Evidence map›Paper›PMID 42598835›Full record

ArticleJournal of applied clinical medical physics2026

A preference-integrated optimization system for medical physics shift scheduling.

Benjamin S Rosen, Zheng Zhang, Karolyn M Hopfensperger, Kelly C Paradis, Choonik Lee, Lana Critchfield, Ikechi Ozoemelam, Charles K Matrosic, Christopher Kehayias, Scott W Hadley and 13 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors.

Benjamin S RosenDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Zheng ZhangDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Karolyn M HopfenspergerDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Kelly C ParadisDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Choonik LeeDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Lana CritchfieldDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Ikechi OzoemelamDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Charles K MatrosicDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Christopher KehayiasDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Scott W HadleyDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
James M BalterDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Claire ZhangDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Lise WeiDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Seyyedeh Azar Oliaei MotlaghDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Alexandar MoncionDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Dale W LitzenbergDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Cory KnillDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Maria G DitmanDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Elizabeth L CovingtonDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Charles S MayoDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Sean P DevanDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Martha M MatuszakDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.
Joann I PrisciandaroDepartment of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA.

Funding

University of Michigan Department of Radiation Oncology
6 · The paper itself

Abstract

backgroundClinical shift scheduling for medical physicists is challenging given multidisciplinary roles and competing clinical, research, and service demands. Manual workflows often lack the flexibility and transparency needed for this complex field. PURPOSE: To develop, deploy, and evaluate a preference-driven, optimization-based scheduling platform for an academic radiation oncology medical physics division.

methodsUsing a graphical user interface, each physicist encoded half-day shift slots as unavailable (0), available but not preferred (0.5), available (1), or preferred (2) for each two-month scheduling block over two years of clinical use. Shift requirements were defined across ten service categories with weighted effort units, and individual targets were adjusted for non-clinical effort (e.g., research, teaching, administrative service) and planned time off. Scheduling was formulated as a constrained binary integer optimization problem, with coverage, sequencing, and availability constraints, solved using a genetic algorithm to minimize a preference-based fitness function. Final schedules were exported to clinical calendars. Retrospective evaluation computed descriptive statistics per scheduling period and overall, including the distribution of assigned shifts by preference category and concordance between assignments and stated availability/preferences. Qualitative feedback from users and scheduling leads was collected to further assess operational impact.

resultsFrom January 2024 through February 2026, 4980 shifts were assigned across fifteen scheduling blocks for 23 physicists. Across 25,796 preference entries, 95% of assignments matched stated preference and availability, with non-preferred assignments accounting for only 5% of total assignments. All clinical coverage requirements were met, and no staff were scheduled for shifts marked as unavailable. Manual interventions were rare, and user feedback indicated improved transparency, satisfaction, and reduced administrative burden.

conclusionsPreference-integrated optimization reliably produced feasible, clinically robust schedules for medical physics shift scheduling. This approach is readily implementable and supports efficient, equitable scheduling in multidisciplinary settings.

Indexed as

AlgorithmsAppointments and SchedulesHealth PhysicsPersonnel Staffing and SchedulingRadiation OncologyRadiotherapy Planning, Computer-AssistedWorkflowHumansRetrospective Studiesclinical operationsefficiencyworkflows

Identifiers

PMID42598835
PMCPMC13474317

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.