Solving Operating Room Scheduling Problems with Surgical Teams via Answer Set Programming

The optimization of daily operating room surgery schedule can be problematic because of many constraints, like to determine the starting time of different surgeries and allocating the required resources, including the availability of surgical teams for complete surgical procedures. Recently, Answer Set Programming (ASP) has been successfully employed for addressing and solving real-life scheduling and planning problems in the healthcare domain. In this paper we present an enhanced solution using ASP for scheduling operating rooms taking explicitly into consideration availability of surgical teams, that include a surgeon and an anesthetist in different specialties for the entire duration of the surgery. We tested our solution on different benchmarks with realistic parameters for schedule’s length up to the target 5-days planning. The results of our experiments show that ASP is a suitable methodology for solving also such enhanced problem

Operating Room (Re)Scheduling with Bed Management via ASP

The Operating Room Scheduling (ORS) problem is the task of assigning patients to operating rooms (ORs), taking into account different specialties, lengths, and priority scores of each planned surgery, OR session durations, and the availability of beds for the entire length of stay (LOS) both in the Intensive Care Unit (ICU) and in the wards. A proper solution to the ORS problem is of primary importance for the healthcare service quality and the satisfaction of patients in hospital environments. In this paper we first present a solution to the problem based on Answer Set Programming (ASP). The solution is tested on benchmarks with realistic sizes and parameters, on three scenarios for the target length on 5-day scheduling, common in small-medium-sized hospitals, and results show that ASP is a suitable solving methodology for the ORS problem in such setting. Then, we also performed a scalability analysis on the schedule length up to 15 days, which still shows the suitability of our solution also on longer plan horizons. Moreover, we also present an ASP solution for the rescheduling problem, that is, when the offline schedule cannot be completed for some reason. Finally, we introduce a web framework for managing ORS problems via ASP that allows a user to insert the main parameters of the problem, solve a specific instance, and show results graphically in real time

Integer programming for building robust surgery schedules.

This paper proposes and evaluates a number of models for building robust cyclic surgery schedules. The developed models involve two types of constraints. Demand constraints ensure that each surgeon (or surgical group) obtains a specific number of operating room (OR) blocks. Capacity con- straints limit the available OR blocks on each day. Furthermore, the number of operated patients per block and the length of stay (LOS) of each operated patient are dependent on the type of surgery. Both are considered stochas- tic, following a multinomial distribution. We develop a number of MIP-based heuristics and a metaheuristic to minimize the expected total bed shortage and present computational results.Constraint; Demand; Distribution; Expected; Heuristic; Integer programming; Model; Models; Resource leveling; Surgery scheduling;

Application of Artificial Intelligence declarative methods for Solving Operating Room Scheduling problems in Hospital Environments

Digital health is a relatively new but already important field in which digitalization meets the need to automatically and efficiently solve problems in healthcare to improve the quality of life for patients. The need to efficiently solve some of these problems has become even more pressing due to the Covid-19 pandemic that significantly increased stress and demand on hospitals. Hospitals have long waiting lists, surgery cancellations, and even worse, resource overload—issues that negatively impact the level of patient satisfaction and the quality of care provided. Within every hospital, operating rooms (ORs) are an important unit. The Operating Room Scheduling (ORS) problem is the task of assigning patients to operating rooms, taking into account different specialties, lengths and priority scores of each planned surgery, operating room session durations, and the availability of beds for the entire length of stay both in the Intensive Care Unit and in the wards. A proper solution to the ORS problem is of primary importance for the quality of healthcare service and the satisfaction of patients in hospital environments. In this thesis, we provide several contributions to the ORS problem. We first present a solution to the problem based on Knowledge Representation and Reasoning via modeling and solving approaches using Answer Set Programming (ASP). This first basic solution builds on a previous solution but takes into account explicitly beds and ICU units because in the pandemic we understood how important and limiting they were. Moreover, we also present an ASP solution for the rescheduling problem, i.e., when the off-line schedule cannot be completed for some reasons, and a further extension where surgical teams are also considered. Another technical contribution is a second solution for the basic ORS problem with beds and an ICU unit, whose modeling departs from the guidelines previously used and shows efficiency improvements. Finally, we introduce a web framework for managing ORS problems via ASP that allows a user to insert the main parameters of the problem, solve a specific instance, and show results graphically in real time

An ASP-based Approach to Master Surgical Scheduling.

The problem of finding Master Surgical Schedules (MSS) consists of scheduling different specialties to the operating rooms of a hospital clinic. To produce a proper MSS, each specialty must be assigned to some operating rooms. The number of assignments is different for each specialty and can vary during the considered planning horizon. Realizing a satisfying schedule is of upmost importance for a hospital clinic. A poorly scheduled MSS may lead to unbalanced specialties availability and increase patients’ waiting list, negatively affecting both the administrative costs of the hospital and the patient satisfaction. In this paper, we present a compact solution based on Answer Set Programming (ASP) to the MSS problem. We tested our solution on different scenarios: experiments show that our ASP solution provides satisfying results in short time, also when compared to other logic-based formalisms. Finally, we describe a web application we have developed for easy usage of our solution

How stochasticity and emergencies disrupt the surgical schedule

In health care system, the operating theatre is recognized as having an important role, notably in terms of generated income and cost. Its management, and in particular its scheduling, is thus a critical activity, and has been the sub ject of many studies. However, the stochasticity of the operating theatre environment is rarely considered while it has considerable effect on the actual working of a surgical unit. In practice, the planners keep a safety margin, let’s say 15% of the capacity, in order to absorb the effect of unpredictable events. However, this safety margin is most often chosen sub jectively, from experience. In this paper, our goal is to rationalize this process. We want to give insights to managers in order to deal with the stochasticity of their environment, at a tactical–strategic decision level. For this, we propose an analytical approach that takes account of the stochastic operating times as well as the disruptions caused by emergency arrivals. From our model, various performance measures can be computed: the emergency disruption rate, the waiting time for an emergency, the distribution of the working time, the probability of overtime, the average overtime, etc. In particular, our tool is able to tell how many operations can be scheduled per day in order to keep the overtime limited.health care, surgical schedule, emergencies, Markov chain.

CASP Solutions for Planning in Hybrid Domains

CASP is an extension of ASP that allows for numerical constraints to be added in the rules. PDDL+ is an extension of the PDDL standard language of automated planning for modeling mixed discrete-continuous dynamics. In this paper, we present CASP solutions for dealing with PDDL+ problems, i.e., encoding from PDDL+ to CASP, and extensions to the algorithm of the EZCSP CASP solver in order to solve CASP programs arising from PDDL+ domains. An experimental analysis, performed on well-known linear and non-linear variants of PDDL+ domains, involving various configurations of the EZCSP solver, other CASP solvers, and PDDL+ planners, shows the viability of our solution.Comment: Under consideration in Theory and Practice of Logic Programming (TPLP

Rescheduling rehabilitation sessions with answer set programming

The rehabilitation scheduling process consists of planning rehabilitation physiotherapy sessions for patients, by assigning proper operators to them in a certain time slot of a given day, taking into account several requirements and optimizations, e.g. patient’s preferences and operator’s work balancing. Being able to efficiently solve such problem is of upmost importance, in particular as a consequence of the COVID-19 pandemic that significantly increased rehabilitation’s needs. The problem has been recently successfully solved via a two-phase solution based on answer set programming (ASP). In this paper, we focus on the problem of rescheduling the rehabilitation sessions, which comes into play when the original schedule cannot be implemented, for reasons that involve the unavailability of operators and/or the absence of patients. We provide rescheduling solutions based on ASP for both phases, considering different scenarios. Results of experiments performed on real benchmarks, provided by ICS Maugeri, show that also the rescheduling problem can be solved in a satisfactory way. Finally, we present a web application that supports the usage of our solution

An ASP-Based Approach to Scheduling Pre-operative Assessment Clinic

The problem of scheduling Pre-Operative Assessment Clinic (PAC) consists of assigning patients to a day for the exams needed before a surgical procedure, taking into account patients with different priority levels, due dates, and operators availability. Realizing a satisfying schedule is of upmost importance for a clinic, since delay in PAC can cause delay in the subsequent phases, causing a decrease in patients’ satisfaction. In this paper, we divide the problem in two sub-problems: In the first sub-problem patients are assigned to a day taking into account a default list of exams; then, in the second sub-problem, having the actual list of exams needed by each patient, we use the results of the first sub-problem to assign a starting time to each exam. We first present a mathematical formulation for both problems. Then, we present solutions based on Answer Set Programming (ASP): The first solution is a genuine ASP encoding of the sub-problems, while the second introduces domain-specific optimizations. Experiments show that both solutions provide satisfying results in short time, while the second is able to prove optimality faster