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Approximate dynamic programming approaches for appointment scheduling with patient preferences.

Abstract：

:During the appointment booking process in out-patient departments, the level of patient satisfaction can be affected by whether or not their preferences can be met, including the choice of physicians and preferred time slot. In addition, because the appointments are sequential, considering future possible requests is also necessary for a successful appointment system. This paper proposes a Markov decision process model for optimizing the scheduling of sequential appointments with patient preferences. In contrast to existing models, the evaluation of a booking decision in this model focuses on the extent to which preferences are satisfied. Characteristics of the model are analysed to develop a system for formulating booking policies. Based on these characteristics, two types of approximate dynamic programming algorithms are developed to avoid the curse of dimensionality. Experimental results suggest directions for further fine-tuning of the model, as well as improving the efficiency of the two proposed algorithms.

journal_name

Artif Intell Med

journal_title

Artificial intelligence in medicine

authors

Li X,Wang J,Fung RYK

doi

10.1016/j.artmed.2018.02.001

subject

Has Abstract

pub_date

2018-04-01 00:00:00

pages

16-25

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(17)30168-9

journal_volume

85

pub_type

杂志文章

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