Female medical school faculty neither advance as rapidly nor are compensated as well as professionally similar male colleagues. Deficits for female physicians are greater than those for nonphysician female faculty, and for both physicians and nonphysicians, women's deficits are greater for faculty with more seniority.

were grouped into primary care, medical, and surgical specialties (eTable 2 in the Supplement).Variables were compared before (March 2018 to February 2020) and during (March 2020 to June 2021) the COVID-19 pandemic. To assess whether the onset of the pandemic was an inflection point in PMARs, message volume per physician per day was modeled by piecewise linear regression using a spline for month with a single knot at March 2020 and Huber-White SEs. Three months of inbox message data were missing (3 of 40 months [7.5%]) and excluded from the analysis. To test for significance (P < .05), we used a 2-sided Wald test for equivalence of the coefficients. We used Stata statistical software version 16 (StataCorp) for data analyses. ResultsForty months of inbox messages were analyzed, including 10 850 401 messages to 419 unique physicians from 38 specialties across 141 practice sites (Figure). Overall, primary care, medical, and surgical physicians received 49.3, 33.4, and 20.7 messages per day, respectively. Between March 2020 and June 2021, mean (SD) total messages per day increased from 45.0 (27.4) to 46.0 (27.4) messages per day for primary care physicians, from 29.3 (20.7) to 32.0 (20.8) messages per day for medical physicians, and from 16.6 (11.9) to 23.3 (17.9) messages per day for surgical physicians.Patient-originated messages also increased, including PMARs (from a mean [SD] of 1.8 [1.8] to 3.9 [3.2] messages per day for primary care physicians; from 1.0 [1.7] to 2.2 [2.9] messages per day for medical physicians; and from 0.4 [0.5] to 1.1 [1.3] messages per day for surgical physicians), patient calls, and time in the inbox (from 21.7 [12.7] to 25.1 [13.7] minutes per day for primary care physicians;

Objective To derive 7 proposed core electronic health record (EHR) use metrics across 2 healthcare systems with different EHR vendor product installations and examine factors associated with EHR time. Materials and Methods A cross-sectional analysis of ambulatory physicians EHR use across the Yale-New Haven and MedStar Health systems was performed for August 2019 using 7 proposed core EHR use metrics normalized to 8 hours of patient scheduled time. Results Five out of 7 proposed metrics could be measured in a population of nonteaching, exclusively ambulatory physicians. Among 573 physicians (Yale-New Haven N = 290, MedStar N = 283) in the analysis, median EHR-Time8 was 5.23 hours. Gender, additional clinical hours scheduled, and certain medical specialties were associated with EHR-Time8 after adjusting for age and health system on multivariable analysis. For every 8 hours of scheduled patient time, the model predicted these differences in EHR time (P &lt; .001, unless otherwise indicated): female physicians +0.58 hours; each additional clinical hour scheduled per month −0.01 hours; practicing cardiology −1.30 hours; medical subspecialties −0.89 hours (except gastroenterology, P = .002); neurology/psychiatry −2.60 hours; obstetrics/gynecology −1.88 hours; pediatrics −1.05 hours (P = .001); sports/physical medicine and rehabilitation −3.25 hours; and surgical specialties −3.65 hours. Conclusions For every 8 hours of scheduled patient time, ambulatory physicians spend more than 5 hours on the EHR. Physician gender, specialty, and number of clinical hours practicing are associated with differences in EHR time. While audit logs remain a powerful tool for understanding physician EHR use, additional transparency, granularity, and standardization of vendor-derived EHR use data definitions are still necessary to standardize EHR use measurement.

To evaluate current practices regarding intensive-care units (ICU's), we collected data on 2693 consecutive admissions to a medical ICU during a two-year period and studied indications for admission, specific interventions, costs, and outcomes. The need for noninvasive monitoring rather than immediate major interventions prompted 77 per cent of the admissions. Only 10 per cent of monitored patients had subsequent indications for major interventions. The 23 per cent who required immediate interventions accounted for disproportionate shares of total charges (37 per cent) and deaths during hospitalization (58 per cent). Demographic and diagnostic data indicate that the aged and chronically ill have become the principal consumers of intensive care. Overall mortality during hospitalization was 10 per cent; cumulative mortality during follow-up study (mean duration, 15 months) was 25 per cent. We conclude that identification of sensitive predictors of complications and specific predictors of mortality can lead to more efficient and effective ICU practices.

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