scholarly journals Physician Stress During Electronic Health Record Inbox Work: In Situ Measurement With Wearable Sensors

10.2196/24014 ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. e24014
Author(s):  
Fatema Akbar ◽  
Gloria Mark ◽  
Stephanie Prausnitz ◽  
E Margaret Warton ◽  
Jeffrey A East ◽  
...  
Keyword(s):  
Heart Rate ◽  
Electronic Health Record ◽  
Physiological Stress ◽  
Work Hours ◽  
Health Record ◽  
Work Patterns ◽  
Work Duration ◽  
Electronic Health ◽  
Physician Stress ◽  
Stress Patterns

Background Increased work through electronic health record (EHR) messaging is frequently cited as a factor of physician burnout. However, studies to date have relied on anecdotal or self-reported measures, which limit the ability to match EHR use patterns with continuous stress patterns throughout the day. Objective The aim of this study is to collect EHR use and physiologic stress data through unobtrusive means that provide objective and continuous measures, cluster distinct patterns of EHR inbox work, identify physicians’ daily physiologic stress patterns, and evaluate the association between EHR inbox work patterns and physician physiologic stress. Methods Physicians were recruited from 5 medical centers. Participants (N=47) were given wrist-worn devices (Garmin Vivosmart 3) with heart rate sensors to wear for 7 days. The devices measured physiological stress throughout the day based on heart rate variability (HRV). Perceived stress was also measured with self-reports through experience sampling and a one-time survey. From the EHR system logs, the time attributed to different activities was quantified. By using a clustering algorithm, distinct inbox work patterns were identified and their associated stress measures were compared. The effects of EHR use on physician stress were examined using a generalized linear mixed effects model. Results Physicians spent an average of 1.08 hours doing EHR inbox work out of an average total EHR time of 3.5 hours. Patient messages accounted for most of the inbox work time (mean 37%, SD 11%). A total of 3 patterns of inbox work emerged: inbox work mostly outside work hours, inbox work mostly during work hours, and inbox work extending after hours that were mostly contiguous to work hours. Across these 3 groups, physiologic stress patterns showed 3 periods in which stress increased: in the first hour of work, early in the afternoon, and in the evening. Physicians in group 1 had the longest average stress duration during work hours (80 out of 243 min of valid HRV data; P=.02), as measured by physiological sensors. Inbox work duration, the rate of EHR window switching (moving from one screen to another), the proportion of inbox work done outside of work hours, inbox work batching, and the day of the week were each independently associated with daily stress duration (marginal R2=15%). Individual-level random effects were significant and explained most of the variation in stress (conditional R2=98%). Conclusions This study is among the first to demonstrate associations between electronic inbox work and physiological stress. We identified 3 potentially modifiable factors associated with stress: EHR window switching, inbox work duration, and inbox work outside work hours. Organizations seeking to reduce physician stress may consider system-based changes to reduce EHR window switching or inbox work duration or the incorporation of inbox management time into work hours.

scholarly journals Physician Stress During Electronic Health Record Inbox Work: In Situ Measurement With Wearable Sensors (Preprint)

2020 ◽  
Author(s):  
Fatema Akbar ◽  
Gloria Mark ◽  
Stephanie Prausnitz ◽  
E Margaret Warton ◽  
Jeffrey A East ◽  
...  
Keyword(s):  
Heart Rate ◽  
Electronic Health Record ◽  
Physiological Stress ◽  
Work Hours ◽  
Health Record ◽  
Work Patterns ◽  
Work Duration ◽  
Electronic Health ◽  
Physician Stress ◽  
Stress Patterns

BACKGROUND Increased work through electronic health record (EHR) messaging is frequently cited as a factor of physician burnout. However, studies to date have relied on anecdotal or self-reported measures, which limit the ability to match EHR use patterns with continuous stress patterns throughout the day. OBJECTIVE The aim of this study is to collect EHR use and physiologic stress data through unobtrusive means that provide objective and continuous measures, cluster distinct patterns of EHR inbox work, identify physicians’ daily physiologic stress patterns, and evaluate the association between EHR inbox work patterns and physician physiologic stress. METHODS Physicians were recruited from 5 medical centers. Participants (N=47) were given wrist-worn devices (Garmin Vivosmart 3) with heart rate sensors to wear for 7 days. The devices measured physiological stress throughout the day based on heart rate variability (HRV). Perceived stress was also measured with self-reports through experience sampling and a one-time survey. From the EHR system logs, the time attributed to different activities was quantified. By using a clustering algorithm, distinct inbox work patterns were identified and their associated stress measures were compared. The effects of EHR use on physician stress were examined using a generalized linear mixed effects model. RESULTS Physicians spent an average of 1.08 hours doing EHR inbox work out of an average total EHR time of 3.5 hours. Patient messages accounted for most of the inbox work time (mean 37%, SD 11%). A total of 3 patterns of inbox work emerged: inbox work mostly outside work hours, inbox work mostly during work hours, and inbox work extending after hours that were mostly contiguous to work hours. Across these 3 groups, physiologic stress patterns showed 3 periods in which stress increased: in the first hour of work, early in the afternoon, and in the evening. Physicians in group 1 had the longest average stress duration during work hours (80 out of 243 min of valid HRV data; <i>P</i>=.02), as measured by physiological sensors. Inbox work duration, the rate of EHR window switching (moving from one screen to another), the proportion of inbox work done outside of work hours, inbox work batching, and the day of the week were each independently associated with daily stress duration (marginal <i>R<sup>2</sup></i>=15%). Individual-level random effects were significant and explained most of the variation in stress (conditional <i>R<sup>2</sup></i>=98%). CONCLUSIONS This study is among the first to demonstrate associations between electronic inbox work and physiological stress. We identified 3 potentially modifiable factors associated with stress: EHR window switching, inbox work duration, and inbox work outside work hours. Organizations seeking to reduce physician stress may consider system-based changes to reduce EHR window switching or inbox work duration or the incorporation of inbox management time into work hours. CLINICALTRIAL

scholarly journals Reducing Violations to the 80-hour Work Rule with Electronic Health Record to Establish Rapid Culture Change, Does It Work?

2021 ◽  
Vol 73 ◽  
Author(s):  
Enoch Yeung ◽  
Daniel Golden ◽  
Jean Miner ◽  
Silviu Marica ◽  
Burt Cagir
Keyword(s):  
Learning Environment ◽  
Electronic Health Record ◽  
Residency Program ◽  
Work Hours ◽  
Health Record ◽  
Pass Rates ◽  
Improvement Project ◽  
Management Software ◽  
Work Hour ◽  
Electronic Health

Objectives: In our free-standing general surgery residency program, it was noted over the past decade that we had an exorbitant number of resident work hours violations. This resulted in multiple citations from the Accreditation Council for Graduate Medical Education (ACGME) with subsequent probation. To restore accreditation requirements and provide trainees with a balanced learning environment, work hours were restructured. Reporting of work hours in the residency management software was authenticated by the organizational Electronic Health Record (EHR). This quality improvement project evaluated the effectiveness of compliance to the 80-hour work rules in a single rural surgical training residency program with the aid of EHR. Methods: The hours are actively monitored in the residency management software, New-Innovations (NI) and cumulative weekly reports were created. With the design, residents were scheduled to work a maximum of 13 hours per day beginning at 0600 and operating room (OR) time concluding by 1700. During each 4-week rotation, residents were assigned one Friday call, one Saturday call and four hours of transitional call. The primary outcome of this project was the number of resident violations to the 80 hours rule before and after implementation of those measures. The secondary outcomes were the residents’ comparative academic and clinical performances. This project also evaluated the overall cultural change and satisfaction with the program using ACGME survey data. Results: Compared with the non EHR era (2013-2015), the number of violations during the EHR era decreased significantly. (Mean non EHR= 167.3, EHR = 24.6) (p =0.0009) Case volumes and board pass rates were a central focus throughout the changes. No decrease in operative volume was noted for graduating residents (N = 8, non EHR= 1,062, Mean EHR = 1,110) (p = 0.5). Over the three years since the changes, the board pass rates have improved from 64% to 80% in Certifying Examination (CE) (N = 8, Passing % non EHR = 64%, EHR = 80%) (p = 0.03) Qualifying Examination (QE) (N = 8, Passing % non EHR = 100%, EHR = 93%) (p =0.1). Conclusion: Reduction in work hour violations can be achieved with a reliable schedule, promotion of accurate reporting by residents, and monitoring through EHR reports. Adherence to work hour guidelines is essential for resident well-being and a healthy and conducive clinical learning environment without diminishing operative experience.

scholarly journals Longitudinal cohorts for harnessing the electronic health record for disease prediction in a US population

BMJ Open ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. e044353
Author(s):  
Sheila M Manemann ◽  
Jennifer L St Sauver ◽  
Hongfang Liu ◽  
Nicholas B Larson ◽  
Sungrim Moon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Heart Rate ◽  
Electronic Health Record ◽  
Validation Cohort ◽  
Complex Diseases ◽  
Health Record ◽  
Discovery Cohort ◽  
Diagnosis Codes ◽  
Electronic Health

PurposeThe depth and breadth of clinical data within electronic health record (EHR) systems paired with innovative machine learning methods can be leveraged to identify novel risk factors for complex diseases. However, analysing the EHR is challenging due to complexity and quality of the data. Therefore, we developed large electronic population-based cohorts with comprehensive harmonised and processed EHR data.ParticipantsAll individuals 30 years of age or older who resided in Olmsted County, Minnesota on 1 January 2006 were identified for the discovery cohort. Algorithms to define a variety of patient characteristics were developed and validated, thus building a comprehensive risk profile for each patient. Patients are followed for incident diseases and ageing-related outcomes. Using the same methods, an independent validation cohort was assembled by identifying all individuals 30 years of age or older who resided in the largely rural 26-county area of southern Minnesota and western Wisconsin on 1 January 2013.Findings to dateFor the discovery cohort, 76 255 individuals (median age 49; 53% women) were identified from which a total of 9 644 221 laboratory results; 9 513 840 diagnosis codes; 10 924 291 procedure codes; 1 277 231 outpatient drug prescriptions; 966 136 heart rate measurements and 1 159 836 blood pressure (BP) measurements were retrieved during the baseline time period. The most prevalent conditions in this cohort were hyperlipidaemia, hypertension and arthritis. For the validation cohort, 333 460 individuals (median age 54; 52% women) were identified and to date, a total of 19 926 750 diagnosis codes, 10 527 444 heart rate measurements and 7 356 344 BP measurements were retrieved during baseline.Future plansUsing advanced machine learning approaches, these electronic cohorts will be used to identify novel sex-specific risk factors for complex diseases. These approaches will allow us to address several challenges with the use of EHR.

scholarly journals Automatic Detection of Front-Line Clinician Hospital Shifts: A Novel Use of Electronic Health Record Timestamp Data

2019 ◽  
Vol 10 (01) ◽  
pp. 028-037 ◽  
Author(s):  
Adam Dziorny ◽  
Evan Orenstein ◽  
Robert Lindell ◽  
Nicole Hames ◽  
Nicole Washington ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Work Hours ◽  
Self Report ◽  
Health Record ◽  
Front Line ◽  
Hours Worked ◽  
Inpatient Settings ◽  
Electronic Health ◽  
End Times ◽  
Junior Residents

Objective Excess physician work hours contribute to burnout and medical errors. Self-report of work hours is burdensome and often inaccurate. We aimed to validate a method that automatically determines provider shift duration based on electronic health record (EHR) timestamps across multiple inpatient settings within a single institution. Methods We developed an algorithm to calculate shift start and end times for inpatient providers based on EHR timestamps. We validated the algorithm based on overlap between calculated shifts and scheduled shifts. We then demonstrated a use case by calculating shifts for pediatric residents on inpatient rotations from July 1, 2015 through June 30, 2016, comparing hours worked and number of shifts by rotation and role. Results We collected 6.3 × 107 EHR timestamps for 144 residents on 771 inpatient rotations, yielding 14,678 EHR-calculated shifts. Validation on a subset of shifts demonstrated 100% shift match and 87.9 ± 0.3% overlap (mean ± standard error [SE]) with scheduled shifts. Senior residents functioning as front-line clinicians worked more hours per 4-week block (mean ± SE: 273.5 ± 1.7) than senior residents in supervisory roles (253 ± 2.3) and junior residents (241 ± 2.5). Junior residents worked more shifts per block (21 ± 0.1) than senior residents (18 ± 0.1). Conclusion Automatic calculation of inpatient provider work hours is feasible using EHR timestamps. An algorithm to assess provider work hours demonstrated criterion validity via comparison with scheduled shifts. Differences between junior and senior residents in calculated mean hours worked and number of shifts per 4-week block were also consistent with differences in scheduled shifts and duty-hour restrictions.

Abstract P346: Cohort Identification to Harness the Electronic Health Record for Cardiovascular Disease Prediction

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Sheila M Manemann ◽  
Jennifer St Sauver ◽  
Janet E Olson ◽  
Nicholas B Larson ◽  
Paul Y Takahashi ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Heart Rate ◽  
Electronic Health Record ◽  
Risk Prediction ◽  
Health Record ◽  
Next Generation ◽  
Cvd Risk ◽  
Electronic Health

Background: Current cardiovascular disease (CVD) risk scores are derived from research cohorts and are particularly inaccurate in women, older adults, and those with missing data. To overcome these limitations, we aimed to develop a cohort to capitalize on the depth and breadth of clinical data within electronic health record (EHR) systems in order to develop next-generation sex-specific risk prediction scores for incident CVD. Methods: All individuals 30 years of age or older residing in Olmsted County, Minnesota on 1/1/2006 were identified. We developed and validated algorithms to define a variety of risk factors, thus building a comprehensive risk profile for each patient. Outcomes including myocardial infarction (MI), percutaneous intervention (PCI), coronary artery bypass graft (CABG), and CVD death were ascertained through 9/30/2017. Results: We identified 73,069 individuals without CVD (Table). We retrieved a total of 14,962,762 lab results; 14,534,466 diagnoses; 17,062,601 services/procedures; 1,236,998 outpatient prescriptions; 1,079,065 heart rate measurements; and 1,320,115 blood pressure measurements. The median number of blood pressure and heart rate measurements ascertained per individuals were 11 and 9, respectively. The five most prevalent conditions were: hypertension, hyperlipidemia, arthritis, depression, and cardiac arrhythmias. During follow-up 1,455 MIs, 1,581 PCI, 652 CABG, and 2,161 CVD-related deaths occurred. Conclusions: We developed a cohort with comprehensive risk profiles and follow-up for each patient. Using sophisticated machine learning approaches, this electronic cohort will be utilized to develop next-generation sex-specific CVD risk prediction scores. These approaches will allow us to address several challenges with use of EHR data including the ability to 1) deal with missing values, 2) assess and utilize a large number of variables without over-fitting, 3) allow non-linear relationships, and 4) use time-to-event data.

scholarly journals Physician Burnout and Timing of Electronic Health Record Use

ACI Open ◽  
2020 ◽  
Vol 04 (01) ◽  
pp. e1-e8 ◽  
Author(s):  
Mark A. Micek ◽  
Brian Arndt ◽  
Wen-Jan Tuan ◽  
Elizabeth Trowbridge ◽  
Shannon M. Dean ◽  
...  
Keyword(s):  
Internal Medicine ◽  
Primary Care ◽  
Electronic Health Record ◽  
Work Hours ◽  
Care Practice ◽  
Health Record ◽  
Physician Burnout ◽  
After Hours ◽  
Electronic Health ◽  
The Relationship

Abstract Background Rates of burnout among physicians have been high in recent years. The electronic health record (EHR) is implicated as a major cause of burnout. Objective This article aimed to determine the association between physician burnout and timing of EHR use in an academic internal medicine primary care practice. Methods We conducted an observational cohort study using cross-sectional and retrospective data. Participants included primary care physicians in an academic outpatient general internal medicine practice. Burnout was measured with a single-item question via self-reported survey. EHR time was measured using retrospective automated data routinely captured within the institution's EHR. EHR time was separated into four categories: weekday work-hours in-clinic time, weekday work-hours out-of-clinic time, weekday afterhours time, and weekend/holiday after-hours time. Ordinal regression was used to determine the relationship between burnout and EHR time categories. Results EHR use during in-clinic sessions was related to burnout in both bivariate (odds ratio [OR] = 1.04, 95% confidence interval [CI]: 1.01, 1.06; p = 0.007) and adjusted (OR = 1.07, 95% CI: 1.03, 1.1; p = 0.001) analyses. No significant relationships were found between burnout and after-hours EHR use. Conclusion In this small single-institution study, physician burnout was associated with higher levels of in-clinic EHR use but not after-hours EHR use. Improved understanding of the variability of in-clinic EHR use, and the EHR tasks that are particularly burdensome to physicians, could help lead to interventions that better integrate EHR demands with clinical care and potentially reduce burnout. Further studies including more participants from diverse clinical settings are needed to further understand the relationship between burnout and after-hours EHR use.

scholarly journals Physicians’ electronic inbox work patterns and factors associated with high inbox work duration

2020 ◽  
Author(s):  
Fatema Akbar ◽  
Gloria Mark ◽  
E Margaret Warton ◽  
Mary E Reed ◽  
Stephanie Prausnitz ◽  
...  
Keyword(s):  
Primary Care Physicians ◽  
Older Patients ◽  
Demographic Data ◽  
Work Hours ◽  
Health Record ◽  
Work Patterns ◽  
Work Time ◽  
Factors Associated ◽  
Use Patterns ◽  
Work Duration

Abstract Objectives Electronic health record systems are increasingly used to send messages to physicians, but research on physicians’ inbox use patterns is limited. This study’s aims were to (1) quantify the time primary care physicians (PCPs) spend managing inboxes; (2) describe daily patterns of inbox use; (3) investigate which types of messages consume the most time; and (4) identify factors associated with inbox work duration. Materials and Methods We analyzed 1 month of electronic inbox data for 1275 PCPs in a large medical group and linked these data with physicians’ demographic data. Results PCPs spent an average of 52 minutes on inbox management on workdays, including 19 minutes (37%) outside work hours. Temporal patterns of electronic inbox use differed from other EHR functions such as charting. Patient-initiated messages (28%) and results (29%) accounted for the most inbox work time. PCPs with higher inbox work duration were more likely to be female (P &lt; .001), have more patient encounters (P &lt; .001), have older patients (P &lt; .001), spend proportionally more time on patient messages (P &lt; .001), and spend more time per message (P &lt; .001). Compared with PCPs with the lowest duration of time on inbox work, PCPs with the highest duration had more message views per workday (200 vs 109; P &lt; .001) and spent more time on the inbox outside work hours (30 minutes vs 9.7 minutes; P &lt; .001). Conclusions Electronic inbox work by PCPs requires roughly an hour per workday, much of which occurs outside scheduled work hours. Interventions to assist PCPs in handling patient-initiated messages and results may help alleviate inbox workload.

Business: The Electronic Health Record: Front Line Experience

2011 ◽  
Vol 21 (1) ◽  
pp. 18-22
Author(s):  
Rosemary Griffin
Keyword(s):  
Health Care ◽  
Information Technology ◽  
Electronic Health Record ◽  
Financial Incentives ◽  
Best Practice ◽  
Health Care Systems ◽  
The United States ◽  
Health Record ◽  
Health Trends ◽  
Electronic Health

National legislation is in place to facilitate reform of the United States health care industry. The Health Care Information Technology and Clinical Health Act (HITECH) offers financial incentives to hospitals, physicians, and individual providers to establish an electronic health record that ultimately will link with the health information technology of other health care systems and providers. The information collected will facilitate patient safety, promote best practice, and track health trends such as smoking and childhood obesity.

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