Automated and Optimized Neurosurgery Scheduling System Improves Resident Satisfaction.


Journal

Neurosurgery
ISSN: 1524-4040
Titre abrégé: Neurosurgery
Pays: United States
ID NLM: 7802914

Informations de publication

Date de publication:
08 Jan 2024
Historique:
received: 07 10 2023
accepted: 22 11 2023
medline: 8 1 2024
pubmed: 8 1 2024
entrez: 8 1 2024
Statut: aheadofprint

Résumé

Neurosurgery residency involves a complex structure with multiple hospitals, services, and clinic days, leading to challenges in creating equitable call schedules. Manually prepared scheduling systems are prone to biases, error, and perceived unfairness. To address these issues, we developed an automated scheduling system (Automated Optimization of Neurosurgery Scheduling System [AONSS]) to reduce biases, accommodate resident requests, and optimize call variation, ultimately enhancing the educational experience by promoting diverse junior-senior-attending relationships. AONSS was developed and tailored to the University of Florida program, with inaugural use in 2021-2022 and mandatory implementation in the 2022-2023 academic year. 2019-2021 academic years were used as control. Residents were surveyed using Google Forms before and after implementation to assess its impact. Outcome measures included call and pairing variations, duty hours, as well as subjective factors such as satisfaction, fairness, and perceived biases. Twenty-six residents (28%-39% female/year) were included in the study. AONSS was used for 6/13 blocks during the 2021-2022 academic year and 13/13 blocks for the 2022-2023 academic year. Overall call variation reduced by 70%. All other objective secondary measures have improved with AONSS. Weekly and monthly duty hours were reduced and less varied. Satisfaction scores improved from 21% reporting being somewhat satisfied or very satisfied to 90%. Fairness scores improved from 43% reporting being somewhat fair or very fair to 95%. Perception of gender bias decreased from 29% to 0%. No resident felt there was racial bias in either system. Our newly developed automated scheduling system effectively reduces variation among calls in a complex neurosurgery residency, which, in return, was found to increase residents' satisfaction with their schedule, improve their perception of fairness with the schedule, and has completely removed the perception of sexual bias in a program that has a large percentage of females. In addition, it was found to be associated with decreased duty hours.

Sections du résumé

BACKGROUND AND OBJECTIVES OBJECTIVE
Neurosurgery residency involves a complex structure with multiple hospitals, services, and clinic days, leading to challenges in creating equitable call schedules. Manually prepared scheduling systems are prone to biases, error, and perceived unfairness. To address these issues, we developed an automated scheduling system (Automated Optimization of Neurosurgery Scheduling System [AONSS]) to reduce biases, accommodate resident requests, and optimize call variation, ultimately enhancing the educational experience by promoting diverse junior-senior-attending relationships.
METHODS METHODS
AONSS was developed and tailored to the University of Florida program, with inaugural use in 2021-2022 and mandatory implementation in the 2022-2023 academic year. 2019-2021 academic years were used as control. Residents were surveyed using Google Forms before and after implementation to assess its impact. Outcome measures included call and pairing variations, duty hours, as well as subjective factors such as satisfaction, fairness, and perceived biases.
RESULTS RESULTS
Twenty-six residents (28%-39% female/year) were included in the study. AONSS was used for 6/13 blocks during the 2021-2022 academic year and 13/13 blocks for the 2022-2023 academic year. Overall call variation reduced by 70%. All other objective secondary measures have improved with AONSS. Weekly and monthly duty hours were reduced and less varied. Satisfaction scores improved from 21% reporting being somewhat satisfied or very satisfied to 90%. Fairness scores improved from 43% reporting being somewhat fair or very fair to 95%. Perception of gender bias decreased from 29% to 0%. No resident felt there was racial bias in either system.
CONCLUSION CONCLUSIONS
Our newly developed automated scheduling system effectively reduces variation among calls in a complex neurosurgery residency, which, in return, was found to increase residents' satisfaction with their schedule, improve their perception of fairness with the schedule, and has completely removed the perception of sexual bias in a program that has a large percentage of females. In addition, it was found to be associated with decreased duty hours.

Identifiants

pubmed: 38189465
doi: 10.1227/neu.0000000000002821
pii: 00006123-990000000-01013
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © Congress of Neurological Surgeons 2024. All rights reserved.

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Auteurs

Ken Porche (K)

College of Medicine, University of Florida, Gainesville, Florida, USA.
Lillian S. Wells Department of Neurosurgery, University of Florida, Gainesville, Florida, USA.

Arvind Mohan (A)

College of Medicine, University of Florida, Gainesville, Florida, USA.
Lillian S. Wells Department of Neurosurgery, University of Florida, Gainesville, Florida, USA.

Jamie Dow (J)

Lillian S. Wells Department of Neurosurgery, University of Florida, Gainesville, Florida, USA.

Kaitlyn Melnick (K)

College of Medicine, University of Florida, Gainesville, Florida, USA.
Lillian S. Wells Department of Neurosurgery, University of Florida, Gainesville, Florida, USA.

Dimitri Laurent (D)

College of Medicine, University of Florida, Gainesville, Florida, USA.
Lillian S. Wells Department of Neurosurgery, University of Florida, Gainesville, Florida, USA.

Brian Hoh (B)

College of Medicine, University of Florida, Gainesville, Florida, USA.
Lillian S. Wells Department of Neurosurgery, University of Florida, Gainesville, Florida, USA.

Gregory Murad (G)

College of Medicine, University of Florida, Gainesville, Florida, USA.
Lillian S. Wells Department of Neurosurgery, University of Florida, Gainesville, Florida, USA.

Classifications MeSH