The Design and Implementation of a Broadly Applicable Algorithm for Optimizing Intra-Day Surgical Scheduling

• URL: http://arxiv.org/abs/2203.08146v1
• Date: Mon, 14 Mar 2022 04:19:25 GMT
• Title: The Design and Implementation of a Broadly Applicable Algorithm for Optimizing Intra-Day Surgical Scheduling
• Authors: Jin Xie, Teng Zhang, Jose Blanchet, Peter Glynn, Matthew Randolph, David Scheinker
• Abstract summary: We present the BEDS (better elective day of surgery) algorithm, a greedy algorithm for smoothing unit-specific surgical admissions days. BEDS is readily implementable with the limited tools available to most hospitals, does not require reductions to surgeon autonomy or centralized scheduling, and is compatible with changes to hospital capacity or patient volumes. We argue that algorithms generated by this framework retain many of the desirable characteristics of BEDS while being compatible with a wide range of objectives and constraints.
• Score: 10.92813727735562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Surgical scheduling optimization is an active area of research. However, few algorithms to optimize surgical scheduling are implemented and see sustained use. An algorithm is more likely to be implemented, if it allows for surgeon autonomy, i.e., requires only limited scheduling centralization, and functions in the limited technical infrastructure of widely used electronic medical records (EMRs). In order for an algorithm to see sustained use, it must be compatible with changes to hospital capacity, patient volumes, and scheduling practices. To meet these objectives, we developed the BEDS (better elective day of surgery) algorithm, a greedy heuristic for smoothing unit-specific surgical admissions across days. We implemented BEDS in the EMR of a large pediatric academic medical center. The use of BEDS was associated with a reduction in the variability in the number of admissions. BEDS is freely available as a dashboard in Tableau, a commercial software used by numerous hospitals. BEDS is readily implementable with the limited tools available to most hospitals, does not require reductions to surgeon autonomy or centralized scheduling, and is compatible with changes to hospital capacity or patient volumes. We present a general algorithmic framework from which BEDS is derived based on a particular choice of objectives and constraints. We argue that algorithms generated by this framework retain many of the desirable characteristics of BEDS while being compatible with a wide range of objectives and constraints.

Related papers

• Random-Key Algorithms for Optimizing Integrated Operating Room Scheduling [0.16385815610837165]
  This study introduces a novel concept of Random-Key (RKO), rigorously tested on literature and new real-world inspired instances. Our literature optimization problem incorporates multi-room scheduling, equipment scheduling, and complex availability constraints. The RKO approach represents solutions as points in a continuous space, which are then mapped in the problem solution space via a deterministic function known as a decoder.
  arXiv  Detail & Related papers  (2025-01-17T15:11:30Z)
• A Contrastive Pretrain Model with Prompt Tuning for Multi-center Medication Recommendation [50.43785306804359]
  We introduce a novel conTrastive prEtrain Model with Prompt Tuning (TEMPT) for multi-center medication recommendation. We devise a novel prompt tuning method to capture the specific information of each hospital rather than adopting the common finetuning. To validate the proposed model, we conduct extensive experiments on the public eICU multi-center medical dataset.
  arXiv  Detail & Related papers  (2024-12-28T06:12:02Z)
• Efficient MedSAMs: Segment Anything in Medical Images on Laptop [69.28565867103542]
  We organized the first international competition dedicated to promptable medical image segmentation. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline. The best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption.
  arXiv  Detail & Related papers  (2024-12-20T17:33:35Z)
• What is Metaheuristics? A Primer for the Epidemiologists [1.2783241540121182]
  This paper reviews the basic BAT algorithm and its variants, including their applications in various fields. As a specific application, we apply the BAT algorithm to a biostatistical estimation problem and show it has some clear advantages over existing algorithms.
  arXiv  Detail & Related papers  (2024-10-26T02:13:00Z)
• OSSAR: Towards Open-Set Surgical Activity Recognition in Robot-assisted Surgery [13.843251369739908]
  We introduce an innovative Open-Set Surgical Activity Recognition (OSSAR) framework. Our solution leverages the hyperspherical reciprocal point strategy to enhance the distinction between known and unknown classes in the feature space. To support our assertions, we establish an open-set surgical activity benchmark utilizing the public JIGSAWS dataset.
  arXiv  Detail & Related papers  (2024-02-10T16:23:12Z)
• GLSFormer : Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos [57.93194315839009]
  We propose a vision transformer-based approach to learn temporal features directly from sequence-level patches. We extensively evaluate our approach on two cataract surgery video datasets, Cataract-101 and D99, and demonstrate superior performance compared to various state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-07-20T17:57:04Z)
• Length of Stay prediction for Hospital Management using Domain Adaptation [0.2624902795082451]
  Inpatient length of stay (LoS) is an important managerial metric which if known in advance can be used to efficiently plan admissions, allocate resources and improve care. Using historical patient data and machine learning techniques, LoS prediction models can be developed. Ethically, these models can not be used for patient discharge in lieu of unit heads but are of utmost necessity for hospital management systems in charge of effective hospital planning.
  arXiv  Detail & Related papers  (2023-06-29T09:58:21Z)
• The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation [81.72197368690031]
  We present a new benchmarking suite designed specifically for medical sequential decision making. The Medkit-Learn(ing) Environment is a publicly available Python package providing simple and easy access to high-fidelity synthetic medical data.
  arXiv  Detail & Related papers  (2021-06-08T10:38:09Z)
• Automatic Liver Segmentation from CT Images Using Deep Learning Algorithms: A Comparative Study [0.0]
  This paper addresses to propose the most efficient DL architectures for Liver segmentation. It is aimed to reveal the most effective and accurate DL architecture for fully automatic liver segmentation. Results reveal that DL algorithms are able to automate organ segmentation from DICOM images with high accuracy.
  arXiv  Detail & Related papers  (2021-01-25T10:05:46Z)
• LRTD: Long-Range Temporal Dependency based Active Learning for Surgical Workflow Recognition [67.86810761677403]
  We propose a novel active learning method for cost-effective surgical video analysis. Specifically, we propose a non-local recurrent convolutional network (NL-RCNet), which introduces non-local block to capture the long-range temporal dependency. We validate our approach on a large surgical video dataset (Cholec80) by performing surgical workflow recognition task.
  arXiv  Detail & Related papers  (2020-04-21T09:21:22Z)
• Robust Medical Instrument Segmentation Challenge 2019 [56.148440125599905]
  Intraoperative tracking of laparoscopic instruments is often a prerequisite for computer and robotic-assisted interventions. Our challenge was based on a surgical data set comprising 10,040 annotated images acquired from a total of 30 surgical procedures. The results confirm the initial hypothesis, namely that algorithm performance degrades with an increasing domain gap.
  arXiv  Detail & Related papers  (2020-03-23T14:35:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.