Planning and Scheduling in Digital Health with Answer Set Programming

TL;DR

This paper explores the application of Answer Set Programming to solve complex hospital scheduling problems, aiming to improve efficiency, patient satisfaction, and explainability of AI-driven solutions in healthcare.

Contribution

It models healthcare scheduling problems using Answer Set Programming and investigates methods to enhance solution explainability, addressing real-world constraints and data.

Findings

Developed ASP models for hospital scheduling problems

Proposed explainability methodologies for AI solutions

Validated approaches with real healthcare data

Abstract

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.