SNOMED CT survey: an assessment of implementation in EMR/EHR applications.
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A descriptive study of health information technology (HIT) vendors was conducted to identify which EMR/EHR vendors currently work or anticipate working with SNOMED CT, determine the prevalence of SNOMED CT integration in electronic medical record (EMR) and electronic health record (EHR) products, identify the available and potential future applications for SNOMED CT in EMR/EHR systems, and learn what prompts vendors to include SNOMED CT in EMR/EHR systems. The Web-based survey consisting of 25 questions was fielded in November-December 2006. Seventy-two responses were received. The results from this survey on SNOMED CT show a mixed message from respondents with regard to the prevalence of SNOMED CT integration in EMR/EHR products. Those with plans for implementation cited strategic reasons most often. However, HIT vendors who have not yet obtained a SNOMED CT license are waiting for market forces to drive deployment in their systems. Finally, survey respondents currently working with SNOMED CT indicated an expected increase over the next three years in EMR/EHR applications where SNOMED CT will be implemented. (+info)
Direct comparison of MEDCIN and SNOMED CT for representation of a general medical evaluation template.
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BACKGROUND: Two candidate terminologies to support entry of general medical data are SNOMED CT and MEDCIN. We compare the ability of SNOMED CT and MEDCIN to represent concepts and interface terms from a VA general medical examination template. METHODS: We parsed the VA general medical evaluation template and mapped the resulting expressions into SNOMED CT and MEDCIN. Internists conducted double independent reviews on 864 expressions. Exact concept level matches were used to evaluate reference coverage. Exact term level matches were required for interface terms. RESULTS: Sensitivity of SNOMED CT as a reference terminology was 83% vs. 25% for MEDCIN (p<0.001). The sensitivity of SNOMED CT as an interface terminology was 53% vs. 7% for MEDCIN (P< 0.001). DISCUSSION: The content coverage of SNOMED CT as a reference terminology and as an interface terminology outperformed MEDCIN. We did not evaluate other aspects of interface terminologies such as richness of clinical linkages. (+info)
Would SNOMED CT benefit from realism-based ontology evolution?
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If SNOMED CT is to serve as a biomedical reference terminology, then steps must be taken to ensure comparability of information formulated using successive versions. New releases are therefore shipped with a history mechanism. We assessed the adequacy of this mechanism for its treatment of the distinction between changes occurring on the side of entities in reality and changes in our understanding thereof. We found that these two types are only partially distinguished and that a more detailed study is required to propose clear recommendations for enhancement along at least the following lines: (1) explicit representation of the provenance of a class; (2) separation of the time-period during which a component is stated valid in SNOMED CT from the period it is (or has been) valid in reality, and (3) redesign of the historical relationships table to give users better assistance for recovery in case of introduced mistakes. (+info)
Analysis of error concentrations in SNOMED.
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Two high-level abstraction networks for the knowledge content of a terminology, known respectively as the "area taxonomy" and "p-area taxonomy," have previously been defined. Both are derived automatically from partitions of the terminology's concepts. An important application of these networks is in auditing, where a number of systematic regimens have been formulated utilizing them. In particular, the taxonomies tend to highlight certain kinds of concept groups where errors are more likely to be found. Using results garnered from applications of our auditing regimens to SNOMED CT, an investigation into the concentration of errors among such groups is carried out. Three hypotheses pertaining to the error distributions are put forth. The results support the fact that certain groups presented by the taxonomies show higher error percentages as compared to other groups. The bootstrap is used to assess their statistical significance. This knowledge will help direct auditing efforts to increase their impact. (+info)
Lessons extracting diseases from discharge summaries.
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We developed a program to extract diseases and procedures from discharge summaries and have applied this program to 96 cases annotated by physicians. We compared the concepts extracted by the program to those extracted by the annotators. The program extracts 93% of the desired concepts including some more specific than the annotators. Concepts were missed because phrases were ambiguous, phrases were missing words or were separated, or deduction was needed, among other reasons. The false positives were either insignificant findings, ambiguous phrases, or did not apply to the patient now. The analysis shows that extraction of medical concepts from discharge summaries with limited natural language processing and no domain inference is effective with still more potential. (+info)
Evaluation of the VA/KP problem list subset of SNOMED as a clinical terminology for electronic prescription clinical decision support.
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A standardized terminology for medical indications is essential for building e-prescription applications with decision support. The FDA has adopted the Veteran Administration and Kaiser Permanente (VA/KP) Problem List Subset of SNOMED as the terminology to represent indications in electronic labels. In this paper, we evaluate the ability of this subset to represent the text phrases extracted from a medication decision support system and the indications section of existing drug labels. We compiled a test set of 1265 distinct indication phrases and mapped them to (1) UMLS, (2) Entire SNOMED, (3) All Precoordinated concepts from the "Clinical Finding" hierarchy of SNOMED, and (4) VA/KP Subset. 95% of the phrases mapped to concepts in UMLS, 90.3% to SNOMED, 79.5% to SNOMED Precordinated and 71.1% mapped completely or partially to concepts in the VA/KP subset. Our study suggests that the VA/KP Subset has significant limitations for coding drug indications; however, when focusing on indications as medical conditions only, the coverage seems more adequate. (+info)
Unambiguous data modeling to ensure higher accuracy term binding to clinical terminologies.
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Work in the field of recording standard, coded data is important to reduce medical errors caused by misinterpretation and misrepresentation of data. The paper discusses the need to ensure that the source of the data i.e. the clinical data model is unambiguous to increase the quality and accuracy of the data mapping to terminology codes. The study chooses one especially ambiguous data model and remodels it to make clearer both the structure of the data, as well as its intended use and semantics. By ensuring an unambiguous model, results of the data mapping increased in accuracy from 64.7% to 80.55%. The clinical experts evaluating the models found it easier working with the revised model and agreed on the mappings 93.1% times as against 48.57% times previously. The aim of the study is to encourage good modeling practice to enable clinicians to record and code patient data unambiguously and accurately. (+info)