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WO2025179010A1 - Systèmes et procédés de planification optimale de salle d'opération soumise à une incertitude - Google Patents

Systèmes et procédés de planification optimale de salle d'opération soumise à une incertitude

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Publication number
WO2025179010A1
WO2025179010A1 PCT/US2025/016581 US2025016581W WO2025179010A1 WO 2025179010 A1 WO2025179010 A1 WO 2025179010A1 US 2025016581 W US2025016581 W US 2025016581W WO 2025179010 A1 WO2025179010 A1 WO 2025179010A1
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WIPO (PCT)
Prior art keywords
operating room
prospective
schedule
mathematical
distributions
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PCT/US2025/016581
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English (en)
Inventor
Michael M. ZAVLANOS
Yi SHEN
Bruce Rogers
Nicoleta ECONOMOU-ZAVLANOS
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Duke University
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Duke University
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Publication of WO2025179010A1 publication Critical patent/WO2025179010A1/fr
Pending legal-status Critical Current
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/22—Social work or social welfare, e.g. community support activities or counselling services
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G06Q10/109—Time management, e.g. calendars, reminders, meetings or time accounting

Definitions

  • the scheduling framework utilizes mathematically defined distributions of durations for surgical and post-surgical activities, as opposed to using point estimates (e.g., single values).
  • the mathematical distributions are constructed in a data driven manner that leverages historical data captured within electronic health records (EHR) and internal hospital systems.
  • EHR electronic health records
  • a method optimizes operating room scheduling under uncertainty by: receiving a set of prospective activities to be scheduled, the prospective activities relating to operating room surgeries that consume healthcare resources for a duration of time; generating a set of mathematical distributions associated with a collection of prospective activity types, where a prospective activity type can be defined in terms of the surgery code, the surgeon ID, and other attributes, wherein each mathematical distribution quantifies the uncertainty with regard to the time duration for the retrospective prospective activity type and is constructed using stored historical data relating to the operating room surgeries; and constructing at least one schedule for the prospective activities that optimizes the healthcare resources within a defined set of constraints using the mathematical distributions such that the at least one schedule incorporates the quantified uncertainty.
  • one or more schedules are able to be generated, where all the generated schedules quantity 7 the uncertainty 7 . Multiple schedules may, for example, tune the tradeoff between the different risk metrics and objectives in different ways.
  • a system optimizes operating room scheduling under uncertainty including: a past history data store containing information relating to operating room surgeries that consume health care resources for a duration of time; a processor with access to the past history data store, the processor executing instructions to: receive a set of prospective activities to be scheduled, said prospective activities relating to the operating room surgeries; generate a set of mathematical distributions using stored historical data relating to the operating room surgeries, wherein each of the set of mathematical distributions quantifies uncertainty with regard to time for the respective prospective activity based on the historical data; and construct at least one schedule for the prospective activities that optimizes the healthcare resources within a defined set of constraints using the mathematical distributions such that the at least one schedule incorporates the quantified uncertainty'.
  • a computer-readable storage medium optimizes operating room scheduling under uncertainty 7 , by receiving a set of prospective activities to be scheduled, said prospective activities relating to operating room surgeries that consume healthcare resources for a duration of time; generating a set of mathematical distributions using stored historical data relating to the operating room surgeries, wherein each of the set of mathematical distributions quantifies uncertainty 7 with regard to time for the respective prospective activity based on the historical data; and constructing at least one schedule for the prospective activities that optimizes the healthcare resources within a defined set of constraints using the mathematical distributions such that the at least one schedule incorporates the quantified uncertainty.
  • FIG. IB shows a schematic diagram of the type of historical surgery data contained in data store, which is used to generate mathematical distributions.
  • FIG. 1C illustrates surgery' time distributions for a procedure for different surgeons.
  • FIG. ID illustrates length of stay distributions for the same surgery across different surgeons.
  • FIG. 4A shows one workflow utilized when constructing schedules that optimize health care resources.
  • the scheduling framework is also adaptive, meaning that as new healthcare data is acquired over time the predictive models and the distributions that are produced are updated to reflect any changes captured within the new data.
  • the optimized scheduling practices reliant on the distributions streamline operating room management, while increasing patient satisfaction and improving hospital resource efficiency.
  • Certain embodiments described provide an operating room (OR) scheduling framework, system, or method that leverages past electronic health records (EHR) and other historical data as well as current operational/resource constraints.
  • the scheduling framework is able to respect and adapt to new operational constraints and requirements.
  • the scheduling framework improves the efficiency and predictability of both surgical and post-surgical operations.
  • the scheduling framework includes an ability to update underlying models and generated mathematical distributions to capture changes in the historical data.
  • the operating room scheduling methodology does not necessarily replace human schedulers but instead is able to seamlessly work with human schedulers, surgeons, and patients to improve scheduling efficiency, lower costs, and improve stakeholder experiences.
  • risk metrics are calculated, specific objectives and operational/resource constraints (e.g., operating room scheduling optimization, operating room overtime, patient throughput, surgeon and staff preferences, patient preferences, step-down bed utilization, etc.) are weighed, and a controlled tradeoff between the various obj ectives is used when constructing the optimized schedules. Controlling tradeoffs are possible only because of the mathematical distributions being able to capture and quantify uncertainty That is. defining risk measures is possible because of the mathematical distributions. Controlling tradeoffs is possible because the scheduling framework is a mathematical optimization model that explicitly captures objectives (including those that measure risks) and operational constraints.
  • the mathematical distributions of surgery duration and/or length of stay can be used to calculate important risk metrics (e.g. the Conditional Value at Risk (CVaR), Value at Risk (VaR), and Mean- Variance) that can capture different levels of performance, ranging from risk-neutral (e.g., expected value) to worst-case (e.g.. very conservative).
  • CVaR Conditional Value at Risk
  • VaR Value at Risk
  • Mean- Variance e.g. the conditional Value at Risk
  • surgery duration distributions for surgeon-surgery pairs allow risk-averse surgery duration thresholds to be calculated, which can avoid large operating room overtimes.
  • the variability of daily discharges can be controlled conditioned on schedules optimized by the predictive model.
  • the prospective activities can be “guaranteed” to be scheduled within a prespecified time window (e.g., within two weeks, three weeks, etc.). The length of this window is decided so that all cases during each round can be scheduled within the decided time frame. This guaranteed performance is another contribution of the proposed mathematical optimization model.
  • FIG. 1A shows a flow chart of an optimization scheme for optimizing operating room scheduling under uncertainty.
  • a set of prospective activities 110 to be scheduled is received by modeler 120.
  • Each prospective activity 1 10 relates to operating room surgery and can be an activity' that requires a duration of time during which a level of healthcare resources is consumed.
  • a modeler 120, or similar computing component is a predictive modeler that uses historical surgery’ data of data store 122 to produce a mathematical distribution 124 quantifying uncertainty related to the time duration of relevant activities.
  • a schedule optimizer 128, or similar computing component uses the mathematical distributions 124 as well as objectives and operational/resource constraints and requirements. Since the model is adaptive, objectives and operational constraints and requirements can be updated at every round to capture changes, the schedule optimizer 128 constructs one or more schedules 130 that optimizes the healthcare resources. Healthcare resources and their constraints can be defined within data store 126.
  • Operational constraints affect both surgery scheduling and post-operational treatment.
  • the post-operational treatment requires a length of stay in a recovery room, hospital room, or hospital bed during which staff monitors the post operation recovery 7 process.
  • Recovery rooms, operating rooms, and respective staff, nurses, and doctors are all constrained healthcare resources.
  • patients have lives and commitments, so schedules 130 can be constrained by patient blackout dates.
  • Surgeons, anesthesiologists, recovery physicians, nurses, and staff all have work schedules that need to be considered when scheduling, which are also treated as constraints. For example, it is common for surgeons to be available for operating room activities for certain days of the week and not others.
  • Data store 126 can include data on all relevant resources and their availability and constraints.
  • FIG. IB shows a schematic diagram of the type of historical surgery data contained in data store 122, which is used to generate mathematical distributions 124.
  • a set of historical records are maintained (records 0 to 4 shown), where each record includes a patient identifier, a primary procedure identifier, a surgery room identifier, a physician (surgeon) identifier, minutes spent in a care facility outside an operating room, a length of stay in days, a creation day for a prospective surgery 7 , a discharge day, and the like.
  • This information is not comprehensive and is a simplistic example of a portion of maintained records able to be used by modeler 120 to create mathematical distributions 124 that incorporate or quantify uncertainty in prospective activities, which affects schedules 130.
  • the schedules 130 created herein can be presented to a manual human scheduling agent that interacts with a hospital’s scheduling computing system.
  • the schedules 130 can be advisory in nature in certain embodiments.
  • components e.g., modeler 120 and schedule optimizer 128, and features expressed herein can be implemented as plug-ins or functional components integrated within existing healthcare software products to provide integrated scheduling optimizations.
  • FIG. 1C illustrates surgery time distributions for a procedure for different surgeons.
  • the left-hand side is labeled in minutes for the duration of the surgery procedure.
  • the bottom of the chart shows eight different surgeons by ID number.
  • the time in minutes per the distributions span from approximately three hundred minutes to approximately five hundred and sixty minutes.
  • the distribution in minutes shows an uncertainty' of approximately fifty minutes for most surgeons.
  • surgeon identity and procedure type is a significant factor in determining an accurate mathematical distribution 124 when modeling (using modeler 120) a prospective activity 1 10 (e g., the procedure having the noted Procedure ID).
  • FIG. ID illustrates length of stay distributions for the same surgery' across different surgeons.
  • the left-hand side is labeled days of length of stay within a post-operation recovery room.
  • the bottom of the graph denotes the same eight surgeon identifiers from FIG. 1C.
  • the surgeon with a specific ID has a significantly' greater uncertainty or variance for length of stay post procedure as a different surgeon.
  • the historical data in data store 122 will create different mathematical distributions 124 for the same procedure based on the assigned surgeon. As new procedures are performed, the underlying historical data is updated and the distributions 124 can change accordingly.
  • modeler 120 estimates statistical distributions for surgeonsurgery' t pe pairings that do not have sufficient data within the historical data store/repository. Transfer learning based on different procedures the surgeon performs in relation to performance of other surgeons for the different procedures as well as performance of different surgeon performance for the procedure of note are leveraged to estimate a surgeon’s distribution for the procedure of note. In other words, a determination can be made that sufficient historical data is lacking to construct an accurate mathematical distribution for a procedure of note based on the assigned physician performing that procedure in the past. Mathematical distributions can be constructed for the procedure at least in part by using a combination of surgery duration of similar surgeons performing the same type of prospective surgery'.
  • the mathematical distributions constructed in the manner can use a weighted mean of distributions. In one implementation, a Wasserstein barycenter distance algorithm can be used. [0035] Generally, use of the mathematical distributions 124 for scheduling reduces variance otherwise experienced. The variance is reduced because uncertainty quantified within the distributions is taken into account in a mathematical optimization framework. The operating room scheduling problem, resolved at least in part by using the distributions 124 when scheduling is graphically expressed in charts of FIG. IE and IF.
  • FIG. IE shows a chart of typical scheduling for patient surgeries within an operating room environment. As shown, three different operating rooms, each a healthcare resource, exist. In week 1, day 1, operating room 2 is utilized in a manner resulting in overtime, while operating room 1 was underutilized. Overtime pay will often occur when tightly scheduled surgeries run over a time estimate that conventional scheduling utilizes. Overtime pay can also result in surgery cancelations. Implementing the mathematical distributions 124 for scheduling minimizes the variance in usage rates between operating rooms and minimizes overtime while maintaining or increasing patient throughput.
  • FIG. IF shows a chart of typical patient discharging after surgery recovery. Because patient discharges occur responsive to and after surgery completion (plus recovery time) operating room scheduling affects daily patient discharges. When variance is reduced in operating room scheduling, variance in patient discharges is also reduced. Further, rises or falls in patient discharging (e.g., high variability) places increased pressure on recovery staff and adds variability to the number of recovery rooms (e.g., a healthcare resource) being utilized. Use of mathematical distributions 124 to quantify uncertainty in the length of stay enables the variability of discharges to be minimized, which equates to more efficient use of recovery rooms.
  • FIG. 2 shows a schematic diagram of an OR scheduling system including modeler 120 and schedule optimizer 128.
  • the OR scheduling system 200 is a computer system having at least one processor 210, circuitry 214, and a set of executable instructions 212, which include software.
  • the processor 210 is part of a set of one or more computer processors, which may be local to a computing device, distributed across network 230, or contained in the cloud.
  • the circuitry 214 includes motherboards, network interface cards, memory, and the like.
  • the instructions 212 can include executables written in a computer language, which cause the computer system and/or processor 210 to perform a series of defined steps.
  • the instructions 212 can include a machine learning algorithm, generative Al programming, a trainable neural network, and/or an optimization algorithm, which is utilized at least in part to generate the mathematical distributions 124 and schedules.
  • the operating room scheduling system 200 can be hosted by a server having access to a network 230, which includes one or more intranets and the internet.
  • Hospital systems can be complex and can utilize numerous tailored software applications, including an Electronic Health Record (EHR) application 240, a patient billing application 242, a patient scheduling application 244, a staff scheduling application 246, a room scheduling application, mobile health applications, recovery tracking and communication applications, additional software for medical records and data collection, and the like.
  • EHR Electronic Health Record
  • Patient scheduling applications 244 may calculate moving averages of previous cases for the same surgery code, but are presently limited to providing point estimates of surgery and LOS duration. These types of programs can be extended and improved via use of the mathematical distributions 124 to ensure uncertainty is captured and taken into account.
  • Other known systems for operating room scheduling are reactive, scheduling cases one-at-a-time as they arrive. Such systems are challenging to optimize and generally cannot correct for scheduling mistakes in the past.
  • System 200 which can be integrated to improve existing scheduling applications, overcomes these challenges using a scheduling interval or workflow formula as elaborated upon in FIG. 4A, 4B, and 4C.
  • Historical data and health care resource data are indicated as being stored in data store 122 and 126.
  • various user interface 226 components can be implemented to permit user to machine interactions.
  • the user interfaces 226 may enable patients to enter a set of available dates for a surgery or other prospective activity. Other coordination efforts, such as surgeon availability and/or confirmation can be performed via the user interface 226.
  • the user interfaces 226 can include intranet user interfaces restricted to hospital staff use, interactive Web pages, mobile application interfaces, extended reality' interfaces, and the like.
  • the modeler 120 and schedule optimizer 128 can include third party software and may be able to read and write spreadsheet, database, or other structured files (e.g., character separated values or CVS files).
  • the PYTHON OPTIMAL TRANSPORTION (POT) or similar library routines can be helpful in generating risk values and mathematical distributions 124, as can the GUROBI SOLVER and other mathematical optimization/analytic platforms.
  • the mathematical distributions 124 and scheduling functions detailed herein can be integrated with various operating room scheduling platforms such as SURGISTREAM, MAX-OR, CASECTRL, HEALTHSTREAM, and the like.
  • FIG. 3A illustrates a computer implemented method for optimizing operating room scheduling under uncertainty.
  • a set of prospective activities to be scheduled are received. These activities can relate to an operating room, scheduling operating room surgeries, reserving, and/or scheduling post-operative resources, and the like.
  • a set of mathematical distributions representing uncertainty are generated using historical data.
  • a schedule is constructed for the set of prospective activities that optimizes the health care resources needed for these activities.
  • FIG. 3B illustrates a use case of FIG. 3A, where risk metrics are calculated and utilized.
  • Steps 302 and 304 are the same as detailed in FIG. 3 A.
  • a set of risk metrics are calculated from the set of mathematical distributions.
  • various objectives are defined for optimizing the health care resources. That is, often tradeoffs are made to optimize for divergent or conflicting factors and the objectives inform the method of a specific manner in which a balance between the tradeoffs is to occur.
  • Step 314 controls the tradeoff between the various objectives using the risk metrics when constructing a schedule that optimizes health care resources.
  • FIG. 3C illustrates an embodiment where mathematical distributions 124 reflecting uncertainty are based at least in part upon surgeon and procedure pairing.
  • step 320 executes.
  • a set of surgeons available and able to perform the surgical procedure are determined.
  • the surgical procedure is one of the prospective activities being modeled and scheduled as discussed.
  • time constraints for performing the surgical procedure from the time a need is identified can exist.
  • the scheduling and optimizing system can be biased or weighted to ensure a patient is able to have a preferred surgeon perform their operation.
  • a surgeon from the set of surgeons is assigned in accordance with availability and established preferences. The process may be iterative, as prior to a surgery' an initially assigned surgeon can be re-tasked and another surgeon assigned.
  • step 324 historical data for the amount of time the assigned surgeon is likely to take for the procedure for which that surgeon was assigned.
  • the mathematical distributions 124 can be data-driven, at least in part, as opposed to being solely reliant on surgeon provided estimations, which are often inaccurate.
  • a particular surgeon can be shown to provide especially accurate or inaccurate estimates and a weight or adjustment applied to the surgeon estimate can be adjusted accordingly.
  • An accurate distribution based on a surgeon/procedure pairing may require a threshold or minimal set of historical data in order to be relied upon.
  • a mathematical distribution is constructed, and the method proceeds from step 324 to step 328.
  • transfer learning techniques can be applied.
  • a combination of similar surgeons and/or surgeries can be obtained from the historical data and used for constructing the mathematical distribution 124.
  • the mathematical distributions (possibility adjusted for risk factors, objectives, and tradeoffs as noted in FIG. 3B) are used to construct a schedule that optimizes the health care resources.
  • the mathematical distributions are not adjusted for risk factors but are instead leamt from historical data.
  • the mathematical distributions capture the uncertainty' in the case length and the length of stay that are inputs to the mathematical optimization problem that determines schedules.
  • the mathematical optimization problem controls the tradeoff between risk factors and enforces operational/resource constraints among others.
  • the input received prior to constructing a schedule for a scheduling interval includes a set of new requests for patient surgeries. Sometimes, recovery rooms are at a premium, due to past surgeries having extensive length of stays.
  • the scheduler e.g.. schedule optimizer 128, considers availability of recovery rooms when scheduling. For each schedule run, anticipated surgery' time in an operating room and anticipated recovery' time post-surgery is modeled as a mathematical distribution 124 by modeler 120. As shown, the output of a scheduling run is a set of assigned surgeries by time and operating room as yvell as a set of post-operative resources (e.g., recovery rooms) anticipated.
  • An automated caller, or human agent can confirm the scheduled dates with patients and surgeons, and the schedule can be updated, as necessary. Appreciably, use of the workflow of FIG. 4A and the mathematical optimization model that relies on mathematical distributions 124 to quantify uncertainty- reduces variability in both patient scheduling and discharge dates, significance of which was elaborated upon in reference to FIG. IE and IF.
  • FIG. 4B illustrates a mathematical description of the operating room scheduling problem resulted/mitigated herein.
  • FIG. 4C provides an example of a scheduling problem flow consistent with the mathematical description and conventions of FIG. 4B.
  • FIG. 4C also shows the type of data, including operational/resource constraints and requirements used herein.
  • the mathematical description of FIG. 4B and/or derivatives thereof is used by the schedule optimizer 128.
  • the schedule optimizer is not limited in this regard and other approaches exist in the art and can be used in other contemplated use cases.
  • the schedule optimizer 128 leverages past Electronic Health Record (EHR) data and other historic data to improve the efficiency of OR utilization and the predictability of postoperative patient flow.
  • modeler 120 uses predictive models of mathematical distributions 124 (compared to only point estimates) of surgery durations and length of stay (LOS).
  • the mathematical distributions 124 quantify uncertainty and can be calculated in a data- driven manner using historical data.
  • the mathematical distributions are based in part upon unique combinations or pairings of surgeon and surgery procedure, which is referred to as a surgeon-surgery pair.
  • T ⁇ Nt,Wt, Pt.Ct,Dt,Xt,R ⁇ --
  • Variable t is a time index, also called a scheduling round, which captures the time when the schedule is updated, (e.g., once a week, twice a week, etc.)
  • Nt represents the set of new cases recorded at time step t.
  • Wt represents a given time horizon starting at time t during which the new Nt cases need to be scheduled.
  • Pt is the prior OR schedule containing cases that were scheduled before time step t but have not been performed yet.
  • Ct is the set of constraints that the new' schedule at time step t must satisfy, (e.g., surgeon availability, patient blackout dates, etc.)
  • Dt denotes the dataset that contains information on all cases that have been performed and discharged before time step t.
  • the optimal schedule Xt at each time step is defined as follows:
  • X(Pt,Ct,Wt) denotes the set of all schedules that respect the prior schedule Pt, satisfy the constraints in Ct. and finish all new cases within the window Wt.
  • the new cases Nt and data Dt constitute inputs to the OR scheduling problem during scheduling round t.
  • FIG. 4C provides example of the elements in the tuple T.
  • Finding the optimal schedule Xt that optimizes health care resources requires the ability to evaluate a schedule at time step t. Since the outcome of a schedule Xt cannot be observed at the decision time step t, except for perhaps some counting statistics (e.g., the number of daily scheduled cases and weekly scheduled cases for each surgeon), a predictive model P(Xt) (see modeler 120) is trained from the dataset Dt and captures the randomness in the outcome of the schedule Xt. (e.g., the distribution of surgery duration and LOS).
  • Embodiments of the described decision management system and LLM Business Knowledge Base can be embodied as a computing system implemented as a single system but may also be implemented across multiple systems or sub-systems co-located or distributed relative to each other.
  • Computing systems generally include one or more processors that transform or manipulate data according to the instructions of software, one or more storage devices on which the software and data are stored, and communication systems for wired and/or wireless communication across devices and systems.
  • storage media In no case do the terms “storage media,” “computer-readable storage media” or “computer-readable storage medium” consist of transitory carrier waves or propagating signals. Instead, “storage” media refers to non- transitory media.

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Abstract

Un procédé optimise la planification de salle d'opération soumise à une incertitude par : la réception d'un ensemble d'activités potentielles à planifier, lesdites activités potentielles se rapportant à des chirurgies de salle d'opération qui consomment des ressources de soins de santé pendant une durée ; la génération d'un ensemble de distributions mathématiques associées à une collection de types d'activité potentielle, un type d'activité potentielle pouvant être défini en fonction du code chirurgical, de l'ID de chirurgien et d'autres attributs, chaque distribution mathématique quantifiant l'incertitude par rapport à la durée pour le type d'activité potentielle rétrospective et étant construite à l'aide de données historiques stockées relatives aux chirurgies de salle d'opération ; et la construction d'au moins un calendrier pour les activités potentielles qui optimise l'utilisation de ressources de soins de santé dans un ensemble défini de contraintes à l'aide des distributions mathématiques de telle sorte que le ou les calendriers incorporent l'incertitude quantifiée. Le procédé peut s'adapter à de nouvelles données historiques et à de nouvelles contraintes fonctionnelles.
PCT/US2025/016581 2024-02-20 2025-02-20 Systèmes et procédés de planification optimale de salle d'opération soumise à une incertitude Pending WO2025179010A1 (fr)

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040249676A1 (en) * 2003-06-05 2004-12-09 W. John S. Marshall Management systems and methods
US20090132332A1 (en) * 2007-10-18 2009-05-21 Washington State University Computer implemented scheduling systems and associated methods
US20180344308A1 (en) * 2012-09-17 2018-12-06 DePuy Synthes Products, Inc. Systems and methods for surgical and interventional planning, support, post-operative follow-up, and functional recovery tracking
US20190148008A1 (en) * 2014-08-29 2019-05-16 General Electric Company Optimizing state transition set points for schedule risk management
US20190180867A1 (en) * 2017-12-07 2019-06-13 International Business Machines Corporation Dynamic operating room scheduler using machine learning
TW202349407A (zh) * 2022-06-01 2023-12-16 奇美醫療財團法人奇美醫院 智慧型床位即時自動預約系統

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040249676A1 (en) * 2003-06-05 2004-12-09 W. John S. Marshall Management systems and methods
US20090132332A1 (en) * 2007-10-18 2009-05-21 Washington State University Computer implemented scheduling systems and associated methods
US20180344308A1 (en) * 2012-09-17 2018-12-06 DePuy Synthes Products, Inc. Systems and methods for surgical and interventional planning, support, post-operative follow-up, and functional recovery tracking
US20190148008A1 (en) * 2014-08-29 2019-05-16 General Electric Company Optimizing state transition set points for schedule risk management
US20190180867A1 (en) * 2017-12-07 2019-06-13 International Business Machines Corporation Dynamic operating room scheduler using machine learning
TW202349407A (zh) * 2022-06-01 2023-12-16 奇美醫療財團法人奇美醫院 智慧型床位即時自動預約系統

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