Project Abstract

Abstract
Time series analysis plays a crucial role in understanding patterns and trends in various fields, including healthcare. In this study, we focus on applying time series analysis techniques to patient attendance data in a healthcare setting. The primary objective is to analyze historical patient attendance patterns to forecast future patient numbers accurately. The dataset used in this research comprises daily patient attendance records over a period of several years. The data include information such as the date, time of arrival, patient demographics, and reason for visit. By leveraging this dataset, we aim to identify seasonal trends, day-of-week effects, and other patterns that may influence patient attendance. To achieve our goal, we employ time series analysis methodologies, including decomposition, autocorrelation analysis, and forecasting models such as ARIMA (AutoRegressive Integrated Moving Average). Decomposition helps us separate the time series data into its components of trend, seasonality, and residual variations. Autocorrelation analysis allows us to explore the relationships between past and present patient attendance numbers. Furthermore, we develop ARIMA models to predict future patient attendance based on historical trends and patterns. These models consider factors like seasonality and trend changes to generate accurate forecasts. By evaluating the performance of these models through metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), we can assess the predictive capabilities of our time series analysis. The results of the study demonstrate the effectiveness of time series analysis in understanding and forecasting patient attendance in a healthcare setting. We observe significant seasonal variations in patient numbers, with peaks during certain times of the year. Day-of-week effects also play a role, influencing the distribution of patient attendance across different days. Through accurate forecasting models, we can anticipate future patient attendance levels with reasonable precision. These forecasts are valuable for healthcare providers in resource allocation, staffing decisions, and overall operational planning. By leveraging time series analysis techniques, healthcare institutions can optimize their services to meet patient demand effectively. In conclusion, this research highlights the importance of time series analysis in understanding patient attendance patterns and making informed decisions in healthcare management. By leveraging historical data and advanced forecasting models, healthcare providers can improve their operational efficiency and enhance patient care services.

Project Overview

1.1    INTRODUCTION

Hajia Gambo Sawaba General Hospital since its inception in 1975 has received considerable amount of people, for treatment medical advice, family planning and a host of other reason. Different categories of people have patronized the hospital for its efficiency.

It is therefore in the best interest of the researcher to use his or her knowledge of statistic application is the attendance of ill health (patients) attending the hospital. It does not and here the research as will look forward to classifying, arranging and recording the monthly, quarterly, annual, bi-annual attendance of patients in the hospital.

In an attempt to introduce efficient methods and routine towards comparing the total attendance of, in and out patient this in general comprises of male, female and children patient attending the hospital.

To crown it all, it shall be in form of data (secondary, primary data) depending on the set of people wishing to use it and purpose or criterion behind using the research, the data collected will be analysis organized, summarized and compiled. Since hospital patronage is consistent and continuous process, it will be an efficient data collection, centres and will promote statistical application and voluminous data i.e. moving average and time series analysis.

1.2    AIMS OF OBJECTIVE

1.       To determine whether there is an increase or decrease in patients’ attendance.

2.       To forecast the patient attendance by using linear trend method

3.       To forecast for patient attendance using the fitted trend equation from 2008 to 2012

1.3    SCOPE AND LIMITATION

This research will limit it analysis on the comparison of the attendance of patient. (IN and OUT) based on secondary data collected from the hospital Hajiya Gambo Sawaba General Hospital Zaria from (1998 to 2007).

1.4    HISTORICAL BACKGROUND OF SOURCE OF DATA

The hospital was established and commissioned by His Excellency the Governor of Kano State Late Alaji Audu Bako in 1975. The hospital is directly under Kaduna State Ministry of Health.

VARIOUS DEPARTMENT OF THE HOSPITAL

The hospital consists of nine (9) departments. The various departments include:

Medical Department: The Medical Director is the overall boss of the hospital. He is only answerable to the Kaduna State Commissioner of Health. He is in charge of all medical cases.

Administration Department: Hospital secretary is the head of this department. He heads all the administrative staff of various departments of the hospital and all heads of department are under him and answerable them, as every head administered on his behalf.  

Nursing Service Department: The nursing service department is in charge with central of nurses, their posting, their duty roster, their shifting training to other post basic courses.

Medical Record Department: This department deals with keeping record and collection of data.

Laboratory: Technologist works in the laboratory and for the operation of laboratories equipment.

Pharmacy Department: The pharmaceutical department is in charge with the supply of drugs, drugs custody, drug protection and storage etc.

Radiology Department: The radiology is in charge with x-ray e.g. chinstraps etc.

Ophthalmology Department: Ophthalmology department is in charge with all cases of eye, its treatment etc.

E.N.T Department: This department is in charge with all cases of ear.