DESIGN AND IMPLEMENTATION FOR THE DIAGNOSIS OF AN EXPERT SYSTEM FOR SICKLE CELL DISEASE

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DESIGN AND IMPLEMENTATION FOR THE DIAGNOSIS OF AN EXPERT SYSTEM FOR SICKLE CELL DISEASE

ABSTRCT

Using sickle cell disease (SCD) as a model, the objective of this study was to create a comprehensive learning healthcare system to support disease management and research. A multidisciplinary team developed a SCD clinical data dictionary to standardize bedside data entry and inform a scalable environment capable of converting complex electronic healthcare records (EHRs) into knowledge accessible in real time.

The need for emergency medical diagnosis especially in developing countries where the access to well trained and qualified medical personnel is quite difficult and due to the very high patient to doctor ratio has given rise to the design of a Web-based medical diagnosis expert system application developed using the Waterfall Design Model. The medical diagnostic expert system has been designed to diagnose based on the individual’s symptoms using knowledge base rule and suggests basic precautionary steps subject to the time the patient can access medical personnel. The medical diagnostic expert system was designed with PHP and CSS to provide a friendly User Interface and MySQL for the database. The system has been able to accurately diagnose some common ailments such as malaria, typhoid which are peculiar to the students.

linicians expert in SCD care developed a data dictionary to describe important SCD-associated health maintenance and adverse events. The SCD data dictionary was deployed in the EHR using EPIC SmartForms, an efficient bedside data entry tool. Additional data elements were extracted from the EHR database (Clarity) using Pentaho Data Integration and stored in a data analytics database (SQL). A custom application, the Sickle Cell Knowledgebase, was developed to improve data analysis and visualization. Utilization, accuracy, and completeness of data entry were assessed.

The SCD Knowledgebase facilitates generation of patient-level and aggregate data visualization, driving the translation of data into knowledge that can impact care. A single patient can be selected to monitor health maintenance, comorbidities, adverse event frequency and severity, and medication dosing/adherence.

Disease-specific data dictionaries used at the bedside will ultimately increase the meaningful use of EHR datasets to drive consistent clinical data entry, improve data accuracy, and support analytics that will facilitate quality improvement and research.

TABLE OF CONTENTS

Title page

Certification Page

Approval Page

Dedication

Acknowledgement

Abstract

Organization of Work

Table of contents

CHAPTER ONE

1.0 INTRODUCTION

Problem Statement
1. Aims / Objective of the Study
1. Purpose Of The Study
1. Signification Of The Study
1. Scope / Delimitation
1. Limitation / Constraints
1. Definition Of Terms

CHAPTER TWO

2.0 REVIEW OF RELEVANT LITERATURE

CHAPTER THREE

3.0 DESCRIPTION AND ANALYSIS OF THE PRESENT SYSTEM

Methodology
- General Overview of System
- Organogram
- Information Flow Diagram
- Output Analysis
- Input Analysis
- Process
- Problems Of The Existing System
- Justification Of The New System

CHAPTER FOUR

4.0 DESIGN AND IMPLEMENTATION

Design Standard
- Output Specification And Design
- Input Specification And Design
- File Design
- Procedure Chart
- System Flow- Chart
- Implementation
  - Program Flowchart
  - Psuedo Code
- Coding
- System Requirements
- Testing
- Cutover Process
- Documentation