Computer Science Project Topics

Design and Implementation of Electronic Diagnosis System – a Study of General Hospital Ishiagu

Design and Implementation of Electronic Diagnosis System – a Study of General Hospital Ishiagu





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 (TDS) 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.


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 Cimono2006.:

  1. Accurate data
  2. Applicable knowledge
  3. 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).


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).

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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 paper-based 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 decision-making.

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 institution-specific data.


There have been multiple attempts through history to construct a computer or program, which would assist clinicians with their decisions concerning diagnosis and therapy. Ledley and Lusted published the first article evolving around this idea in 1959. The first really functional CDSS didn’t appear until the 1970s.

Some of them are reviewed below: Leeds abdominal pain, MYCIN, HELP and Internist-1.

Leeds Abdominal Pain

  1. T. de Dombal and his co-workers at University of Leeds developed Leeds abdominal pain. It used Bayesian reasoning on basis of surgical and pathological diagnoses. These pieces of information were gathered from thousands of patients and put into systems’ database. The Leeds abdominal pain system used sensitivity, specificity and disease prevalence data for various signs, symptoms and test results. With help of Bayes’ theorem it calculated the probability of seven possible diagnoses resulting in acute abdominal pain: appendicitis, diverticulitis, perforated ulcer, cholecystitis, small-bowel obstruction, pancreatitis, and nonspecific abdominal pain. The system assumed that each patient with abdominal pain had one of these seven conditions, thus selected the most likely diagnose on the basis of recorded observations. Evaluation of the system was done by de Dombal et al. in 1972. It showed that the clinicians’ diagnoses were correct in only 65 to 80 percent of the 304 cases, whereas the program’s diagnoses were correct in 91.8 percent of cases. Surprisingly, the system has never achieved similar results of diagnostic accuracy in practice outside the Leeds University. The most likely reason for that is the variation of data that clinicians entered into the system for acquiring correct diagnoses (de Dombal, Leaper et al. 1972).


This was a consultation system that emphasized appropriate management of patients who had infections rather than just finding their diagnosis. The developers of this system formed production rules (IF-THEN rules), on basis of current knowledge about infectious diseases. The MYCIN program determined which rules to use and how to chain them together in order to make decisions about a specific case. System developers could update the system’s knowledge structure rapidly by removing, altering, or adding rules, without reprogramming or restructuring other parts of the system (Shortliffe 1976).

The HELP System

The HELP system is actually an integrated hospital information system with the ability to generate alerts when data abnormalities in the patient record are noted. It can output data either automatically, in form of printed reports, or it can display specific information, if so requested. Furthermore, the system has an event-driven mechanism for generation of specialized warnings, alerts and reports (Burke, Classen et al. 1991).


This was an experimental CDSS designed by Pople and Myers at the University of Pittsburg in 1974. It was a rule-based expert system capable of making multiple, complex diagnoses in internal medicine based on patient observations. The Internist-I was using a tree-structured database that linked symptoms with diseases. The evaluation of the system revealed that it was not sufficiently reliable for clinical application. Nevertheless, the most valuable product of the system was its medical knowledge base. This was used as a basis for successor systems including CADUCEUS and Quick Medical Reference (QMR), a commercialized diagnostic CDSS for internists (Miller, Pople et al. 1982).



The Athena decision support system was deployed in 2002 as a tool to implement guidelines for hypertension. It encourages blood pressure control and issues recommendations about a suitable choice of therapy, concordant with latest guidelines. It also considers co-morbidities of the specific patient in question. ATHENA DSS has an easily changeable knowledge base that specifies criteria for eligibility, risk stratification, set blood pressure margins, it includes relevant co-morbid states and guideline-recommendation, specific for patients with present co-morbidities. The knowledge base also comprises of preferences for certain drugs within antihypertensive drug groups according to the latest evidence.

New pieces of evidence are constantly changing protocols of best hypertension management; ATHENA is thus designed to be accessible to clinicians for knowledge base-customization and to custom local interpretations of guidelines according to the local population structure and other factors.

The system was designed to be independently integrated into a variety of EMR-systems, and is thus interchangeable and adaptable for various health information-systems. The effectiveness, accuracy and success of implementation has been researched and reviewed on many occasions (Goldstein, Coleman et al. 2004; Lai, Goldstein et al. 2004).


Isabel is a web-based diagnosis decision support system that was created in 2001 by physicians. It offers diagnosis decision support at the point of care. The system is eligible for all aged patients, from neonates to geriatrics. Its database covers major specialties like Internal Medicine, Surgery, Gynecology & Obstetrics, Pediatrics, Geriatrics, Oncology, Toxicology and Bioterrorism. Isabel produces an instant list of likely diagnoses for a given set of clinical features (symptoms, signs, results of tests and investigations etc), followed by suggesting the administration of suitable drugs. This is executed by reconciling (i.e. pattern-matching technology) patient data sets with data sets as described in established medical literature. The system allows clinicians to follow their assumptions about differential diagnoses; it hence restricts searches to specific body systems, relatively to diagnoses in question. The system is interfaced with EMR, which allows it to extract existing diagnoses and other patient-specific data.

Furthermore it contains a feature to help Isabel has been extensively validated and been shown to enhance clinician’s cognitive skills and thereby improves patient safety and the quality of patient care (Ramnarayan, Tomlinson et al. 2004; OpenClinical 2006).


LISA is a CDSS that consists of two main components. The first is a centralized Oracle database, holding all patient information about drug schedules, blood and toxicity results, doses prescribed etc. The database is accessible by health professionals from different sectors and locations. The second component represents a web-based decision support module, which is using the PROforma guideline development technology to provide advice about dose adjustments in treatment of acute childhood lymphoblastic leukemia. Clinicians answer their questions with up to date knowledge from textbooks and journals.



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