Scheduling outpatient day service operations for rheumatology diseases

Appointment scheduling systems represent a method to manage patient waiting lists effectively. This work advances an innovative quantitative approach for the outpatient appointment scheduling problems, based on an optimization model, to manage outpatient Day Service operations. It focuses on outpati...

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Vydáno v:Flexible services and manufacturing journal Ročník 32; číslo 1; s. 102 - 128
Hlavní autoři: Guido, Rosita, Ielpa, Giuseppe, Conforti, Domenico
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.03.2020
Springer Nature B.V
Témata:
Artificial intelligence
Combinatorial analysis
Computing time
Declarative programming
Engineering
Heuristic methods
Knowledge representation
Machines
Manufacturing
Mathematical programming
Operations Management
Operations Research/Decision Theory
Optimization
Processes
Production scheduling
Prototyping
Rheumatology
Scheduling
System effectiveness
68N17
Day service
68T27
68T37
68T35
Patient appointment scheduling
Answer set programming
90B35
Clinical pathways
Patient-centred policies
68T30
Artificial intelligence
Ambulatory care
ISSN:1936-6582, 1936-6590
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Shrnutí:Appointment scheduling systems represent a method to manage patient waiting lists effectively. This work advances an innovative quantitative approach for the outpatient appointment scheduling problems, based on an optimization model, to manage outpatient Day Service operations. It focuses on outpatient appointment scheduling. We start from earlier works in the literature to design models with the objective to maximize the number of patients’ appointments, to reduce patient’s waiting time, and to increase patient’s satisfaction. The proposed combinatorial problem is solved by Answer Set Programming, which is a declarative logic formalism, widely used in Artificial Intelligence and recognized as a powerful tool for Knowledge Representation and Reasoning, to show the advantages of declarative programming for modelling and fast prototyping problem requirements. We apply the model to solve real-life scenarios of the Rheumatology domain. We compare the results on the real instance already solved in our earlier work and extend the computational experiments on some new generated and realistic instances. Since the computational times increase with the size of instances, we develop a three-phase solution approach based on patient’s priority. The heuristic approach is hierarchical and enables to solve more instances than the one-run approach within the computational time limit.
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ISSN:1936-6582
1936-6590
DOI:10.1007/s10696-019-09354-7