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Beyond duty hours: leveraging large-scale paging data to monitor resident workload

Monitoring and managing resident workload is a cornerstone of policy in graduate medical education, and the duty hours metric is the backbone of current regulations. While the duty hours metric measures hours worked, it does not capture differences in intensity of work completed during those hours,...

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Autores principales: Kaushal, Amit, Katznelson, Laurence, Harrington, Robert A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6733865/
https://www.ncbi.nlm.nih.gov/pubmed/31531394
http://dx.doi.org/10.1038/s41746-019-0165-2
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author Kaushal, Amit
Katznelson, Laurence
Harrington, Robert A.
author_facet Kaushal, Amit
Katznelson, Laurence
Harrington, Robert A.
author_sort Kaushal, Amit
collection PubMed
description Monitoring and managing resident workload is a cornerstone of policy in graduate medical education, and the duty hours metric is the backbone of current regulations. While the duty hours metric measures hours worked, it does not capture differences in intensity of work completed during those hours, which may independently contribute to fatigue and burnout. Few such metrics exist. Digital data streams generated during the usual course of hospital operations can serve as a novel source of insight into workload intensity by providing high-resolution, minute-by-minute data at the individual level; however, study and use of these data streams for workload monitoring has been limited to date. Paging data is one such data stream. In this work, we analyze over 500,000 pages—two full years of pages in an academic internal medicine residency program—to characterize paging patterns among housestaff. We demonstrate technical feasibility, validity, and utility of paging burden as a metric to provide insight into resident workload beyond duty hours alone, and illustrate a general framework for evaluation and incorporation of novel digital data streams into resident workload monitoring.
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spelling pubmed-67338652019-09-17 Beyond duty hours: leveraging large-scale paging data to monitor resident workload Kaushal, Amit Katznelson, Laurence Harrington, Robert A. NPJ Digit Med Brief Communication Monitoring and managing resident workload is a cornerstone of policy in graduate medical education, and the duty hours metric is the backbone of current regulations. While the duty hours metric measures hours worked, it does not capture differences in intensity of work completed during those hours, which may independently contribute to fatigue and burnout. Few such metrics exist. Digital data streams generated during the usual course of hospital operations can serve as a novel source of insight into workload intensity by providing high-resolution, minute-by-minute data at the individual level; however, study and use of these data streams for workload monitoring has been limited to date. Paging data is one such data stream. In this work, we analyze over 500,000 pages—two full years of pages in an academic internal medicine residency program—to characterize paging patterns among housestaff. We demonstrate technical feasibility, validity, and utility of paging burden as a metric to provide insight into resident workload beyond duty hours alone, and illustrate a general framework for evaluation and incorporation of novel digital data streams into resident workload monitoring. Nature Publishing Group UK 2019-09-09 /pmc/articles/PMC6733865/ /pubmed/31531394 http://dx.doi.org/10.1038/s41746-019-0165-2 Text en © This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Brief Communication
Kaushal, Amit
Katznelson, Laurence
Harrington, Robert A.
Beyond duty hours: leveraging large-scale paging data to monitor resident workload
title Beyond duty hours: leveraging large-scale paging data to monitor resident workload
title_full Beyond duty hours: leveraging large-scale paging data to monitor resident workload
title_fullStr Beyond duty hours: leveraging large-scale paging data to monitor resident workload
title_full_unstemmed Beyond duty hours: leveraging large-scale paging data to monitor resident workload
title_short Beyond duty hours: leveraging large-scale paging data to monitor resident workload
title_sort beyond duty hours: leveraging large-scale paging data to monitor resident workload
topic Brief Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6733865/
https://www.ncbi.nlm.nih.gov/pubmed/31531394
http://dx.doi.org/10.1038/s41746-019-0165-2
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