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PubMed2006

Modeling end-users' acceptance of a knowledge authoring tool.

OBJECTIVES: Knowledge bases comprise a vital component in the classic medical expert system model, yet the knowledge acquisition process by which they are created has been characterized as highly iterative and labor-intensive. The difficulty of this process underscores the importance of knowledge authoring tools that satisfy the demands of end-users. The authors hypothesize that the acceptability of a knowledge authoring tool for the creation of medical knowledge base content can be predicted by an accepted model in the information technology (IT) field, specifically the Technology Acceptance Model (TAM). METHODS: An online survey was conducted amongst knowledge base authors who had previously established experience with the authoring tool software. The Likert-based questions in the survey were patterned directly after accepted TAM constructs with minor modifications to particularize them to the software being used. The results were analyzed using structural equation modeling. RESULTS: The TAM performed well in predicting endusers' behavioral intentions to use the knowledge authoring tool. Five out of seven goodness-of-fit statistics indicate that the model represents the behavioral intentions of the authors well. All but one of the hypothesized relationships specified by the TAM were significant with p values less than 0.05. CONCLUSIONS: The TAM provides an adequate means by which development teams can anticipate and better understand what aspects of a knowledge authoring tool are most important to their target audience. Further research involving other behavioral models and an expanded user base will be necessary to better understand the scope of issues that factor into acceptability.

Attitude↗
PubMedDate not supplied

From data to knowledge through concept-oriented terminologies: experience with the Medical Entities Dictionary.

Knowledge representation involves enumeration of conceptual symbols and arrangement of these symbols into some meaningful structure. Medical knowledge representation has traditionally focused more on the structure than the symbols. Several significant efforts are under way, at local, national, and international levels, to address the representation of the symbols though the creation of high-quality terminologies that are themselves knowledge based. This paper reviews these efforts, including the Medical Entities Dictionary (MED) in use at Columbia University and the New York Presbyterian Hospital. A decade's experience with the MED is summarized to serve as a proof-of-concept that knowledge-based terminologies can support the use of coded patient data for a variety of knowledge-based activities, including the improved understanding of patient data, the access of information sources relevant to specific patient care problems, the application of expert systems directly to the care of patients, and the discovery of new medical knowledge. The terminological knowledge in the MED has also been used successfully to support clinical application development and maintenance, including that of the MED itself. On the basis of this experience, current efforts to create standard knowledge-based terminologies appear to be justified.

Artificial Intelligence↗
PubMed2004

Knowledge acquisition in the fuzzy knowledge representation framework of a medical consultation system.

This paper describes the fuzzy knowledge representation framework of the medical computer consultation system MedFrame/CADIAG-IV as well as the specific knowledge acquisition techniques that have been developed to support the definition of knowledge concepts and inference rules. As in its predecessor system CADIAG-II, fuzzy medical knowledge bases are used to model the uncertainty and the vagueness of medical concepts and fuzzy logic reasoning mechanisms provide the basic inference processes. The elicitation and acquisition of medical knowledge from domain experts has often been described as the most difficult and time-consuming task in knowledge-based system development in medicine. It comes as no surprise that this is even more so when unfamiliar representations like fuzzy membership functions are to be acquired. From previous projects we have learned that a user-centered approach is mandatory in complex and ill-defined knowledge domains such as internal medicine. This paper describes the knowledge acquisition framework that has been developed in order to make easier and more accessible the three main tasks of: (a) defining medical concepts; (b) providing appropriate interpretations for patient data; and (c) constructing inferential knowledge in a fuzzy knowledge representation framework. Special emphasis is laid on the motivations for some system design and data modeling decisions. The theoretical framework has been implemented in a software package, the Knowledge Base Builder Toolkit. The conception and the design of this system reflect the need for a user-centered, intuitive, and easy-to-handle tool. First results gained from pilot studies have shown that our approach can be successfully implemented in the context of a complex fuzzy theoretical framework. As a result, this critical aspect of knowledge-based system development can be accomplished more easily.

Decision Making↗
PubMed2005

A graphical framework for specification of clinical guidelines at multiple representation levels.

Formalization of a clinical guideline for purposes of automated application and quality assessment mainly involves conversion of its free-text representation into a machine comprehensible representation, i.e., a formal language, thus enabling auto-mated support. The main issues involved in this process are related to the collaboration between the expert physician and the knowledge engineer. We introduce GESHER - a graphical framework for specification of clinical guidelines at multiple representation levels. The GESHER architecture facilitates incremental specification through a set of views adapted to each representation level, enabling this process to proceed smoothly and in a transparent fashion, fostering extensive collaboration among the various types of users. The GESHER framework supports specification of guidelines at multiple representation levels, in more than one specification language, and uses the DeGeL digital guideline library architecture as its knowledge base. The GESHER architecture also uses a temporal abstraction knowledge base to store its declarative knowledge, and a standard medical-vocabularies server for generic specification of key terms, thus enabling reuse of the specification at multiple sites.

Computer Graphics↗
PubMed2002

Intelligent healthcare information assistant: towards agent-based healthcare knowledge management.

Initiatives in healthcare knowledge management have provided some interesting solutions for the implementation of large-scale information repositories vis-à-vis the implementation of Healthcare Enterprise Memories (HEM). In this paper, we present an agent-based Intelligent Healthcare Information Assistant (IHIA) for dynamic information gathering, filtering and adaptation from a HEM comprising an amalgamation of (i) databases storing empirical knowledge, (ii) case-bases storing experiential knowledge, (iii) scenario-bases storing tacit knowledge and (iv) document-bases storing explicit knowledge. The featured work leverages intelligent agents and medical ontologies for autonomous HEM-wide navigation, approximate content matching, inter- and intra-repositories content correlation and information adaptation to meet the user's information request. We anticipate that the use of IHIA will empower healthcare stakeholders to actively communicate with an 'information/knowledge-rich' HEM and will be able to retrieve with ease 'useful' task-specific information via the presentation of cognitively intuitive queries.

Internet↗
PubMed1999

The core content of the undergraduate curriculum in Manchester.

AIM: To identify the core content for the new undergraduate medical curriculum in Manchester. METHOD: The initial step was to produce a list of 'index clinical situations' (ICSs), for which a newly graduated doctor must have a required level of competence. Using repeated consultation with consultants and general practitioners involved in medical education in the North-West of England, a list of 215 ICSs was agreed. Specialists and generalists were then asked to identify the components of the knowledge base and the performance (skills) base for each ICS. The knowledge base was divided into technical (biomedical facts/concepts) and contextual (effect/management of disease within the individual, family and society) domains. The performance base was divided into intellectual (problem solving and decision making) and interpersonal (history, examination, communication and procedural skills) domains. RESULTS: Forty specialties were consulted and 11,021 items (defined as a piece of knowledge, a concept or a skill) were identified. There was considerable overlap in the items listed, such that when the returns for each ICS were amalgamated, the 215 ICSs contained 6434 items with a mean of 34 +/- 14.2 per situation (range 6-85). UTILISATION: We have used the defined ICSs in the design of the trigger material used in the weekly problem-based learning sessions. Over 4 years almost all (207/215, 96%) of the ICS are covered, with many being revisited at several points in the curriculum.

Competency-Based Education↗
PubMed2001

Achieving reuse of computable guideline systems.

We describe an architecture for reusing computable guidelines and the programs used to interpret them across varied legacy clinical systems. Developed for the PRODIGY 3 project, our architecture aims to support interactive, point of care use of guidelines in primary care. Legacy medical record systems in UK primary care are diverse, using different terminologies, different data models, and varying user-interface philosophies. However, our goal is to provide common guideline knowledge bases and system components, while achieving full integration with the host medical record system, and a user interface tailored to that system. In conjunction with system suppliers, we identified areas of standardization required to achieve this goal. Firstly, standardized interfaces were created for mediation with the legacy system medical record and for act management. Secondly, a standard interface was developed for communication with the User Interface for guideline interaction. Thirdly, a terminology mapping knowledge base and system component was provided. Lastly, we developed a numeric unit conversion knowledge base and system component. The standardization of this architecture was achieved by close collaboration with existing vendors of Primary Care computing systems in the UK. The work has been verified by two suppliers successfully building and deploying systems with User Interfaces which mirror their normal look and feel, communicating fully with existing medical records, while using identical Guideline Interpreter components and knowledge bases. Encouragingly further experiments in other areas of clinical decision support have not required extension of our interfaces.

Artificial Intelligence↗
PubMed2000

A test ordering system with automated reminders for primary care based on practice guidelines.

In this article we describe a real-time automated reminder system that has been developed to change Family Physicians' (FP) test ordering behavior. The system focuses on the appropriateness of test requests. We aim at using the system as a substitute for written feedback by human experts. The reminder system consists of a knowledge base, an order entry system and modules to provide passive and active support in the form of reminders to FPs. The system generates critical comments about the rationality of the test requests at the moment the FP orders a test that is not in line with national or regional guidelines. For the first validation of the knowledge base we compared the comments of a human expert to the comments of the reminder system on three random samples of test requests. The overall agreement in the subsequent validation rounds was 46, 60 and 69%. The corrections made in the knowledge base after each validation round resulted in a reminder system with 149 reminders concerning various medical problems. Due to the corrections in the knowledge base the reminder system reacts better over the subsequent validation rounds.

Diagnostic Tests, Routine↗
PubMed1994

Artificial intelligence within the chemical laboratory.

Various techniques within the area of artificial intelligence such as expert systems and neural networks may play a role during the problem-solving processes within the clinical biochemical laboratory. Neural network analysis provides a non-algorithmic approach to information processing, which results in the ability of the computer to form associations and to recognize patterns or classes among data. It belongs to the machine learning techniques which also include probabilistic techniques such as discriminant function analysis and logistic regression and information theoretical techniques. These techniques may be used to extract knowledge from example patients to optimize decision limits and identify clinically important laboratory quantities. An expert system may be defined as a computer program that can give advice in a well-defined area of expertise and is able to explain its reasoning. Declarative knowledge consists of statements about logical or empirical relationships between things. Expert systems typically separate declarative knowledge residing in a knowledge base from the inference engine: an algorithm that dynamically directs and controls the system when it searches its knowledge base. A tool is an expert system without a knowledge base. The developer of an expert system uses a tool by entering knowledge into the system. Many, if not the majority of problems encountered at the laboratory level are procedural. A problem is procedural if it is possible to write up a step-by-step description of the expert's work or if it can be represented by a decision tree. To solve problems of this type only small expert system tools and/or conventional programming are required.(ABSTRACT TRUNCATED AT 250 WORDS)

Artificial Intelligence↗
PubMed1997

Arden Syntax: the emerging standard language for representing medical knowledge in computer systems.

The Arden Syntax for Medical Logic Modules standard language for knowledge-based computer systems is described. Knowledge systems can use computerized patient data in decision-making. Although they have the potential to reduce adverse drug events and infection rates, improve drug dosing, and decrease the cost of care, knowledge systems have not yet reached the average patient. Arden Syntax for Medical Logic Modules is the standard language for defining clinical decision rules that drive alerts, reminders, clinical guidelines, and data interpretations. Each medical logic module (MLM) in an Arden Syntax knowledge base is designed to make one type of decision. MLMs usually represent either rules that can be encoded, such as generating a warning that the potassium concentration is decreasing in a patient taking digoxin, or complex decision trees for individual patient care plans and clinical protocols. An MLM contains maintenance slots (title, file name, version, originating institution, author, date, specialist, validation information), library slots (stating the MLM's purpose and providing keywords for searching), and knowledge slots (containing the "essence" of the MLM). Arden Syntax is receiving growing support from the medical and information systems communities as the standard language for medical knowledge systems, but legal, ethical, regulatory, and ownership issues remain. If pharmacy is to grow and prosper as a knowledge profession, it should adopt an accepted standard language for representing active, applied knowledge in computer systems.

Decision Support Techniques↗
PubMed1993

Assessing the clinical ethical competence of undergraduate medical students.

At the University of Newcastle, health law and ethics is taught and assessed in each year of the five-year curriculum. However, the critical question for assessment remains: 'Does teaching ethics have a measurable effect on the clinical activity of medical students who have had such courses?' Those responsible for teaching confront this question each year they sit down to construct their assessment tools. Should they assess what the student knows? Should they assess the student's moral reasoning, that is, what decisions the student makes, and, how these decisions are justified, or should they assess what the student actually does when dealing with patients in the clinical setting, and how he or she does it? From 1982 to 1991, assessment at Newcastle was primarily aimed at determining the quality of the students' ethics knowledge base. This paper describes the strengths and limitations of a purely knowledge-based method of evaluation and why in 1992, we are now attempting to redefine and assess, what we call 'clinical ethical competence' in terms of how students actually apply this knowledge base in a controlled clinical context.

Australia↗
PubMed1986

[Contradiction, incitement or hindrance to the development of knowledge].

Based on sequences of psychological examinations and the analysis of mental functioning of school children with learning difficulties, the authors propose a discussion around the concept of contradiction, such as envisaged by Piaget as having a fundamental role in the process of access to knowledge. The manner in which the dysfunction is manifested leads them to define different forms of reactions to the contradiction which compromises more or less seriously the development of knowledge. Finally, faced by the quasi existential confrontation which the contradiction represents for these children, they weigh the extent of its consequences on thought mechanisms.

Child↗
PubMed1991

Using computer software to improve group decision-making.

This article provides a review of some of the work done in the area of knowledge-based systems for strategic planning. Since 1985, with the founding of the Center for Knowledge-based Systems for Business Management, the project has focused on developing knowledge-based systems (KBS) based on these models. In addition, the project also involves developing a variety of computer and non-computer methods and techniques for assisting both technical and non-technical managers and individuals to do decision modelling and KBS development. This paper presents a summary of one segment of the project: a description of integrative groupware useful in strategic planning. The work described here is part of an ongoing research project. As part of this project, for example, over 200 non-technical and technical business managers, most of them working full-time during the project, developed over 160 KBS prototype systems in conjunction with MBA course in strategic planning and management decision making. Based on replies to a survey of this test group, 28 per cent of the survey respondents reported their KBS were used at work, 21 per cent reportedly received promotions, pay rises or new jobs based on their KBS development work, and 12 per cent reported their work led to participation in other KBS development projects at work. All but two of the survey respondents reported that their work on the KBS development project led to a substantial increase in their job knowledge or performance.

Artificial Intelligence↗
PubMed1995

Using scientific evidence to improve information practice.

The recent policy statement of the Medical Library Association (MLA) takes the position that scientific evidence is the basis for improving the quality of library and information sciences now and in the future. Research activity is seen as the foundation of an evolving knowledge base for the profession--a knowledge base that will set health sciences librarians apart from others in an increasingly competitive world of information service providers. The statement represents the culmination of many years of activity by association members, during which the role of research in health information practice has been debated. Over a similar time period, the quality movement, with its increasing demand for the collection and use of data, has been growing. Developments such as total quality management (TQM) and continuous quality improvement (CQI) reinforce the centrality of research with its increasing demand for the collection and use of data, has been growing. Developments such as total quality management (TQM) and continuous quality improvement (CQI) reinforce the centrality of research and its relationship to efficient and effective information practice as envisioned in the MLA policy statement.

Artificial Intelligence↗
PubMed1995

Evaluation of a problem-based access knowledge system using triangulation methods.

A microcomputer-based system designed to provide nurses and physicians access to expert synthesized knowledge in the area of pulmonary arterial waveform troubleshooting has been developed and implemented. Evaluation using triangulation methods show that there was a substantive increase in knowledge in both nurses and physicians.¿.

Attitude of Health Personnel↗
PubMed1994

Functionality of gray-scale display workstation hardware and software in clinical radiology.

This article examines the functional factors crucial for the successful conversion from film-based radiography to radiologic gray-scale display systems, including hardware architecture and software requirements, radiologic workstation operations, and a multilayered intelligent user interface. Radiologic workstation operations are logically decomposed into case preparation, case selection, case presentation, case interpretation, and documentation and presentation of the diagnosis. A multilayered software architecture for an adaptive, intelligent user interface is proposed: a hardware interface layer, an object-oriented layer, and a knowledge-based layer. The knowledge-based layer is composed of three elements: image presentation based on context-dependent models of diagnostic requirements, knowledge-based expert systems for assistance in diagnostic decision making, and computer-assisted diagnosis to alert the radiologist to potential lesions or abnormalities.

Diagnosis, Computer-Assisted↗
PubMed2004

A physical reference state unifies the structure-derived potential of mean force for protein folding and binding.

Extracting knowledge-based statistical potential from known structures of proteins is proved to be a simple, effective method to obtain an approximate free-energy function. However, the different compositions of amino acid residues at the core, the surface, and the binding interface of proteins prohibited the establishment of a unified statistical potential for folding and binding despite the fact that the physical basis of the interaction (water-mediated interaction between amino acids) is the same. Recently, a physical state of ideal gas, rather than a statistically averaged state, has been used as the reference state for extracting the net interaction energy between amino acid residues of monomeric proteins. Here, we find that this monomer-based potential is more accurate than an existing all-atom knowledge-based potential trained with interfacial structures of dimers in distinguishing native complex structures from docking decoys (100% success rate vs. 52% in 21 dimer/trimer decoy sets). It is also more accurate than a recently developed semiphysical empirical free-energy functional enhanced by an orientation-dependent hydrogen-bonding potential in distinguishing native state from Rosetta docking decoys (94% success rate vs. 74% in 31 antibody-antigen and other complexes based on Z score). In addition, the monomer potential achieved a 93% success rate in distinguishing true dimeric interfaces from artificial crystal interfaces. More importantly, without additional parameters, the potential provides an accurate prediction of binding free energy of protein-peptide and protein-protein complexes (a correlation coefficient of 0.87 and a root-mean-square deviation of 1.76 kcal/mol with 69 experimental data points). This work marks a significant step toward a unified knowledge-based potential that quantitatively captures the common physical principle underlying folding and binding. A Web server for academic users, established for the prediction of binding free energy and the energy evaluation of the protein-protein complexes, may be found at http://theory.med.buffalo.edu.

Antigen-Antibody Complex↗
PubMed1994

Data driven medical decision support based on Arden Syntax within the HELIOS environment.

Methods and tools for development of data driven decision support systems (DSSs) is an integral part of the HELIOS software engineering environment. The DSS development environment includes tools for knowledge acquisition and knowledge base construction, provides trigger mechanisms for evocation of the knowledge base and an inference engine for execution control of appropriate parts of the knowledge base during run-time. The tools are part of the HELIOS service components and are integrated within the HELIOS communication framework. The work is based on the Arden Syntax, a standard for knowledge representation, allowing medical logic modules (MLMs) to be shared and transferred between institutions.

Artificial Intelligence↗