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), 2024. Published by Science Publishing Group |
Markov Chain Models, Patient Flow, Steady State, Transition Probability Matrix
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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
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
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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}
}
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 -
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