Clinical Guideline Audit and Knowledge Elicitation Using the MDS Tool and Techniques

This paper outlines a study, utilising the MDS methodology and tool to create a knowledge model based on clinical experts’ interpreted knowledge of clinical guidelines. The study demonstrated the elicitation of tacit expert knowledge when the formalised processes of the MDS were applied to model a clinical expert’s interpretation of the knowledge content of a clinical guideline onto the specialised MDS architecture

Impact of an informed choice invitation on uptake of screening for diabetes in primary care (DICISION): randomised trial

Objective To compare the effect of an invitation promoting informed choice for screening with a standard invitation on attendance and motivation to engage in preventive action

The role of guidelines and the patient's life-style in GPs' management of hypercholesterolaemia

BACKGROUND: Recent Swedish and joint European guidelines on hyperlipidaemia stress the high coronary risk for patients with already established arterio-sclerotic disease (secondary prevention) or diabetes. For the remaining group, calculation of the ten-year risk for coronary events using the Framingham equation is suggested. There is evidence that use of and adherence to guidelines is incomplete and that tools for risk estimations are seldom used. Intuitive risk estimates are difficult and systematically biased. The purpose of the study was to examine how GPs use knowledge of guidelines in their decisions to recommend or not recommend a cholesterol-lowering drug and the reasons for their decisions. METHODS: Twenty GPs were exposed to six case vignettes presented on a computer. In the course of six screens, successively more information was added to the case. The doctors were instructed to think aloud while processing the cases (Think-Aloud Protocols) and finally to decide for or against drug treatment. After the six cases they were asked to describe how they usually reason when they meet patients with high cholesterol values (Free-Report Protocols). The two sets of protocols were coded for cause-effect relations that were supposed to reflect the doctors' knowledge of guidelines. The Think-Aloud Protocols were also searched for reasons for the decisions to prescribe or not to prescribe. RESULTS: According to the protocols, the GPs were well aware of the importance of previous coronary heart disease and diabetes in their decisions. On the other hand, only a few doctors mentioned other arterio-sclerotic diseases like stroke and peripheral artery disease as variables affecting their decisions. There were several instances when the doctors' decisions apparently deviated from their knowledge of the guidelines. The arguments for the decisions in these cases often concerned aspects of the patient's life-style like smoking or overweight- either as risk-increasing factors or as alternative strategies for intervention. CONCLUSIONS: Coding verbal protocols for knowledge and for decision arguments seems to be a valuable tool for increasing our understanding of how guidelines are used in the on treatment of hypercholesterolaemia. By analysing arguments for treatment decisions it was often possible to understand why departures from the guidelines were made. While the need for decision support is obvious, the current guidelines may be too simple in some respects

Using conceptual graphs for clinical guidelines representation and knowledge visualization

The intrinsic complexity of the medical domain requires the building of some tools to assist the clinician and improve the patient’s health care. Clinical practice guidelines and protocols (CGPs) are documents with the aim of guiding decisions and criteria in specific areas of healthcare and they have been represented using several languages, but these are difficult to understand without a formal background. This paper uses conceptual graph formalism to represent CGPs. The originality here is the use of a graph-based approach in which reasoning is based on graph-theory operations to support sound logical reasoning in a visual manner. It allows users to have a maximal understanding and control over each step of the knowledge reasoning process in the CGPs exploitation. The application example concentrates on a protocol for the management of adult patients with hyperosmolar hyperglycemic state in the Intensive Care Unit

An ontological clinical decision support system based on clinical guidelines for diabetes patients in Sri Lanka

Health professionals should follow the clinical guidelines to decrease healthcare costs to avoid unnecessary testing and to minimize the variations among healthcare providers. In addition, this will minimize the mistakes in diagnosis and treatment processes. To this end, it is possible to use Clinical Decision Support Systems that implement the clinical guidelines. Clinical guidelines published by international associations are not suitable for developing countries such as Sri Lanka, due to the economic background, lack of resources, and unavailability of some laboratory tests. Hence, a set of clinical guidelines has been formulated based on the various published international professional organizations from a Sri Lankan context. Furthermore, these guidelines are usually presented in non-computer-interpretable narrative text or non-executable flow chart formats. In order to fill this gap, this research study finds a suitable approach to represent/organize the clinical guidelines in a Sri Lankan context that is suitable to be used in a clinical decision support system. To this end, we introduced a novel approach which is an ontological model based on the clinical guidelines. As it is revealed that there are 4 million diabetes patients in Sri Lanka, which is approximately twenty percent of the total population, we used diabetes-related guidelines in this research. Firstly, conceptual models were designed to map the acquired diabetes-related clinical guidelines using Business Process Model and Notation 2.0. Two models were designed in mapping the diagnosis process of Type 1 and Type 2 Diabetes, and Gestational diabetes. Furthermore, several conceptual models were designed to map the treatment plans in guidelines by using flowcharting. These designs were validated by domain experts by using questionnaires. Grüninger and Fox’s method was used to design and evaluate the ontology based on the designed conceptual models. Domain experts’ feedback and several real-life diabetic scenarios were used to validate and evaluate the developed ontology. The evaluation results show that all suggested answers based on the proposed ontological model are accurate and well addressed with respect to the real-world scenarios. A clinical decision support system was implemented based on the ontological knowledge base using the Jena Framework, and this system can be used to access the diabetic information and knowledge in the Sri Lankan context. However, this contribution is not limited to diabetes or a local context, and can be applied to any disease or any context

COGNITIVE IMPACT OF INTERACTIVE MULTIMEDIA

Technology has been gradually introduced in heath education. One of the most attractive features of this technology-based education is the use of multimedia. In this article we explore the research evidence about the role that multimedia is playing in education. From that analysis we describe the most relevant features of this technology to prepare a common ground of discussion about the evaluation of its impact on educational outcomes. As part of this analysis, we organize current research evidence on the use of technology in medical education, distinguishing diverse variables involved in the process, like knowledge (declarative, procedural), learner characteristics, curricular scenario, etc. This article presents an overview of the Distributed Representations theory and its relationship with research on educational outcomes and multimedia. Next we discuss the relationship between media and diverse learning theories, proposing a theory based taxonomy for educational multimedia

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers