The problem of scheduling chemotherapy treatments in oncology clinics is a complex problem, given that the solution has to satisfy (as much as possible) several requirements such as the cyclic nature of chemotherapy treatment plans, maintaining a constant number of patients, and the availability of resources, for example, treatment time, nurses, and drugs. At the same time, realizing a satisfying schedule is of upmost importance for obtaining the best health outcomes. In this paper we first consider a specific instance of the problem which is employed in the San Martino Hospital in Genova, Italy, and present a solution to the problem based on Answer Set Programming (ASP). Then, we enrich the problem and the related ASP encoding considering further features often employed in other hospitals, desirable also in S. Martino, and/or considered in related papers. Results of an experimental analysis, conducted on the real data provided by the San Martino Hospital, show that ASP is an effective solving methodology also for this important scheduling problem.

The problem of scheduling chemotherapy treatments in oncology clinics is a complex problem, given that the solution has to satisfy (as much as possible) several requirements such as the cyclic nature of chemotherapy treatment plans, maintaining a constant number of patients, and the availability of resources, e.g., treatment time, nurses, and drugs. At the same time, realizing a satisfying schedule is of upmost importance for obtaining the best health outcomes.In this paper we first consider a specific instance of the problem which is employed in the San Martino Hospital in Genova, Italy, and present a solution to the problem based on Answer Set Programming (ASP). Then, we enrich the problem and the related ASP encoding considering further features often employed in other hospitals, desirable also in S. Martino, and/or considered in related papers. Results of an experimental analysis, conducted on the real data provided by the San Martino Hospital, show that ASP is an effective solving methodology also for this important scheduling problem. Under consideration for acceptance in TPLP.

In the hospital world there are several complex combinatory problems, and solving these problems is important to increase the degree of patients' satisfaction and the quality of care offered. The problems in the healthcare are complex since to solve them several constraints and different type of resources should be taken into account. Moreover, the solutions must be evaluated in a small amount of time to ensure the usability in real scenarios. We plan to propose solutions to these kind of problems both expanding already tested solutions and by modelling solutions for new problems, taking into account the literature and by using real data when available. Solving these kind of problems is important but, since the European Commission established with the General Data Protection Regulation that each person has the right to ask for explanation of the decision taken by an AI, without developing Explainability methodologies the usage of AI based solvers e.g. those based on Answer Set programming will be limited. Thus, another part of the research will be devoted to study and propose new methodologies for explaining the solutions obtained.

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 hospital, since delay in PAC can cause delay in the subsequent phases, thus lowering patients’ satisfaction. In this paper, we propose a two-phase solution to the PAC problem: in the first phase, patients are assigned to a day taking into account a default list of exams; then, in the second phase, having the actual list of exams needed by each patient, we use the results of the first phase to assign a starting time to each exam. We first present a mathematical formulation for both problems. Further, we present a solution where modeling and solving are done via answer set programming. We then introduce a rescheduling solution that may come into play when the scheduling solution cannot be applied fully. Experiments employing synthetic benchmarks on both scheduling and rescheduling show that both solutions provide satisfying results in short time. We finally show the implementation and usage of a web application that allows to run our scheduling solution and analyze the results graphically in a transparent way.

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.