IAIMS: an overview from the National Library of Medicine.
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Biomedical subjects
Publications and source records attributed to D A Lindberg.
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The goal of the UMLS Project is to give practitioners and researchers easy access to machine-readable information from diverse sources. Assessment of the first experimental versions of the UMLS Knowledge Sources is essential to measuring progress toward that goal and to identifying needed enhancements. As of July 30, 1991, copies of the first edition of the UMLS Knowledge Sources had been distributed to 143 individuals and institutions; 66 had provided initial feedback information. The information received indicates that the UMLS Knowledge Sources will undergo broad testing in the patient care, medical education, library service, and product development environments. Preliminary data support the hypothesis that expanded coverage of routine clinical concepts is needed. Key enhancements planned for 1992 and beyond include expanded coverage of ICD-9-CM and CPT.
An adaptation of the Critical Incident Technique for the evaluation of an online information system is described. 552 users of the National Library of Medicine's MEDLINE database, interviewed by telephone and responding to a highly structured set of open-ended questions, reported 1,158 incidents in which the results of a MEDLINE search was especially helpful (or not helpful) in carrying out professional activities. Systematic analysis of these "critical incidents" produced three comprehensive and detailed views of the purposes and outcomes of MEDLINE searches: (1) why information is sought from MEDLINE; (2) the impact of MEDLINE-derived information on medical decision-making; and (3) the ultimate outcome of having (or not having) the desired information on medical situations prompting a MEDLINE search. Results revealed that MEDLINE is used to satisfy a diversity of medical needs concerning patient care, the progress of biomedical research, the quality of education received by health professionals in training, the safety and effectiveness of health care institutions, the operation of the system of third-party reimbursement, for legal decisions, and for the knowledge of the public.
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In many educational applications of the computer, systems are designed to contain information or knowledge that is to be conveyed to the student or user via an interactive learning scheme. In contrast, most NLM systems strive merely to increase the user's access to the broad range of published scientific literature, or to link the user to appropriate factual databases. Providing or enhancing access is, of course, made more difficult than in didactic systems because of the relatively large size of the files that make up the bibliographic and factual databases. For example, the MEDLARS files contain over 12 million records, made up of roughly thirty billion online characters. Users send electronic queries to these files on-line via commercial value-added networks (24 hrs/day, 365 days/year), in total about 4 million queries per year. Of the 4 million, approximately half of the queries were (in the opinion of the users polled) prompted by questions that arose in caring for a particular patient. The remaining 2 million queries concerned research, teaching, and public policy.
AI/RHEUM is a knowledge-based computer consultant system for the diagnosis of rheumatic diseases. Its diagnostic accuracy was evaluated using information that was supplied by Japanese rheumatologists on 59 patients with connective tissue diseases. The diagnoses of the AI/RHEUM model were in full or partial agreement with those of the Japanese rheumatologists in 54 of 59 cases (92%). Preliminary evaluation of the criteria tissue disease showed a sensitivity of 90% and a specificity of 96%.
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Medical informatics attempts to provide the theoretic and scientific basis for the use of automated information systems in biomedicine. Even though a new field, its roots are in the 19th century. The National Library of Medicine (NLM) began classifying the medical literature and publishing the Index Medicus in 1897; in the early 1960s, the growth of the index gave rise to MEDLARS, the first successful, large-scale, computerized bibliographic system. In 1971, about the time MEDLARS evolved into a nationwide on-line retrieval system known as MEDLINE, a committee of the Association of American Medical Colleges published a report calling for the NLM to exert strong leadership in developing computer applications for information transfer in medicine. The NLM has sponsored several training and research programs in this area and is now developing the concept of "centers of excellence" in medical informatics. In addition, there are a number of current research and development activities within the NLM internal and extramural programs that may influence the progress of medical informatics.
The future of the National Library of Medicine will be shaped by a number of scientific, technical, and social influences. Among these are the continuing rapid development of computer technology and storage systems. Artificial intelligence techniques, factual databases, the emergence of medical informatics as a formal discipline, and the development of Integrated Academic Information Management Systems (IAIMS) are also important influences on the direction of the library. Public policy issues will influence the future of NLM--among them, sharing of scientific information between nations and the role of federal agencies in dissemination of information domestically. The formal, long-range plan now being prepared for the library by panels of expert advisers will be a guide for future programs and goals.
A knowledge-based computer consultant system in the medical specialty area of rheumatology is described. The system, called AI/RHEUM, uses artificial intelligence techniques to provide nonrheumatologist physicians with diagnostic assistance in this specialty area. AI/RHEUM contains in a structured knowledge base formal criteria for 26 rheumatologic diseases. The system has been retrospectively tested against 384 clinical cases, with an overall correct diagnostic performance of 94%.
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