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PubMed1998

Pathology information systems: data mining leads to knowledge discovery.

Information systems in pathology provide opportunities for pathologists and clinical laboratory scientists to impact both clinical care and modern research agendas. The paradigm shift in health care from individualized care to population-based and standardized delivery systems has created both of these opportunities. In research, pathology information systems can provide key databases for health services research and new informatics-based approaches to database research. The latter is characterized by utilization of pathology databases for data mining to discover new patterns that provide new knowledge. The multidisciplinary knowledge discovery and data mining program at the University of Alabama at Birmingham focuses on this health care application, which has the potential to make a major impact on health care research and delivery.

Artificial Intelligence↗
PubMed1998

Risk perception during information system development in non-profit health care organizations.

The perception of risk exposure among design team members during the early phases of information system development projects can provide valuable strategic information for clinical organizations. To develop a typology of perceived risks during information system development projects in health care, interviews were performed with key team members from a specialist clinic, primary health care, and an informatics research group, during the requirements specification. Phenomenological data analysis and secondary integration of the results in available theories were performed. System objectives, the user requirements definition procedure, the communication pattern between design team members and project management were found to be perceived as the main risk areas. In the secondary analysis, the technical factors, identified as preventing a maximization of the use of the resources, were lack of informatics knowledge among economic decision makers and differences between customers and suppliers regarding their views on the nature of system design. During the implementation of a given strategy, decision makers may consider the requests of their own sponsors in the first place and maximize the use of the project resources in the second place. Informatics knowledge plays a key role in risk perception during the development of an information system in health care. Political considerations by team members are important to take into regard, since these may influence technical and economic decisions.

Humans↗
PubMed1998

The nature of lexical knowledge.

This paper considers the nature of lexical knowledge and its role in language and information processing. The lexicon is the central component of language and plays a pivotal role in current linguistic theory [3, 4] and, increasingly, in natural language processing systems [5-7]. The lexicon embodies information about the lexical items of the language and serves as the foundation for morphologic, syntactic, and semantic processing. The differences as well as commonalities among dictionaries, thesauri, and lexicons are discussed, and distinctions between words, lexical items, and terms are drawn. Next, the scope and content of the SPECIALIST lexicon are presented, followed by a discussion of certain writing conventions that can be troublesome for text processing applications. One approach to handling orthographic and other lexical variation is discussed in a section that reports on the design and implementation of the SPECIALIST lexical programs. The paper concludes with a discussion of controlled terminologies for the medical domain. Throughout the discussion, examples are drawn from the SPECIALIST lexicon and from the other UMLS knowledge sources [8, 9].

Humans↗
PubMed1998

Three types of IS-A statement in diagnostic classifications: three types of knowledge needed for development and maintenance.

Update mechanisms for diagnostic classifications should capture changes in medical knowledge but also allow for comparability across versions. This paper provides a basis for such a mechanism by describing types of IS-A statement and types of knowledge used in the construction of diagnostic classifications. Three types of IS-A statement are used: 'A is by definition a B', 'A is probably a B' and 'A is in theory necessarily a B'. Each relates to a different type of knowledge: knowledge of linguistic conventions, of probabilities, and of empirical theories and their status, respectively. Consequently, the development and maintenance of diagnostic classifications requires a collaboration of medical terminologists and medical scientists. The role of the latter is especially important during updating. Updating is necessitated by changing probabilities and by the introduction or changing status of empirical theories. The linguistic notion of hyponymy oversimplifies the issue.

Artificial Intelligence↗
PubMed1998

Cooperative knowledge evolution: a construction-integration approach to knowledge discovery in medicine.

In this paper, we perform a cognitive analysis of knowledge discovery processes. As a result of this analysis, the construction-integration theory is proposed as a general framework for developing cooperative knowledge evolution systems. We thus suggest that for the acquisition of new domain knowledge in medicine, one should first construct pluralistic views on a given topic which may contain inconsistencies as well as redundancies. Only thereafter does this knowledge become consolidated into a situation-specific circumscription and the early inconsistencies become eliminated. As a proof for the viability of such knowledge acquisition processes in medicine, we present the IDEAS system, which can be used for the intelligent documentation of adverse events in clinical studies. This system provides a better documentation of the side-effects of medical drugs. Thereby, knowledge evolution occurs by achieving consistent explanations in increasingly larger contexts (i.e., more cases and more pharmaceutical substrates). Finally, it is shown how prototypes, model-based approaches and cooperative knowledge evolution systems can be distinguished as different classes of knowledge-based systems.

Artificial Intelligence↗
PubMed1998

Thesauri and formal classifications: terminologies for people and machines.

Terminologies are now software. They are key components of the integration of electronic patient records, decision support systems and information retrieval systems. To be used as software, the different types of content in traditional terminologies must be separated, which we term here: conceptual, linguistic, inferential and pragmatic. The conceptual knowledge at the heart of the terminology needs to be expressed formally in order to provide a dependable framework for the other types of knowledge. Information left implicit in most existing coding and classification systems must be made explicit. The test of the resulting terminologies is how well they support software for key functions: including data entry, information retrieval, mediation, indexing, and authoring.

Abstracting and Indexing↗
PubMedDate not supplied

Evolution of medical informatics societies in the United States.

Medical informatics, the application of computers to medicine, was supported by engineering groups in the 1950s, by biomedical engineering societies in the 1960s, and by medical informatics organizations in the 1970s and 1980s. Because of the highly specialized and technical nature of medical informatics, the dissemination of early articles on the subject was largely dependent on publication of the proceedings and transactions of meetings of professional organizations. The American Medical Informatics Association (AMIA) was recently formed from the merger of three professional organizations, each dedicated to medical informatics: the American Association for Medical systems and Informatics (AAMSI), the American College for Medical Informatics (ACMI), and the Symposium on Computer Applications in Medical Care (SCAMC). An increase in professional interest and activity in medical informatics is anticipated in the 1990s.

Directories as Topic↗
PubMedDate not supplied

APA Summit on Medical Student Education Task Force on Informatics and Technology: learning about computers and applying computer technology to education and practice.

OBJECTIVE: This article provides a brief overview of important issues for educators regarding medical education and technology. METHODS: The literature describes key concepts, prototypical technology tools, and model programs. A work group of psychiatric educators was convened three times by phone conference to discuss the literature. Findings were presented to and input was received from the 2005 Summit on Medical Student Education by APA and the American Directors of Medical Student Education in Psychiatry. RESULTS: Knowledge of, skills in, and attitudes toward medical informatics are important to life-long learning and modern medical practice. A needs assessment is a starting place, since student, faculty, institution, and societal factors bear consideration. Technology needs to "fit" into a curriculum in order to facilitate learning and teaching. CONCLUSION: Learning about computers and applying computer technology to education and clinical care are key steps in computer literacy for physicians.

Computer User Training↗
PubMed1993

Introducing medical students to medical informatics.

Medical informatics (MI) has been introduced to medical students in several countries. Before outlining a course plan it was necessary to conduct a survey on students' computer literacy. A questionnaire was designed for students, focusing on knowledge and previous computer experience. The questions reproduced a similar questionnaire submitted to medical students from North Carolina University in Chapel Hill (NCU). From the results it is clear that although almost 80% of students used computers, less than 30% used general purpose applications, and utilization of computer-aided search of databases or use in the laboratory was exceptional. Men reported more computer experience than women in each area investigated by our questionnaire but this did not appear to be related to academic performance, age or course. Our main objectives when planning an MI course were to give students a general overview of the medical applications of computers and instruct them in the use of computers in future medical practice. As our medical school uses both Apple Macintosh and IBM compatibles, we decided to provide students with basic knowledge of both. The programme was structured with a mix of theoretico-practical lectures and personalized practical sessions in the computer laboratory. As well as providing a basic overview of medical informatics, the course and computer laboratory were intended to encourage other areas of medicine to incorporate the computer into their teaching programmes.

Computer Literacy↗
PubMed2002

Medical Imaging Informatics and Medical Informatics: opportunities and constraints. Findings from the IMIA yearbook of Medical Informatics 2002.

OBJECTIVES: The Yearbook of Medical Informatics is published annually by the International Medical Informatics Association (IMIA) and contains a selection of recent excellent papers on medical informatics research (http://www.yearbook.uni-hd.de). The 2002 Yearbook of Medical Informatics took as its theme the topic of Medical Imaging Informatics. In this paper, we will summarize the contributions of medical informatics researchers to the development of medical imaging informatics, discuss challenges and opportunities of imaging informatics, and present the lessons learned from the IMIA Yearbook 2002. RESULTS AND CONCLUSIONS: Medical informatics researchers have contributed to the development of medical imaging methods and systems since the inception of this field approximately 40 years ago. The Yearbook presents selected papers and reviews on this important topic. In addition, as usual, the Yearbook 2002 also contains a variety of papers and reviews on other subjects relevant to medical informatics, such as Bioinformatics, Computer-supported education, Health and clinical management, Health information systems, Knowledge processing and decision support, Patient records, and Signal processing.

Computer-Assisted Instruction↗
PubMed1997

Aims and tasks of medical informatics.

Ten major long-term aims and tasks, so to speak 'grand challenges', for research in the field of medical informatics, including health informatics, are proposed and described. These are the further development of methods and tools of information processing for: (1) diagnostics ('the visible body'); (2) therapy ('medical intervention with as little strain on the patient as possible'); (3) therapy simulation; (4) early-recognition and prevention; (5) compensating physical handicaps; (6) health consulting ('the informed patient'); (7) health reporting; (8) health care information systems; (9) medical documentation and (10) comprehensive documentation of medical knowledge and knowledge-based decision support. Work is, in part, already in progress. To all these aims and tasks medical informatics can and may be should make substantial contributions. Prior to outlining the above aims and tasks, an account is given of the meaning of medical informatics, of the objective it pursues in general and of its achievements so far. The present paper intends to contribute to a broad public discussion of the aims and tasks for research in the field of medical informatics.

Artificial Intelligence↗