An analysis of patient flow using markov Chains: a case of Kapsabet county referral Hospital

Abstract

Hospital is an essential welfare of society. It provides management of illnesses through treatmentandpreventioninterventionsbymedicalandhealthprofessionals. Duetogrowing population and rise in chronic diseases, there is an increased demand for health care services. Thiscausescongestionandovercrowdinginmosthospitals. Hospitalovercrowding is a major problem to patients, hospital administration and to the general health workers. Hospitals in many jurisdictions struggle to reduce congestion and improve the patient flow across the continuum of care. In this project report, we have developed an objective patient flow assessment through an analysis of Markov chains using weekly data from Kapsabet county referral hospital to check on the patient’s flow at the hospital. Weusedweeklydatatoconstructtransitionmatricesforeachdayinaweektoportraythe weekly routine amount of the patients in the hospital using the states; high, medium,low and very low. Steady state transition matrices were also computed for each day of the week to reflect the future flow for each week. It was found that the patient flow had some pattern observed through the steady states. The probability of patient flow being high tends to be up on Mondays with probability of 0.57, medium on Tuesdays to Thursdays with probabilities of being high ranging from 0.36 on Tuesdays and 0.3 on Thursdays, on Fridays is when the steady states of being high starting to decrease upto Sunday with steady state probabilities of 0.22 on Friday, 0.17 on Saturday and 0.12 on Sunday. ThroughtheanalysisofpatientflowusingMarkovchains,wehaveidentifiedsomepattern of how the patient flow throughout the week. Generally through this study, the patient flow congestion can be easily understood, handled and hence controlled