ABSTRACT

Cardiac outpatients are those with heart -related diseases but are not on admission. In the present study, a stochastic approach was used for modeling the cardiac outpatient flow in Nnamdi Azikiwe University Teachin g Hospital (NAUTH) Nnewi in a way to solving the long waiting times cardiac outpatient experienced before they are being attended to. In this study, Monte Carlo Simulation Method and queuing theory were used to analyse the inter -arrival and service time of the outpatient and measure of system performance, respectively. On the basis of the results obtained from the models in Table 4.7.2 and 4.82, it is vividly clear that having one doctor (S = 1) in morning shift would be inadequate for providing relatively prompt treatment needed by patients.

1.1 INTRODUCTION

CHAPTER ONE

INTRODUCTION

The simple, but elusive goals in health care delivery are “to deliver the right care, to the right patient”, “at the right time”.
“To the right patient”, means that the health care delivery system must be able to discriminate among patients with different types and severities of disease so that an individual patient is neither under -or over-treated with an appropriate therapy.
“At the right time” means that each patient must have access to care within a time frame that is medically appropriate for his or her illness.
For example, long waiting times by patients seeking consultation has been a long term complaint. Enhancing productivity while maintaining a high level of quality has become a challenge for healt hcare managers. The major factor for patients in terms of quality concerns waiting time which has become a significant portion of determining the service quality.
This project surveys the contributions and applications of queueing theory in the field of healthcare processes, in which patients arrive, wait for service, obtain service and then depart.
Windsor star (Health J ournal), of 29th J une 2000, Toronto – Canada reported that fifty -five people have died while waiting for heart operations in Ontario i n the last ten months, a “significant” increase on previous years that has experts worried. A new study yet to be published concludes that “excessive waiting times” are a factor in such deaths, a spokesman for Ontario’s Cardiac Care Network said the length of Cardiac Surgery waiting lists in the province soared by almost 30 percent last year.
Right now, waits at peak hours are long, sometimes more then six hours, said J ohn Greenaway, the Antonio Deluca hospital’s chief of staff. “Our patients don’t like t hat, our staff doesn’t like that, and our board doesn’t like that” reported by Brain Cross, Star Health/Science Reporter.
Therefore excessive waiting time by patents has become everybody’s headache in Health care institution and all hands must be on deck to tame this monster.

1.2 STATEMENT OF THE PROBLEM
In the outpatient department, long waiting times for treatment followed by short consultations have long been complaints of patients. The Windsor Star -Health J ournal in Canada, reported that some Canad ian doctors believe that hospital emergency departments are being hit with fallout of increased waiting times; the longer patients wait, the worse their illness becomes, and the more likely they are to end up in emergency. Thus, Health Managers have a numb er of very good reasons to be concerned with waiting lines. Chief among these reasons are the following:
🠹 The cost to provide waiting space;
🠹 A possible loss of goodwill and health deterioration;
🠹 A possible reduction in customer satisfaction;
🠹 The resulting congestion may disrupt other business operation and/or customers.

1.3 OBJ ECTIVES
These are:
(a) Improvement of patients flow to avoid congestion;
(b) Reducing doctor’s stress and improve patient safety from life threatening cardiac attack;

(c) To ameliorate patient dissatisfaction from long waits coupled with incessant bumping into the physicians.

1.4 SCOPE OF THE STUDY
This includes the following
(a) Queueing theory is to be used in modeling cardiac outpatient flow in NAUTH. The outpatient flo w involves the arrival and service time of the patient that follows exponential distribution by assumption. This assumption has to be verified.
(b) The mathematical estimation of measure of system performance (i.e. △, P0, Ls, Lq, Ws, Wq) of M/M/S model will be determined on a single server (S = 1) or multiple server (S = 2). By implication, the two alternative being considered are to continue to having just one Chief consultant doctor on clinic day or add a second doctor.
(c) The mathematical estimation o f measure of system performance of formulated priority – discipline queueing model will be determined on a single doctor (S = 1) or multiple doctor (S = 2).
(d) Lastly, given that the mean service rate does increase as the queue size increases, it is desir able to develop a theoretical model (state – dependent service rate) that seems to describe the pattern by which it increases. This model not only should bed a reasonable approximation of the actual pattern but also should be simple enough to be practical for implementation.

1.5 SIGNIFICANCE OF THE STUDY
The significance of this work cannot be overemphasized. This study, when completed will be of tremendous relevance to Health care managers who take decisions in hospital management without the help of q uantitative model – based analyses, but will now have queueing theory to model a health care process at their disposal.

1.6 DEFINITION OF SOME QUEUEING THEORY TERMINOLOGIES
(a) Balking : This is where customers decide not to join the queue.
(b) Blocking : Blocking occurs when a queueing system places a limit on queue length. In a hospital, patients who find all beds occupied are refused admissions.
(c) Queue Length : This is the number of customers (patients) waiting in the queue.
(d) Reneging : This is when a customer (patient) joining the queue leaves it afterward without being served.
(e) Steady State : This is the state of the system in the long run. This is, when there is stability in its component parts – the arrival rate, the service facilities and service rate.
(f) Transient State : This is the opposite of a steady state. It describes a situation whereby the component parts of the queuing system change. Also the probability of a given number of customers (patients) in the system at any point in time changes from time to time.
(g) FCFS: first – come, first – served
(h) PS: Priority served

1.7 ORGANIZATION OF THE STUDY
The study is aimed at modeling cardiac outpatient flow in NAUTH – an application of queuing theory.
The work is organized in fiv e systematic chapters. Chapter one is made up of the introduction to the study, including its background. Other sub -topics considered in this chapter include: the statement of the problem, the objectives of the study, the scope and significance of the stud y, some queueing theory terminologies and the organization of the study.
In chapter two, the related literature are reviewed as to ascertaining what various scholars had said. In this chapter, the following are considered: variable arrival rate, priority queuing discipline and appointment systems.