Dataset Information

Trends and Perceptions of Electronic Health Record Usage among Plastic Surgeons.

ABSTRACT: Background:Electronic health records (EHRs) should help physicians stay organized, improve patient safety, and facilitate communication with both patients and fellow healthcare providers. However, few studies have directly evaluated physician satisfaction with EHR and its perceived impact on patient care. This study assessed trends and perceptions of EHR within the American plastic surgery community. Methods:An Institutional Review Board-approved survey that assessed demographics, patterns of EHR use, and attitudes toward EHR was deployed by the American Society of Plastic Surgeons Member Survey Research Services. Statistical analyses were performed using Stata 14.2 and QDA Miner Lite software (Version 2.0; Provalis, Montreal, Canada). Significance level was P < 0.05. Results:Among plastic surgeons who use EHR, EPIC Systems software (Epic, Verona, Wisc.) was the most common vendor, with users noting a net positive effect on the quality of care they provided to patients. Younger age and less years of experience were correlated with a more positive attitude toward EHR. Positive attitude was closely linked to shared responsibility among support staff over data entry, whereas negative attitude was tightly tied to the perceived time wasted because of EHR, followed by poor technical support and design. Conclusions:EHR use among plastic surgeons was more common in academic-associated specialties and larger practice groups. Overall, age and practice type had weak associations with perceptions of EHR usage. On average, there were slightly more positive perceptions of EHR usage than negative. The most commonly perceived issues with EHR were wasted time and barriers to user-friendliness. These findings suggest the need for greater physician involvement in EHR optimization.

SUBMITTER: Seu M

PROVIDER: S-EPMC7209869 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Trends and Perceptions of Electronic Health Record Usage among Plastic Surgeons.

Seu Michelle M Cho Brian H BH Pigott Rachel R Sarmiento Samuel S Pedreira Rachel R Bhat Deepa D Sacks Justin J

Plastic and reconstructive surgery. Global open 20200424 4

<h4>Background</h4>Electronic health records (EHRs) should help physicians stay organized, improve patient safety, and facilitate communication with both patients and fellow healthcare providers. However, few studies have directly evaluated physician satisfaction with EHR and its perceived impact on patient care. This study assessed trends and perceptions of EHR within the American plastic surgery community.<h4>Methods</h4>An Institutional Review Board-approved survey that assessed demographics, ...[more]

PMID: 32440400

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Project description:BackgroundCollaboration is vital within health care institutions, and it allows for the effective use of collective health care worker (HCW) expertise. Human-computer interactions involving electronic health records (EHRs) have become pervasive and act as an avenue for quantifying these collaborations using statistical and network analysis methods.ObjectiveWe aimed to measure HCW collaboration and its characteristics by analyzing concurrent EHR usage.MethodsBy extracting concurrent EHR usage events from audit log data, we defined concurrent sessions. For each HCW, we established a metric called concurrent intensity, which was the proportion of EHR activities in concurrent sessions over all EHR activities. Statistical models were used to test the differences in the concurrent intensity between HCWs. For each patient visit, starting from admission to discharge, we measured concurrent EHR usage across all HCWs, which we called temporal patterns. Again, we applied statistical models to test the differences in temporal patterns of the admission, discharge, and intermediate days of hospital stay between weekdays and weekends. Network analysis was leveraged to measure collaborative relationships among HCWs. We surveyed experts to determine if they could distinguish collaborative relationships between high and low likelihood categories derived from concurrent EHR usage. Clustering was used to aggregate concurrent activities to describe concurrent sessions. We gathered 4 months of EHR audit log data from a large academic medical center's neonatal intensive care unit (NICU) to validate the effectiveness of our framework.ResultsThere was a significant difference (P<.001) in the concurrent intensity (proportion of concurrent activities: ranging from mean 0.07, 95% CI 0.06-0.08, to mean 0.36, 95% CI 0.18-0.54; proportion of time spent on concurrent activities: ranging from mean 0.32, 95% CI 0.20-0.44, to mean 0.76, 95% CI 0.51-1.00) between the top 13 HCW specialties who had the largest amount of time spent in EHRs. Temporal patterns between weekday and weekend periods were significantly different on admission (number of concurrent intervals per hour: 11.60 vs 0.54; P<.001) and discharge days (4.72 vs 1.54; P<.001), but not during intermediate days of hospital stay. Neonatal nurses, fellows, frontline providers, neonatologists, consultants, respiratory therapists, and ancillary and support staff had collaborative relationships. NICU professionals could distinguish high likelihood collaborative relationships from low ones at significant rates (3.54, 95% CI 3.31-4.37 vs 2.64, 95% CI 2.46-3.29; P<.001). We identified 50 clusters of concurrent activities. Over 87% of concurrent sessions could be described by a single cluster, with the remaining 13% of sessions comprising multiple clusters.ConclusionsLeveraging concurrent EHR usage workflow through audit logs to analyze HCW collaboration may improve our understanding of collaborative patient care. HCW collaboration using EHRs could potentially influence the quality of patient care, discharge timeliness, and clinician workload, stress, or burnout.

Project description:BackgroundTo address the obesity epidemic, there is a need for novel paradigms, including those that address the timing of eating and sleep in relation to circadian rhythms. Electronic health records (EHRs) are an efficient way to identify potentially eligible participants for health research studies. Mobile health (mHealth) apps offer available and convenient data collection of health behaviors, such as timing of eating and sleep.ObjectiveThe aim of this descriptive analysis was to report on recruitment, retention, and app use from a 6-month cohort study using a mobile app called Daily24.MethodsUsing an EHR query, adult patients from three health care systems in the PaTH clinical research network were identified as potentially eligible, invited electronically to participate, and instructed to download and use the Daily24 mobile app, which focuses on eating and sleep timing. Online surveys were completed at baseline and 4 months. We described app use and identified predictors of app use, defined as 1 or more days of use, versus nonuse and usage categories (ie, immediate, consistent, and sustained) using multivariate regression analyses.ResultsOf 70,661 patients who were sent research invitations, 1021 (1.44%) completed electronic consent forms and online baseline surveys; 4 withdrew, leaving a total of 1017 participants in the analytic sample. A total of 53.79% (n=547) of the participants were app users and, of those, 75.3% (n=412), 50.1% (n=274), and 25.4% (n=139) were immediate, consistent, and sustained users, respectively. Median app use was 28 (IQR 7-75) days over 6 months. Younger age, White race, higher educational level, higher income, having no children younger than 18 years, and having used 1 to 5 health apps significantly predicted app use (vs nonuse) in adjusted models. Older age and lower BMI predicted early, consistent, and sustained use. About half (532/1017, 52.31%) of the participants completed the 4-month online surveys. A total of 33.5% (183/547), 29.3% (157/536), and 27.1% (143/527) of app users were still using the app for at least 2 days per month during months 4, 5, and 6 of the study, respectively.ConclusionsEHR recruitment offers an efficient (ie, high reach, low touch, and minimal participant burden) approach to recruiting participants from health care settings into mHealth research. Efforts to recruit and retain less engaged subgroups are needed to collect more generalizable data. Additionally, future app iterations should include more evidence-based features to increase participant use.

Project description:BackgroundClinical guidelines and public health authorities lack recommendations on scalable approaches to defining and monitoring the occurrence and severity of bleeding in populations prescribed antithrombotic therapy.MethodsWe examined linked primary care, hospital admission and death registry electronic health records (CALIBER 1998-2010, England) of patients with newly diagnosed atrial fibrillation, acute myocardial infarction, unstable angina or stable angina with the aim to develop algorithms for bleeding events. Using the developed bleeding phenotypes, Kaplan-Meier plots were used to estimate the incidence of bleeding events and we used Cox regression models to assess the prognosis for all-cause mortality, atherothrombotic events and further bleeding.ResultsWe present electronic health record phenotyping algorithms for bleeding based on bleeding diagnosis in primary or hospital care, symptoms, transfusion, surgical procedures and haemoglobin values. In validation of the phenotype, we estimated a positive predictive value of 0.88 (95% CI 0.64, 0.99) for hospitalised bleeding. Amongst 128,815 patients, 27,259 (21.2%) had at least 1 bleeding event, with 5-year risks of bleeding of 29.1%, 21.9%, 25.3% and 23.4% following diagnoses of atrial fibrillation, acute myocardial infarction, unstable angina and stable angina, respectively. Rates of hospitalised bleeding per 1000 patients more than doubled from 1.02 (95% CI 0.83, 1.22) in January 1998 to 2.68 (95% CI 2.49, 2.88) in December 2009 coinciding with the increased rates of antiplatelet and vitamin K antagonist prescribing. Patients with hospitalised bleeding and primary care bleeding, with or without markers of severity, were at increased risk of all-cause mortality and atherothrombotic events compared to those with no bleeding. For example, the hazard ratio for all-cause mortality was 1.98 (95% CI 1.86, 2.11) for primary care bleeding with markers of severity and 1.99 (95% CI 1.92, 2.05) for hospitalised bleeding without markers of severity, compared to patients with no bleeding.ConclusionsElectronic health record bleeding phenotyping algorithms offer a scalable approach to monitoring bleeding in the population. Incidence of bleeding has doubled in incidence since 1998, affects one in four cardiovascular disease patients, and is associated with poor prognosis. Efforts are required to tackle this iatrogenic epidemic.

Dataset Information

Trends and Perceptions of Electronic Health Record Usage among Plastic Surgeons.

Publications

Trends and Perceptions of Electronic Health Record Usage among Plastic Surgeons.

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