Two broad themes of this research are, 1) to develop a generalized framework for studying the process of patient information acquisition and 2) to develop and evaluate automated techniques for identifying domain-specific vocabulary terms contained in, or missing from, a standardized controlled medical vocabulary with emphasis on those terms necessary for representing the canine physical examination.

A generalized framework for studying the process of patient information acquisition is addressed by the Patient Information Acquisition Model (PIAM). PIAM illustrates the decision-to-perception chain which links a clinician's decision to collect information, either personally or through another, with the perception of the resulting information. PIAM serves as a framework for a systematic approach to identifying causes of missing or inaccurate information.

The vocabulary studies in this research were conducted using free-text with two objectives in mind, 1) develop and evaluate automated techniques for identifying canine physical examination terms contained in the Systematized Nomenclature of Medicine and Veterinary Medicine (SNOMED), version 3.3 and 2) develop and evaluate automated techniques for identifying canine physical examination terms not documented in the 1997 release of the Unified Medical Language System (UMLS).

Two lexical matching techniques for identifying SNOMED concepts contained in free-text were evaluated, 1) lexical matching using SNOMED version 3.3 terms alone and 2) Metathesaurus-enhanced lexical matching. Metathesaurus-enhanced lexical matching utilized non-SNOMED terms from the source vocabularies of the Metathesaurus of the Unified Medical Language System to identify SNOMED concepts in free-text using links among synonymous terms contained in the Metathesaurus.

Explicit synonym disagreement between the Metathesaurus and its source vocabularies was identified during the Metathesaurus-enhanced lexical matching studies. Explicit synonym disagreement occurs, 1) when terms within a single concept group in a source vocabulary are mapped to multiple Metathesaurus concepts, and 2) when terms from multiple concept groups in a source vocabulary are mapped to a single Metathesaurus concept. Five causes of explicit synonym disagreement between a source vocabulary and the Metathesaurus were identified in this research, 1) errors within a source vocabulary, 2) errors within the Metathesaurus, 3) errors in mapping between the Metathesaurus and a source vocabulary, 4) systematic differences in vocabulary management between the Metathesaurus and a source vocabulary, and 5) differences regarding synonymy among domain experts, based on perspective or context. Three approaches to reconciling differences among domain experts are proposed. First, document which terms are involved. Second, provide a mechanism for selecting either vocabulary-based or Metathesaurus-based synonymy. Third, assign a "basis of synonymy" attribute to each set of synonymous terms in order to identify the perspective or context of synonymy explicitly.

The second objective, identifying canine physical examination terms not documented in the 1997 release of the UMLS was accomplished using lexical matching, domain-specific free-text, the Metathesaurus and the SPECIALIST Lexicon. Terms contained in the Metathesaurus and SPECIALIST Lexicon were removed from free-text and the remaining character strings were presented to domain experts along with the original sections of text for manual review.