Data-Driven Two-stage Appointment Radiotherapy Scheduling Model for Resource Optimization at a Tertiary Cancer Center Article Swipe
Fan Jia
,
Michael Carter
,
Srinivas Raman
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2693973/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2693973/v1
Background: The timely delivery of radiotherapy (RT) is crucial to cancer care, and excessive delays in RT have been associated with detrimental oncological and psychological outcomes. Prior to receiving RT on treatment units (Linear accelerators), there are a few processes that need to take place including simulation (on CT simulators), radiotherapy plan generation/optimization and quality assurance. The assignment of patient schedules on CT simulators and Linear accelerators is currently done manually at most cancer centers. We propose that data-driven optimization of patient scheduling has the potential to improve wait-times, and optimize use of departmental resources. Methods: A two-stage Mixed Integer Programming model was developed to optimize the patient appointment scheduling process and to forecast machine utilization. The model was tested with historical institutional data from Princess Margaret Cancer Center. By analyzing the dataset and simulating historical patient arrivals, the model output is evaluated by comparing patient wait time statistics and monthly machine utilization against what occurred during this time frame. Results: Testing our model on data from 2019-06 to 2020-02, we found a reduction in average wait time from 11.2 to 6.7 business days for standard category patients. The number of standard patients exceeding the wait time target of 10 business days were reduced from 118 to 15 patients each month. In addition, our model could accurately estimate future machine utilization for both CT simulators and linear accelerators based on the model output appointments, which could facilitate better planning and utilization of departmental resources. Conclusion: Our scheduling model has the potential to reduce the standard patient wait time for radiation treatment without compromising the wait time for urgent patients. The model can be also used to forecast department resources and machine utilization based on the output of the scheduling model. Radiotherapy departments could use this model to generate patient appointment schedules as well as to reduce machine idle time or appointment over-booking.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-2693973/v1
- https://www.researchsquare.com/article/rs-2693973/latest.pdf
- OA Status
- green
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4327912898
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4327912898Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-2693973/v1Digital Object Identifier
- Title
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Data-Driven Two-stage Appointment Radiotherapy Scheduling Model for Resource Optimization at a Tertiary Cancer CenterWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-03-20Full publication date if available
- Authors
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Fan Jia, Michael Carter, Srinivas RamanList of authors in order
- Landing page
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https://doi.org/10.21203/rs.3.rs-2693973/v1Publisher landing page
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https://www.researchsquare.com/article/rs-2693973/latest.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
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https://www.researchsquare.com/article/rs-2693973/latest.pdfDirect OA link when available
- Concepts
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Scheduling (production processes), Computer science, Integer programming, Quality assurance, Linear programming, Operations management, Operations research, Algorithm, Engineering, External quality assessmentTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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16Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(Linear | 34 |
| abstract_inverted_index.2019-06 | 168 |
| abstract_inverted_index.Center. | 129 |
| abstract_inverted_index.Integer | 100 |
| abstract_inverted_index.Testing | 162 |
| abstract_inverted_index.against | 154 |
| abstract_inverted_index.average | 176 |
| abstract_inverted_index.crucial | 9 |
| abstract_inverted_index.dataset | 133 |
| abstract_inverted_index.improve | 88 |
| abstract_inverted_index.machine | 115, 152, 220, 281, 307 |
| abstract_inverted_index.monthly | 151 |
| abstract_inverted_index.patient | 60, 82, 108, 137, 146, 256, 299 |
| abstract_inverted_index.process | 111 |
| abstract_inverted_index.propose | 77 |
| abstract_inverted_index.quality | 55 |
| abstract_inverted_index.reduced | 204 |
| abstract_inverted_index.without | 262 |
| abstract_inverted_index.2020-02, | 170 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Margaret | 127 |
| abstract_inverted_index.Methods: | 96 |
| abstract_inverted_index.Princess | 126 |
| abstract_inverted_index.Results: | 161 |
| abstract_inverted_index.business | 183, 201 |
| abstract_inverted_index.category | 187 |
| abstract_inverted_index.centers. | 75 |
| abstract_inverted_index.delivery | 4 |
| abstract_inverted_index.estimate | 218 |
| abstract_inverted_index.forecast | 114, 277 |
| abstract_inverted_index.generate | 298 |
| abstract_inverted_index.manually | 71 |
| abstract_inverted_index.occurred | 156 |
| abstract_inverted_index.optimize | 91, 106 |
| abstract_inverted_index.patients | 193, 209 |
| abstract_inverted_index.planning | 239 |
| abstract_inverted_index.standard | 186, 192, 255 |
| abstract_inverted_index.addition, | 213 |
| abstract_inverted_index.analyzing | 131 |
| abstract_inverted_index.arrivals, | 138 |
| abstract_inverted_index.comparing | 145 |
| abstract_inverted_index.currently | 69 |
| abstract_inverted_index.developed | 104 |
| abstract_inverted_index.evaluated | 143 |
| abstract_inverted_index.exceeding | 194 |
| abstract_inverted_index.excessive | 14 |
| abstract_inverted_index.including | 46 |
| abstract_inverted_index.outcomes. | 26 |
| abstract_inverted_index.patients. | 188, 269 |
| abstract_inverted_index.potential | 86, 251 |
| abstract_inverted_index.processes | 40 |
| abstract_inverted_index.radiation | 260 |
| abstract_inverted_index.receiving | 29 |
| abstract_inverted_index.reduction | 174 |
| abstract_inverted_index.resources | 279 |
| abstract_inverted_index.schedules | 61, 301 |
| abstract_inverted_index.treatment | 32, 261 |
| abstract_inverted_index.two-stage | 98 |
| abstract_inverted_index.accurately | 217 |
| abstract_inverted_index.assignment | 58 |
| abstract_inverted_index.associated | 20 |
| abstract_inverted_index.assurance. | 56 |
| abstract_inverted_index.department | 278 |
| abstract_inverted_index.facilitate | 237 |
| abstract_inverted_index.historical | 122, 136 |
| abstract_inverted_index.resources. | 95, 244 |
| abstract_inverted_index.scheduling | 83, 110, 247, 289 |
| abstract_inverted_index.simulating | 135 |
| abstract_inverted_index.simulation | 47 |
| abstract_inverted_index.simulators | 64, 225 |
| abstract_inverted_index.statistics | 149 |
| abstract_inverted_index.Background: | 1 |
| abstract_inverted_index.Conclusion: | 245 |
| abstract_inverted_index.Programming | 101 |
| abstract_inverted_index.appointment | 109, 300, 311 |
| abstract_inverted_index.data-driven | 79 |
| abstract_inverted_index.departments | 292 |
| abstract_inverted_index.detrimental | 22 |
| abstract_inverted_index.oncological | 23 |
| abstract_inverted_index.utilization | 153, 221, 241, 282 |
| abstract_inverted_index.wait-times, | 89 |
| abstract_inverted_index.Radiotherapy | 291 |
| abstract_inverted_index.accelerators | 67, 228 |
| abstract_inverted_index.compromising | 263 |
| abstract_inverted_index.departmental | 94, 243 |
| abstract_inverted_index.optimization | 80 |
| abstract_inverted_index.radiotherapy | 6, 51 |
| abstract_inverted_index.simulators), | 50 |
| abstract_inverted_index.utilization. | 116 |
| abstract_inverted_index.appointments, | 234 |
| abstract_inverted_index.institutional | 123 |
| abstract_inverted_index.over-booking. | 312 |
| abstract_inverted_index.psychological | 25 |
| abstract_inverted_index.accelerators), | 35 |
| abstract_inverted_index.generation/optimization | 53 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5022511452 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I185261750, https://openalex.org/I2802654988 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.0562163 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |