Result: Decomposition approaches for scheduling chronic outpatients’ clinical pathways in Answer Set Programming

Title:
Decomposition approaches for scheduling chronic outpatients’ clinical pathways in Answer Set Programming
Source:
Journal of Logic and Computation. 33:1851-1871
Publisher Information:
Oxford University Press (OUP), 2023.
Publication Year:
2023
Document Type:
Academic journal Article
File Description:
application/pdf
Language:
English
ISSN:
1465-363X
0955-792X
DOI:
10.1093/logcom/exad038
Rights:
OUP Standard Publication Reuse
Accession Number:
edsair.doi.dedup.....a871bd39ce79b47b34f87910b6e4d6f7
Database:
OpenAIRE

Further Information

Chronic patients suffering from non-communicable diseases are often enrolled into a diagnostic and therapeutic care program featuring a personalized care plan. Healthcare is mostly provided at the patient’s home, but those examinations and treatments that must be delivered at the hospital have to be explicitly booked. Booking is not trivial due to, on the one hand, the several time constraints that become particularly tight in the case of comorbidity, on the other hand, the limited availability of both staff and equipment at the hospital care units. This suggests that the scheduling of the clinical pathways for enrolled outpatients should be managed in a centralized manner, taking advantage of the fact that demand for services is known well in advance. The aim is to serve as many requests as possible (unattended requests are supplied by contracted private health facilities) in a timely manner, taking patients priority into account. Booking involves setting a date and a time for each selected health service, which is rather complex. In this work, we provide a declarative approach by encoding the problem in Answer Set Programming (ASP). In order to improve the scalability of the ASP approach, we present and compare two heuristic approaches, respectively based on service demand and time decomposition. All approaches are tested on instances of increasing size to assess scalability with respect to time horizon and number of requests.