MODELING CARDIAC OUT–PATIENT FLOW IN NNAMDI AZIKIWE UNIVERSITY TEACHING HOSPITAL (NAUTH) NNEWI WITH MONTE CARLO SIMULATION: AN APPLICATION ON QUEUEING THEORY

MODELING CARDIAC OUT–PATIENT FLOW IN NNAMDI AZIKIWE UNIVERSITY TEACHING HOSPITAL (NAUTH) NNEWI WITH MONTE CARLO SIMULATION: AN APPLICATION ON QUEUEING THEORY

  • The Complete Research Material is averagely 70 pages long and it is in Ms Word Format, it has 1-5 Chapters.
  • Major Attributes are Abstract, All Chapters, Figures, Appendix, References.
  • Study Level: BTech, BSc, BEng, BA, HND, ND or NCE.
  • Full Access Fee: ₦6,000

Get the complete project » Instant Download Active

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 Teaching 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.


CHAPTER ONE

INTRODUCTION

1.1      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 healthcare managers. The major factor for patients in terms of quality concerns waiting time which


1


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 Journal), of 29th June 2000, Toronto – Canada reported that fifty-five people have died while waiting for heart operations in Ontario in 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 John Greenaway, the Antonio Deluca hospital’s chief of staff. “Our patients don’t like that, 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.


2


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 Journal in Canada, reported that some Canadian 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 number 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      OBJECTIVES

These are:

(a)         Improvement of patients flow to avoid congestion;

(b)        Reducing doctor’s stress and improve patient safety from life threatening cardiac attack;


3


(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 flow 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. r, 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 of measure of system performance of formulated priority – discipline queueing


4


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 desirable 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 quantitative model – based

analyses, but will now have queueing theory to model a health

care process at their disposal.


5


1.6      DEFINITION OF SOME QUEUEING THEORY TERMINOLOGIES

(a)         Balking: This is where customers decide not to join the queue.

(b)       


You either get what you want or your money back. T&C Apply





Share a Comment


You can find more project topics easily, just search

Quick Project Topic Search