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Characteristics of patient arrivals and service utilization in outpatient departments

conference contribution
posted on 2019-01-01, 00:00 authored by Y He, B Chen, Y Li, C Wang, Zili ZhangZili Zhang, L Tao
The characteristics of patient arrivals and service utilization are the theoretical foundation for modeling and simulating healthcare service systems. However, some commonly acknowledged characteristics of outpatient departments (e.g., the Gaussian distribution of the patient numbers, or the exponential distribution of diagnosis time) may be doubted because many outpatients make prior appointment before they come to a hospital in recent years. In this study, we aim to discover the characteristics of patient arrivals and service utilization in five outpatient departments in a big and heavy load hospital in Chongqing, China. Based on the outpatient registration data from 2016 to 2017, we have the following interesting findings: (1) the variation of outpatient arrival numbers in each day is non-linear and can be characterized as pink noise; (2) the distribution of daily arrivals follows a bimodal distribution; (3) the outpatient arrivals in distinct departments exhibit different seasonal patterns; (4) the registration intervals of outpatient arrivals and the doctors’ diagnosis time in all the departments except the Geriatrics department exhibit a power law with cutoff distribution. These empirical findings provide some new insights into the dynamics of patient arrivals and service utilization in outpatient departments and thus enable us to make more reasonable assumptions when modeling the behavior of outpatient departments.

History

Event

China Computer Federation. Academic Conference (7th : 2019 : Wuhan, China)

Volume

1120

Series

China Computer Federation Academic Conference

Pagination

341 - 350

Publisher

Springer

Location

Wuhan, China

Place of publication

Singapore

Start date

2019-09-26

End date

2019-09-28

ISSN

1865-0929

eISSN

1865-0937

ISBN-13

9789811518980

Language

eng

Publication classification

E1.1 Full written paper - refereed

Editor/Contributor(s)

H Jin, X Lin, X Cheng, X Shi, N Xiao, Y Huang

Title of proceedings

CCF Big Data 2019 : Gathering the data, intelligent computing for the future : Proceedings of the 7th CCF Academic Conference on BigData

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