The effects of competency requirements in Part II MFPHM submission--a study of the abstracts of successful reports.
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BACKGROUND: The objectives of this study are to explore the effects of the new 1996 guidance requiring explicit demonstration of competencies on the nature of successful MFPHM Part II reports, how successful candidates claimed competency areas in their two reports and the effects of the subject areas of the report on the specific competencies claimed. METHODS: The abstracts of candidates who passed the examination from January 1996 to January 1999 were studied. Information was extracted on candidate's region, year of the abstract, examination guidance, subject area, methods and data used, format of the abstract, and level (e.g. national, regional, etc.) for which the work was performed. RESULTS: Compared with reports submitted under the 1992 guidance, those submitted under the 1996 guidance were more likely to have a structured abstract, and to employ descriptive epidemiological methods and routine data, and were less likely to be case-control or retrospective studies. There were no other significant differences in the level for which the work was performed, the subject area, or the methods and data used. Thirty-nine per cent of candidates under the 1996 guidance claimed at least one identical competency area in both reports, most frequently for health needs assessment and literature review. Each of the four competencies was demonstrated by a significant proportion of reports in each subject areas. CONCLUSIONS: The new examination guidance had only minor effects on the nature of successful Part II reports. Candidates used different strategies for claiming competencies, apparently at the choice of individual trainees and trainers. The competency requirements did not appear to limit the range of work performed. (+info)
The NLM Indexing Initiative.
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The objective of NLM's Indexing Initiative (IND) is to investigate methods whereby automated indexing methods partially or completely substitute for current indexing practices. The project will be considered a success if methods can be designed and implemented that result in retrieval performance that is equal to or better than the retrieval performance of systems based principally on humanly assigned index terms. We describe the current state of the project and discuss our plans for the future. (+info)
Automated indexing for full text information retrieval.
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We report our experience with a statistically based method of generating sentence-level indexing based on identified UMLS concepts and query and vector-space models. We evaluated the system using the consensus markup of two domain experts as the gold standard. UMLS concepts identified both from HTML headings and in paragraph text were valuable in proposing markup. Using both sources of concepts, the model proposed the correct set of concepts in the form of a query prototype 71% of the time. The correct query prototype was ranked first or second in 79% of cases. (+info)
A randomized controlled trial of concept based indexing of Web page content.
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OBJECTIVE: Medical information is increasingly being presented in a web-enabled format. Medical journals, guidelines, and textbooks are all accessible in a web-based format. It would be desirable to link these reference sources to the electronic medical record to provide education, to facilitate guideline implementation and usage and for decision support. In order for these rich information sources to be accessed via the medical record they will need to be indexed by a single comparable underlying reference terminology. METHODS: We took a random sample of 100 web pages out of the 6,000 web pages on the Mayo Clinic's Health Oasis web site. The web pages were divided into four datasets each containing 25 pages. These were humanly reviewed by four clinicians to identify all of the health concepts present (R1DA, R2DB, R3DC, R4DD). The web pages were simultaneously indexed using the SNOMED-RT beta release. The indexing engine has been previously described and validated. A new clinician reviewed the indexed web pages to determine the accuracy of the automated mappings as compared with the human identified concepts (R4DA, R3DB, R2DC, R1DD). RESULTS: This review found 13,220 health concepts. Of these 10,383 concepts were identified by the initial human review (78.5% +/- 3.6%). The automated process identified 10,083 concepts correctly (76.3% +/- 4.0%) from within this corpus. The computer identified 2,420 concepts, which were not identified by the clinician's review but were upon further consideration important to include as health concepts. There was on average a 17.1% +/- 3.5% variability in the human reviewers ability to identify the important health concepts within web page content. Concept Based Indexing provided a positive predictive value (PPV) of finding a health concept of 79.3% as compared with keyword indexing which only has a PPV of 33.7% (p < 0.001). CONCLUSION: SNOMED-RT is a reasonable ontology for web page indexing. Concept based indexing provides a significantly greater accuracy in identifying health concepts when compared with keyword indexing. (+info)
Automated coding of diagnoses--three methods compared.
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In Germany, new legal requirements have raised the importance of the accurate encoding of admission and discharge diseases for in- and outpatients. In response to emerging needs for computer-supported tools we examined three methods for automated coding of German-language free-text diagnosis phrases. We compared a language-independent lexicon-free n-gram approach with one which uses a dictionary of medical morphemes and refines the query by a mapping to SNOMED codes. Both techniques produced a ranked output of possible diagnoses within a vector space framework for retrieval. The results did not reveal any significant difference: The correct diagnosis was found in approximately 40% for three-digit codes, and 30% for four-digit codes. The lexicon-based method was then modified by substituting the vector space ranking by a heuristic approach that capitalizes on the semantic structure of SNOMED, thus raising the number of correct diagnoses significantly (approximately 50% for three-digit codes, and 40% for four-digit codes). As a result, we claim that lexicon-based retrieval methods do not perform better than the lexicon-free ones, unless conceptual knowledge is added. (+info)
Development of a MeSH-based index of faculty research interests.
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An index of faculty research interests terms has many uses for an institution's researchers and administrators. This paper describes the Faculty Research Interests Project (FRIP), which addresses vocabulary and compliance problems inherent in research interests index development. FRIP creates an index using Medical Subject Headings (MeSH) associated with the MEDLINE-indexed publications of faculty authors. Following a preliminary study, a Web-based term selection component was developed that allows faculty users not only to choose MeSH terms but also to add both additional author names under which they have published and original terms in real time. In a study involving 136 medical school faculty, users successfully navigated the term selection component, and more than 90 percent of the terms they selected were MeSH terms, confirming MeSH's usefulness for indexing research interests. (+info)
A formal approach to integrating synonyms with a reference terminology.
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Medical terminologies continue to grow in scope, completeness and detail. The emerging generation of terminology systems define concepts in terms of their position within a categorical structure. It is still necessary, however, to access and represent the concepts using everyday spoken and written language, which introduces both lexical and semantic ambiguity. This ambiguity can have a negative impact on both selectivity and recall when it comes to associating free-form textual phrases with their coded equivalent. Lexical ambiguity issues can often be addressed algorithmically, but semantic ambiguity presents a more difficult problem. A common solution to the semantic problem is to associate many different representational permutations with a given target concept. This approach has several drawbacks. An alternate solution is to build separate synonym tables that can serve as permuted indices into the terms representing the underlying concepts. A potential shortcoming of this approach, however, is a further reduction in the lookup selectivity. One possible source of loss of selectivity could be "meaning drift"--the gradual change in meaning that can be introduced when following a chain of nearly synonymous words. We posited that organizing synonyms into separate "meaning clusters" might reduce this loss in precision, but the results of this study did not bear that out. (+info)
Having our cake and eating it too: how the GALEN Intermediate Representation reconciles internal complexity with users' requirements for appropriateness and simplicity.
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Clinical terminologies are complex objects, getting more complex as the requirements on them grow, and as more complex technologies are used in their construction. But to the clinical end-user, functionality and utility is important, not inherent complexity--the simpler a clinical terminology can be for the end-user, the better. To reconcile these contradictory requirements, the GALEN Programme has developed an Intermediate Representation that allows the OpenGALEN Clinical Terminology to retain a high degree of internal complexity, whilst allowing it to be efficiently maintained, and easily used. This paper describes the elements of the Intermediate Representation, how it works, and some experience of its use. (+info)