Design and Implementation of Electronic Diagnosis System (A Study of General Hospital, Ishiagu)
Chapter One
OBJECTIVES OF THE STUDY
The major objective of this work is to develop an expert system for diagnosing non-communicable diseases.
It also targets contributing to academic research work.
- To develop modern interactive diagnostic software to aid clinicians in diagnostic procedures.
- To offer a prescription of medication.
- To enable flexibility in access to information through the World Wide Web or comprehensive knowledge bases.
- It is also to ascertain whether the diseases could be diagnosed based on signs and symptoms.
- It will also examine a patient based on simple clinical signs, and to improve family and community health
CHAPTER TWO
REVIEW OF REATED LITERATURE
INTRODUCTION
Medical diagnosis, (often simply termed diagnosis) refers both to the process of attempting to determine or identifying a possible disease or disorder to the opinion reached by this process. A diagnosis in the sense of diagnostic procedure can be regarded as an attempt at classifying an individual’s health condition into separate and distinct categories that allow medical decisions about treatment and prognosis to be made. Subsequently, a diagnostic opinion is often described in terms of a disease or other conditions.
In the medical diagnostic system procedures, elucidation of the etiology of the disease or conditions of interest, that is, what caused the disease or condition and its origin is not entirely necessary. Such elucidation can be useful to optimize treatment, further specify the prognosis or prevent recurrence of the disease or condition in the future.
Clinical decision support systems (CDSS) are interactive computer programs designed to assist healthcare professionals such as physicians, physical therapists, optometrists, healthcare scientists, dentists, pediatrists, nurse practitioners or physical assistants with decision making skills. The clinician interacts with the software utilizing both the clinician’s knowledge and the software to make a better analysis of the patient’s data than neither humans nor software could make on their own.
Typically, the system makes suggestions for the clinician to look through and the he picks useful information and removes erroneous suggestions.
To diagnose a disease, a physician is usually based on the clinical history and physical examination of the patient, visual inspection of medical images, as well as the results of laboratory tests. In some cases, confirmation of the diagnosis is particularly difficult because it requires specialization and experience, or even the application of interventional methodologies (e.g., biopsy). Interpretation of medical images (e.g., Computed Tomography, Magnetic Resonance Imaging, Ultrasound, etc.) usually performed by radiologists, is often limited due to the non-systematic search patterns of humans, the presence of structure noise (camouflaging normal anatomical background) in the image, and the presentation of complex disease states requiring the integration of vast amounts of image data and clinical information. Computer-Aided Diagnosis (CAD), defined as a diagnosis made by a physician who uses the output from a computerized analysis of medical data as a “second opinion” in detecting lesions, assessing disease severity, and making diagnostic decisions, is expected to enhance the diagnostic capabilities of physicians and reduce the time required for accurate diagnosis. With CAD, the final diagnosis is made by the physician.
The first CAD systems were developed in the early 1950s and were based on production rules (Shortliffe, 1976) and decision frames (Engelmore& Morgan, 1988). More complex systems were later developed, including blackboard systems (Engelmore& Morgan, 1988) to extract a decision, Bayes models (Spiegelhalter, Myles, Jones, & Abrams, 1999) and artificial neural networks (ANNs) (Haykin, 1999). Recently, a number of CAD systems have been implemented to address a number of diagnostic problems. CAD systems are usually based on biosignals, including the electrocardiogram (ECG), electroencephalogram (EEG), and so on or medical images from a number of modalities, including radiography, computed tomography, magnetic resonance imaging, ultrasound imaging, and so on.
CHAPTER THREE
METHODOLOGY AND SYSTEM ANALYSIS
PREAMBLE
Procedures used in data collection and information gathering are here, outlined and analyzed. Data was carefully collated and objectively evaluated in order to define as well as ultimately provide solutions to the problems for which the research work is based.
During the research work, data collection was carried out in many places. In gathering and collecting necessary data and information needed for system analysis, two major fact-finding techniques were used in this work and they are:
- Primary source
- Secondary source
Primary source:
Primary source refers to the sources of collecting original data in which the researcher made use of empirical approach such as personal interview and questionnaires.
This involved series of orally conducted interviews with select clinicians in public and private healthcare practice on the diagnostic procedures they adopt. Also, some patients were interviewed with a view to getting information about their opinion on how medical diagnoses affected them.
Secondary Source:
Perusals through online journals and e-books as well as visits to relevant websites, medical dictionaries and other research materials increased my knowledge and aided my comprehension of diagnostic processes.
METHODS OF DATA COLLECTION
Oral Interview
This was done between the researcher and the doctors in the hospital used for the studies, and the lab attendance was interviewed. Reliable facts were got based on the questions posed to the staff by the researcher.
Study of Manuals
Manuals and report based used by lab attendance were studied and a lot of information concerning the system in question was obtained.
Evaluation of Forms
Some forms that are necessary and available were assed. These include admission card, lab form, test result, bill card Etc. These forms help in the design of the new system.
CHAPTER FOUR
DESIGN, TESTING AND IMPLEMENTATION OF SYSTEM
DESIGN STANDARD
The major objective of this design is to achieve a new system that is more reliable and robust than the existing system in terms of rapid disease prognosis, diagnosis and treatment prescription based on the accurate disease symptoms as provided by the patient in the course of examination and the expert system’s inference.
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATIONS
SUMMARY
The primary goal of clinical decision support systems development, as for any branch of biomedical research, is to improve the overall health of the population. CDSSs may contribute to this by improving the quality of healthcare services, as well as by controlling the cost-effectiveness of medical examinations and treatment.
The ultimate acceptance of CDS systems will depend not only on the performance of the computerized method alone, but also on how well the human performs the task when the computer output is used as an aid and on the ability to integrate the computerized analysis method into routine clinical practice (Hunt, Haynes, Hanna & Smith, 1998).
Issues, such as a friendly user-interface, a short system response time and low cost, are critical for the daily routine use of CDS systems. Obviously, the development of CDS systems requires close collaboration of two scientific areas: medicine and computer science. This collaboration aims to codify knowledge and define the logical procedures used by the physician to reach a conclusion.
As a result, the engineer must “extract” knowledge from the physician and reproduce it appropriately. This is particularly difficult because the physician’s decisions are the result of a complex procedure combining special knowledge and experience.
CONCLUSION
The coupling of CDSS technology with evidence-based medicine brings together two potentially powerful methods for improving health care quality. To realize the potentials of this synergy, literature-based and practice-based evidence must be captured into computable knowledge bases, technical and methodological foundations for evidence-adaptive CDSSs must be developed and maintained, and public policies must be established to finance the implementation of electronic medical records and CDSSs and to reward health care quality improvement.
RECOMMENDATIONS
Based on the remarkable successes recorded by clinical decision support systems in robust health care delivery, this research work is therefore recommended to approved health institutions such as: hospitals, primary health centers, medical laboratoriesetc to further enhance diagnostic processes by clinicians hereby guaranteeing efficiency in drug or therapy prescription and ultimately ensuring effective treatment.
Quoting Delaney, Fitzmaurice, Riaz& Hobbs, 1999, future trends and challenges in the area of CDS systems include the creation of links to patient electronic medical records and a universally-agreed upon medical vocabulary, so that the entries in the medical records can have well-defined meanings. In addition to this, studies that evaluate the performance of CDS systems in clinical practice, in conjunction with demonstrations of cost-effectiveness, are a critical stage in further developing CDS systems. Users should be responsible for carefully monitoring the introduction of any new system carefully.
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