Biomedical subjects

Steven H Brown

Publications and source records attributed to Steven H Brown.

16 recordsLinked to original sources

PubMed2006

Interface terminologies: facilitating direct entry of clinical data into electronic health record systems.

Previous investigators have defined clinical interface terminology as a systematic collection of health care-related phrases (terms) that supports clinicians' entry of patient-related information into computer programs, such as clinical "note capture" and decision support tools. Interface terminologies also can facilitate display of computer-stored patient information to clinician-users. Interface terminologies "interface" between clinicians' own unfettered, colloquial conceptualizations of patient descriptors and the more structured, coded internal data elements used by specific health care application programs. The intended uses of a terminology determine its conceptual underpinnings, structure, and content. As a result, the desiderata for interface terminologies differ from desiderata for health care-related terminologies used for storage (e.g., SNOMED-CT), information retrieval (e.g., MeSH), and classification (e.g., ICD9-CM). Necessary but not sufficient attributes for an interface terminology include adequate synonym coverage, presence of relevant assertional knowledge, and a balance between pre- and post-coordination. To place interface terminologies in context, this article reviews historical goals and challenges of clinical terminology development in general and then focuses on the unique features of interface terminologies.

Clinical Medicine↗

PubMed2006

Standardization of microarray and pharmacogenomics data.

This chapter provides a bottom-up perspective on bioinformatics data standards, beginning with a historical perspective on biochemical nomenclature standards. Various file format standards were soon developed to convey increasingly complex and voluminous data that nomenclature alone could not effectively organize without additional structure and annotation. As areas of biochemistry and molecular biology have become more integral to the practice of modern medicine, broader data representation models have been created, from corepresentation of genomic and clinical data as a framework for drug research and discovery to the modeling of genotyping and pharmacogenomic therapy within the broader process of the delivery of health care.

Computational Biology↗

PubMed2006

eQuality: electronic quality assessment from narrative clinical reports.

OBJECTIVE: To evaluate an electronic quality (eQuality) assessment tool for dictated disability examination records. METHODS: We applied automated concept-based indexing techniques to automated quality screening of Department of Veterans Affairs spine disability examinations that had previously undergone gold standard quality review by human experts using established quality indicators. We developed automated quality screening rules and refined them iteratively on a training set of disability examination reports. We applied the resulting rules to a novel test set of spine disability examination reports. The initial data set was composed of all electronically available examination reports (N=125,576) finalized by the Veterans Health Administration between July and September 2001. RESULTS: Sensitivity was 91% for the training set and 87% for the test set (P-.02). Specificity was 74% for the training set and 71% for the test set (P=.44). Human performance ranged from 4% to 6% higher (P<.001) than the eQuality tool in sensitivity and 13% to 16% higher in specificity (P<.001). In addition, the eQuality tool was equivalent or higher in sensitivity for 5 of 9 individual quality indicators. CONCLUSION: The results demonstrate that a properly authored computer-based expert systems approach can perform quality measurement as well as human reviewers for many quality indicators. Although automation will likely always rely on expert guidance to be accurate and meaningful, eQuality is an important new method to assist clinicians in their efforts to practice safe and effective medicine.

Algorithms↗

PubMed2006

Evaluation of the content coverage of SNOMED CT: ability of SNOMED clinical terms to represent clinical problem lists.

OBJECTIVE: To evaluate the ability of SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) version 1.0 to represent the most common problems seen at the Mayo Clinic in Rochester, Minn. MATERIAL AND METHODS: We selected the 4996 most common nonduplicated text strings from the Mayo Master Sheet Index that describe patient problems associated with inpatient and outpatient episodes of care. From July 2003 through January 2004, 2 physician reviewers compared the Master Sheet Index text with the SNOMED CT terms that were automatically mapped by a vocabulary server or that they identified using a vocabulary browser and rated the "correctness" of the match. If the 2 reviewers disagreed, a third reviewer adjudicated. We evaluated the specificity, sensitivity, and positive predictive value of SNOMED CT. RESULTS: Of the 4996 problems in the test set, SNOMED CT correctly identified 4568 terms (true-positive results); 36 terms were true negatives, 9 terms were false positives, and 383 terms were false negatives. SNOMED CT had a sensitivity of 92.3%, a specificity of 80.0%, and a positive predictive value of 99.8%. CONCLUSION: SNOMED CT, when used as a compositional terminology, can exactly represent most (92.3%) of the terms used commonly in medical problem lists. Improvements to synonymy and adding missing modifiers would lead to greater coverage of common problem statements. Health care organizations should be encouraged and provided incentives to begin adopting SNOMED CT to drive their decision-support applications.

Humans↗

PubMed2005

A controlled trial of automated classification of negation from clinical notes.

BACKGROUND: Identification of negation in electronic health records is essential if we are to understand the computable meaning of the records: Our objective is to compare the accuracy of an automated mechanism for assignment of Negation to clinical concepts within a compositional expression with Human Assigned Negation. Also to perform a failure analysis to identify the causes of poorly identified negation (i.e. Missed Conceptual Representation, Inaccurate Conceptual Representation, Missed Negation, Inaccurate identification of Negation). METHODS: 41 Clinical Documents (Medical Evaluations; sometimes outside of Mayo these are referred to as History and Physical Examinations) were parsed using the Mayo Vocabulary Server Parsing Engine. SNOMED-C was used to provide concept coverage for the clinical concepts in the record. These records resulted in identification of Concepts and textual clues to Negation. These records were reviewed by an independent medical terminologist, and the results were tallied in a spreadsheet. Where questions on the review arose Internal Medicine Faculty were employed to make a final determination. RESULTS: SNOMED-CT was used to provide concept coverage of the 14,792 Concepts in 41 Health Records from John's Hopkins University. Of these, 1,823 Concepts were identified as negative by Human review. The sensitivity (Recall) of the assignment of negation was 97.2% (p < 0.001, Pearson Chi-Square test; when compared to a coin flip). The specificity of assignment of negation was 98.8%. The positive likelihood ratio of the negation was 81. The positive predictive value (Precision) was 91.2% CONCLUSION: Automated assignment of negation to concepts identified in health records based on review of the text is feasible and practical. Lexical assignment of negation is a good test of true Negativity as judged by the high sensitivity, specificity and positive likelihood ratio of the test. SNOMED-CT had overall coverage of 88.7% of the concepts being negated.

Abstracting and Indexing↗

PubMed2004

U.S. Department of Veterans Affairs Enterprise Reference Terminology strategic overview.

The Veterans Health Affairs (VHA) branch of the Department of Veterans Affairs has undertaken an Enterprise Reference Terminology (ERT). VHA, arguably the largest integrated healthcare provider in the United States, has completely computerized virtually all clinical transactions, including physician orders and documentation. The VA is now integrating its clinical records across hundreds of sites of care by means of a Health Data Repository (HDR) project. ERT has been designed to provide a terminology development environment, terminology services, and maintenance services for the clinical and business content in HDR and other VHA applications. Drug, laboratory observations, and clinical document title files have been developed, and the ERT will encompass all HDR domains by 2008. Commercial tools are used to host the VHA's ERT terminology development and server environments. We will select and adopt both open-source and licensable terminology systems to provide ERT content, as well as reuse existing VA-specific terminology content.

Hospital Information Systems↗

PubMed2004

VA National Drug File Reference Terminology: a cross-institutional content coverage study.

BACKGROUND: Content coverage studies provide valuable information to potential users of terminologies. We detail the VA National Drug File Reference Terminology's (NDF-RT) ability to represent dictated medication list phrases from the Mayo Clinic. NDF-RT is a description logic-based resource created to support clinical operations at one of the largest healthcare providers in the US. METHODS: Medication list phrases were extracted from dictated patient notes from the Mayo Clinic. Algorithmic mappings to NDF-RT using the SmartAccess Vocabulary Server (SAVS) were presented to two non-VA physicians. The physicians used a terminology browser to determine the accuracy of the algorithmic mapping and the content coverage of NDF-RT. RESULTS: The 509 extracted documents on 300 patients contained 847 medication concepts in medication lists. NDF-RT covered 97.8% of concepts. Of the 18 phrases that NDF-RT did not represent, 10 were for OTC's and food supplements, 5 were for prescription medications, and 3 were missing synonyms. The SAVS engine properly mapped 773 of 810 phrases with an overall sensitivity (precision) was 95.4% and positive predictive value (recall) of 99.9%. CONCLUSIONS: This study demonstrates that NDF-RT has more general utility than its initial design parameters dictated

Abstracting and Indexing↗

PubMed2004

Evaluation of SNOMED coverage of Veterans Health Administration terms.

Veterans Health Administration (VHA) is now evaluating use of SNOMED-CT. This paper reports the first phase of this evaluation, which examines the coverage of SNOMED-CT for problem list entries. Clinician expressions in VA problem lists are quite diverse compared to the content of the current VA terminology Lexicon. We selected a random set of 5054 narratives that were previously "unresolved" against the Lexicon. These narratives were mapped to SNOMED-CT using two automated tools. Experts reviewed a subset of the tools' matched, partly matched, and un-matched narratives. The automated tools produced exact or partial matches for over 90% of the 5054 unresolved narratives. SNOMED-CT has promise as a coding system for clinical problems. In subsequent studies, VA will examine the coverage of SNOMED for other clinical domains, such as drugs, allergies, and physician orders.

Forms and Records Control↗

PubMed2003

VistA--U.S. Department of Veterans Affairs national-scale HIS.

The Veterans Health Administration of the U.S. Department of Veterans Affairs has a long, successful, and interesting history of using information technology to meet its mission. Each medical center is computerized to a degree that surprises the uninitiated. For example, medical documentation and ordering are computerized at every facility. A sophisticated national infrastructure has been developed to replicate, support, and evolve single-center successes. With advances in inter-facility networking, data sharing, and specialized central support and technical tools, VistA is becoming a single, highly scalable national health information system (HIS) solution. In this paper, we present an historical overview of VistA's development, describe its current functionality, and discuss its emergence as a national-scale hospital information system.

Forms and Records Control↗

PubMed2003

A formal representation for messages containing compositional expressions.

Clinically useful controlled vocabularies should represent healthcare concepts completely and with high reliability. Anticipating and pre-coordinating all possible expressions (e.g. 'fracture of the left femur' and 'fracture of the right femur') is not feasible. Variation in practice styles, requirements for the granularity of content, the exponential growth of terminology size, and increased cost of maintaining pre-coordinated terminologies lead us to conclude that no enumerated terminology can ever be truly comprehensive. Compositional terminologies are one potential solution to the problem of content completeness, but carry a risk of generating expressions whose equivalency cannot be easily determined. In order for post-coordinated expressions to be comparable, a sufficiently detailed formal mechanism for information representation is necessary. Comparable data for post-coordinated expressions requires normalization of both the contents and the semantics of the contents of the terminology with the information captured in post-coordinated expressions. In addition, comparable data requires a storage and messaging paradigm robust enough to faithfully represent the information contained within arbitrarily complex compositional expressions. We present a formalism for storing, and sending messages containing compositional expressions using a large-scale reference terminology. It is our intent that this formalism be used to algorithmically determine whether or not messages contain comparable data. In addition, we advocate transmitting the upward transitive closure of subsumption of all concepts, to improve comparability of data and to decrease reliance on locally stored versions of the underlying reference terminology.

Abstracting and Indexing↗

PubMed2003

Data representation in healthcare: a design for a freely-available openly-developed international reference terminology for healthcare.

The importance of data representation in healthcare has been of concern for centuries. In the last fifty years there has been an increasing awareness of the need for formal representations of terminological systems. We propose that terminological system development should be consensus driven and the product of iterative open refinement in order to practically serve the needs of the healthcare community. The system of development and maintenance of such a system must involve recruitment of the best and the brightest in the medical community to take responsibility for insuring the accuracy and completeness of such an effort. We suggest a method for algorithmically building a starting point for such a reference terminology. Further we suggest a distributed authoring environment that would allow domain experts to contribute regardless of their location or language. Open intellectual contribution is the necessary ingredient for a consensus based international health reference terminology.

Algorithms↗

PubMed2003

Coverage of oncology drug indication concepts and compositional semantics by SNOMED-CT.

OBJECTIVE: To evaluate SNOMED-CT 's ability to represent simple and compositional concepts in FDA approved oncology drug indications. METHODS: Oncology drug indications were decomposed into single and compositional concepts. SNOMED-CT's coverage of single concepts and the semantics needed to create compositional concepts were evaluated using automated and manual techniques. RESULTS: SNOMED-CT covered 86.3% of single concepts present in oncology drug indications; 11.3% of indications were covered completely. Coverage was best for concepts describing diseases, anatomy, and patient characteristics. Medications accounted for 50.5% of missing concepts. Excluding drug names, 45.2% of indications were completely represented. SNOMED-CT's semantics completely represented 60.1% of compositional expressions. CONCLUSIONS: SNOMED-CT's overall coverage of the concepts in oncology drug indications was good. Improvements or alternatives are needed for medications and semantics.

Antineoplastic Agents↗

PubMed2003

Adequacy of representation of the National Drug File Reference Terminology Physiologic Effects reference hierarchy for commonly prescribed medications.

The National Drug File Reference Terminology contains a novel reference hierarchy to describe physiologic effects (PE) of drugs. The PE reference hierarchy contains 1697 concepts arranged into two broad categories; organ specific and generalized systemic effects. This investigation evaluated the appropriateness of the PE concepts for classifying a random selection of commonly prescribed medications. Ten physician reviewers classified the physiologic effects of ten drugs and rated the accuracy of the selected term. Inter reviewer agreement, overall confidence, and concept frequencies were assessed and were correlated with the complexity of the drug's known physiologic effects. In general, agreement between reviewers was fair to moderate (kappa 0.08-0.49). The physiologic effects modeled became more disperse with drugs having and inducing multiple physiologic processes. Complete modeling of all physiologic effects was limited by reviewers focusing on different physiologic processes. The reviewers were generally comfortable with the accuracy of the concepts selected. Overall, the PE reference hierarchy was useful for physician reviewers classifying the physiologic effects of drugs. Ongoing evolution of the PE reference hierarchy as it evolves should take into account the experiences of our reviewers.

Drug Therapy↗

PubMed2002

Guideline and quality indicators for development, purchase and use of controlled health vocabularies.

Developers and purchasers of controlled health terminologies require valid mechanisms for comparing terminological systems. By Controlled Health Vocabularies, we refer to terminologies and terminological systems designed to represent clinical data at a granularity consistent with the practice of today's healthcare delivery. Comprehensive criterion for the evaluation of such systems historically have been lacking and the known criteria are inconsistently applied. Although there are many papers which describe specific desirable features of a controlled health vocabulary, to date there is not a consistent guide for evaluators of terminologies to reference, which will help them compare implementations of terminological systems on an equal footing [Methods Inf. Med. 37 (1998) 394, J. Am. Med. Inform. Assoc. 5 (1998) 503]. This guideline serves to fill the gap between academic enumeration of desirable terminological characteristics and the practical implementation or rigorous evaluations which will yield comparable data regarding the quality of one or more controlled health vocabularies.

Guidelines as Topic↗

PubMed2002

Initializing the VA medication reference terminology using UMLS metathesaurus co-occurrences.

We developed and evaluated a UMLS Metathesaurus Co-occurrence mining algorithm to connect medications and diseases they may treat. Based on 16 years of co-occurrence data, we created 977 candidate drug-disease pairs for a sample of 100 ingredients (50 commonly prescribed and 50 selected at random). Our evaluation showed that more than 80% of the candidate drug-disease pairs were rated "APPROPRIATE" by physician raters. Additionally, there was a highly significant correlation between the overall frequency of citation and the likelihood that the connection was rated "APPROPRIATE." The drug-disease pairs were used to initialize term definitions in an ongoing effort to build a medication reference terminology for the Veterans Health Administration. Co-occurrence mining is a valuable technique for initializing term definitions in a large-scale reference terminology creation project.

Algorithms↗

PubMed2002

A semantic normal form for clinical drugs in the UMLS: early experiences with the VANDF.

A semantic normal form (SNF) for a clinical drug, designed to represent the meaning of an expression typically seen in a practitioner's medication order, has been developed and is being created in the UMLS Metathesaurus. The long term goal is to establish a relationship for every concept in the Metathesaurus with semantic type "clinical drug" with one or more of these semantic normal forms. First steps have been taken using the Veterans Administration National Drug File (VANDF). 70% of the entries in the VANDF could be parsed algorithmically into the SNF. Next steps include parsing other drug vocabularies included in the UMLS Metathesaurus and performing human review of the parsed vocabularies. After machine parsed forms have been merged in the Metathesaurus Information Database (MID), editors will be able to edit matched SNFs for accuracy and establish relationships and relationship attributes with other clinical drug concepts.

Algorithms↗