Controlled medical vocabularies. A long-fallow field gets ready to bear important fruit.
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Centers for Disease Control and Prevention, U.S.↗
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Portability issues for a structured clinical vocabulary: mapping from Yale to the Columbia medical entities dictionary.
OBJECTIVE: To examine the issues involved in mapping an existing structured controlled vocabulary, the Medical Entities Dictionary (MED) developed at Columbia University, to an institutional vocabulary, the laboratory and pharmacy vocabularies of the Yale New Haven Medical Center. DESIGN: 200 Yale pharmacy terms and 200 Yale laboratory terms were randomly selected from database files containing all of the Yale laboratory and pharmacy terms. These 400 terms were then mapped to the MED in three phases: mapping terms, mapping relationships between terms, and mapping attributes that modify terms. RESULTS: 73% of the Yale pharmacy terms mapped to MED terms. 49% of the Yale laboratory terms mapped to MED terms. After certain obsolete and otherwise inappropriate laboratory terms were eliminated, the latter rate improved to 59%. 23% of the unmatched Yale laboratory terms failed to match because of differences in granularity with MED terms. The Yale and MED pharmacy terms share 12 of 30 distinct attributes. The Yale and MED laboratory terms share 14 of 23 distinct attributes. CONCLUSION: The mapping of an institutional vocabulary to a structured controlled vocabulary requires that the mapping be performed at the level of terms, relationships, and attributes. The mapping process revealed the importance of standardization of local vocabulary subsets, standardization of attribute representation, and term granularity.
Clinical Laboratory Information Systems↗
Linguistic analysis: terms and phrases used by patients in e-mail messages to nurses.
While some researchers are focusing on mapping free-text within health care fields into controlled vocabularies and classifications, many researchers are focusing on consumers' vocabularies. Using natural language processing (NLP) tools, such as MetaMap, to extract and map into terms in a controlled vocabulary is one way of understanding the pattern of terms used by lay people. Before an NLP tool can be effectively and efficiently used to extract concepts and create machine-understandable interpretations of the data, the appropriateness of the tool needs to be determined. This study aims were to determine the appropriateness of linguistic meaning captured for terms and phrases used by patients in electronic mail messages to nurses, using nursing-specific MetaMap output. Twenty messages were randomly selected from the 241 messages data set. Results indicated that four out of six nursing classification systems captured more than 50 % of the parsed word's linguistic meaning. This study demonstrates that it is possible to automatically extract and capture the linguistic meaning of the terms patients use in their electronic mail messages.
Electronic Mail↗
The Common Data Elements for cancer research: remarks on functions and structure.
OBJECTIVES: The National Cancer Institute (NCI) has developed the Common Data Elements (CDE) to serve as a controlled vocabulary of data descriptors for cancer research, to facilitate data interchange and inter-operability between cancer research centers. We evaluated CDE's structure to see whether it could represent the elements necessary to support its intended purpose, and whether it could prevent errors and inconsistencies from being accidentally introduced. We also performed automated checks for certain types of content errors that provided a rough measure of curation quality. METHODS: Evaluation was performed on CDE content downloaded via the NCI's CDE Browser, and transformed into relational database form. Evaluation was performed under three categories: 1) compatibility with the ISO/IEC 11179 metadata model, on which CDE structure is based, 2) features necessary for controlled vocabulary support, and 3) support for a stated NCI goal, set up of data collection forms for cancer research. RESULTS: Various limitations were identified both with respect to content (inconsistency, insufficient definition of elements, redundancy) as well as structure--particularly the need for term and relationship support, as well as the need for metadata supporting the explicit representation of electronic forms that utilize sets of common data elements. CONCLUSIONS: While there are numerous positive aspects to the CDE effort, there is considerable opportunity for improvement. Our recommendations include review of existing content by diverse experts in the cancer community; integration with the NCI thesaurus to take advantage of the latter's links to nationally used controlled vocabularies, and various schema enhancements required for electronic form support.
Biomedical Research↗
KOBAS server: a web-based platform for automated annotation and pathway identification.
There is an increasing need to automatically annotate a set of genes or proteins (from genome sequencing, DNA microarray analysis or protein 2D gel experiments) using controlled vocabularies and identify the pathways involved, especially the statistically enriched pathways. We have previously demonstrated the KEGG Orthology (KO) as an effective alternative controlled vocabulary and developed a standalone KO-Based Annotation System (KOBAS). Here we report a KOBAS server with a friendly web-based user interface and enhanced functionalities. The server can support input by nucleotide or amino acid sequences or by sequence identifiers in popular databases and can annotate the input with KO terms and KEGG pathways by BLAST sequence similarity or directly ID mapping to genes with known annotations. The server can then identify both frequent and statistically enriched pathways, offering the choices of four statistical tests and the option of multiple testing correction. The server also has a 'User Space' in which frequent users may store and manage their data and results online. We demonstrate the usability of the server by finding statistically enriched pathways in a set of upregulated genes in Alzheimer's Disease (AD) hippocampal cornu ammonis 1 (CA1). KOBAS server can be accessed at http://kobas.cbi.pku.edu.cn.
Alzheimer Disease↗
A hospital-wide clinical findings dictionary based on an extension of the International Classification of Diseases (ICD).
The use of a controlled vocabulary set in a hospital-wide clinical information system is of crucial importance for many departmental database systems to communicate and exchange information. In the absence of an internationally recognized clinical controlled vocabulary set, a new extension of the International statistical Classification of Diseases (ICD) is proposed. It expands the scope of the standard ICD beyond diagnosis and procedures to clinical terminology. In addition, the common Clinical Findings Dictionary (CFD) further records the definition of clinical entities. The construction of the vocabulary set and the CFD is incremental and manual. Tools have been implemented to facilitate the tasks of defining/maintaining/publishing dictionary versions. The design of database applications in the integrated clinical information system is driven by the CFD which is part of the Medical Questionnaire Designer tool. Several integrated clinical database applications in the field of diabetes and neuro-surgery have been developed at the HUG.
Databases as Topic↗
A unified approach to the design of clinical reporting systems.
Computer-based Clinical Reporting Systems (CRS) for diagnostic departments that use structured data entry have a number of functional and structural affinities suggesting that a common software architecture for CRS may be defined. Such an architecture should allow easy expandability and reusability of a CRS. We report the development methodology and the architecture of SISCOPE, a CRS originally designed for gastrointestinal endoscopy that is expandable and reusable. Its main components are a patient database, a knowledge base, a reports base, and screen and reporting engines. The knowledge base contains the description of the controlled vocabulary and all the information necessary to control the menu system, and is easily accessed and modified with a conventional text editor. The structure of the controlled vocabulary is formally presented as an entity-relationship diagram. The screen engine drives a dynamic user interface and the reporting engine automatically creates a medical report; both engines operate by following a set of rules and the information contained in the knowledge base. Clinical experience has shown this architecture to be highly flexible and to allow frequent modifications of both the vocabulary and the menu system. This structure provided increased collaboration among development teams, insulating the domain expert from the details of the database, and enabling him to modify the system as necessary and to test the changes immediately. The system has also been reused in several different domains.
Artificial Intelligence↗
An evaluation of three policies for updating product categories in the National Retail Data Monitor.
A problem in biosurveillance is how frequently to update controlled vocabularies that identify various data elements such as laboratory tests and over-the-counter healthcare products. More frequent updates improve completeness of data captured over time, but introduction of new codes into a surveillance system may cause false alarms when codes are aggregated into analytic categories. We studied the effect of three policies for updating UPCs, the controlled vocabulary for over-the-counter healthcare products used by the National Retail Data Monitor. To compare different policies for updating, we analyzed historical data from two cities for the 18 product categories of the National Retail Data Monitor under annual, quarterly, or monthly UPC update policies. We measured the effect on data completeness and false alarm rate. We found that the monthly update policy had the highest data completeness and led to the fewest number of additional false alarms. Overall, monthly updating of UPCs was the superior policy.
Commerce↗
Computer-interpretable clinical practice guidelines. Where are we and where are we going ?
OBJECTIVES: To provide a comprehensive overview of computer-interpretable guideline (CIG) systems aimed at non-experts. The overview includes the history of efforts to develop CIGs, features of and relationships among current major CIG systems, current status of standards developments pertinent to CIGs and identification of unsolved problems and needs for future research. METHODS: Literature review based on PubMed, AMIA conference proceedings and key references from publications identified. Search terms included practice guidelines, decision support, controlled vocabulary and medical record systems. Papers were reviewed by both authors and summarized narratively. RESULTS: There is a consensus that guideline delivery systems must be integrated with electronic health records (EHRs) to be most effective. Several evolving CIG formalisms have in common, use of a task network model. There is currently no dominant CIG system. The major challenge in development of interoperable CIGs, is agreement on a standard controlled vocabulary. Such standards are under development, but not widely used, particularly in commercial EHR systems. The Virtual Medical Record (VMR) concept has been proposed as a standard that would serve as an intermediary between guideline vocabulary and that used in EHR implementation. CONCLUSION: CIG systems are in a state of evolution. Standards efforts promise to improve interoperability without compromising innovation. The VMR concept can assist guideline development even before clinical systems routinely adhere to standards. Frontiers for future work include using the principles learned by computer implementation of guidelines to improve the guideline development process and evaluation methods that isolate the effects of specific CIG features.
Decision Making, Computer-Assisted↗
Barriers to evaluation of clinical vocabularies.
BACKGROUND: The importance of standard vocabularies for representing clinical data is widely accepted. The selection of suitable vocabularies for a given task relies upon criteria for systematically differentiating vocabularies. However, methods and measures for comparing vocabularies are only now emerging. Substantial barriers inhibit progress in the development of these measures. METHOD: As part of a larger project, the authors are developing quantitative measures for characteristics of controlled vocabulary as identified in the literature. RESULTS: Seven barriers have been identified that inhibit the development of quantitative measures of the characteristics of controlled vocabularies: 1) application dependence; 2) empirical v. independent assessment; 3) dichotomous v. continuous measures of characteristics; 4) poor definition of characteristics; 5) number of characteristics; 6) multiple levels of significance of characteristics; and 7) interdependence of characteristics. CONCLUSION: Progress toward quantitative assessment of clinical vocabularies is dependent upon overcoming barriers to the development of appropriate measures. Such progress requires innovative solutions and collaboration among investigators.
Evaluation Studies as Topic↗
A randomized controlled trial of automated term composition.
OBJECTIVE: To compare the ability of an Automated Term Composition (ATC) algorithm with non-compositional mappings to provide coverage (exact mappings to a controlled vocabulary) for a randomly selected set of free text entries which were entered as headings to the Impression section of the clinical notes system at the Mayo Foundation. We also compare the results of four evaluators to determine the inter-observer variability and the variance between term sets, with respect to the accuracy of the mappings and the reliability of the failure analysis. METHODS: From a corpus of approximately 1,000,000 unique terms entered into the Impression/Report/Plan section of the clinical notes system in the calendar year 1997, we randomly selected 1,000 terms. We then further randomized these 1,000 terms into two groups of 500 (Sets A and B). We constructed two copies of the same term matching interface, one without ATC (alpha) and one with ATC (beta). We took four expert Indexers and assigned them to one of the following tasks. The first reviewer (R1) compared set A using the alpha program and then set B using the beta program (R1(Aalpha + Bbeta)). The second compared set A using the alpha program and then set B using the alpha program (R2(A + B) alpha). The third compared set B using the beta program and then set A using the beta program (R3(B + A) beta). The fourth compared set A using the beta program and then set B using the alpha program (R4(Abeta + Balpha)). RESULTS: The program with Automated Term Composition mapped 540 out of the 1,000 Concepts correctly (54.0%). The same program without ATC mapped only 276 out of the 1,000 Concepts correctly (27.6%). Therefore the program with ATC was significantly more effective at matching concepts in our problem lists than the same search engine without ATC (p < 0.0001; McNemar Method). These figures result from the comparison of the alpha program with the beta program by reviewers one and four. Failure analysis showed that with the alpha version 425 out of the 724 mismatches were because a base concept was missing from the retrieval set (58.7%) and 299 mismatches were from missing qualifiers or modifiers or both (41.3%). In the beta version of the program (with ATC) 340 out of the 460 mismatches were secondary to there being a missing base concept in the retrieval set (73.9%) and only 120 mismatches due to missing modifiers and or qualifiers (26.1%). CONCLUSIONS: Automated term composition provided significantly better coverage of a randomly chosen set of patient problems, diagnosed at the Mayo Clinic during the 1997 calendar year, when compared with the same information retrieval system without ATC. We believe that these results speak further to the excellent content coverage provided by the UMLS metathesaurus. These authors believe that increased structure, normalization of UMLS content and semantics, and better tools to make use of the currently available content such as automated term composition, are what is needed to leverage the production of commercially viable tools that provide access to controlled vocabularies for medicine.
Abstracting and Indexing↗
Self-contained patient data in ORCA to cope with an evolving vocabulary.
Because of the benefits of standardization in healthcare data for research, decision support, and quality assessment, much research effort focuses on collection of structured patient data. Many strategies to obtain such data are based on controlled vocabularies to guide data entry in a far more flexible way than a fixed-form approach. Medical controlled vocabularies evolve, but change is difficult to reconcile with standardization. Retrieval of data, collected with different versions of vocabularies, is not straightforward and has consequences for patient care and research. There are several strategies to cope with these problems: keep each version, keep a record of changes, or conversion of previously collected data. Each of these strategies has pros and cons regarding storage consumption, performance during patient care, and research. The approach in ORCA (Open Record for Care) is based on self-contained patient data and combines the strengths of these strategies.
Humans↗
Indexing consistency in MEDLINE.
The quality of indexing of periodicals in a bibliographic data base cannot be measured directly, as there is no one "correct" way to index an item. However, consistency can be used to measure the reliability of indexing. To measure consistency in MEDLINE, 760 twice-indexed articles from 42 periodical issues were identified in the data base, and their indexing compared. Consistency, expressed as a percentage, was measured using Hooper's equation. Overall, checktags had the highest consistency. Medical Subject Headings (MeSH) and subheadings were applied more consistently to central concepts than to peripheral points. When subheadings were added to a main heading, consistency was lowered. "Floating" subheadings were more consistent than were attached subheadings. Indexing consistency was not affected by journal indexing priority, language, or length of the article. Terms from MeSH Tree Structure categories A, B, and D appeared more often than expected in the high-consistency articles; whereas terms from categories E, F, H, and N appeared more often than expected in the low-consistency articles. MEDLINE, with its excellent controlled vocabulary, exemplary quality control, and highly trained indexers, probably represents the state of the art in manually indexed data bases.
Abstracting and Indexing↗
A vocabulary development and visualization tool based on natural language processing and the mining of textual patient reports.
Medical terminologies are critical for automated healthcare systems. Some terminologies, such as the UMLS and SNOMED are comprehensive, whereas others specialize in limited domains (i.e., BIRADS) or are developed for specific applications. An important feature of a terminology is comprehensive coverage of relevant clinical terms and ease of use by users, which include computerized applications. We have developed a method for facilitating vocabulary development and maintenance that is based on utilization of natural language processing to mine large collections of clinical reports in order to obtain information on terminology as expressed by physicians. Once the reports are processed and the terms structured and collected into an XML representational schema, it is possible to determine information about terms, such as frequency of occurrence, compositionality, relations to other terms (such as modifiers), and correspondence to a controlled vocabulary. This paper describes the method and discusses how it can be used as a tool to help vocabulary builders navigate through the terms physicians use, visualize their relations to other terms via a flexible viewer, and determine their correspondence to a controlled vocabulary.
Algorithms↗
CaseLog: semantic network interface to a student computer-based patient record system.
We have developed a computer program called CaseLog, which serves as an exemplary, computer-based patient record (CPR) system. The program allows for the introduction of the students to issues unique to patient record systems. These include record security, unique patient identifiers, and the use of controlled vocabularies. A particularly challenging aspect of the development of this program was allowing for student entry of controlled vocabulary terms. There were four goals we wished to achieve: students should be able to find the terms they are looking for; once a term has been found, it should be easy to find contextually related terms; it should be easy to determine that a sought-for term is not in the vocabulary; and the structure of the vocabulary should be dynamically altered by contextual information to allow its use for a variety of purposes. We chose a semantic network for our vocabulary structure. Within the processing power of the equipment we were working with, we achieved our goals. This paper will describe the development of the vocabulary, the design of the CaseLog program, and the feedback from student users of the program.
Computer Systems↗
Computer databases of medical school curricula.
As the pace of curriculum reform in medical education has accelerated during the past decade, so too have demands on curriculum managers to supply increasingly detailed information about the curriculum. In response, a number of schools have joined together to begin work on designs for computer databases of the curriculum. The authors describe three of the most mature curriculum database prototypes, developed by groups at the medical schools of the University of North Carolina at Chapel Hill (UNC), The University of Maryland, and the University of Miami. All three groups have employed relational database management systems to organize information about each "instructional unit" in the preclinical curriculum, including a set of keywords defining the major concepts presented. The keywords are indexed to a controlled vocabulary, either the Medical Subject Headings (MeSH) or a MeSH derivative. The UNC database also employs a textfile management system to provide users with an overview of the entire curriculum. Future work will focus on identifying a suitable controlled vocabulary; capturing content in greater contextual detail; incorporating alternative learning formats, such as problem-based learning; creating links between content items and examination questions; and capturing information generated by student-patient interactions in clinical settings. As a result of recent collaboration with the Association of American Medical Colleges, work to define a prototype national database has begun and a consortium of interested schools is addressing further development activities.
Abstracting and Indexing↗
PEN-Ivory: the design and evaluation of a pen-based computer system for structured data entry.
PEN-Ivory is a pen-based computer system that uses structured data entry for creating patient progress notes. Users make simple gestures such as circles, lines, and scratch-outs to enter medical findings from a controlled vocabulary. The result of an interaction with PEN-Ivory is a computer-generated patient progress note in English prose. We designed PEN-Ivory's user interface in a principled way. We first created multiple working prototypes, each differing in one of three user-interface characteristics. Then we empirically evaluated the prototypes in a controlled, experimental setting for their efficiencies in enabling users to create patient progress notes. The prototype that allowed the fastest data entry had the following three user-interface characteristics: it used a paging form, used a fixed palette of modifiers, and made available all findings from the controlled vocabulary at once.
Handwriting↗
Toward a veterinary informatics research agenda: an analysis of the PubMed-indexed literature.
PURPOSE: Veterinary medicine and human health are inextricably intertwined. Effective tracking of veterinary information - veterinary informatics - impacts not only veterinary medicine, but also public health, informatics research, and clinical care. However, veterinary informatics has received little attention from the general biomedical informatics community. METHODS: To identify both active and under-researched areas in veterinary informatics, we retrieved Medical Subject Heading (MeSH) descriptors for veterinary informatics-related citations and analyzed them by topic category, animal type, and journal. RESULTS: We found that the categories of veterinary informatics with the most growth were information/bibliographical retrieval, hardware/programming, and radiology/imaging. Less than two articles per year were published in the areas of computerized veterinary medical records, clinical decision support, standards, and controlled vocabularies. Veterinary informatics articles primarily address production animals such as cattle and sheep, and companion animals such as cats and dogs. Six journals account for 31% of the veterinary informatics literature, 35 journals account for 66%. CONCLUSIONS: Veterinary informatics remains an embryonic field with relatively few publications. With the exception of radiology/imaging, published articles are primarily focused on non-clinical areas such as hardware/programming and information retrieval. There are very few publications on controlled vocabularies, standards, methodologies for integrating disparate systems, computerized medical records, clinical decision support systems, and system usability. The lack of publications in these areas may hamper efforts to collect and track animal health data at a time when such data are potentially critical to human health.
Medical Informatics↗