Dynamic appointment scheduling in outpatient department with patient preferences
考慮病人選擇的動態門診預約排序研究
Student thesis: Doctoral Thesis
Author(s)
Detail(s)
Awarding Institution | |
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Award date | 16 Feb 2015 |
Link(s)
Permanent Link | https://scholars.cityu.edu.hk/en/theses/theses(209c24ce-d2b2-4430-a6ea-ef3240210332).html |
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Other link(s) | Links |
Abstract
Traditional healthcare systems are proving to be inadequate as they come under
pressure from aging populations. In response, appointment systems aimed at
improving resource utilization and service efficiency are being installed. However,
current systems still have many shortcomings in terms of satisfying patient
preferences and stochastically optimizing service durations and patient arrival
patterns. Of these, the issue of patient preferences is particularly serious because
it is a matter that affects the patients directly.
Real time appointment scheduling is essentially a problem of sequencing.
Schedulers make decisions upon receiving a patient's request online. A
well-developed appointment system should take into account future events (such
as future patient requests). This turns the problem usually into a dynamic
programming (DP) type. Since this is usually a complex and computationally
intensive problem, its solution demands a methodical approach.
In summary, the main challenge in appointment scheduling is to efficiently
handle patients' appointments online with due consideration of patient preferences.
This thesis tackles this challenge by modeling and optimizing patient appointments
using a DP model.
The thesis begins with an examination of the dynamic appointment
scheduling problems associated with single patient preference scenarios in
outpatient departments. A two-dimensional choice model that takes into account
preferences with regard to physicians as well as time slots is developed. A DP
model then optimizes the scheduling of sequential appointments constrained by
patient preferences. An improvement over existing models is the evaluation of
booking decisions on the basis of the degree to which patient preferences are
satisfied. Next, the characteristics of the model are analyzed to help formulate
booking policies. Based on the characteristics, two kinds of approximate
dynamic programming algorithms are developed in order to avoid the curse of
dimensionality typically associated with DP models. Experimental results have
pointed to the need for further fine-tuning of the model and improving the
efficiencies of the two proposed algorithms.
The thesis then proceeds to consider the dynamic appointment problem with
multiple preferences. Unlike in the case of single preferences, patient decisions
after receiving the scheduler's assignments are also considered. Next, a DP
model is proposed for scheduling sequential appointments in a manner
maximizing patient satisfaction levels. Certain adaptive dynamic programming
algorithms are formulated to avoid the curse of dimensionality. The algorithms
are capable of dynamically capturing patient preferences, updating the values of
being in a state, and thus raising the quality of decision-making. Numerical
experiments are conducted next to evaluate the performance of the algorithms.
Evaluation considerations include the convergence behaviors under different
settings, i.e., the number of iterations needed before convergence and the
accuracy of results.
In some cases, the patient might want to choose a specific appointment time
slot on his/her own rather than being assigned one by the scheduler unilaterally.
A mechanism for facilitating negotiations between the patient and the scheduler
is therefore included in the proposed model. The scheduler does not just assign a
patient to a particular physician and time slot, instead a set of options is offered
to the patient. A separate DP model is used to deal with this requirement. A
decomposition method and a column generation algorithm are used while
performing the computations needed in solving the model. Numerical studies
have shown that these are acceptably efficient and accurate. The effects of
booking horizon and patient arrival rate are also studied. A policy concerning
how one can make use of the solutions proposed by the model is introduced next
along with a quasi-optimal policy aimed at mitigating computational complexity.
Since no-shows are common in advance appointment systems, appointment
scheduling with no-shows is also examined. Finally, an overbooking policy is
introduced to reduce losses resulting from no-shows.
- Hospitals, Medical appointments and schedules, Waiting lists, Dynamic programming