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      Physicians’ gender and their use of electronic health records: findings from a mixed-methods usability study

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          Abstract

          Objective

          Physician burnout associated with EHRs is a major concern in health care. A comprehensive assessment of differences among physicians in the areas of EHR performance, efficiency, and satisfaction has not been conducted. The study sought to study relationships among physicians’ performance, efficiency, perceived workload, satisfaction, and usability in using the electronic health record (EHR) with comparisons by age, gender, professional role, and years of experience with the EHR.

          Materials and Methods

          Mixed-methods assessments of the medical intensivists' EHR use and perceptions. Using simulated cases, we employed standardized scales, performance measures, and extensive interviews. NASA Task Load Index (TLX), System Usability Scale (SUS), and Questionnaire on User Interface Satisfaction surveys were deployed.

          Results

          The study enrolled 25 intensive care unit (ICU) physicians (11 residents, 9 fellows, 5 attendings); 12 (48%) were men, with a mean age of 33 (range, 28-55) years and a mean of 4 (interquartile range, 2.0-5.5) years of Epic experience. Overall task performance scores were similar for men (90% ± 9.3%) and women (92% ± 4.4%), with no statistically significant differences (P = .374). However, female physicians demonstrated higher efficiency in completion time (difference = 7.1 minutes; P = .207) and mouse clicks (difference = 54; P = .13). Overall, men reported significantly higher perceived EHR workload stress compared with women (difference = 17.5; P < .001). Men reported significantly higher levels of frustration with the EHR compared with women (difference = 33.15; P < .001). Women reported significantly higher satisfaction with the ease of use of the EHR interface than men (difference = 0.66; P =.03). The women’s perceived overall usability of the EHR is marginally higher than that of the men (difference = 10.31; P =.06).

          Conclusions

          Among ICU physicians, we measured significant gender-based differences in perceived EHR workload stress, satisfaction, and usability—corresponding to objective patterns in EHR efficiency. Understanding the reasons for these differences may help reduce burnout and guide improvements to physician performance, efficiency, and satisfaction with EHR use.

          Design

          Mixed-methods assessments of the medical intensivists’ EHR use and perceptions. Using simulated cases, we employed standardized scales, performance measures, and extensive interviews.

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          Most cited references28

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          Race, gender, and partnership in the patient-physician relationship.

          Many studies have documented race and gender differences in health care received by patients. However, few studies have related differences in the quality of interpersonal care to patient and physician race and gender. To describe how the race/ethnicity and gender of patients and physicians are associated with physicians' participatory decision-making (PDM) styles. Telephone survey conducted between November 1996 and June 1998 of 1816 adults aged 18 to 65 years (mean age, 41 years) who had recently attended 1 of 32 primary care practices associated with a large mixed-model managed care organization in an urban setting. Sixty-six percent of patients surveyed were female, 43% were white, and 45% were African American. The physician sample (n = 64) was 63% male, with 56% white, and 25% African American. Patients' ratings of their physicians' PDM style on a 100-point scale. African American patients rated their visits as significantly less participatory than whites in models adjusting for patient age, gender, education, marital status, health status, and length of the patient-physician relationship (mean [SE] PDM score, 58.0 [1.2] vs 60.6 [3.3]; P = .03). Ratings of minority and white physicians did not differ with respect to PDM style (adjusted mean [SE] PDM score for African Americans, 59.2 [1.7] vs whites, 61.7 [3.1]; P = .13). Patients in race-concordant relationships with their physicians rated their visits as significantly more participatory than patients in race-discordant relationships (difference [SE], 2.6 [1.1]; P = .02). Patients of female physicians had more participatory visits (adjusted mean [SE] PDM score for female, 62.4 [1.3] vs male, 59.5 [3.1]; P = .03), but gender concordance between physicians and patients was not significantly related to PDM score (unadjusted mean [SE] PDM score, 76.0 [1.0] for concordant vs 74.5 [0.9] for discordant; P = .12). Patient satisfaction was highly associated with PDM score within all race/ethnicity groups. Our data suggest that African American patients rate their visits with physicians as less participatory than whites. However, patients seeing physicians of their own race rate their physicians' decision-making styles as more participatory. Improving cross-cultural communication between primary care physicians and patients and providing patients with access to a diverse group of physicians may lead to more patient involvement in care, higher levels of patient satisfaction, and better health outcomes.
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            Comparison of Hospital Mortality and Readmission Rates for Medicare Patients Treated by Male vs Female Physicians.

            Studies have found differences in practice patterns between male and female physicians, with female physicians more likely to adhere to clinical guidelines and evidence-based practice. However, whether patient outcomes differ between male and female physicians is largely unknown.
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              Tethered to the EHR: Primary Care Physician Workload Assessment Using EHR Event Log Data and Time-Motion Observations

              PURPOSE Primary care physicians spend nearly 2 hours on electronic health record (EHR) tasks per hour of direct patient care. Demand for non–face-to-face care, such as communication through a patient portal and administrative tasks, is increasing and contributing to burnout. The goal of this study was to assess time allocated by primary care physicians within the EHR as indicated by EHR user-event log data, both during clinic hours (defined as 8:00 am to 6:00 pm Monday through Friday) and outside clinic hours. METHODS We conducted a retrospective cohort study of 142 family medicine physicians in a single system in southern Wisconsin. All Epic (Epic Systems Corporation) EHR interactions were captured from “event logging” records over a 3-year period for both direct patient care and non–face-to-face activities, and were validated by direct observation. EHR events were assigned to 1 of 15 EHR task categories and allocated to either during or after clinic hours. RESULTS Clinicians spent 355 minutes (5.9 hours) of an 11.4-hour workday in the EHR per weekday per 1.0 clinical full-time equivalent: 269 minutes (4.5 hours) during clinic hours and 86 minutes (1.4 hours) after clinic hours. Clerical and administrative tasks including documentation, order entry, billing and coding, and system security accounted for nearly one-half of the total EHR time (157 minutes, 44.2%). Inbox management accounted for another 85 minutes (23.7%). CONCLUSIONS Primary care physicians spend more than one-half of their workday, nearly 6 hours, interacting with the EHR during and after clinic hours. EHR event logs can identify areas of EHR-related work that could be delegated, thus reducing workload, improving professional satisfaction, and decreasing burnout. Direct time-motion observations validated EHR-event log data as a reliable source of information regarding clinician time allocation.
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                Author and article information

                Journal
                Journal of the American Medical Informatics Association
                Oxford University Press (OUP)
                1527-974X
                December 2019
                December 01 2019
                August 28 2019
                December 2019
                December 01 2019
                August 28 2019
                : 26
                : 12
                : 1505-1514
                Affiliations
                [1 ]Carolina Health Informatics Program, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
                [2 ]School of Nursing, University of North Carolina at Chapel Hill, North Carolina, USA
                [3 ]Preventive Medicine Residency Program, Department of Family Medicine, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA
                [4 ]Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, USA
                [5 ]Pulmonary Diseases and Critical Care Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
                [6 ]Sociology Department and Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
                [7 ]Department of Biomedical Informatics, University at Buffalo, Buffalo, New York, USA
                Article
                10.1093/jamia/ocz126
                7647147
                31504578
                7ce5bb42-4c71-4ec9-a4a3-68d1b67b41c3
                © 2019

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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