1
|
Kwan B, Engel J, Steele B, Oyama L, Longhurst CA, El–Kareh R, Daniel M, Goldberg C, Clay B. An Automated System for Physician Trainee Procedure Logging via Electronic Health Records. JAMA Netw Open 2024; 7:e2352370. [PMID: 38265802 PMCID: PMC10809018 DOI: 10.1001/jamanetworkopen.2023.52370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Accepted: 11/30/2023] [Indexed: 01/25/2024] Open
Abstract
Importance Procedural proficiency is a core competency for graduate medical education; however, procedural reporting often relies on manual workflows that are duplicative and generate data whose validity and accuracy are difficult to assess. Failure to accurately gather these data can impede learner progression, delay procedures, and negatively impact patient safety. Objective To examine accuracy and procedure logging completeness of a system that extracts procedural data from an electronic health record system and uploads these data securely to an application used by many residency programs for accreditation. Design, Setting, and Participants This quality improvement study of all emergency medicine resident physicians at University of California, San Diego Health was performed from May 23, 2023, to June 25, 2023. Exposures Automated system for procedure data extraction and upload to a residency management software application. Main Outcomes and Measures The number of procedures captured by the automated system when running silently compared with manually logged procedures in the same timeframe, as well as accuracy of the data upload. Results Forty-seven residents participated in the initial silent assessment of the extraction component of the system. During a 1-year period (May 23, 2022, to May 7, 2023), 4291 procedures were manually logged by residents, compared with 7617 procedures captured by the automated system during the same period, representing a 78% increase. During assessment of the upload component of the system (May 8, 2023, to June 25, 2023), a total of 1353 procedures and patient encounters were evaluated, with the system operating with a sensitivity of 97.4%, specificity of 100%, and overall accuracy of 99.5%. Conclusions and Relevance In this quality improvement study of emergency medicine resident physicians, an automated system demonstrated that reliance on self-reported procedure logging resulted in significant procedural underreporting compared with the use of data obtained at the point of performance. Additionally, this system afforded a degree of reliability and validity heretofore absent from the usual after-the-fact procedure logging workflows while using a novel application programming interface-based approach. To our knowledge, this system constitutes the first generalizable implementation of an automated solution to a problem that has existed in graduate medical education for decades.
Collapse
Affiliation(s)
- Brian Kwan
- Department of Emergency Medicine, University of California, San Diego, School of Medicine, San Diego
- Department of Biomedical Informatics, University of California, San Diego Health, San Diego
| | - Jeffery Engel
- Department of Information Services, University of California, San Diego Health, San Diego
| | - Brian Steele
- Office of Graduate Medical Education, University of California, San Diego Health, San Diego
| | - Leslie Oyama
- Department of Emergency Medicine, University of California, San Diego, School of Medicine, San Diego
| | - Christopher A. Longhurst
- Office of the Chief Medical Officer and Chief Digital Officer, University of California, San Diego Health, San Diego
- Department of Pediatrics, University of California, San Diego, School of Medicine, San Diego
| | - Robert El–Kareh
- Division of Hospital Medicine, Department of Medicine, University of California, San Diego School of Medicine, San Diego
- Office of the Associate Chief Medical Officer for Transformation and Learning, University of California, San Diego Health, San Diego
| | - Michelle Daniel
- Department of Emergency Medicine, University of California, San Diego, School of Medicine, San Diego
- Office of the Vice Dean for Medical Education, University of California, San Diego, School of Medicine, San Diego
| | - Charles Goldberg
- Office of Graduate Medical Education, University of California, San Diego Health, San Diego
- Division of Hospital Medicine, Department of Medicine, University of California, San Diego School of Medicine, San Diego
- Office of the Associate Dean for Graduate Medical Education, University of California, San Diego, School of Medicine, San Diego
| | - Brian Clay
- Division of Hospital Medicine, Department of Medicine, University of California, San Diego School of Medicine, San Diego
- Office of the Associate Chief Medical Officer, University of California, San Diego Health, San Diego
| |
Collapse
|
2
|
Lees AF, Beni C, Lee A, Wedgeworth P, Dzara K, Joyner B, Tarczy-Hornoch P, Leu M. Uses of Electronic Health Record Data to Measure the Clinical Learning Environment of Graduate Medical Education Trainees: A Systematic Review. ACADEMIC MEDICINE : JOURNAL OF THE ASSOCIATION OF AMERICAN MEDICAL COLLEGES 2023; 98:1326-1336. [PMID: 37267042 PMCID: PMC10615720 DOI: 10.1097/acm.0000000000005288] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
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.
Collapse
Affiliation(s)
- A Fischer Lees
- A. Fischer Lees is a clinical informatics fellow, Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, Washington
| | - Catherine Beni
- C. Beni is a general surgery resident, Department of Surgery, University of Washington School of Medicine, Seattle, Washington
| | - Albert Lee
- A. Lee is a clinical informatics fellow, Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, Washington
| | - Patrick Wedgeworth
- P. Wedgeworth is a clinical informatics fellow, Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, Washington
| | - Kristina Dzara
- K. Dzara is assistant dean for educator development, director, Center for Learning and Innovation in Medical Education, and associate professor of medical education, Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, Washington
| | - Byron Joyner
- B. Joyner is vice dean for graduate medical education and a designated institutional official, Graduate Medical Education, University of Washington School of Medicine, Seattle, Washington
| | - Peter Tarczy-Hornoch
- P. Tarczy-Hornoch is professor and chair, Department of Biomedical Informatics and Medical Education, and professor, Department of Pediatrics (Neonatology), University of Washington School of Medicine, and adjunct professor, Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington
| | - Michael Leu
- M. Leu is professor and director, Clinical Informatics Fellowship, Department of Biomedical Informatics and Medical Education, and professor, Department of Pediatrics, University of Washington School of Medicine, Seattle, Washington
| |
Collapse
|
3
|
Vennemeyer S, Kinnear B, Gao A, Zhu S, Nattam A, Knopp MI, Warm E, Wu DT. User-Centered Evaluation and Design Recommendations for an Internal Medicine Resident Competency Assessment Dashboard. Appl Clin Inform 2023; 14:996-1007. [PMID: 38122817 PMCID: PMC10733060 DOI: 10.1055/s-0043-1777103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Accepted: 10/25/2023] [Indexed: 12/23/2023] Open
Abstract
OBJECTIVES Clinical Competency Committee (CCC) members employ varied approaches to the review process. This makes the design of a competency assessment dashboard that fits the needs of all members difficult. This work details a user-centered evaluation of a dashboard currently utilized by the Internal Medicine Clinical Competency Committee (IM CCC) at the University of Cincinnati College of Medicine and generated design recommendations. METHODS Eleven members of the IM CCC participated in semistructured interviews with the research team. These interviews were recorded and transcribed for analysis. The three design research methods used in this study included process mapping (workflow diagrams), affinity diagramming, and a ranking experiment. RESULTS Through affinity diagramming, the research team identified and organized opportunities for improvement about the current system expressed by study participants. These areas include a time-consuming preprocessing step, lack of integration of data from multiple sources, and different workflows for each step in the review process. Finally, the research team categorized nine dashboard components based on rankings provided by the participants. CONCLUSION We successfully conducted user-centered evaluation of an IM CCC dashboard and generated four recommendations. Programs should integrate quantitative and qualitative feedback, create multiple views to display these data based on user roles, work with designers to create a usable, interpretable dashboard, and develop a strong informatics pipeline to manage the system. To our knowledge, this type of user-centered evaluation has rarely been attempted in the medical education domain. Therefore, this study provides best practices for other residency programs to evaluate current competency assessment tools and to develop new ones.
Collapse
Affiliation(s)
- Scott Vennemeyer
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Ohio, United States
| | - Benjamin Kinnear
- Department of Pediatrics, College of Medicine, University of Cincinnati, Ohio, United States
- Department of Internal Medicine, College of Medicine, University of Cincinnati, Ohio, United States
| | - Andy Gao
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Ohio, United States
- Medical Sciences Baccalaureate Program, College of Medicine, University of Cincinnati, Ohio, United States
| | - Siyi Zhu
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Ohio, United States
- School of Design, College of Design, Architecture, Art, and Planning (DAAP), University of Cincinnati, Ohio, United States
| | - Anunita Nattam
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Ohio, United States
- Medical Sciences Baccalaureate Program, College of Medicine, University of Cincinnati, Ohio, United States
| | - Michelle I. Knopp
- Department of Internal Medicine, College of Medicine, University of Cincinnati, Ohio, United States
- Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Department of Pediatrics, College of Medicine, University of Cincinnati, Ohio, United States
| | - Eric Warm
- Department of Internal Medicine, College of Medicine, University of Cincinnati, Ohio, United States
| | - Danny T.Y. Wu
- Department of Biomedical Informatics, College of Medicine, University of Cincinnati, Ohio, United States
- Department of Pediatrics, College of Medicine, University of Cincinnati, Ohio, United States
- Medical Sciences Baccalaureate Program, College of Medicine, University of Cincinnati, Ohio, United States
- School of Design, College of Design, Architecture, Art, and Planning (DAAP), University of Cincinnati, Ohio, United States
| |
Collapse
|
4
|
Smirnova A, Chahine S, Milani C, Schuh A, Sebok-Syer SS, Swartz JL, Wilhite JA, Kalet A, Durning SJ, Lombarts KM, van der Vleuten CP, Schumacher DJ. Using Resident-Sensitive Quality Measures Derived From Electronic Health Record Data to Assess Residents' Performance in Pediatric Emergency Medicine. ACADEMIC MEDICINE : JOURNAL OF THE ASSOCIATION OF AMERICAN MEDICAL COLLEGES 2023; 98:367-375. [PMID: 36351056 PMCID: PMC9944759 DOI: 10.1097/acm.0000000000005084] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
PURPOSE Traditional quality metrics do not adequately represent the clinical work done by residents and, thus, cannot be used to link residency training to health care quality. This study aimed to determine whether electronic health record (EHR) data can be used to meaningfully assess residents' clinical performance in pediatric emergency medicine using resident-sensitive quality measures (RSQMs). METHOD EHR data for asthma and bronchiolitis RSQMs from Cincinnati Children's Hospital Medical Center, a quaternary children's hospital, between July 1, 2017, and June 30, 2019, were analyzed by ranking residents based on composite scores calculated using raw, unadjusted, and case-mix adjusted latent score models, with lower percentiles indicating a lower quality of care and performance. Reliability and associations between the scores produced by the 3 scoring models were compared. Resident and patient characteristics associated with performance in the highest and lowest tertiles and changes in residents' rank after case-mix adjustments were also identified. RESULTS 274 residents and 1,891 individual encounters of bronchiolitis patients aged 0-1 as well as 270 residents and 1,752 individual encounters of asthmatic patients aged 2-21 were included in the analysis. The minimum reliability requirement to create a composite score was met for asthma data (α = 0.77), but not bronchiolitis (α = 0.17). The asthma composite scores showed high correlations ( r = 0.90-0.99) between raw, latent, and adjusted composite scores. After case-mix adjustments, residents' absolute percentile rank shifted on average 10 percentiles. Residents who dropped by 10 or more percentiles were likely to be more junior, saw fewer patients, cared for less acute and younger patients, or had patients with a longer emergency department stay. CONCLUSIONS For some clinical areas, it is possible to use EHR data, adjusted for patient complexity, to meaningfully assess residents' clinical performance and identify opportunities for quality improvement.
Collapse
Affiliation(s)
- Alina Smirnova
- A. Smirnova is clinical assistant professor, Department of Family Medicine, University of Calgary, Calgary, Alberta, Canada, and adjunct assistant professor, Kern Institute for the Transformation of Medical Education, Medical College of Wisconsin, Milwaukee, Wisconsin; ORCID: https://orcid.org/0000-0003-4491-3007
| | - Saad Chahine
- S. Chahine is associate professor of measurement and assessment, Faculty of Education, Queen’s University, Kingston, Ontario, Canada; ORCID: https://orcid.org/0000-0003-0488-773X
| | - Christina Milani
- C. Milani is clinical research assistant, Bruyère Research Institute, Ottawa, Ontario, Canada
| | - Abigail Schuh
- A. Schuh is associate professor of pediatrics, Division of Emergency Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin; ORCID: http://orcid.org/0000-0002-6422-2361
| | - Stefanie S. Sebok-Syer
- S.S. Sebok-Syer is assistant professor, Department of Emergency Medicine, Stanford University School of Medicine, Palo Alto, California; ORCID: http://orcid.org/0000-0002-3572-5971
| | - Jordan L. Swartz
- J.L. Swartz is clinical associate professor, Ronald O. Perelman Department of Emergency Medicine, NYU Grossman School of Medicine, and director of clinical informatics, Department of Emergency Medicine, NYU Langone Health, New York, New York
| | - Jeffrey A. Wilhite
- J.A. Wilhite is senior research coordinator, Department of Medicine, NYU Langone Health, New York, New York; ORCID: https://orcid.org/0000-0003-4096-8473
| | - Adina Kalet
- A. Kalet is professor and Steven and Shelagh Roell Chair, Robert D. and Patricia P. Kern Institute for the Transformation of Medical Education, Medical College of Wisconsin, Milwaukee, Wisconsin; ORCID: http://orcid.org/0000-0003-4855-0223
| | - Steven J. Durning
- S.J. Durning is professor and vice chair, Department of Medicine, and director, Center for Health Professions Education, Uniformed Services University of the Health Sciences, Bethesda, Maryland; ORCID: http://orcid.org/0000-0001-5223-1597
| | - Kiki M.J.M.H. Lombarts
- K.M.J.M.H. Lombarts is professor of professional performance, Department of Medical Psychology, Amsterdam University Medical Centers, University of Amsterdam, and Amsterdam Public Health research institute, Amsterdam, The Netherlands; ORCID: https://orcid.org/0000-0001-6167-0620
| | - Cees P.M. van der Vleuten
- C.P.M. van der Vleuten is professor of education, Department of Educational Development and Research. Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands; ORCID: http://orcid.org/0000-0001-6802-3119
| | - Daniel J. Schumacher
- D.J. Schumacher is professor of pediatrics, Division of Emergency Medicine, Cincinnati Children’s Hospital Medical Center/University of Cincinnati College of Medicine, Cincinnati, Ohio; ORCID: http://orcid.org/0000-0001-5507-8452
| |
Collapse
|
5
|
Sundaresan J, Ferrell ST, Hron JD. A Model for Work Intensity in a Pediatric Training Program. J Grad Med Educ 2022; 14:714-718. [PMID: 36591429 PMCID: PMC9765907 DOI: 10.4300/jgme-d-22-00323.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 08/23/2022] [Accepted: 10/13/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The Accreditation Council for Graduate Medical Education (ACGME) requires residency programs to monitor scheduling, work intensity, and work compression. OBJECTIVE We aimed to create a model for assessing intern work intensity by examining patient and clinical factors in our electronic health systems using multiple linear regression. METHODS We identified measurable factors that may contribute to resident work intensity within our electronic health systems. In the spring of 2021, we surveyed interns on pediatric hospital medicine rotations each weekday over 5 blocks to rank their daily work intensity on a scale from -100 (bored) to +100 (exasperated). We queried our electronic systems to identify patient care activities completed by study participants on days they were surveyed. We used multiple linear regression to identify factors that correlate with subjective scores of work intensity. RESULTS Nineteen unique interns provided 102 survey responses (28.3% response rate) during the study period. The mean work intensity score was 9.82 (SD=44.27). We identified 19 candidate variables for the regression model. The most significantly associated variables from our univariate regression model were text messages (β=0.432, P<.0009, R2=0.105), orders entered (β=0.207, P<.0002, R2=0.128), and consults ordered (β=0.268, P=.022, R2=0.053). Stepwise regression produced a reduced model (R2=0.247) including text messages (β=0.379, P=.002), patient transfers (β=-1.405, P=.15), orders entered (β=0.186, P<.001), and national patients (β=-0.873, P=.035). CONCLUSIONS Our study demonstrates that data extracted from electronic systems can be used to estimate resident work intensity.
Collapse
Affiliation(s)
- Janani Sundaresan
- Janani Sundaresan, MD, MSc, is a PGY-3 Resident, Boston Combined Residency Program, Department of Pediatrics Boston Children's Hospital and Boston Medical Center
| | - Sebastian Ty Ferrell
- Sebastian Ty Ferrell, BA, is a former Graduate Medical Education Data Analyst, Department of Medical Education, Boston Children's Hospital
| | - Jonathan D. Hron
- Jonathan D. Hron, MD, is a Pediatric Hospitalist, Division of General Pediatrics, Boston Children's Hospital, and Assistant Professor of Pediatrics, Harvard Medical School
| |
Collapse
|
6
|
Yarahuan JKW, Lo HY, Bass L, Wright J, Hess LM. Design, Usability, and Acceptability of a Needs-Based, Automated Dashboard to Provide Individualized Patient-Care Data to Pediatric Residents. Appl Clin Inform 2022; 13:380-390. [PMID: 35294985 PMCID: PMC8926457 DOI: 10.1055/s-0042-1744388] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Pediatric residency programs are required by the Accreditation Council for Graduate Medical Education to provide residents with patient-care and quality metrics to facilitate self-identification of knowledge gaps to prioritize improvement efforts. Trainees are interested in receiving this data, but this is a largely unmet need. Our objectives were to (1) design and implement an automated dashboard providing individualized data to residents, and (2) examine the usability and acceptability of the dashboard among pediatric residents. METHODS We developed a dashboard containing individualized patient-care data for pediatric residents with emphasis on needs identified by residents and residency leadership. To build the dashboard, we created a connection from a clinical data warehouse to data visualization software. We allocated patients to residents based on note authorship and created individualized reports with masked identities that preserved anonymity. After development, we conducted usability and acceptability testing with 11 resident users utilizing a mixed-methods approach. We conducted interviews and anonymous surveys which evaluated technical features of the application, ease of use, as well as users' attitudes toward using the dashboard. Categories and subcategories from usability interviews were identified using a content analysis approach. RESULTS Our dashboard provides individualized metrics including diagnosis exposure counts, procedure counts, efficiency metrics, and quality metrics. In content analysis of the usability testing interviews, the most frequently mentioned use of the dashboard was to aid a resident's self-directed learning. Residents had few concerns about the dashboard overall. Surveyed residents found the dashboard easy to use and expressed intention to use the dashboard in the future. CONCLUSION Automated dashboards may be a solution to the current challenge of providing trainees with individualized patient-care data. Our usability testing revealed that residents found our dashboard to be useful and that they intended to use this tool to facilitate development of self-directed learning plans.
Collapse
Affiliation(s)
- Julia K W Yarahuan
- Division of Pediatric Hospital Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts, United States
| | - Huay-Ying Lo
- Section of Pediatric Hospital Medicine, Department of Pediatrics, Baylor College of Medicine/Texas Children's Hospital, Houston, Texas, United States
| | - Lanessa Bass
- Section of Pediatric Hospital Medicine, Department of Pediatrics, Baylor College of Medicine/Texas Children's Hospital, Houston, Texas, United States
| | - Jeff Wright
- Information Services, Texas Children's Hospital, Houston, Texas, United States
| | - Lauren M Hess
- Section of Pediatric Hospital Medicine, Department of Pediatrics, Baylor College of Medicine/Texas Children's Hospital, Houston, Texas, United States
| |
Collapse
|
7
|
Goldflam K, Tsyrulnik A, Flood C, Bod J, Coughlin R, Della-Giustina D. Resident Perceptions of a Publicly Disclosed Daily Productivity Dashboard. West J Emerg Med 2022; 23:86-89. [PMID: 35060869 PMCID: PMC8782132 DOI: 10.5811/westjem.2021.10.53874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Accepted: 10/29/2021] [Indexed: 11/19/2022] Open
Abstract
Introduction Following resident requests, we created a public metrics dashboard to inform residents of their daily productivity. Our goal was to iteratively improve the dashboard based on resident feedback and to measure the impact of reviewing aggregate data on self-perceived productivity. Methods A 10-question anonymous survey was completed by our postgraduate year 1–3 residents. Residents answered questions on the dashboard and rated their own productivity before and after reviewing aggregate peer-comparison data. Using the Wilcoxon signed-rank test we calculated summary statistics for survey questions and compared distributions of pre- and post-test, self-rated productivity scores. Results All 43 eligible residents completed the survey (response rate 100%). Thirteen (30%) residents “rarely” or “never” reviewed the dashboard. No respondents felt the dashboard measured their productivity or quality of care “extremely accurately” or “very accurately.” Seven (16%) residents felt “very” or “extremely pressured” to change their practice patterns based on the metrics provided, and 28 (65%) would have preferred private over public feedback. Fifteen residents (35%) changed their self-perceived rank after viewing peer-comparison data, although not significantly in a particular direction (z = 0.71, P = 0.48). Conclusion Residents did not view the presented metrics as reflective of their productivity or quality of care. Viewing the dashboard did not lead to statistically significant changes in resident self-perception of productivity. This finding highlights the need for expanding the resident conversation and education on metrics, given their frequent inclusion in attending physician workforce payment and incentive models.
Collapse
Affiliation(s)
- Katja Goldflam
- Yale School of Medicine, Department of Emergency Medicine, New Haven, Connecticut
| | - Alina Tsyrulnik
- Yale School of Medicine, Department of Emergency Medicine, New Haven, Connecticut
| | - Colin Flood
- Yale School of Medicine, Department of Emergency Medicine, New Haven, Connecticut
| | - Jessica Bod
- Yale School of Medicine, Department of Emergency Medicine, New Haven, Connecticut
| | - Ryan Coughlin
- Yale School of Medicine, Department of Emergency Medicine, New Haven, Connecticut
| | - David Della-Giustina
- Yale School of Medicine, Department of Emergency Medicine, New Haven, Connecticut
| |
Collapse
|
8
|
Hong P, Herigon JC, Uptegraft C, Samuel B, Brown DL, Bickel J, Hron JD. Use of clinical data to augment healthcare worker contact tracing during the COVID-19 pandemic. J Am Med Inform Assoc 2021; 29:142-148. [PMID: 34623426 DOI: 10.1093/jamia/ocab231] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/28/2021] [Accepted: 10/06/2021] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE This work examined the secondary use of clinical data from the electronic health record (EHR) for screening our healthcare worker (HCW) population for potential exposures to patients with coronavirus disease 2019. MATERIALS AND METHODS We conducted a cross-sectional study at a free-standing, quaternary care pediatric hospital comparing first-degree, patient-HCW pairs identified by the hospital's COVID-19 contact tracing team (CTT) to those identified using EHR clinical event data (EHR Report). The primary outcome was the number of patient-HCW pairs detected by each process. RESULTS Among 233 patients with COVID-19, our EHR Report identified 4,116 patient-HCW pairs, including 2,365 (30.0%) of the 7,890 pairs detected by the CTT. The EHR Report also revealed 1,751 pairs not identified by the CTT. The highest number of patient-HCW pairs per patient was detected in the inpatient care venue. Nurses comprised the most frequently identified HCW role overall. CONCLUSION Automated methods to screen HCWs for potential exposure to patients with COVID-19 using clinical event data from the EHR are likely to improve epidemiologic surveillance by contact tracing programs and represent a viable and readily available strategy which should be considered by other institutions.
Collapse
Affiliation(s)
- Peter Hong
- Division of General Pediatrics, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts, USA.,Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| | - Joshua C Herigon
- Division of Pediatric Infectious Diseases, Department of Pediatrics, Children's Mercy Kansas City, Kansas City, Missouri, USA.,Department of Pediatrics, University of Missouri-Kansas City School of Medicine, USA, Kansas City, Missouri
| | - Colby Uptegraft
- Health Informatics Branch, Defense Health Agency, Falls Church, Virginia, USA
| | - Bassem Samuel
- Information Services Department, Boston Children's Hospital, Boston, Massachusetts, USA
| | - D Levin Brown
- Information Services Department, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Jonathan Bickel
- Division of General Pediatrics, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts, USA.,Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA.,Information Services Department, Boston Children's Hospital, Boston, Massachusetts, USA.,Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Jonathan D Hron
- Division of General Pediatrics, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts, USA.,Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
9
|
Sebok‐Syer SS, Shepherd L, McConnell A, Dukelow AM, Sedran R, Lingard L. "EMERGing" Electronic Health Record Data Metrics: Insights and Implications for Assessing Residents' Clinical Performance in Emergency Medicine. AEM EDUCATION AND TRAINING 2021; 5:e10501. [PMID: 33898906 PMCID: PMC8052996 DOI: 10.1002/aet2.10501] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 07/02/2020] [Accepted: 07/07/2020] [Indexed: 05/30/2023]
Abstract
OBJECTIVES Competency-based medical education requires that residents are provided with frequent opportunities to demonstrate competence as well as receive effective feedback about their clinical performance. To meet this goal, we investigated how data collected by the electronic health record (EHR) might be used to assess emergency medicine (EM) residents' independent and interdependent clinical performance and how such information could be represented in an EM resident report card. METHODS Following constructivist grounded theory methodology, individual semistructured interviews were conducted in 2017 with 10 EM faculty and 11 EM residents across all 5 postgraduate years. In addition to open-ended questions, participants were presented with an emerging list of EM practice metrics and asked to comment on how valuable each would be in assessing resident performance. Additionally, we asked participants the extent to which each metric captured independent or interdependent performance. Data collection and analysis were iterative; analysis employed constant comparative inductive methods. RESULTS Participants refined and eliminated metrics as well as added new metrics specific to the assessment of EM residents (e.g., time between signup and first orders). These clinical practice metrics based on data from our EHR database were organized along a spectrum of independent/interdependent performance. We conclude with discussions about the relationship among these metrics, issues in interpretation, and implications of using EHR for assessment purposes. CONCLUSIONS Our findings document a systematic approach for developing EM resident assessments, based on EHR data, which incorporate the perspectives of both clinical faculty and residents. Our work has important implications for capturing residents' contributions to clinical performances and distinguishing between independent and interdependent metrics in collaborative workplace-based settings.
Collapse
Affiliation(s)
- Stefanie S. Sebok‐Syer
- Department of Emergency Medicine at Stanford University School of MedicineStanford UniversityPalo AltoCAUSA
| | - Lisa Shepherd
- Division of Emergency Medicine at Schulich School of Medicine and DentistryWestern UniversityLondonOntarioCanada
| | - Allison McConnell
- Division of Emergency Medicine at Schulich School of Medicine and DentistryWestern UniversityLondonOntarioCanada
| | - Adam M. Dukelow
- Division of Emergency Medicine at Schulich School of Medicine and DentistryWestern UniversityLondonOntarioCanada
| | - Robert Sedran
- Division of Emergency Medicine at Schulich School of Medicine and DentistryWestern UniversityLondonOntarioCanada
| | - Lorelei Lingard
- Department of Medicine and Faculty of Education and the Centre for Education, Research, and Innovation at Schulich School of Medicine and DentistryWestern UniversityLondonOntarioCanada
| |
Collapse
|
10
|
Loftus KV, Schumacher DJ, Mittiga MR, McDonough E, Sobolewski B. A Descriptive Analysis of the Cumulative Experiences of Emergency Medicine Residents in the Pediatric Emergency Department. AEM EDUCATION AND TRAINING 2021; 5:e10462. [PMID: 33796805 PMCID: PMC7995924 DOI: 10.1002/aet2.10462] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 04/25/2020] [Accepted: 04/27/2020] [Indexed: 06/12/2023]
Abstract
OBJECTIVES Most children seeking emergency care are evaluated in general emergency departments (EDs). The cumulative pediatric clinical experiences of emergency medicine (EM) residents are largely unknown. This study examined EM resident pediatric clinical experience through the lens of the Accreditation Council for Graduate Medical Education requirements and the Model of the Clinical Practice of Emergency Medicine. METHODS Retrospective, observational study of the cumulative clinical experience of two classes of EM residents from a 4-year training program at two pediatric EDs of a quaternary care pediatric center. A database of resident patient encounters was generated from the electronic medical record. Experiences classified included: diagnosis categories per the Model of the Clinical Practice of Emergency Medicine, procedures, and resuscitations. Results were stratified by age, acuity, and disposition. RESULTS Twenty-five EM residents evaluated 17,642 patients (median = 723). Most patients (73.5%) were emergent acuity (Emergency Severity Index triage level 2 or 3 or non-intensive care admission); 2% were critical. Residents participated in 598 (median = 22) medical resuscitations and 483 (median = 19) trauma resuscitations. Minor procedures (e.g., laceration repair) were commonly performed; critical procedures (e.g., intubation) were rare. Exposure to neonates was infrequent and pediatric deaths were rare. Abdominal pain (5.7%), asthma exacerbation (4.6%), and fever (3.8%) were the most common diagnoses. CONCLUSIONS Emergency medicine residents encountered a wide array of pediatric diagnoses throughout training and performed a substantial number of common pediatric procedures. Exposure to critical acuity and procedures, neonatal pathology, and certain pediatric-specific diagnoses, such as congenital heart disease, was limited despite training in a large, quaternary care children's hospital. Curriculum development and collaboration should focus on these areas.
Collapse
Affiliation(s)
- Kirsten V. Loftus
- From theDepartment of PediatricsNorthwestern University Feinberg School of Medicine & Division of Pediatric Emergency MedicineAnn and Robert H. Lurie Children’s Hospital of ChicagoChicagoILUSA
- theDepartment of PediatricsUniversity of Cincinnati College of Medicine & Division of Emergency MedicineCincinnati Children’s Hospital Medical CenterCincinnatiOHUSA
| | - Daniel J. Schumacher
- theDepartment of PediatricsUniversity of Cincinnati College of Medicine & Division of Emergency MedicineCincinnati Children’s Hospital Medical CenterCincinnatiOHUSA
| | - Matthew R. Mittiga
- and theDepartment of PediatricsUniversity of Colorado School of Medicine & Section of Emergency MedicineChildren's Hospital ColoradoAuroraCOUSA
| | - Erin McDonough
- and theDepartment of Emergency MedicineUniversity of Cincinnati College of MedicineCincinnatiOHUSA
| | - Brad Sobolewski
- theDepartment of PediatricsUniversity of Cincinnati College of Medicine & Division of Emergency MedicineCincinnati Children’s Hospital Medical CenterCincinnatiOHUSA
| |
Collapse
|
11
|
Mai MV, Orenstein EW, Manning JD, Luberti AA, Dziorny AC. Attributing Patients to Pediatric Residents Using Electronic Health Record Features Augmented with Audit Logs. Appl Clin Inform 2020; 11:442-451. [PMID: 32583389 DOI: 10.1055/s-0040-1713133] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVE Patient attribution, or the process of attributing patient-level metrics to specific providers, attempts to capture real-life provider-patient interactions (PPI). Attribution holds wide-ranging importance, particularly for outcomes in graduate medical education, but remains a challenge. We developed and validated an algorithm using EHR data to identify pediatric resident PPIs (rPPIs). METHODS We prospectively surveyed residents in three care settings to collect self-reported rPPIs. Participants were surveyed at the end of primary care clinic, emergency department (ED), and inpatient shifts, shown a patient census list, asked to mark the patients with whom they interacted, and encouraged to provide a short rationale behind the marked interaction. We extracted routine EHR data elements, including audit logs, note contribution, order placement, care team assignment, and chart closure, and applied a logistic regression classifier to the data to predict rPPIs in each care setting. We also performed a comment analysis of the resident-reported rationales in the inpatient care setting to explore perceived patient interactions in a complicated workflow. RESULTS We surveyed 81 residents over 111 shifts and identified 579 patient interactions. Among EHR extracted data, time-in-chart was the best predictor in all three care settings (primary care clinic: odds ratio [OR] = 19.36, 95% confidence interval [CI]: 4.19-278.56; ED: OR = 19.06, 95% CI: 9.53-41.65' inpatient: OR = 2.95, 95% CI: 2.23-3.97). Primary care clinic and ED specific models had c-statistic values > 0.98, while the inpatient-specific model had greater variability (c-statistic = 0.89). Of 366 inpatient rPPIs, residents provided rationales for 90.1%, which were focused on direct involvement in a patient's admission or transfer, or care as the front-line ordering clinician (55.6%). CONCLUSION Classification models based on routinely collected EHR data predict resident-defined rPPIs across care settings. While specific to pediatric residents in this study, the approach may be generalizable to other provider populations and scenarios in which accurate patient attribution is desirable.
Collapse
Affiliation(s)
- Mark V Mai
- Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States.,Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| | - Evan W Orenstein
- Department of Pediatrics, Children's Healthcare of Atlanta, Atlanta, Georgia, United States
| | - John D Manning
- Department of Emergency Medicine, Atrium Health's Carolinas Medical Center, Charlotte, North Carolina, United States
| | - Anthony A Luberti
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States.,Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| | - Adam C Dziorny
- Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States.,Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| |
Collapse
|
12
|
Rosenbluth G. Trainee and Program Director Perspectives on Meaningful Patient Attribution and Clinical Outcomes Data. J Grad Med Educ 2020; 12:295-302. [PMID: 32595849 PMCID: PMC7301928 DOI: 10.4300/jgme-d-19-00730.1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2019] [Revised: 02/24/2020] [Accepted: 02/29/2020] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND The Accreditation Council for Graduate Medical Education specifies that trainees must receive clinical outcomes and quality benchmark data at specific levels related to institutional patient populations. Program directors (PDs) are challenged to identify meaningful data and provide them in formats acceptable to trainees. OBJECTIVE We sought to understand what types of patients, data/metrics, and data delivery systems trainees and PDs prefer for supplying trainees with clinical outcomes data. METHODS Trainees (n = 21) and PDs (n = 12) from multiple specialties participated in focus groups during academic year 2017-2018. They described key themes for providing clinical outcomes data to trainees. RESULTS Trainees and PDs differed in how they identified patients for clinical outcomes data for trainees. Trainees were interested in encounters where they felt a sense of responsibility or had autonomy/independent decision-making opportunities, continuity, or learned something new; PDs used broader criteria including all patients cared for by their trainees. Both groups thought trainees should be given trainee-level metrics and consistently highlighted the importance of comparison to peers and/or benchmarks. Both groups found value in "push" and "pull" data systems, although trainees wanted both, while PDs wanted one or the other. Both groups agreed that trainees should review data with specific faculty. Trainees expressed concern about being judged based on their patients' clinical outcomes. CONCLUSIONS Trainee and PD perspectives on which patients they would like outcomes data for differed, but they overlapped for types of metrics, formats, and review processes for the data.
Collapse
|
13
|
Epstein JA, Noronha C, Berkenblit G. Smarter Screen Time: Integrating Clinical Dashboards Into Graduate Medical Education. J Grad Med Educ 2020; 12:19-24. [PMID: 32064058 PMCID: PMC7012529 DOI: 10.4300/jgme-d-19-00584.1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
|
14
|
Schumacher DJ, Wu DTY, Meganathan K, Li L, Kinnear B, Sall DR, Holmboe E, Carraccio C, van der Vleuten C, Busari J, Kelleher M, Schauer D, Warm E. A Feasibility Study to Attribute Patients to Primary Interns on Inpatient Ward Teams Using Electronic Health Record Data. ACADEMIC MEDICINE : JOURNAL OF THE ASSOCIATION OF AMERICAN MEDICAL COLLEGES 2019; 94:1376-1383. [PMID: 31460936 DOI: 10.1097/acm.0000000000002748] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
PURPOSE To inform graduate medical education (GME) outcomes at the individual resident level, this study sought a method for attributing care for individual patients to individual interns based on "footprints" in the electronic health record (EHR). METHOD Primary interns caring for patients on an internal medicine inpatient service were recorded daily by five attending physicians of record at University of Cincinnati Medical Center in August 2017 and January 2018. These records were considered gold standard identification of primary interns. The following EHR variables were explored to determine representation of primary intern involvement in care: postgraduate year, progress note author, discharge summary author, physician order placement, and logging clicks in the patient record. These variables were turned into quantitative attributes (e.g., progress note author: yes/no), and informative attributes were selected and modeled using a decision tree algorithm. RESULTS A total of 1,511 access records were generated; 116 were marked as having a primary intern assigned. All variables except discharge summary author displayed at least some level of importance in the models. The best model achieved 78.95% sensitivity, 97.61% specificity, and an area under the receiver-operator curve of approximately 91%. CONCLUSIONS This study successfully predicted primary interns caring for patients on inpatient teams using EHR data with excellent model performance. This provides a foundation for attributing patients to primary interns for the purposes of determining patient diagnoses and complexity the interns see as well as supporting continuous quality improvement efforts in GME.
Collapse
Affiliation(s)
- Daniel J Schumacher
- D.J. Schumacher is associate professor of pediatrics, Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, Ohio. D.T.Y. Wu is assistant professor of biomedical informatics and pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio. K. Meganathan is senior clinical data analyst, Center for Health Informatics, University of Cincinnati College of Medicine, Cincinnati, Ohio. L. Li is research associate, Center for Health Informatics, University of Cincinnati College of Medicine, Cincinnati, Ohio. B. Kinnear is assistant professor of pediatrics and internal medicine, Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, Ohio. D.R. Sall is assistant professor of internal medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio. E. Holmboe is senior vice president for milestones development and evaluation, Accreditation Council for Graduate Medical Education, Chicago, Illinois. C. Carraccio is vice president of competency-based assessment, American Board of Pediatrics, Chapel Hill, North Carolina. C. van der Vleuten is professor of education, Department of Educational Development and Research, Faculty of Health, Medicine, and Life Sciences, and scientific director, School of Health Professions Education (SHE), Maastricht University, Maastricht, The Netherlands. J. Busari is consultant pediatrician and associate professor of medical education, Maastricht University, Maastricht, The Netherlands. M. Kelleher is assistant professor of pediatrics and internal medicine, Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, Ohio. D. Schauer is associate professor of internal medicine, University of Cincinnati College of Medicine, Cincinnati, Ohio. E. Warm is professor of medicine and internal medicine program director, University of Cincinnati College of Medicine, Cincinnati, Ohio
| | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
15
|
Cox ML, Farjat AE, Risoli TJ, Peskoe S, Goldstein BA, Turner DA, Migaly J. Documenting or Operating: Where Is Time Spent in General Surgery Residency? JOURNAL OF SURGICAL EDUCATION 2018; 75:e97-e106. [PMID: 30522828 PMCID: PMC10765321 DOI: 10.1016/j.jsurg.2018.10.010] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2018] [Revised: 07/25/2018] [Accepted: 10/11/2018] [Indexed: 06/09/2023]
Abstract
OBJECTIVE The utilization of electronic health records (EHR) has become essential in the daily activities of physicians for documentation and as an information source. However, the amount of time spent by residents utilizing the EHR has not been thoroughly evaluated, particularly within surgical specialties. This study aims to analyze EHR usage by general surgery residents and to assess the association between this use and case volume at a single academic institution. DESIGN For general surgery residents in clinical years (CY) 1-5, de-identified login and logout time data between September 2016 and June 2017 were retrospectively extracted from the Epic EHR (Verona, WI). A binary time series was created for each resident to indicate and track over time whether he or she was utilizing the EHR system. Comparisons between categorical variables were performed with Fisher's exact test. Continuous variables were compared using Wilcoxon rank sum test. Longitudinal linear mixed-effects models were used to assess the EHR usage among the surgery residents. The association between EHR time and the number of operative cases logged was evaluated with Pearson's correlation coefficient. SETTING This study was performed by the Department of Surgery in conjunction with the Office of Graduate Medical Education at Duke University Health System. PARTICIPANTS All active general surgery residents during the 2016-2017 academic year. RESULTS Thirty-six general surgery residents (28 males, 8 females) spent a median of 2.4 hours per day and 23.7 hours per week using the EHR. CY2 had the highest median usage per week (28.9 hours), while CY3 had the lowest (16.7 hours) but no significant difference based on EHR usage was found among the analyzed CYs (p = 0.164). Residents spent significantly more time logged into the EHR during the week compared to weekends and during the day compared to nights (all p < 0.001). For the residency program as a whole, a median of 151.5 total work hours per day was dedicated to documentation. On average, interns on dedicated night rotations spent 7% of their login time outside regularly scheduled duty hours while interns on dedicated day rotations spent 27%. There was no overall correlation between monthly case logs and EHR usage (r = 0.06, p = 0.30); however, CY2 had a significant negative correlation (r = -0.2, p = 0.038). CONCLUSIONS In the era of a maximum 80-hour work week, general surgery residents spend a substantial portion, at least 30%, of their time utilizing the EHR. One third of EHR usage by interns occurred outside the scheduled 12-hour shift, demonstrating the difficulties of completing paperwork as part of the scheduled work day. Additionally, the lack of correlation to case logs is likely due to an underestimation of the documentation burden associated with operating, which includes preparatory effort and operative notes. Ultimately, these quantitative EHR usage results will be correlated to burnout prior to implementing programs to improve efficiency and decrease the burden of charting.
Collapse
Affiliation(s)
- Morgan L Cox
- Department of Surgery, Duke University, Durham, North Carolina.
| | - Alfredo E Farjat
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
| | - T J Risoli
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
| | - Sarah Peskoe
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
| | - Benjamin A Goldstein
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
| | - David A Turner
- Graduate Medical Education, Duke University Hospital and Health System, Durham, North Carolina
| | - John Migaly
- Department of Surgery, Duke University, Durham, North Carolina
| |
Collapse
|
16
|
Li J, Roosevelt G, McCabe K, Preotle J, Pereira F, Takayesu JK, Monuteaux M, Bachur RG. Pediatric Case Exposure During Emergency Medicine Residency. AEM EDUCATION AND TRAINING 2018; 2:317-327. [PMID: 30386842 PMCID: PMC6194046 DOI: 10.1002/aet2.10130] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 08/28/2018] [Accepted: 09/04/2018] [Indexed: 05/12/2023]
Abstract
OBJECTIVE While emergency medicine (EM) physicians treat the majority of pediatric EM (PEM) patients in the United States, little is known about their PEM experience during training. The primary objective was to characterize the pediatric case exposure and compare to established EM residency training curricula among EM residents across five U.S. residency programs. METHODS We performed a multicenter medical record review of all pediatric patients (aged < 18 years) seen by the 2015 graduating resident physicians at five U.S. EM training programs. Resident-level counts of pediatric patients were measured and specific counts were classified by the 2016 Model of Clinical Practice of Emergency Medicine (MCP) and Pediatric Emergency Care Applied Research Network (PECARN) diagnostic categories. We assessed variability between residents and between programs. RESULTS A total of 36,845 children were managed by 68 residents across all programs. The median age was 6 years. The median number of patients per resident was 660 with an interquartile range of 336. The most common PECARN diagnostic categories were trauma, gastrointestinal, and respiratory disease. Thirty-two core MCP diagnoses (43% of MCP list) were not seen by at least 50% of the residents. We found statistically significant variability between programs in both PECARN diagnostic categories (p < 0.01) and MCP diagnoses (p < 0.01). CONCLUSION There is considerable variation in the number of pediatric patients and the diagnostic case volume seen by EM residents. The relationship between this case variability and competence upon graduation is unknown; further investigation is warranted to better inform program-specific curricula and to guide training requirements in EM.
Collapse
Affiliation(s)
- Joyce Li
- Division of Emergency MedicineBoston Children's HospitalBostonMA
- Department of Emergency Medicine and PediatricsHarvard Medical SchoolBostonMA
| | - Genie Roosevelt
- Denver Health Medical CenterDepartment of Emergency MedicineUniversity of Colorado School of MedicineDenverCO
| | - Kerry McCabe
- Boston Medical CenterDepartment of Emergency MedicineBoston University School of MedicineBostonMA
| | - Jane Preotle
- Hasbro Children's HospitalDepartment of Emergency MedicineAlpert Medical School of Brown UniversityProvidenceRI
| | - Faria Pereira
- Texas Children's HospitalDivision of Emergency MedicineBaylor College of MedicineHoustonTX
| | - James K. Takayesu
- Massachusetts General HospitalDivision of Emergency MedicineHarvard Medical SchoolBostonMA
| | - Michael Monuteaux
- Division of Emergency MedicineBoston Children's HospitalBostonMA
- Department of Emergency Medicine and PediatricsHarvard Medical SchoolBostonMA
| | - Richard G. Bachur
- Division of Emergency MedicineBoston Children's HospitalBostonMA
- Department of Emergency Medicine and PediatricsHarvard Medical SchoolBostonMA
| |
Collapse
|