Design And Implementation Of A Medical Diagnostic System

Design And Implementation Of A Medical Diagnostic System

CHAPTER ONE

INTRODUCTION

1.0 BACKGROUND OF STUDY

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. In therapy, the selection of the optimal therapeutic scheme for a specific patient is a complex procedure that requires sound judgement based on clinical  expertise, and knowledge of patient values and preferences, in addition to evidence from research. Usually, the procedure for the selection of the therapeutic scheme is enhanced by the use of simple statistical tools applied to empirical data. In general, decision making about therapy is typically based on recent and older information about the patient and the disease, whereas information or prediction about the potential evolution of the specific patient disease or response to therapy is not available. Recent advances in hardware and software allow the development of modern Therapeutic Decision Support ITDS) systems, which make use of advanced simulation techniques and available patient data to optimize and individualize patient treatment, including diet, drug treatment, or radiotherapy treatment. In addition to this, CDS systems may be used to generate warning messages in unsafe situations, provide information about abnormal values of laboratory tests, present complex research results, and predict morbidity and mortality based on epidemiological data.

1.2 STATEMENT OF THE PROBLEM

Disease diagnosis and treatment constitute the major work of physicians. Some of the time, diagnosis is wrongly done leading to error in drug prescription and further complications in the patient’s health. It has also been noticed that much time is spent in physical examination and interview of patients before treatment commences. The clinical decision support system (CDSS) shall address these problems by effectively providing quality diagnosis in real-time.

1.3 OBJECTIVES OF THE STUDY

To develop modern interactive diagnostic software that will aid clinicians in diagnostic procedures. To offer prescription of medication. To enable flexibility in access to information through the World Wide Web or comprehensive knowledge bases. To offer information on effective disease prevention. To provide for real-time overall effective, efficient and accurate service delivery by clinicians in line with global medical health standards.

 1.4 SIGNIFICANCE OF STUDY

Advances in the areas of computer science and artificial intelligence have allowed for development of computer systems that support clinical diagnostic or therapeutic decisions based on individualized patient data. Clinical decision support (CDS) systems aim to codify and strategically manage biomedical knowledge to handle challenges in clinical practice using mathematical modeling tools, medical data processing techniques and artificial intelligence (A.I.) methods. Its significance is also seen in its ability to: Provide diagnostic support and model the possibility of occurrence of various diseases or the efficiency of alternative therapeutic schemes. Reduce the potential for harmful drug interactions, prescription errors and adverse drug reactions. Enable clinicians report adverse drug reactions to the relevant authorities.
Promote better patient care by enhancing collaboration between physicians and pharmacists.

1.5 SCOPE OF THE STUDY

Due to the fact that it is difficult to develop an expert system for diagnosing all diseases at a time, financial and time constraints, this research is limited to medical diagnosis and treatment for malaria, typhoid fever and pneumonia. The therapy covers severe and uncomplicated cases of the treatment of extreme or severe associated cases in patients such as cerebral malaria which causes insanity, blondness, asthma, tuberculosis and so on. The study will also involve method(s) of diagnosis especially the patient history, physical examination and request for clinical laboratory test but will not go into how these tests are carried out. Rather, it will only make use of the laboratory and treatment.

1.6 LIMITATIONS OF THE STUDY

In the course of this study, a major constraint experienced was that of time factor and insufficient finance. Others include the inevitability of human error and bias as some information were obtained via interpersonal interactions, interviews and research, making some inconsistent with existing realities or outrightly incorrect. Great pains were however taken to ensure that these limitations are at their very minimum and less impactful on the outcome of the work.

1.7 DEFINITION OF RELATED TERMS

Here, the researcher shall try as much as possible to explain certain technical terms used during the course of his study. Prognosis: This is a medical opinion as to the likely outcome of a disease Etiology: This is the branch of medicine that investigates the causes and origin of diseases.
Diagnostic Criteria: This term designates the specific combination of signs, symptoms, and test results that the clinician uses to attempt to determine the correct diagnosis.
Therapy critiquing and consulting: This function of a clinician implies assessing of the therapy looking for inconsistencies, errors, cross-references for drug interactions and prevents prescribing of allergenic drugs.
Allergen: A substance that causes an allergy.Epidemiology: The scientific and medical study of the causes and transmission of disease within a population.

CHAPTER TWO

REVIEW OF REATED LITERATURE

2.0 CLINICAL DIAGNOSTIC SUPPORT SYSTEMS

Advances in the areas of computer science and artificial intelligence have allowed for the development of computer systems that support clinical diagnostic or therapeutic decisions based on individualized patient data(Berner and Bell, 1998; Shortliffe, Pennault, Wiederhold, and Fagan, 1990). Medical diagnostic systems according to Wikipedia—the online encyclopedia are interactive computer programs designed to assist healthcare professionals with decision making tasks. Bankman, 2000, elucidates further by asserting that Clinical Decision Support (CDS) systems aim to codify and strategically manage biomedical knowledge to handle challenges in clinical practice using mathematical modeling tools, medical data processing techniques and Artificial Intelligence (AI) methods. In other words, CDSS are active knowledge systems which use two or more items of patient data to generate case-specific advice (Wyatt and Spiegelhalter, 1991) This kind of software uses relevant knowledge rules within a knowledge base and relevant patient and clinical data to improve clinical decision making on topics like preventive, acute and chronic care, diagnostics, specific test ordering, prescribing practices. Clinicians, health-care staff or patients can manually enter patient characters into the computer system; alternatively, electronic medical records can be queried for retrieval of patient characteristics. These kinds of decision-support systems allow the clinicians to spot and choose the most appropriate treatment.
However, Delaney, Fitzmaurice et al. 1991; Pearson, Moxey et al. 2009) warns that ―regardless of how we choose to define CDS systems, we have to accept  that the field of CDSS is rapidly advancing and unregulated. ―it has a potential for harm if systems are poorly designed and inadequately evaluated, as well as a huge potential to benefit , especially in health care provider performance,,
quality of care and patient outcomes.‖ CDS system is one of the areas addressed by the clinical information systems (CIS). Clinical information systems provide a clinical data repository that stores clinical data such as the patient’s history of illness, diagnosis proferred, treatment as well as interactions with care providers. There are some principal categories to take into account while striving for excellent decision making as outlined by Shortliffe and Cimono 2006.:
a. Accurate data
b. Applicable knowledge
c. Appropriate problem solving skills.

Patient data must be adequate to make a valid decision. The problem arises when the clinician is met with an overwhelming amount of specific and unspecific data, which he/she cannot satisfactorily process. Therefore, it is important to access when additional facts will confuse rather than clarify the patient’s case. For example, a usual setting for such a problem is intensive-care units where practitioners must absorb large amounts of data from various monitors, be aware of the clinical status, patient history, accompanying chronic illness, patient’s medication and adverse drug interactions, etc – and on top of that make an appropriate decision about the course of action. The quality of available data is of equal importance. Measuring instruments and monitors serious adverse effect on patient-care decisions.
Knowledge used in decision making process must be accurate and current. It is a major importance that the deciding clinician has a broad spectrum of medical knowledge and access to information resources, where it is possible to constantly revise and validate that knowledge. For a patient to receive appropriate care, the clinician must be aware of the latest evidence based guidelines and development in the area of the case in question. It is in the clinician’s hands to bring proven therapists from research papers to the fore. CDSS analogously needs an extensive well structured and current source of knowledge to appropriately serve the clinician.ood problem solving skills are needed to utilize available data and knowledge.
Above all, good problem solving skills are needed to utilize available data and knowledge deciding clinicians must set appropriate goals for each task, know how to reason about each goal and taste in to account the trade-offs between costs and benefits of therapy and diagnostics. By incorporating patient specific data and evidence based guidelines or applicable knowledge base, the CDSS can improve quality of care with enhancing the clinical decision making process, (General Practice Electronic Decision Support 2000). In order to be able to construct applicable CDS systems, it is imperative to have a broader-based understanding of medical decision making as it occurs in the natural setting. Designing CDSS without understanding the cognitive processes underlying medical reasoning and decision analysis is pliable for ineffectiveness and failure for implementation into clinical workflow (Patel, Kaufman et al.2002).

2.1 SUCCESS FACTORS OF CDS SYSTEMS

Despite the fact that the computerized CDS systems were continuously in development since the 1970s, their impact on routine clinical practice has not
been as strong as expected. The potential benefits of using electronic decision support systems in clinical practice fall into three broad categories (Coiera 2003):
1. Improved patient safety (reduced medication errors and unwanted adverse events, refined ordering of medication and tests); 2. Improved quality of care (increasing clinicians’ time allocated directly to patient care, increased application of clinical pathways and guidelines, accelerate and encourage the use of latest clinical findings, improved clinical documentation and patient satisfaction); 3. Improved efficiency of health-care (reducing costs through faster order processing, reductions in test duplication, decreased adverse events, and changed patterns of drug prescribing, favoring cheaper but equally effective generic brands).
Developing CDSSs is a challenging process, which may lead to a failure despite our theoretical knowledge about the topic. Understanding the underlying causes, which lead either to success or either to failure, may help to improve the efficiency of CDSS development and deployment in day-to-day practice. Failures can originate from various developmental and implementation phases  failure to technically complete an appropriate system, failure to get the system accepted by the users and failure to integrate the system in the organizational or user environment (Brender, Ammenwerth et al. 2006).
There is an estimation that 45% of computerized medical information systems fail because of user resistance, even though these systems are technologically coherent. Some reasons for such a high percentage of failure may derive from insufficient computer ability, diminished professional autonomy, lack of awareness of long-term benefits of CDSS-use and lack of desire to change the daily workflow (Zheng, Padman et al. 2005). There is also clear evidence that CDSS services are not always used when available, since too numerous systems’ alerts are being overridden or ignored by physicians (Moxey, Robertson et al. 2010).
Despite the problems and failures that might accompany CDSSs, these systems have still been proven to improve drug selection and dosing suggestions, reduce serious medication errors by flagging potential drug reactions, drug allergies and identifying duplication of therapy, they enhance the delivery of preventive care services and improve adherence to recommended care standards. Recent studies suggest that there are some CDSS features crucial to success of these systems (Kawamoto, Houlihan et al. 2005; Shortliffe and Cimino 2006;
Pearson, Moxey et al. 2009; Moxey, Robertson et al. 2010):

CDSS should provide decision support automatically as part of clinicians’ workflow, since systems where clinicians were required to seek out advice manually have not been proven as successful.
Decision support should be delivered at the time and location of decision making. If the clinician has to interrupt the normal pattern of patient care to move to a separate workstation or to follow complex, time-consuming startup procedures it is not likely that such system will be good accepted. Systems that were provided as an integrated component of charting or ordering systems were significantly more likely to succeed than alone standing systems.
Generally speaking, the decision-support element should be incorporated into a larger computer system that is already part of the users’ professional routine, thus making decision support a byproduct of practitioners’ ordinary work practices.
Computerized systems have been reported to be advantageous over paperbased systems.
Systems should provide recommendation rather than just state a patient assessment. For instance, system recommends that the clinician prescribes diuretics for a patient rather just identifying patient being cardiologically decompensated.
CDSS should request the clinician to record a reason for not following the systems’ advice (the clinician is asked to justify the decision with a reason, e.g. ―The patient refused―).
It should promote clinicians’ action rather than inaction.
No need for additional clinical data entry. Due to clinicians’ effort required for entering new patient data, they tend to avoid this process, which is essential for new decision support. Systems should rather acquire new data automatically (e.g. data retrieval from EMR).
The system should be easy to navigate and use, e.g. with quick access and minimal mouse clicks for desired information.
Timing and frequency of prompts are of great importance. For instance if there are too many messages, this might only lead to ignoring all of them and consequently to missing important information. The timing is as well of great importance – the alerts shouldn’t appear at inappropriate times and interrupt the workflow.
The presentation of data or information on CDSSs shouldn’t be too dense or the text to small. Researchers also suggest the use of blinking icons for  important tasks or the arrangement of interactions according to their urgency.
Decision support results should be provided to both clinicians and patients. Studies have shown beneficial effect of such actions, because they stimulate the clinicians to discuss treatment options with patients, and consequently make the latter feel more involved in their medical treatment. Periodic feedback about clinician’s compliance with system decisionmaking. What these features have in common is that they all make it easier for clinicians to implement the CDSS into their workflow, thus making it easier to use. An effective CDSS must minimize the effort to receive and act on system recommendations. Clinicians found it also very practical if the CDSS would back up its decision-making with linking it to other knowledge resources across the intranet or Internet. In their opinion the safety and drug interaction alerts  were the most helpful feature. Above all the organizational factors, such as computer availability at the point of care and technical perfection of CDSS hardware and software are crucial to implementation (Moxey, Robertson et al.
2010). Kawamoto 2005 suggests that the effectiveness of CDSS remains mainly unchanged when system recommendations are stated more strongly and when the evidence supporting these prompts is expanded and includes institutionspecific data.

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