Introduction The Electronic Health Record (EHR) has become an integral component of healthcare delivery. Survey based studies have estimated that physicians spend 4-6 hours of their workday devoted to EHR. Our study was designed to use computer software to objectively obtain time spent on EHR. Methods We recorded EHR time for 248 physiciansover 2 time intervals. EHR active use was defined as more than 15 keystrokes, or 3 mouse clicks, or 1700 "mouse miles" per minute. We recorded total time and % of work hours spent on EHR, and differences in those based on seniority. Physicians reported duty hours using a standardized toolkit. Results Physicians spent 3.8 (±2) hours on EHR daily, which accounted for 37% (±17%), 41% (±14%), and 45% (±12%) of their day for all clinicians, residents, and interns, respectively. With the progression of training, there was a reduction in EHR time (all p values <0.01). During the first academic quarter, clinicians spent 38% (± 8%) of time on chart review, 17% (± 7%) on orders, 28% (±11%) on documentation (i.e. writing notes) and 17% (±7%) on other activities (i.e. physician hand-off and medication reconciliation). This pattern remained unchanged during the fourth quarter. Conclusions Physicians spend close to 40% of their work day on EHR, with interns spending the most time. There is a significant reduction in time spent on EHR with training and greater experience, although the overall amount of time spent on EHR remained high.
As a part of a larger, mixed-methods research study, we conducted semi-structured interviews with 21 adults with depressive symptoms to understand the role that past health care discrimination plays in shaping help-seeking for depression treatment and receiving preferred treatment modalities. We recruited to achieve heterogeneity of racial/ethnic backgrounds and history of health care discrimination in our participant sample. Participants were Hispanic/Latino ( n = 4), non-Hispanic/Latino Black ( n = 8), or non-Hispanic/Latino White ( n = 9). Twelve reported health care discrimination due to race/ethnicity, language, perceived social class, and/or mental health diagnosis. Health care discrimination exacerbated barriers to initiating and continuing depression treatment among patients from diverse backgrounds or with stigmatized mental health conditions. Treatment preferences emerged as fluid and shaped by shared decisions made within a trustworthy patient–provider relationship. However, patients who had experienced health care discrimination faced greater challenges to forming trusting relationships with providers and thus engaging in shared decision-making processes.
Sexual minority individuals (eg, lesbian, gay, bisexual, and queer people) experience high rates of bullying and violence, a predictor of worse mental health, 1 but little research has examined adverse childhood experiences (ACEs) occurring before age 18 years among sexual minority individuals. This study uses a large, multistate probability sample to (1) characterize population-level prevalence of ACEs by sexual orientation and (2) estimate the association between level of ACE exposure and mental distress in adulthood by sexual orientation.Methods | This cross-sectional study uses a probability-based sample from the 2019 Behavioral Risk Factor Surveillance System (BRFSS) among states implementing both the optional sexual orientation and the ACE modules. The exposure variable was sexual minority identity. Methodology for BRFSS ACE modules has been published elsewhere. 2 No research has found that ACEs cause sexual minority identity; rather, it is hypothesized that perpetrators target socially vulnerable youth (eg, individuals with low income or a disability), including sexual minority individuals. 3 Outcomes included weighted prevalence and odds of ACEs and frequent mental distress 4 (≥14 bad mental health days in the past month) and the number of bad mental health days in the past month, stratified by ACE exposure level. Self-identified race and ethnicity were used to assess demographic differences in subsamples. This study was deemed exempt from review by the Vanderbilt University institutional review board because BRFSS data are publicly available. Statistical analysis was conducted via Stata version 17.0. Logistic regression was used to estimate unadjusted and adjusted odds ratios (ORs), controlling for sex, age, and race and ethnicity.
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