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.

scholarly journals AB1206 SELF-REPORT OF FRACTURE HISTORY COMPARED TO FRACTURE CODES FROM AN ELECTRONIC HEALTH RECORD DATASET

2019 ◽  
Author(s):  
Maria Danila ◽  
Amy Mudano ◽  
Elizabeth Rahn ◽  
Andrea Lacroix ◽  
Jeffrey Curtis ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Self Report ◽  
Health Record ◽  
Fracture History ◽  
Electronic Health

Electronic health record usage among nurse practitioners, physician assistants, and junior residents

2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Michael D. Watson ◽  
Sharbel A. Elhage ◽  
Casey Scully ◽  
Sabrina Peterson ◽  
Marialice Gulledge ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Nurse Practitioners ◽  
Physician Assistants ◽  
Health Record ◽  
Electronic Health ◽  
Junior Residents

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.

Implementation of Patient-Reported Outcomes in Routine Medical Care

2018 ◽  
pp. 122-134 ◽  
Author(s):  
Ethan Basch ◽  
Lisa Barbera ◽  
Carolyn L. Kerrigan ◽  
Galina Velikova
Keyword(s):  
Electronic Health Record ◽  
Patient Reported Outcomes ◽  
Clinical Care ◽  
Self Report ◽  
Routine Practice ◽  
Health Record ◽  
Patient Portals ◽  
Free Standing ◽  
Patient Reported ◽  
Electronic Health

There is increasing interest to integrate collection of patient-reported outcomes (PROs) in routine practice to enhance clinical care. Multiple studies show that systematic monitoring of patients using PROs improves patient-clinician communication, clinician awareness of symptoms, symptom management, patient satisfaction, quality of life, and overall survival. The general approach includes a brief electronic survey, administered via the Web or an app or an automated telephone system, with alerts to clinicians for concerning or worsening issues. Patients have generally been asked to self-report on a regular basis (remotely between visits and/or at visits), with reminders prompting patients to self-report that are sent via email, text, or automated phone message. More recently, care management pathways for patients and clinicians have been triggered by PRO system alerts. PRO systems may be free-standing, integrated into electronic health record systems or patient portals, or native functionality of an electronic health record. Despite potential benefits, there are challenges with integrating PROs into practice for monitoring patient status, as there are with any modifications to existing clinical processes. These challenges range from administrative to technical to workflow. A session at the 2018 ASCO Annual Meeting was dedicated to the implementation of PROs in clinical practice. The session focused on practical examples of PRO implementations, with honest reflections on barriers and strategies that may be generalizable to other systems looking to implement PROs. Panelists for that session are the authors of this paper, which describes their respective experiences implementing PROs in practice settings.

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 New AI Technologies to Enrich Electronic Health Record Data Sets With Self-Report Scores in Geriatrics

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 275-275
Author(s):  
Ricardo Pietrobon
Keyword(s):  
Electronic Health Record ◽  
Self Report ◽  
Health Record ◽  
Data Sets ◽  
Electronic Health Record Data ◽  
Patient Reported ◽  
Record Data ◽  
Data Source ◽  
Electronic Health ◽  
Rich Data

Abstract Although electronic health record data present a rich data source for health service researchers, for the most part, they lack self-report information. Although recent CMS projects have provided hospitals with incentives to collect patient-reported outcomes for select procedures, the process often leads to a substantial percentage of missing data, also being expensive as it requires the assistance of research coordinators. In this presentation, we will cover Artificial Intelligence-based based technologies to reduce the burden of data collection, allowing for its expansion across clinics and conditions. The technology involves the use of algorithms to predict self-report scores based on widely available claims data. Following previous work predicting frailty scores from existing variables, we expand its use with scores related to quality of life, i.e. mental health and physical function, and cognition. Accuracy metrics are presented both in cross-validation as well as external samples.

scholarly journals A Comparison of Smoking History in the Electronic Health Record With Self-Report

2020 ◽  
Vol 58 (4) ◽  
pp. 591-595 ◽  
Author(s):  
Nikhil Patel ◽  
David P. Miller ◽  
Anna C. Snavely ◽  
Christina Bellinger ◽  
Kristie L. Foley ◽  
...  
Keyword(s):  
Electronic Health Record ◽  
Self Report ◽  
Smoking History ◽  
Health Record ◽  
Electronic Health
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