TY - GEN
T1 - Characteristics of Patient Arrivals and Service Utilization in Outpatient Departments
AU - He, Yonghou
AU - Chen, Bo
AU - Li, Yuanxi
AU - Wang, Chunqing
AU - Zhang, Zili
AU - Tao, Li
N1 - This work is supported by Fundamental Research Funds for the Central Universities (XDJK2018C045 and XDJK2019D018).
Publisher Copyright:
© Springer Nature Singapore Pte Ltd 2019.
PY - 2019/11/27
Y1 - 2019/11/27
N2 - 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.
AB - 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.
KW - Characteristics of outpatient arrivals
KW - Power spectrum analysis
KW - Service utilization
KW - Statistical analysis
UR - http://www.scopus.com/inward/record.url?scp=85076912752&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-1899-7_24
DO - 10.1007/978-981-15-1899-7_24
M3 - Conference proceeding
AN - SCOPUS:85076912752
SN - 9789811518980
T3 - Communications in Computer and Information Science
SP - 341
EP - 350
BT - Big Data
A2 - Jin, Hai
A2 - Shi, Xuanhua
A2 - Lin, Xuemin
A2 - Lin, Xuemin
A2 - Cheng, Xueqi
A2 - Xiao, Nong
A2 - Huang, Yihua
PB - Springer
T2 - 7th CCF Academic Conference on BigData, CCF BigData 2019
Y2 - 26 September 2019 through 28 September 2019
ER -
