Characteristics of Patient Arrivals and Service Utilization in Outpatient Departments

  • Yonghou He
  • Bo Chen
  • Yuanxi Li
  • Chunqing Wang
  • Zili Zhang
  • Li TaoEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1120)


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.


Characteristics of outpatient arrivals Service utilization Statistical analysis Power spectrum analysis 



This work is supported by Fundamental Research Funds for the Central Universities (XDJK2018C045 and XDJK2019D018).


  1. 1.
    Kim, S.H., Whitt, W., Cha, W.C.: A data-driven model of an appointment-generated arrival process at an outpatient clinic. INFORMS J. Comput. 30(1), 181–199 (2018)CrossRefGoogle Scholar
  2. 2.
    Biswas, S., Arora, H., et al.: On an application of geiger-muller counter model (type-ii) for optimization relating to hospital administration. Acta Med. Int. 4(2), 16 (2017)CrossRefGoogle Scholar
  3. 3.
    Peter, P.O., Sivasamy, R.: Queueing theory techniques and its real applications to health care systems-outpatient visits. Int. J. Healthcare Manag. 1–9 (2019) Google Scholar
  4. 4.
    Vass, H., Szabo, Z.K.: Application of queuing model to patient flow in emergency department. case study. Proc. Econ. Financ. 32, 479–487 (2015)CrossRefGoogle Scholar
  5. 5.
    Ghamsari, B.N.: Modeling and Improving Patient flow at an Emergency Department in a Local Hospital Using Discrete Event Simulation. Ph.D. thesis, University of Minnesota (2017)Google Scholar
  6. 6.
    Yang, P.C., et al.: Features of online hospital appointment systems in Taiwan: a nationwide survey. Int. J. Environ. Res. Public Health 16(2), 171 (2019)CrossRefGoogle Scholar
  7. 7.
    Zhang, M., Zhang, C., Sun, Q., Cai, Q., Yang, H., Zhang, Y.: Questionnaire survey about use of an online appointment booking system in one large tertiary public hospital outpatient service center in china. BMC Med. Inform. Decis. Making 14(1), 49 (2014)CrossRefGoogle Scholar
  8. 8.
    Zhang, L., Liu, Z.: Empirical analysis of nonlinear characteristics on the patient flows. J. Syst. Manag. 25(3), 527–531 (2016)Google Scholar
  9. 9.
    D’amato, G., et al.: Climate change and air pollution: effects on respiratory allergy. Allergy Asthma Immunol. Res. 8(5), 391–395 (2016)CrossRefGoogle Scholar
  10. 10.
    Kelly, F.J., Fussell, J.C.: Air pollution and public health: emerging hazards and improved understanding of risk. Environ. Geochem. Health 37(4), 631–649 (2015)CrossRefGoogle Scholar
  11. 11.
    Strosnider, H.M., Chang, H.H., Darrow, L.A., Liu, Y., Vaidyanathan, A., Strickland, M.J.: Age-specific associations of ozone and fine particulate matter with respiratory emergency department visits in the united states. Am. J. Respir. Critical Care Med. 199(7), 882–890 (2019)CrossRefGoogle Scholar
  12. 12.
    Bao, X., Tian, X., Yang, C., Li, Y., Hu, Y.: Association between ambient air pollution and hospital admission for epilepsy in Eastern China. Epilepsy Res. 152, 52 (2019)CrossRefGoogle Scholar
  13. 13.
    Mukamal, K.J., Wellenius, G.A., Suh, H.H., Mittleman, M.A.: Weather and air pollution as triggers of severe headaches. Neurology 72(10), 922–927 (2009)CrossRefGoogle Scholar
  14. 14.
    Ren, J., Wang, W.X., Yan, G., Wang, B.H.: Emergence of cooperation induced by preferential learning. arXiv preprint physics/0603007 (2006)Google Scholar
  15. 15.
    Lalwani, A.: Long-range correlations in air quality time series: effect of differencing and shuffling. Aerosol Air Qual. Res. 16(9), 2302–2313 (2016)CrossRefGoogle Scholar
  16. 16.
    Welch, P.: The use of fast fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 15(2), 70–73 (1967)CrossRefGoogle Scholar
  17. 17.
    Alstott, J., Bullmore, E., Plenz, D.: Powerlaw: a python package for analysis of heavy-tailed distributions. PloS One 9(1), e85777 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yonghou He
    • 1
  • Bo Chen
    • 2
  • Yuanxi Li
    • 3
  • Chunqing Wang
    • 1
  • Zili Zhang
    • 1
  • Li Tao
    • 1
    Email author
  1. 1.School of Computer and Information ScienceSouthwest UniversityChongqingChina
  2. 2.The First Affiliated Hospital of Chongqing Medical UniversityChongqingChina
  3. 3.Department of Computer ScienceHong Kong Baptist UniversityKowloon TongChina

Personalised recommendations