ArticlePloS one2020
Implementation of an automated scheduling tool improves schedule quality and resident satisfaction.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Evaluation of an AI-Based Constraint-Optimization Scheduler to Optimize On-Call Schedule Equity and Reduce Administrative Burden in a Pediatric Residency: Retrospective Comparative Study.Journal of medical Internet research · 2026Article
- Artificial Intelligence in Psychiatric Graduate Medical Education.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026Article
- Tackling Administrative Burden in Graduate Medical Education: Consider AI-Powered Tools.Journal of graduate medical education · 2026Article
- Artificial Intelligence for Hospital Scheduling: A Simple, Reproducible, and Effective Model.Cureus · 2026Article
- Equity-promoting integer programming approaches for medical resident rotation scheduling.Health care management science · 2025Article
- Exploring nurse perspectives on AI-based shift scheduling for fairness, transparency, and work-life balance.BMC nursing · 2025Article
- Shift nurses' work quality and job satisfaction after implementing the Inha University hospital nursing AI scheduling system (IH-NASS).BMC nursing · 2025Article
- Creating a master training rotation schedule for emergency medicine residents and challenges in using artificial intelligence.International journal of emergency medicine · 2024Article
- Evaluation of the public health clinical rotation in medical school: a qualitative case study design.Korean journal of medical education · 2023Article
- Research on multi-objective optimal scheduling considering the balance of labor workload distribution.PloS one · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Rotation schedules for residents must balance individual preferences, compliance with Accreditation Council for Graduate Medical Education guidelines, and institutional staffing requirements. Automation has the potential to improve the consistency and quality of schedules. We designed a novel rotation scheduling tool, the Automated Internal Medicine Scheduler (AIMS), and evaluated schedule quality and resident satisfaction and perceptions of fairness after implementation. We compared schedule uniformity, fulfillment of resident preferences, and conflicting shift assignments for the hand-made 2017-2018 schedule, and the AIMS-generated 2018-2019 schedule. Residents were surveyed in September 2018 to assess perception of schedule quality and fairness. With AIMS, 71/74 (96.0%) interns and 66/82 (80.5%) residents were assigned to their first-choice rotation, a significant increase from the 50/72 (69.4%) interns and 25/82 (30.5%) residents assigned their first-choice in the 2017-2018 academic year. AIMS also yielded significant improvements in the number of night shift/day shift conflicts at the time of rotation switches for interns, with a significant decrease to 0.3 conflicts per intern compared to 0.7 with the prior manual schedule. Twenty-two of 82 residents (27%) completed the survey, and average satisfaction and perception of fairness were 0.7 and 0.9 points higher on a 5-point Likert scale for the AIMS-generated schedule when compared to the non-AIMS schedule. There was no significant difference in the preference for assigned vacation blocks, or in variance for night or ICU rotations. Automated scheduling improved several metrics of schedule quality, as well as resident satisfaction. Future directions include evaluation of the tool in other residency programs and comparison with alternative scheduling algorithms.
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