Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Dec;14(6):714-718.
doi: 10.4300/JGME-D-22-00323.1.

A Model for Work Intensity in a Pediatric Training Program

Janani Sundaresan  1 , Sebastian Ty Ferrell  2 , Jonathan D Hron  3
Affiliations

A Model for Work Intensity in a Pediatric Training Program

Janani Sundaresan et al. J Grad Med Educ. 2022 Dec.

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.

PubMed Disclaimer

Conflict of interest statement

Conflict of interest: The authors declare they have no competing interests.

References

    1. Wolpaw JT. It is time to prioritize education and well-being over workforce needs in residency training. Acad Med . 2019;94(11):1640–1642. doi: 10.1097/ACM.0000000000002949. - DOI - PubMed
    1. Patel RS, Bachu R, Adikey A, Malik M, Shah M. Factors related to physician burnout and its consequences: a review. Behav Sci (Basel) . 2018;8(11):98. doi: 10.3390/bs8110098. - DOI - PMC - PubMed
    1. McHill AW, Czeisler CA, Shea SA. Resident physician extended work hours and burnout. Sleep . 2018. 41(8):zsy112. - DOI - PMC - PubMed
    1. Accreditation Council for Graduate Medical Education. ACGME Common Program Requirements Section VI with Background and Intent. Accessed October 24, 2022. https://www.acgme.org/Portals/0/PFAssets/ProgramRequirements/
    1. Fishbein D, Nambiar S, McKenzie K, et al. Objective measures of workload in healthcare: a narrative review. Int J Health Care Qual Assur . 2019;33(1):1–17. doi: 10.1108/IJHCQA-12-2018-0288. - DOI - PubMed

LinkOut - more resources