Surgery Residents Spend Nearly 8 Months of Their 5-Year Training on the Electronic Health Record (EHR)

Deficiencies in Electronic Medical Record Inpatient List Capabilities Negatively Impact Patient Safety, Resident Education, and Wellness

OBJECTIVE

Electronic medical records (EMRs) have streamlined workflows for health care professionals, yet their full potential is not always actualized. Modern EMRs are often capable of generating automated prepopulated inpatient lists, however if these capabilities are not made available to inpatient teams or not designed with the end user in mind, resident physicians may be left to create alternative, manual solutions to ensure reliable and efficient care. The purpose of the current study was to longitudinally compare the impact of both manual and automated inpatient lists on resident education, wellness, and patient safety.

DESIGN

Retrospective standardized surveys were administered to resident physicians in the orthopedic surgery department at a level I trauma center over a 3-year period to evaluate the impact of various automated and manual list iterations coinciding with changes to the EMR. Data collected included post graduate year (PGY) status, arrival time to work, daily time spent preparing and updating the list, perceived impact on patient safety, resident workload, education, and sleep. We compared manual versus automated list data with unpaired t-tests and chi-squared tests.

SETTING

The study was conducted at Brooke Army Medical Center, a level 1 trauma center in San Antonio, Texas. It is an Academic Medical Center and the Department of Defense's largest medical facility.

PARTICIPANTS

A total of 71 surveys were collected from 33 orthopedic surgery residents in all levels of training.

RESULTS

Intern list prep time in the morning was 27 ± 16 minutes for the automated list (n = 17) vs 72 ± 21 minutes for the manual lists (n = 23) (p < 0.0001). Total time spent by interns updating the list for the entire day was on average 83 minutes for the automated list (n = 17) vs 196 minutes for the manual lists (n = 23) (p < 0.0001). In addition, 86% of interns felt the time spent on the manual list impacted their education, with 96% stating that it directly impacted the amount of time they had to study and 100% agreed that it negatively impacted their sleep (n = 23). Only 48% of interns (n = 23) were satisfied with the performance of the manual lists compared to 94% satisfaction (n = 17) with the automated list. Further, 87% of interns felt the manual list negatively impacted patient care and negatively affected their job satisfaction. In comparison, 59% of interns felt the automated list improved their job satisfaction. Ultimately, for an intern an automated list versus a manual list affords them an extra 106 minutes per day for education, sleep, or other activities. PGY2 residents and above noted similar trends regarding their experience with the lists.

CONCLUSIONS

The benefits of utilizing automated inpatient lists as determined by our study are improved efficiency in the morning with less preparation and maintenance throughout the day. Additionally, with automated lists there was more perceived time for education and sleep, with improved job satisfaction. Most importantly, respondents felt that automated lists were safer for patient care when compared to manual lists. This should compel further research and efforts into improving automated EMR tracking lists in hospitals. In summary, as compared to the automated electronic medical record inpatient list, manual lists resulted in substantially more preparation time and maintenance throughout the day thereby negatively impacting resident education and quality of life, while raising concerns for patient safety.

The Impact of Diagnosis-Specific Plan Templates on Admission Note Writing Time: A Quality Improvement Initiative

Background There are limited objective studies regarding the effectiveness of strategies to alleviate the documentation burden on resident physicians. Objective To develop and implement diagnosis-specific templates for the plan of care section of inpatient admission notes, aiming to reduce documentation time. Methods Twelve templates for the plan of care section of admission notes were written by the study authors, reviewed by attending physicians, and shared with the residents through the electronic health record (EHR) on September 23, 2022. EHR audit log data were collected to examine admission note writing times, supplemented by resident feedback on acceptability via an anonymous survey. Feasibility measures included time investment, experience with the EHR, and resident training. Results Between July 1, 2021 and June 30, 2023, 62 pediatric residents contributed 9840 admission notes. The templates were used in 557 admission notes. The mean total time spent on an admission note decreased from 97.9 minutes pre-intervention to 71.0 minutes post-intervention with the use of a template; an adjusted reduction of 23% (95% CI 16%-30%; P<.001). The mean attending time spent editing an admission note was unchanged. The survey results underscored wide acceptability of the templates among the residents. Feasibility data showed that the project required minimal time investment from the health care informatics team and minimal resident training. Conclusions Using templates in the care plan section of admission notes reduces the time residents spend writing admission notes.

Charting Tomorrow's Healthcare: A Traditional Literature Review for an Artificial Intelligence-Driven Future

Electronic health record (EHR) systems have developed over time in parallel with general advancements in mainstream technology. As artificially intelligent (AI) systems rapidly impact multiple societal sectors, it has become apparent that medicine is not immune from the influences of this powerful technology. Particularly appealing is how AI may aid in improving healthcare efficiency with note-writing automation. This literature review explores the current state of EHR technologies in healthcare, specifically focusing on possibilities for addressing EHR challenges through the automation of dictation and note-writing processes with AI integration. This review offers a broad understanding of existing capabilities and potential advancements, emphasizing innovations such as voice-to-text dictation, wearable devices, and AI-assisted procedure note dictation. The primary objective is to provide researchers with valuable insights, enabling them to generate new technologies and advancements within the healthcare landscape. By exploring the benefits, challenges, and future of AI integration, this review encourages the development of innovative solutions, with the goal of enhancing patient care and healthcare delivery efficiency.

Improving healthcare quality by unifying the American electronic medical report system: time for change

BACKGROUND

In recent years, innovation in healthcare technology has significantly improved the efficiency of the healthcare system. Advancements have led to better patient care and more cost-effective services. The electronic medical record (EMR) system, in particular, has enhanced interoperability and collaboration across healthcare departments by facilitating the exchange and utilization of patient data. The COVID-19 pandemic further accelerated this trend, leading to a surge in telemedicine services, which rely on electronic communication to deliver healthcare remotely.

MAIN BODY

Integrating artificial intelligence (AI) and machine learning (ML) in healthcare have been instrumental in analyzing vast data sets, allowing for identifying patterns and trends that can improve care delivery and pinpoint potential issues. The proposal of a unified EMR system in the USA aims to capitalize on these technological advancements. Such a system would streamline the sharing of patient information among healthcare providers, improve the quality and efficiency of care, and minimize the likelihood of errors in patient treatment.

CONCLUSION

A unified electronic medical record system represents a promising avenue for enhancing interoperability within the US healthcare sector. By creating a more connected and accessible network of patient information, it sets the stage for a transformation in healthcare delivery. This change is imperative for maintaining the momentum of progress in healthcare technology and realizing the full potential of recent advancements in patient care and system efficiency.

Uses of Electronic Health Record Data to Measure the Clinical Learning Environment of Graduate Medical Education Trainees: A Systematic Review

PURPOSE

This study systematically reviews the uses of electronic health record (EHR) data to measure graduate medical education (GME) trainee competencies.

METHOD

In January 2022, the authors conducted a systematic review of original research in MEDLINE from database start to December 31, 2021. The authors searched for articles that used the EHR as their data source and in which the individual GME trainee was the unit of observation and/or unit of analysis. The database query was intentionally broad because an initial survey of pertinent articles identified no unifying Medical Subject Heading terms. Articles were coded and clustered by theme and Accreditation Council for Graduate Medical Education (ACGME) core competency.

RESULTS

The database search yielded 3,540 articles, of which 86 met the study inclusion criteria. Articles clustered into 16 themes, the largest of which were trainee condition experience (17 articles), work patterns (16 articles), and continuity of care (12 articles). Five of the ACGME core competencies were represented (patient care and procedural skills, practice-based learning and improvement, systems-based practice, medical knowledge, and professionalism). In addition, 25 articles assessed the clinical learning environment.

CONCLUSIONS

This review identified 86 articles that used EHR data to measure individual GME trainee competencies, spanning 16 themes and 6 competencies and revealing marked between-trainee variation. The authors propose a digital learning cycle framework that arranges sequentially the uses of EHR data within the cycle of clinical experiential learning central to GME. Three technical components necessary to unlock the potential of EHR data to improve GME are described: measures, attribution, and visualization. Partnerships between GME programs and informatics departments will be pivotal in realizing this opportunity.

Surgeon Burnout and Usage of Personal Communication Devices: Examining the Technology "Empowerment/Enslavement Paradox"

INTRODUCTION

Access to patients' electronic medical records (EMRs) on personal communication devices (PCDs) is beneficial but can negatively impact surgeons. In a recent op-ed, Cohen et al. explored this technology "empowerment/enslavement paradox" and its potential effect on surgeon burnout. We examined if there is a relationship between accessing EMRs on PCDs and surgeon burnout.

METHODS

This was a cohort study with retrospective and prospective arms. Trainees and attendings with a background in general surgery completed the Maslach Burnout Index for Medical Personnel, a validated survey scored on three areas of burnout (emotional exhaustion, depersonalization, and low personal accomplishment). Data on login frequency to EMRs on PCDs over the previous 6 mo were obtained. Pearson correlation coefficients were calculated to determine if burnout and login frequency were associated.

RESULTS

There were 52 participants included. Residents were 61.5% (n = 32) of participants. The mean login frequency over 6 mo was 431.0 ± 323.9. The mean scores (out of 6) for emotional exhaustion, depersonalization, and personal accomplishment were 2.3 ± 1.1, 1.9 ± 1.2, and 4.9 ± 0.8, respectively. There was no correlation between burnout and logins. Residents had higher median depersonalization scores (2.3 versus 1.2, P = 0.03) and total logins (417.5 versus 210.0, P < 0.001) than attendings. Participants who overestimated logins had higher median emotional exhaustion and depersonalization scores than those who underestimated (2.6 versus 1.4, P = 0.03, and 2.4 versus 0.8, P = 0.003, respectively).

CONCLUSIONS

Using EMRs on PCDs is common, but frequency of logins did not correlate with burnout scores in this study. However, perception of increased workload may contribute to experiencing burnout.

On-Call Junior Neurosurgery Residents Spend 9 hours of Their On-Call Shift Actively Using the Electronic Health Record

BACKGROUND

The electronic health record (EHR) is central to clinical workflow, yet few studies to date have explored EHR usage patterns among neurosurgery trainees.

OBJECTIVE

To describe the amount of EHR time spent by postgraduate year (PGY)-2 and PGY-3 neurosurgery residents during on-call days and the distribution of EHR activities in which they engage.

METHODS

This cohort study used the EHR audit logs, time-stamped records of user activities, to review EHR usage of PGY-2 and PGY-3 neurosurgery residents scheduled for 1 or more on-call days across 2 calendar years at the University of California San Francisco. We focused on the PGY-2 and PGY-3, which, in our training program, represent the primary participants in the in-house on-call pool.

RESULTS

Over 723 call days, 12 different residents took at least one on-call shift. The median (IQR) number of minutes that residents spent per on-call shift actively using the EHR was 536.8 (203.5), while interacting with an average (SD) of 68.1 (14.7) patient charts. There was no significant difference between Active EHR Time between residents as PGY-2 and PGY-3 on paired t -tests. Residents spent the most time on the following EHR activities: patient reports, notes, order management, patient list, and chart review.

CONCLUSION

Residents spent, on average, 9 hours of their on-call shift actively using the EHR, and there was no improved efficiency as residents gained experience. We noted several areas of administrative EHR burden, which could be reduced.

Perceptions of Mobile Health Technology in Elective Surgery: A Qualitative Study of North American Surgeons

OBJECTIVES

To explore the surgeon-perceived added value of mobile health technologies (mHealth), and determine facilitators of and barriers to implementing mHealth.

BACKGROUND

Despite the growing popularity of mHealth and evidence of meaningful use of patient-generated health data in surgery, implementation remains limited.

METHODS

This was an exploratory qualitative study following the Consolidated Criteria for Reporting Qualitative Research. Purposive sampling was used to identify surgeons across the United States and Canada. The Consolidated Framework for Implementation Research informed development of a semistructured interview guide. Video-based interviews were conducted (September-November 2020) and interview transcripts were thematically analyzed.

RESULTS

Thirty surgeons from 8 specialties and 6 North American regions were interviewed. Surgeons identified opportunities to integrate mHealth data pre- operatively (eg, expectation-setting, decision-making) and during recovery (eg, remote monitoring, earlier detection of adverse events) among higher risk patients. Perceived advantages of mHealth data compared with surgical and patient-reported outcomes included easier data collection, higher interpretability and objectivity of mHealth data, and the potential to develop more patientcentered and functional measures of health. Surgeons identified a variety of implementation facilitators and barriers around surgeon- and patient buy-in, integration with electronic medical records, regulatory/reimbursement concerns, and personnel responsible for mHealth data. Surgeons described similar considerations regarding perceptions of mHealth among patients, including the potential to address or worsen existing disparities in surgical care.

CONCLUSIONS

These findings have the potential to inform the effective and equitable implementation of mHealth for the purposes of supporting patients and surgical care teams throughout the delivery of surgical care.

Using event logs to observe interactions with electronic health records: an updated scoping review shows increasing use of vendor-derived measures

OBJECTIVE

The aim of this article is to compare the aims, measures, methods, limitations, and scope of studies that employ vendor-derived and investigator-derived measures of electronic health record (EHR) use, and to assess measure consistency across studies.

MATERIALS AND METHODS

We searched PubMed for articles published between July 2019 and December 2021 that employed measures of EHR use derived from EHR event logs. We coded the aims, measures, methods, limitations, and scope of each article and compared articles employing vendor-derived and investigator-derived measures.

RESULTS

One hundred and two articles met inclusion criteria; 40 employed vendor-derived measures, 61 employed investigator-derived measures, and 1 employed both. Studies employing vendor-derived measures were more likely than those employing investigator-derived measures to observe EHR use only in ambulatory settings (83% vs 48%, P = .002) and only by physicians or advanced practice providers (100% vs 54% of studies, P < .001). Studies employing vendor-derived measures were also more likely to measure durations of EHR use (P < .001 for 6 different activities), but definitions of measures such as time outside scheduled hours varied widely. Eight articles reported measure validation. The reported limitations of vendor-derived measures included measure transparency and availability for certain clinical settings and roles.

DISCUSSION

Vendor-derived measures are increasingly used to study EHR use, but only by certain clinical roles. Although poorly validated and variously defined, both vendor- and investigator-derived measures of EHR time are widely reported.

CONCLUSION

The number of studies using event logs to observe EHR use continues to grow, but with inconsistent measure definitions and significant differences between studies that employ vendor-derived and investigator-derived measures.

Acute Care Surgeons Spend More Time than General Surgeons on the Electronic Health Record (EHR)

BACKGROUND

The paradigm of Acute Care Surgery (ACS) emerged in response to decreasing operative opportunities for trauma surgeons and increasing need for surgical coverage in disciplines to which the expertise of trauma surgeons naturally extends. While the continued evolution of this specialty remains largely beneficial, unintended consequences may have arisen along the way. One aspect of ACS that remains to be thoroughly investigated is the impact of the electronic health record (EHR). The purpose of this study is to objectively quantify EHR usage for ACS and compare it to other general surgery specialties.

METHODS

EHR user data were collected for fifteen ACS attendings and thirty-seven general surgery attendings from October 2014 to September 2019. Comparative analysis was conducted using two-tailed t-tests. Subgroup analysis was conducted for subspecialties included in the general surgery group.

RESULTS

ACS attendings opened almost 3 times as many charts as general surgery attendings per month (180 vs 64 charts/month, P < .0001), and ultimately spent more time on the EHR as a result (10 vs 6.4 hours/month, P < .0001). Documentation was the most time consuming EHR task for both groups. Although ACS attendings spent less overall time per patient chart, the proportion of time spent on certain EHR tasks was similar to that of general surgery colleagues.

DISCUSSION

The EHR imposes a disproportionate burden on ACS attendings compared to their general surgery counterparts, and additional study is warranted to improve usage. EHR usage burden has workforce implications for trainees considering a career in ACS.

Pilot Testing of Simulation in the Evaluation of a Novel, Rapidly Deployable Electronic Health Record for use in Disaster Intensive Care

OBJECTIVES

The SARS-CoV-2 pandemic has highlighted the need for rapid creation and management of ICU field hospitals with effective remote monitoring which is dependent on the rapid deployment and integration of an Electronic Health Record (EHR). We describe the use of simulation to evaluate a rapidly scalable hub-and-spoke model for EHR deployment and monitoring using asynchronous training.

METHODS

We adapted existing commercial EHR products to serve as the point of entry from a simulated hospital and a separate system for tele-ICU support and monitoring of the interfaced data. To train our users we created a modular video-based curriculum to facilitate asynchronous training. Effectiveness of the curriculum was assessed through completion of common ICU documentation tasks in a high-fidelity simulation. Additional endpoints include assessment of EHR navigation, user satisfaction (Net Promoter), system usability (System Usability Scale-SUS), and cognitive load (NASA-TLX).

RESULTS

A total of 5 participants achieved a 100% task completion on all domains except ventilator data (91%). Systems demonstrated high degrees of satisfaction (Net Promoter = 65.2), acceptable usability (SUS = 66.5), and acceptable cognitive load (NASA-TLX = 41.5); with higher levels of cognitive load correlating with the number of screens employed.

CONCLUSIONS

Clinical usability of a comprehensive and rapidly deployable EHR was acceptable in an intensive care simulation which was preceded by < 1 hour of video education about the EHR. This model should be considered in plans for integrated clinical response with remote and accessory facilities.

Emergency medicine residents spend over 7.5 months of their 3-year residency on the electronic health record

BACKGROUND

Use of the electronic health record (EHR) is a standard component of modern patient care. Although EHRs have improved since inception, cumbersome workflows decrease the time for residents to spend on clinical and educational activities. This study aims to quantify the time spent interacting with the EHR during a 3-year emergency medicine (EM) residency.

METHODS

System records of time spent actively engaged in EHR use were analyzed for 98 unique EM residents over a period of 5 years from July 2015 to June 2020. Time spent on the EHR was totaled to give a career time, with a "work month" defined as a 4-week period of 70.5 h per week, based on Accreditation Council for Graduate Medical Education work hour restrictions for EM residents. Engagement in specific activities such as chart review, documentation preparation, and order entry were separately analyzed.

RESULTS

Over their 3-year training, a resident interacted with the EHR for 2,171 continuous hours. This amounts to 30.8 work weeks or 7.7 work months. Chart review was the most time-intensive activity at 11.42 weeks. Documentation accounted for 9.91 weeks, with an average career total of 7,280 notes created. Additionally, each resident spent 4.57 weeks on order entry, with 46,347 orders entered during training. While the number of charts opened increased after first year of residency, average time spent on each activity per patient decreased.

CONCLUSIONS

This unique study quantifies the total time an EM resident spends on the EHR during a 3-year residency. Use of the EHR accounted for over 7.5 work months or nearly 21% of their training. Residents spend a substantial portion of their training interacting with the EHR and workflow improvements to reduce EHR time are critical for maximizing training time.

The Impact of Electronic Health Record-Based Simulation During Intern Boot Camp: Interventional Study

BACKGROUND

Accurate data retrieval is an essential part of patient care in the intensive care unit (ICU). The electronic health record (EHR) is the primary method for data storage and data review. We previously reported that residents participating in EHR-based simulations have varied and nonstandard approaches to finding data in the ICU, with subsequent errors in recognizing patient safety issues. We hypothesized that a novel EHR simulation-based training exercise would decrease EHR use variability among intervention interns, irrespective of prior EHR experience.

OBJECTIVE

This study aims to understand the impact of a novel, short, high-fidelity, simulation-based EHR learning activity on the intern data gathering workflow and satisfaction.

METHODS

A total of 72 internal medicine interns across the 2018 and 2019 academic years underwent a dedicated EHR training session as part of a week-long boot camp early in their training. We collected data on previous EHR and ICU experience for all subjects. Training consisted of 1 hour of guided review of a high-fidelity, simulated ICU patient chart focusing on best navigation practices for data retrieval. Specifically, the activity focused on using high- and low-yield data visualization screens determined by expert consensus. The intervention group interns then had 20 minutes to review a new simulated patient chart before the group review. EHR screen navigation was captured using screen recording software and compared with data from existing ICU residents performing the same task on the same medical charts (N=62). Learners were surveyed immediately and 6 months after the activity to assess satisfaction and preferred EHR screen use.

RESULTS

Participants found the activity useful and enjoyable immediately and after 6 months. Intervention interns used more individual screens than reference residents (18 vs 20; P=.008), but the total number of screens used was the same (35 vs 38; P=.30). Significantly more intervention interns used the 10 most common screens (73% vs 45%; P=.001). Intervention interns used high-yield screens more often and low-yield screens less often than the reference residents, which are persistent on self-report 6 months later.

CONCLUSIONS

A short, high-fidelity, simulation-based learning activity focused on provider-specific data gathering was found to be enjoyable and to modify navigation patterns persistently. This suggests that workflow-specific simulation-based EHR training throughout training is of educational benefit to residents.