File(s) under permanent embargo
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 TaoThe 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
1120Series
China Computer Federation Academic ConferencePagination
341 - 350Publisher
SpringerLocation
Wuhan, ChinaPlace of publication
SingaporePublisher DOI
Start date
2019-09-26End date
2019-09-28ISSN
1865-0929eISSN
1865-0937ISBN-13
9789811518980Language
engPublication classification
E1.1 Full written paper - refereedEditor/Contributor(s)
H Jin, X Lin, X Cheng, X Shi, N Xiao, Y HuangTitle of proceedings
CCF Big Data 2019 : Gathering the data, intelligent computing for the future : Proceedings of the 7th CCF Academic Conference on BigDataUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC