Estimation of Surgical Resident Duty Hours and Workload in Real Time Using Electronic Health Record Data

Real-Time Reporting of Complications in Hospitalized Surgical Patients by Surgical Team Members Using a Smartphone Application

BACKGROUND

The surgical morbidity and mortality (M&M) conference is a vital part of a resident's surgical education, but methods to collect and store M&M data are often rudimentary and unreliable. The authors propose a Health Insurance Portability and Accountability Act (HIPAA)-compliant, electronic health record (EHR)-connected application and database to report and store complication data.

METHODS

The app is linked to the patient's EHR, and as a result, basic data on each surgical case-including diagnosis, surgery type, and surgeon-are automatically uploaded to the app. In addition, all data are stored in a secure SQL database-with communications between the app and the database end-to-end encrypted for HIPAA compliance. The full surgical team has access to the app, democratizing complications reporting and allowing for reporting in both the inpatient and outpatient settings. This complication information can then be automatically pulled from the app with a premade presentation for the M&M conference. The data can also be accessed by a Power BI dashboard, allowing for easy quality improvement analyses.

RESULTS

When implemented, the app improved data collection for the M&M conference while providing a database for institutional quality improvement use. The authors also identified additional utility of the app, including ensuring appropriate revenue capture. The general appearance of the app and the dashboard can be found in the article.

CONCLUSION

The app developed in this project significantly improves on more common methods for M&M conference complication reporting-transforming M&M data into a valuable resource for resident education and quality improvement.

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.

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.