectation of 1600 (IQR 1450, 1850) patients and a median yearly RVU expectation of 5200 (IQR 4161, 5891) yearly RVUs generated. Staff support was most commonly present for prescription refills and patient portal message checks. It was less commonly available for time sensitive form completion. Occasional clinical coverage for other physicians was an expectation at all practices. CONCLUSIONS: In this study, we characterize the organization of and supports available in academic GIM practices affiliated with the top primary care medical schools. Our findings provide comparative information for leaders of academic GIM practices seeking to enhance primary care delivery for their faculty and trainees. They also highlight areas where standardization may be beneficial across academic GIM.

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.