| dc.description.abstract | Queuing theory is the mathematical study of waiting lines or queues, a phenomenon
which is very common in any service provision facility such as supermarkets, banks,
hospitals, library, transport and telecommunication lines among others. Queues occur
when requests for service is greater than service providers. Long queues are
undesirable in any service provision point because they result in time wastage, anxiety
and boredom leading to poor customer satisfaction, poor sales and reduced
profits, and most disastrously death if a critical patient is not attended to in time.
The research models queuing scenario at Moi Teaching and Referral Hospital, one of
the major health referral facilities in Kenya, located in Eldoret Town of Uasin Gishu
County. The Government of Kenya introduced agenda four flagship development
programs in 2017 one of them being provision of universal health care. This is informed
by the rapidly growing population in Kenya coupled with spread of diseases,
high birth rate and low life expectancy rates. The above led to overcrowding in
Hospitals’ Emergency Departments thus threatening the achievement of the health
agenda and all the agendas in general. The emergence of Covid-19 posed great health
challenge worldwide whereby health care facilities were fully stretched with no space
to admit new critical patients. This motivated us to model the process as a queuing
system with heterogeneous server pools, where the pools represent the wards and
servers are beds. We analyzed this system under various queue-architectures and
routing policies, in search for fairness and optimum operational performance so as to
enhance the level of access to health care in the facility. Focusing only on the stream
of emergency patients, a queuing network model interaction between the Emergency
Department and Internal Wards, which was believed to cause a major proportion
of the blocking at the Emergency Department. Through the use of secondary data
from the Hospital and existing models such as Kendall’s, Erlang’s, Little’s Law and
de Bruin, various ward/unit operating characteristics and sufficient bed count were
determined so as to guarantee certain access standards to care. Results have shown
that redistribution of beds among the wards is significant in reducing congestion in
the facility. | en_US |
