| Peer-Reviewed

Markovian Approach for Analyzing Patient Flow Data: A Study of Kapsabet County Referral Hospital, Kenya

Received: 10 June 2022     Accepted: 30 June 2022     Published: 12 July 2022
Views:       Downloads:
Abstract

Hospital is indispensable and necessary welfare of society. Through it, we can manage our illnesses by treatment and prevention interventions. With the rise incidences of chronic diseases and illnesses, there has been an increased demand for health care services round the world. This demand has subsequently caused a serious pressure resulting to serious episodes of congestion and overcrowding in hospitals. Hospital overcrowding and congestion, has always been a problem to patients, hospital administration and to the general health workers. Hospitals are struggling to alleviate congestion and overcrowding. In this study, we developed an objective patient flow estimation using Markov chain models. Weekly data from Kapsabet County Referral Hospital facility was used to assess the flow. Markov chains’ transition probability matrices were constructed for each day in a week. Markov chain’s four-state model used was; High, Medium, Low and Very Low. The future n step transition probabilities matrices were computed, giving rise to steady state for each day of the week. It was examined that the patient flow had some pattern through the Markov chains’ steady states. The steady state probability of the flow is high on Mondays with highest probability of 0.57. Medium on Tuesdays through to Thursdays with steady state probabilities ranging from 0.36 and 0.3 respectively. On Fridays the probabilities decrease from 0.22 to 0.12 on Sunday. Through this study, we can witness some pattern from steady state of transition matrices. This way, the patient’s population flow throughout the week at this facility is identified. Generally, through this study, the patient flow is understood and hence the patient flow congestion can be easily attenuated.

Published in International Journal of Systems Science and Applied Mathematics (Volume 7, Issue 2)
DOI 10.11648/j.ijssam.20220702.12
Page(s) 39-45
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2022. Published by Science Publishing Group

Keywords

Markov Chain Models, Patient Flow, Steady State, Transition Probability Matrix

References
[1] Houghton (2007). The American Heritage Medical Dictionary. USA: Houghton Mufflin Harcourt.
[2] Fits Gerald, G. S Toloo, J. Rego, J Ting, P. Aitken, V. Pipett (2012). ’Demand for public hospital emergency department services in Australia; 2000-2001 to 2009-2010’ Emergency Medicine Australisia 24 (1); 72-78.
[3] Chiara Canta and Marie-Louise Leroux (2013). Public and private hospitals, congestion and redistribution.
[4] Hall. R., Belson, D., Murali, P., & Dessouky, M. (2006). Modeling Patient flow through the healthcare system. In R. Hall (Ed), patient flow: Reducing delay in healthcare delivery, Vol. 91: ,1-44: springer US.
[5] L. He, Y. Li and S. H Chung (2017). Markov chain based modeling and analysis of colonoscopy screening process. 740-745.
[6] Olwanda, Easter & Shen, Jennifer & Kahn, James & Bryant-Comstock, Katelyn & Huchko, Megan. (2018). Comparison of patient flow and provider efficiency of two delivery strategies for HPV-based cervical cancer screening in Western Kenya: a time and motion study. Global Health Action. 11.1451455. 10.1080/16549716.2018.1451455.
[7] Aeenparast, Afsoon & Farzadi, Faranak & Maftoon, Farzaneh & Yahyazadeh, Hossein. (2019). Patient Flow Analysis in General Hospitals: How Clinical Disciplines Affect Outpatient Wait Times. Hospital Practices and Research. 4.128-133.10.15171/hpr.2019.26.
[8] Tavakoli, M., Tavakkoli Moghaddam, R., Mesbahi, R. (2022). Simulation of the COVID-19 Patient flow and investigation of future patient arrival using a time series prediction model: A real case study. Med. Biol. Eng Comput 60, 969-990 (2022). https://doi.org/10.1007/s11517-022-02525-z
[9] Haifeng, Xie, Chaussalet, T. J., and Millard, P. (2006). A model-based approach to the analysis of patterns of length of stay in institutional long-term Care. Information technology in biomedicine IEEE transactions on 10 (3); 512-518.37.
[10] Garge, L., Mc Clean, S., Meenan, B., Millard P., (2010). A non- homogeneous discrete time Markov model for admission scheduling and resource planning in cost and capacity constrained health care system. Health care management science 13 (2): 155-169.
[11] Cochran, J. K., and Roche, K. T (2009). A multi class queuing network analysis methodology for improving hospital emergency department performance. Computer and operation research, 36 (5): 1497-1512.
[12] Cote, M. J., & Stein, W. E (2007). A stochastic model for a visit to doctor’s office. Mathematical and computer modeling, 45 (3-4): 309-323.
[13] Perez A., Chan, & Dennis W. (2004). Predicting the length of stay of patients admitted for intensive care using first step analysis. Health care management science, 127-138.
[14] Akkerman, R., Knip, M. (2004). Reallocation of beds to reduce waiting time for Cardiac surgery. Health Care Management Science, 119-126.
[15] Mc Clean, S. I., MC Alea, B., & Millard, P. H (1998). Using Markov reward model to estimate spend down cost for geriatric department. Joper Res Soc 49 (10): 1021-1025.
[16] Nicola Bartolomeo, Paulo T., Annamaria M., and Gabriella S., (2008). A Markov model to evaluate hospital re-admission BMC Medical Research Methodology 8 (1) 23.
[17] James, R Broyles, Jeffery K Cochran and Douglas C Montgomery (2010). A statistical Markov chain approximation of transient hospital inpatient inventory. European journal of operational research, 207 (3): 1645-1657.
[18] Ross, S. M (2009). Stochastic process (2ndEd.) New Delhi; John Wiley and sons.
Cite This Article
  • APA Style

    Merary Kipkogei, Edgar Ouko Otumba. (2022). Markovian Approach for Analyzing Patient Flow Data: A Study of Kapsabet County Referral Hospital, Kenya. International Journal of Systems Science and Applied Mathematics, 7(2), 39-45. https://doi.org/10.11648/j.ijssam.20220702.12

    Copy | Download

    ACS Style

    Merary Kipkogei; Edgar Ouko Otumba. Markovian Approach for Analyzing Patient Flow Data: A Study of Kapsabet County Referral Hospital, Kenya. Int. J. Syst. Sci. Appl. Math. 2022, 7(2), 39-45. doi: 10.11648/j.ijssam.20220702.12

    Copy | Download

    AMA Style

    Merary Kipkogei, Edgar Ouko Otumba. Markovian Approach for Analyzing Patient Flow Data: A Study of Kapsabet County Referral Hospital, Kenya. Int J Syst Sci Appl Math. 2022;7(2):39-45. doi: 10.11648/j.ijssam.20220702.12

    Copy | Download

  • @article{10.11648/j.ijssam.20220702.12,
      author = {Merary Kipkogei and Edgar Ouko Otumba},
      title = {Markovian Approach for Analyzing Patient Flow Data: A Study of Kapsabet County Referral Hospital, Kenya},
      journal = {International Journal of Systems Science and Applied Mathematics},
      volume = {7},
      number = {2},
      pages = {39-45},
      doi = {10.11648/j.ijssam.20220702.12},
      url = {https://doi.org/10.11648/j.ijssam.20220702.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssam.20220702.12},
      abstract = {Hospital is indispensable and necessary welfare of society. Through it, we can manage our illnesses by treatment and prevention interventions. With the rise incidences of chronic diseases and illnesses, there has been an increased demand for health care services round the world. This demand has subsequently caused a serious pressure resulting to serious episodes of congestion and overcrowding in hospitals. Hospital overcrowding and congestion, has always been a problem to patients, hospital administration and to the general health workers. Hospitals are struggling to alleviate congestion and overcrowding. In this study, we developed an objective patient flow estimation using Markov chain models. Weekly data from Kapsabet County Referral Hospital facility was used to assess the flow. Markov chains’ transition probability matrices were constructed for each day in a week. Markov chain’s four-state model used was; High, Medium, Low and Very Low. The future n step transition probabilities matrices were computed, giving rise to steady state for each day of the week. It was examined that the patient flow had some pattern through the Markov chains’ steady states. The steady state probability of the flow is high on Mondays with highest probability of 0.57. Medium on Tuesdays through to Thursdays with steady state probabilities ranging from 0.36 and 0.3 respectively. On Fridays the probabilities decrease from 0.22 to 0.12 on Sunday. Through this study, we can witness some pattern from steady state of transition matrices. This way, the patient’s population flow throughout the week at this facility is identified. Generally, through this study, the patient flow is understood and hence the patient flow congestion can be easily attenuated.},
     year = {2022}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Markovian Approach for Analyzing Patient Flow Data: A Study of Kapsabet County Referral Hospital, Kenya
    AU  - Merary Kipkogei
    AU  - Edgar Ouko Otumba
    Y1  - 2022/07/12
    PY  - 2022
    N1  - https://doi.org/10.11648/j.ijssam.20220702.12
    DO  - 10.11648/j.ijssam.20220702.12
    T2  - International Journal of Systems Science and Applied Mathematics
    JF  - International Journal of Systems Science and Applied Mathematics
    JO  - International Journal of Systems Science and Applied Mathematics
    SP  - 39
    EP  - 45
    PB  - Science Publishing Group
    SN  - 2575-5803
    UR  - https://doi.org/10.11648/j.ijssam.20220702.12
    AB  - Hospital is indispensable and necessary welfare of society. Through it, we can manage our illnesses by treatment and prevention interventions. With the rise incidences of chronic diseases and illnesses, there has been an increased demand for health care services round the world. This demand has subsequently caused a serious pressure resulting to serious episodes of congestion and overcrowding in hospitals. Hospital overcrowding and congestion, has always been a problem to patients, hospital administration and to the general health workers. Hospitals are struggling to alleviate congestion and overcrowding. In this study, we developed an objective patient flow estimation using Markov chain models. Weekly data from Kapsabet County Referral Hospital facility was used to assess the flow. Markov chains’ transition probability matrices were constructed for each day in a week. Markov chain’s four-state model used was; High, Medium, Low and Very Low. The future n step transition probabilities matrices were computed, giving rise to steady state for each day of the week. It was examined that the patient flow had some pattern through the Markov chains’ steady states. The steady state probability of the flow is high on Mondays with highest probability of 0.57. Medium on Tuesdays through to Thursdays with steady state probabilities ranging from 0.36 and 0.3 respectively. On Fridays the probabilities decrease from 0.22 to 0.12 on Sunday. Through this study, we can witness some pattern from steady state of transition matrices. This way, the patient’s population flow throughout the week at this facility is identified. Generally, through this study, the patient flow is understood and hence the patient flow congestion can be easily attenuated.
    VL  - 7
    IS  - 2
    ER  - 

    Copy | Download

Author Information
  • Department of Statistics and Actuarial Sciences, Maseno University, Maseno, Kenya

  • Department of Statistics and Actuarial Sciences, Maseno University, Maseno, Kenya

  • Sections