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C G Chute

Publications and source records attributed to C G Chute.

100 records · Page 6Linked to original sources
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Phase II evaluation of clinical coding schemes: completeness, taxonomy, mapping, definitions, and clarity. CPRI Work Group on Codes and Structures.

OBJECTIVE: To compare three potential sources of controlled clinical terminology (READ codes version 3.1, SNOMED International, and Unified Medical Language System (UMLS) version 1.6) relative to attributes of completeness, clinical taxonomy, administrative mapping, term definitions and clarity (duplicate coding rate). METHODS: The authors assembled 1929 source concept records from a variety of clinical information taken from four medical centers across the United States. The source data included medical as well as ample nursing terminology. The source records were coded in each scheme by an investigator and checked by the coding scheme owner. The codings were then scored by an independent panel of clinicians for acceptability. Codes were checked for definitions provided with the scheme. Codes for a random sample of source records were analyzed by an investigator for "parent" and "child" codes within the scheme. Parent and child pairs were scored by an independent panel of medical informatics specialists for clinical acceptability. Administrative and billing code mapping from the published scheme were reviewed for all coded records and analyzed by independent reviewers for accuracy. The investigator for each scheme exhaustively searched a sample of coded records for duplications. RESULTS: SNOMED was judged to be significantly more complete in coding the source material than the other schemes (SNOMED* 70%; READ 57%; UMLS 50%; *p < .00001). SNOMED also had a richer clinical taxonomy judged by the number of acceptable first-degree relatives per coded concept (SNOMED* 4.56, UMLS 3.17; READ 2.14, *p < .005). Only the UMLS provided any definitions; these were found for 49% of records which had a coding assignment. READ and UMLS had better administrative mappings (composite score: READ* 40.6%; UMLS* 36.1%; SNOMED 20.7%, *p < .00001), and SNOMED had substantially more duplications of coding assignments (duplication rate: READ 0%; UMLS 4.2%; SNOMED* 13.9%, *p < .004) associated with a loss of clarity. CONCLUSION: No major terminology source can lay claim to being the ideal resource for a computer-based patient record. However, based upon this analysis of releases for April 1995, SNOMED International is considerably more complete, has a compositional nature and a richer taxonomy. Is suffers from less clarity, resulting from a lack of syntax and evolutionary changes in its coding scheme. READ has greater clarity and better mapping to administrative schemes (ICD-10 and OPCS-4), is rapidly changing and is less complete. UMLS is a rich lexical resource, with mappings to many source vocabularies. It provides definitions for many of its terms. However, due to the varying granularities and purposes of its source schemes, it has limitations for representation of clinical concepts within a computer-based patient record.

Abstracting and Indexing↗
PubMedDate not supplied

A framework for comprehensive health terminology systems in the United States: development guidelines, criteria for selection, and public policy implications. ANSI Healthcare Informatics Standards Board Vocabulary Working Group and the Computer-Based Patient Records Institute Working Group on Codes and Structures.

Health care in the United States has become an information-intensive industry, yet electronic health records represent patient data inconsistently for lack of clinical data standards. Classifications that have achieved common acceptance, such as the ICD-9-CM or ICD, aggregate heterogeneous patients into broad categories, which preclude their practical use in decision support, development of refined guidelines, or detailed comparison of patient outcomes or benchmarks. This document proposes a framework for the integration and maturation of clinical terminologies that would have practical applications in patient care, process management, outcome analysis, and decision support. Arising from the two working groups within the standards community--the ANSI (American National Standards Institute) Healthcare Informatics Standards Board Working Group and the Computer-based Patient Records Institute Working Group on Codes and Structures--it outlines policies regarding 1) functional characteristics of practical terminologies, 2) terminology models that can broaden their applications and contribute to their sustainability, 3) maintenance attributes that will enable terminologies to keep pace with rapidly changing health care knowledge and process, and 4) administrative issues that would facilitate their accessibility, adoption, and application to improve the quality and efficiency of American health care.

Delivery of Health Care↗
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Clinical classification and terminology: some history and current observations.

The evolution of health terminology has undergone glacial transition over time, although this pace has quickened recently. After a long history of near neglect, unimaginative structure, and factitious development, health terminologies are in an era of unprecedented importance, sophistication, and collaboration. The major highlights of this history are reviewed, together with important intellectual advances in health terminology development. The inescapable conclusion is that we are amidst a major revolution in the role and capabilities of health terminologies, entering an age of large-scale systems for health concept representation with international implications.

Classification↗
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Embedded structures and representation of nursing knowledge.

Nursing Vocabulary Summit participants were challenged to consider whether reference terminology and information models might be a way to move toward better capture of data in electronic medical records. A requirement of such reference models is fidelity to representations of domain knowledge. This article discusses embedded structures in three different approaches to organizing domain knowledge: scientific reasoning, expertise, and standardized nursing languages. The concept of pressure ulcer is presented as an example of the various ways lexical elements used in relation to a specific concept are organized across systems. Different approaches to structuring information-the clinical information system, minimum data sets, and standardized messaging formats-are similarly discussed. Recommendations include identification of the polyhierarchies and categorical structures required within a reference terminology, systematic evaluations of the extent to which structured information accurately and completely represents domain knowledge, and modifications or extensions to existing multidisciplinary efforts.

Decision Support Systems, Clinical↗
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Hierarchical distribution analysis for computer-assisted classifications of patient records.

The many existing hierarchical classifications present opportunities for computer-aided classification of medical data. The authors evaluate lexical term mapping followed by hierarchical node activation to determine the semantic locality of diagnoses in the American College of Radiology (ACR) classification. The activated hierarchy exposes conceptual structures behind lexically matched categories and markedly narrows the alternatives for human review. Several distribution analyses are evaluated for narrowing the candidate search space, to reduce ambiguity. The methods were tested on a prototype system, using a set of 78 samples of Mayo radiology diagnoses; their relative effectiveness is illustrated.

Classification↗