VETERINARY INFORMATION
Uses of Informatics to Solve Real World Problemsin Veterinary Medicine
Suzanne L. Santamaria n Kurt L. Zimmerman
ABSTRACTVeterinary informatics is the science of structuring, analyzing, and leveraging information in an effort to advance animalhealth, disease surveillance, research, education, and business practices. Reference and terminology standards arecore components of the informatics infrastructure. This paper focuses on three current activities that use referencestandards in veterinary informatics: (1) the construction of a messaging standard in a national animal health laboratorynetwork, (2) the creation of breed and species terminology lists for livestock disease surveillance, and (3) the develop-ment of a standardized diagnoses list for small animal practices. These and other endeavors will benefit from researchconducted to identify innovative and superior tools, methods, and techniques. The authors believe there are manyareas requiring study and special focus in order to advance veterinary informatics, and this paper highlights some ofthe needs and challenges surrounding these areas.
Key words: veterinary informatics, reference standards, terminology standards
INFORMATICS IS THE SCIENCE OF INFORMATIONInformatics, by strict definition, is the study of the scienceof information. Medical informatics is the science ofstructuring and analyzing information and data to im-prove problem solving and decision making in healthcare.1 Informatics provides the basis by which mean-ingful advancements toward evidence-based veterinarymedicine can be made, and standardized references andterminology are core components of the informatics in-frastructure,2 as demonstrated by their use in electronicmedical records for patient diagnosis and procedures.This helps with the management of patient informa-tion and enables clinicians to aggregate such informationfor epidemiological surveillance, to perform retrospectivestudies, and to drive evidence-based medical decisionmaking. For example, suppose a state health monitoringagency needs to monitor the number of cases of lowerrespiratory disease seen in the past 10 days. Viral orbacterial pneumonia, decreased respiratory function, andbronchioloalveolar adenocarcinoma, among others, areall classified as types of lower respiratory disorders inthe standardized terminology hierarchy. However, stand-ardized terminology could provide the means by whichto gather more specific types of lower respiratory dis-orders. Medical informatics encompasses a wide varietyof topics beyond terminology standards; however, itis too broad of a discipline to fully discuss these otheraspects of medical informatics in a single article. Thegoal of this paper is therefore to highlight the use ofstandardized medical terminology3 in veterinary medi-cine by examining how use of these standards haveaddressed three different problems: (1) the constructionof a messaging standard in a national animal health labo-ratory network, (2) the creation of breed and speciesterminology lists for livestock disease surveillance, and(3) the development of a standardized diagnoses list forsmall animal practice. Finally, this paper will also discuss
some of the challenges and necessary research within thearea of veterinary medical informatics terminology.
REFERENCE STANDARDS IN INFORMATICSReference standards are an integral part of informaticsas they enable unambiguous communication betweendisparate users and systems in order to ensure that twousers derive the same meaning from the same bit ofinformation. The need for reference standards is evidentin all areas of medicine such as in meta-analyzing pub-lished biomedical results, relating phenotypical findingsto clinical microscopic and molecular data, naming thedisorders, and even in describing the patients in terms ofbreeds, reproductive status, and so forth.4–11 Using refer-ence standards allows for the compilation and compari-son of large amounts of data from multiple sources. Forexample, a common terminology language is used totransmit and store diagnoses from eight veterinary teach-ing hospitals to a large data repository at Veterinary Medi-cal Databases (VMDB).12 This data repository can then besearched as a whole and compared to other data sets.13Typically, reference standards either provide the message/report (laboratory report) or the terminology to fit inthe message/report (test performed). A description of onemessage standard and two terminology standards usedin medical informatics follows.
HL7 Standard for MessagesHealth Level Seven (HL7)14 is an international standardthat promotes and enables interoperability in health careby providing a mechanism for electronic informationtransfer within and among clinics. It provides a structureand rules for when a message is instigated (patientadmitted to a hospital), the actual message composition(patient’s name), and how the message is encoded (thefirst and last name are separated by a certain symbol in
doi:10.3138/jvme.38.2.103JVME 38(2) 6 2011 AAVMC 103
the message).15 In some parts of the message, a form ofstandardized terminology may provide the value (patientdiagnosis is recorded using medical terminology). HL7is fee-based and members vote on changes. It is widelyused in human health care. The American VeterinaryMedical Association (AVMA) endorsed HL7 as an officialinformatics standard for veterinary medicine.16–24
LOINC Terminology for Laboratory TestsLogical Observations Identifiers Names and Codes(LOINC),25 a medical terminology managed by the non-profit Regenstrief Institute at Indiana University,26 con-tains over 30,000 terms and numeric codes for laboratorytests and clinical documents (e.g., Over The Counteranimal drug label). Each LOINC term is divided intofive or six parts, including the component or analytetested, its property, timing aspect, the body systemtested, and the scale and method used. For example, theLOINC term for a heartworm serum antigen test (its longname is Dirofilaria immitis Ag [Presence] in Serum and itscode is 31801–4) contains the following parts: componentof Dirofilaria immitis with the subcomponent Antigen,property of Arbitrary Concentration, time of Point in time,Serum system, and Ordinal scale. The terminology can besearched and downloaded online without charge. LOINCis endorsed for use by numerous federal agencies and theAVMA.24,27–29
SNOMED–CT Terminology for MedicineThe Systematized Nomenclature of Medicine–ClinicalTerms (SNOMED–CT)30 is a large, international standar-dized medical terminology managed by the non-profitInternational Health Terminology Standards DevelopmentOrganization of Copenhagen, Denmark.31,32 SNOMED–CT attempts to describe the whole discipline of medicinethrough its 19 interrelated hierarchies of concepts: clini-cal finding, procedure, observable entity, body structure,organism, substance, pharmaceutical/biologic product,specimen, special concept, linkage concept, physical force,event, environment or geographic location, social context,situation with explicit context, staging and scales, physicalobject, qualifier value, and record artifact.33 SNOMED–CTconcepts have numeric identifiers and computable defini-tions created through the use of attribute-value triplesand inheritance from parental concepts.31,32,34-40 For ex-ample, the computable definition of the concept viralkeratitis includes a causative agent of virus, pathologicalprocess of infectious process, morphology of inflammation,and a finding site of the cornea. Figure 1 provides a
graphical representation of this concept. SNOMED–CTcontains over 300,000 medical concepts and 900,000 syn-onyms or alternate descriptions. SNOMED–CT has beenadopted by numerous federal agencies, the AVMA, andother veterinary organizations.24,41 It is free to usersin the United States and available for download, butit requires users to consent to a licensure agreement.41SNOMED–CT has an extension mechanism31 wherebyorganizations can create concepts and descriptions fortheir specific needs which still fit into the SNOMED frame-work. The Veterinary Terminology Services Laboratoryat Virginia Tech maintains an extension of SNOMED–CT to house additional veterinary content.42
TERMINOLOGIES IN ACTIONDescriptions of three examples of cases where standardswere used to overcome a challenge in veterinary medi-cine follow. Information on the stakeholders, their needs,and the solution are discussed.
Common Structure for Individual LaboratoriesReporting to the National Veterinary Laboratory
The ChallengeThe National Veterinary Services Laboratory (NVSL), adivision of the US Department of Agriculture, Animaland Plant Health Inspection Services–Veterinary Services(USDA, APHIS–VS), is a national, regional, and interna-tional veterinary diagnostic reference laboratory.43 TheNVSL protects US animal health, public health, and inter-national trade through disease surveillance and emer-gency response. State veterinary diagnostic laboratoriessubmit laboratory test results to the NVSL, which thencompiles the reports to detect and analyze emerginganimal-health events. Efficient categorization and analysisof the laboratory reports have proven difficult for multiplereasons. One problem has been that different structures,different terms, and paper submissions provided inade-quate turn-around time during emergencies. Other chal-lenges included the lack of a reliable Internet connectionand insufficiently configured computers at some labo-ratories. Also, many laboratory personnel do not haveknowledge or experience in informatics.
The SolutionThe National Animal Health Laboratory Network(NAHLN)44 was created to coordinate the NVSL withinthe existing infrastructure of state and university labora-
Figure 1: Concept map of a SNOMED-CT concept. Viral keratitis and its associated defining relationships areshown. Each concept has a numeric identifier and defining parental relationships (these are omitted herefor brevity). Created using CmapTools (http://cmap.ihmc.us/).
104 JVME 38(2) 6 2011 AAVMC
tories. The state laboratories are equipped with Internetconnections and adequate computers to transmit relevantinformation. A standardized, structured message formatwas created by (1) analyzing the common, necessary in-formation for the laboratory reports (e.g., each reporthas a submitting organization, specimen type, test per-formed, and so forth); (2) creating a new Health LevelSeven (HL7) message structure to support the requiredtypes of information and their organization by consultingthe guidance documents and collaborating with users; (3)developing subsets of multiple terminology standardsto provide values for the HL7 elements (LOINC subset oflaboratory tests, SNOMED–CT subset of breeds, etc.). Ter-minology from the standards was identified through theuse of mapping tools (RELMA25 for LOINC) or browsers(SNOMED–CT) to identify the qualifying terminologycontent.
This standard message enables NVSL to compile all labo-ratory reports submitted from the various state laboratoriesand to use sophisticated data analytics to aid in the de-tection of emerging animal-health events. This solutioncreates a unified electronic laboratory reporting systembetween the state diagnostic laboratories and the federalorganization responsible for ensuring agriculture animalhealth. A representation of part of the NAHLN labora-tory message might appear as follows:
OPU_R25.ACCESSION.SPECIMEN.ORDER
Identifier ¼ 23566–3
Text ¼ VSNJV Ab Ser QI EIA
Name of coding system ¼ LOINC Terminology
The HL7 element ‘‘OPU_R25.ACCESSION.SPECIMEN.ORDER’’ identifies that the information that follows con-cerns the test order. The identifier ‘‘23566–3’’ correspondsto a Vesicular stomatitis virus New Jersey antibody ELISAtest on the serum in the LOINC reference terminology.
In addition, necessary informatics training and support wasprovided to pertinent laboratory personnel for mappingtheir existing reports and term lists onto the NAHLNmessage. The laboratory message and terminology valuesare available to all via an open Web site, and an onlineforum connects and assists NAHLN users and adminis-trators.45
Enhanced Intra- and Inter-agency Communicationand Analytic Possibilities with StandardizedTerminology
The ChallengeThe mission of the USDA Animal and Plant Health In-spection Services–Veterinary Services (VS) is to protectthe health of animals and animal products in the US.46The judicious monitoring of animal health and identifica-tion of and response to disease outbreaks are critical tothe mission. VS consists of multiple internal centers andprograms that work with over 600 field animal-healthworkers to perform animal-health initiatives. In order tosafeguard animal health, it is necessary for VS to collabo-rate with multiple stakeholders including other federal
agencies, state veterinary offices, and industry and foreigngovernments. Information in support of specific programswithin VS was recorded in different formats and could notbe analyzed expediently during disease outbreaks. In fact,VS stated that ‘‘the lack of standardization of data ele-ments and integration within U.S. animal health data sys-tems is the most significant challenge today in conduct-ing successful animal trace back and controlling animaldisease.’’47 VS is currently creating a unified data manage-ment system that will improve the collection and analysisof animal-health events. This system includes a commondatabase repository with many of the data values draw-ing from standardized terminology subsets. This discus-sion focuses on the issue of representing animal typeand taxonomy in this centralized database.
Terminologies to identify the kind of animal by breed,Linnaean classification, or common grouping had beendeveloped independently by various programs within VSto suit a particular program’s needs. The use of differenttaxonomic lists by different programs precluded readycomparison of data or integration with other data sets. Acomputer could not reconcile that a Hampshire breed pigfrom one record was a subtype of Sus scrofa in anotherrecord, nor could it understand how a record of swinewould encompass both of these terms. Therefore, thisconsistent type of taxonomic information is necessary tocompile and analyze records from the multiple animal-health programs.
The SolutionVeterinary Services funded the Veterinary TerminologyServices Laboratory (VTSL) to align (that is, to map) eachindividual program taxonomy list (breed, species, etc.)with a standardized terminology, SNOMED–CT. The tax-onomy lists were mapped in a Microsoft Excel spread-sheet. Candidate SNOMED–CT concepts were identifiedusing the VTSL browser41 of the Veterinary Adaptationof SNOMED–CT. Difficult terms and maps were dis-cussed via conference calls and/or electronic communi-cation. The resulting list of SNOMED–CT concepts wasuploaded with tools developed by VTSL into its data-base. Subsets of the various kinds of information werecreated for ease of use at the USDA (see Table 1 for anexample from the avian breed subset). The hierarchicalstructure of SNOMED–CT enables the integration of datafrom multiple levels of detail, from strains, mixed breeds,and breeds to a species classification and higher. SNOMED–CT provides a common language to use within VS andwith external organizations, allowing for the integrationand analysis of records from multiple sources. VS pub-lishes its taxonomy subsets of SNOMED online.48
Standardized Diagnosis List for Small AnimalPractice
The ChallengeThe American Animal Hospital Association (AAHA) isdedicated to improving the ability of companion animalveterinarians to provide quality care to patients, operatetheir practices, and meet the needs of the public in re-gards to small animal medicine.49 The AAHA’s member-ship includes approximately 6,000 veterinary practices
JVME 38(2) 6 2011 AAVMC 105
and over 40,000 veterinary care providers in the US andother countries. A list of the patients’ problems and diag-noses is central to the current problem-based medicalrecord. In addition to the primary use of documentingpatients’ problems, the diagnoses list can be used forbilling codes, clinical research, targeted marketing, deci-sion support systems,50 and linking to knowledge basedresources.51 The use of electronic medical systems insmall animal practice has increased. However, manypractices do not use a standard diagnosis list within orbetween practices, creating many missed opportunitiesto retrieve and analyze cases in-house and to communi-cate with other veterinary clinical systems, including re-ferral hospitals and Veterinary Medical Databases. Theseproblems preclude the use of larger datasets to identifythe prevalence of disease, effectiveness of treatments,and candidates for clinical trials or targeted marketing.Consider the case of a practitioner who wanted to informher clients that a newly approved treatment was avail-able for diabetes mellitus in dogs. Without an electronicmedical record system that uses a standardized diagnosislist, it would not be feasible to locate all of the ownersof dogs with diabetes mellitus that have been seen inthe clinic and a mailing to every client seen in the clinicwould be wasteful. An electronic system can easily pro-duce a list of all the owners of dogs who had been re-corded as having diabetes mellitus with the help of thestandard diagnosis list, and the owners can thus be con-tacted about the new treatment. This ability to effectivelyuse the information stored in patient records undoubt-edly enables veterinarians to provide better care to theirpatients and clients.
The SolutionThe AAHA embarked on a mission to deliver a standar-dized diagnosis list to small animal practices as part ofa larger effort to support electronic health-record use.52The AAHA developed a list of diagnostic terms com-monly used in general small animal practice and thenfunded VTSL to convert the list to a subset (smaller part)of the standardized terminology SNOMED–CT. Each termwas aligned with a SNOMED–CT concept by the follow-ing process. A terminologist identified a potential matchin SNOMED–CT using VTSL’s browser of the VeterinaryAdaptation of SNOMED–CT.41 VTSL developed a Web-based mapping tool which interacted with the databaseto display the potential maps. Matches were reviewedby another terminologist and three AAHA subject matterexperts via this online system. Differences were recon-ciled by conference calls and electronic communication.SNOMED–CT contained approximately 66% of the medi-cal findings and diagnoses needed for this subset and
the rest were created in the veterinary extension ofSNOMED–CT using tools developed by VTSL. A subsetof SNOMED–CT was formed with the concepts resultingfrom the map.
A subset of SNOMED–CT was desired because only 1%(P3400) of SNOMED’s over 300,000 medical conceptswas needed. This subset allows for the use of a commondiagnostic code to be stored and transmitted and formultiple synonyms of the same disease to be available tousers. For instance, a practitioner may choose cardiopathyor cardiac disease from the subset, but both are connectedto the same medical concept of heart disease. This subsetserves as a common language for diagnostic codes be-tween users. The AAHA released the subset with a free,open-source license using SNOMED–CT’s standard dis-tribution format53 and is currently supporting an onlineforum to discuss the implementation, improvement andmaintenance of the terms.54
OTHER CHALLENGES AND FUTURE ACTIVITIESThis paper included discussions and examples of thestructured recording of information leading to more op-portunities for retrieval and analysis. There are manyother documents in veterinary medicine that could besimilarly standardized (examples include radiology andpathology reports, medical record structure, and continu-ity of care). The need for a common language of standar-dized terminology within and between organizationswas a commonly identified problem in the three exam-ples given and a problem that persists in many specialtiesareas within veterinary medicine. Terminology subsetsshould also be developed across multiple species andgranularities for procedures, treatments, drugs, labora-tory results, breeds,55 and so forth. The creation of afunctional multispecies anatomy terminology is neces-sary. All of these activities lead to the greater integrationand functionality of the system as a whole.
These examples demonstrate the potential of informaticsto improve the practice of veterinary medicine. However,numerous obstacles to the use of reference standards andinformatics exist, such as limited financial resources, thesmall number of trained people and educational pro-grams, the lack of a coordinated effort among stake-holders, and the adaptation of existing human referencestandards to support veterinary medical informatics.
Organizations that work on veterinary informatics issuesinclude the Association for Veterinary Informatics (AVI),Banfield, VTSL, and VMDB. The AVI offers opportuni-ties for collaboration and education on its Web site
Table 1: Data standards at US Department of Agriculture (USDA) Veterinary Services (VS)
Concept name Coding system Concept unique identifier VS concept state
Ancona duck SNOMED–CT 131820006 Active
Ancona chicken SNOMED–CT 69575001 Active
Andalusian chicken SNOMED–CT 78375007 Active
This table shows a small portion of the avian breed subset of SNOMED-CT used in support of Veterinary Services programs(more information and other subsets are available from USDA/APHIS/Veterinary Services)48
106 JVME 38(2) 6 2011 AAVMC
and Google Groups pages. The AVI also coordinates thetwo-day Talbot Symposium on Informatics, currentlyheld at the AVMA’s Annual Convention, which offersprograms and discussions to promote knowledge and in-terest in informatics initiatives. Banfield shares significantfindings from its medical informatics division with theveterinary profession through publications and serviceannouncements. The VTSL provides educational presen-tations and online discussion forums for veterinary ter-minology issues and maintains a veterinary extension ofSNOMED–CT. The VMDB collects case reports frommultiple veterinary teaching hospitals recorded withSNOMED–CT in its database and offers case retrievalservices to the profession. Many of these groups andothers are moving toward an open, standards-based,user-driven consortium.
CONCLUSIONReference terminology standards in informatics providesolutions to real-world problems in veterinary medicine.Using terminology standards enhances communicationwithin and among organizations, leading to greater op-portunities for data aggregation and analysis and linksbetween information resources. The adoption of suchstandards and the continued work toward their develop-ment will help propel veterinary medicine toward moreevidence-based practice. Further training, development,research, and coordination are needed to advance medi-cal informatics and its impact on veterinary medicine.
CONFLICT OF INTEREST DISCLOSURETwo of the organizations mentioned in this paper (USDAand AAHA) fund activities in a laboratory with whichwe are associated (VTSL), but these organizations didnot participate or influence the facts or conclusions ofthis paper.
REFERENCES1 Shortliffe EH. Medical informatics: computerapplications in health care and biomedicine. 2nd ed.New York: Springer; 2001.
2 Bakken S. An informatics infrastructure is essentialfor evidence-based practice. J Am Med Inform Assn.2001;8:199–201. doi:10.1136/jamia.2001.0080199
3 Cimino JJ, Zhu X. The practical impact of ontologieson biomedical informatics. Yearb Med Inform.2006:124–35.
4 Wilcke JR, Green JM, Spackman KA, Martin MK,Case JT, Santamaria SL, et al. Concerning SNOMED–CTcontent for public health case reports; author reply. J AmMed Inform Assoc. 2010;17:613.doi:10.1136/jamia.2010.003756
5 Kamal J, Borlawsky T, Payne PR. Development of anontology-anchored data warehouse meta-model. AMIAAnnu Symp Proc. 2007:1001.
6 O’Connor BD, Day A, Cain S, Arnaiz O, Sperling L,Stein LD. GMODWeb: a web framework for the Generic
Model Organism Database. Genome Biol. 2008;9(6):R102.doi:10.1186/gb-2008-9-6-r102
7 Robinson PN, Kohler S, Bauer S, Seelow D, Horn D,Mundlos S. The Human Phenotype Ontology: a tool forannotating and analyzing human hereditary disease. AmJ Hum Genet. 2008;83:610–5.doi:10.1016/j.ajhg.2008.09.017
8 Osborne JD, Zhu LJ, Lin SM, Kibbe WA. Interpretingmicroarray results with gene ontology and MeSH.Methods Mol Biol. 2007;377:223–42.doi:10.1385/1-59745-390-0:223
9 Washington NL, Haendel MA, Mungall CJ,Ashburner M, Westerfield M, Lewis SE. Linking humandiseases to animal models using ontology-basedphenotype annotation. PLoS Biol. 2009;7(11):e1000247.
10 Kupershmidt I, Su QJ, Grewal A, Sundaresh S,Halperin I, Flynn J, et al. Ontology-based meta-analysisof global collections of high-throughput public data.PLoS One. 2010;5(9):e13066.
11 Cook C, Hannley M, Richardson JK, Michon J,Harker M, Pietrobon R. Real-time updates of meta-analyses of HIV treatments supported by a biomedicalontology. Account Res. 2007;14(1):1–18.doi:10.1080/08989620601003471
12 Veterinary Medical Databases [Internet]. Urbana (IL):Veterinary Medical Databases; c2010 [cited 2010 Nov 23].Available from: http://www.vmdb.org/.
13 Glickman LT, Moore GE, Glickman NW, CaldanaroRJ, Aucoin D, Lewis HB. Purdue University-BanfieldNational Companion Animal Surveillance Program foremerging and zoonotic diseases. Vector-Borne Zoonot.2006;6(1):14–23. doi:10.1089/vbz.2006.6.14
14 Health Level Seven (HL7) [Internet]. Ann Arbor(MI): Health Level Seven International; c2007–2011.Available from: http://www.hl7.org/.
15 Coiera E. Guide to health informatics. 2nd ed.London and New York: Arnold; 2003.
16 Dolin RH, Alschuler L, Boyer S, Beebe C, Behlen FM,Biron PV, et al. HL7 Clinical document architecture,release 2. J Am Med Inform Assoc. 2006;13:30–9.doi:10.1197/jamia.M1888
17 Kawamoto K, Honey A, Rubin K. The HL7-OMGHealthcare Services Specification Project: motivation,methodology, and deliverables for enabling asemantically interoperable service-oriented architecturefor healthcare. J Am Med Inform Assoc. 2009;16:874–81.doi:10.1197/jamia.M3123
18 Kurtz M. HL7 version 3.0: a preview for CIOs,managers, and programmers. J Healthc Inf Manag.2002;16(4):22–3.
19 Oemig F, Blobel BG. HL7 conformance: how to doproper messaging. St Heal T. 2007;127:298–307.
20 Ouagne D, Nadah N, Schober D, Choquet R,Teodoro D, Colaert D, et al. Ensuring HL7-basedinformation model requirements within an ontologyframework. St Heal T. 2010;160(Pt 2):912–6.
JVME 38(2) 6 2011 AAVMC 107
21 Quinsey CA. Using HL7 standards to evaluate anEHR. J AHIMA. 2006;77(4):64A-C.
22 Shaver D. HL7—what you need to know. HealthManag Technol. 2002;23(6):106–7.
23 Tracy WR, Dougherty M. HL7 standard shapescontent, exchange of patient information. J AHIMA.2002;73(8):48–51; quiz 3–4.
24 SNOMED, HL7, LOINC the official informaticsstandards for veterinary medicine [Internet].Schaumburg (IL): American Veterinary MedicalAssociation; 2002 [cited 2010 Nov 28]. Available from:http://www.avma.org/onlnews/javma/jun02/s020601o.asp.
25 Logical Observation Identifiers Names and Codes(LOINC) [Internet]. Regenstrief Institute; c1994-2011[cited 2010 Nov 28]. Available from: http://loinc.org/.
26 Regenstrief Institute at Indiana University [Internet].Regenstrief Institute; c2000-2011 [cited 2011 Nov 28].Available from: http://www.regenstrief.org/.
27 Herbst MR. Another look at LOINC (LogicalObservation and Identifier Codes). J AHIMA.2002;73(1):56, 8.
28 McDonald CJ, Huff SM, Suico JG, Hill G, Leavelle D,Aller R, et al. LOINC, a universal standard foridentifying laboratory observations: a 5-year update. ClinChem. 2003;49:624–33. doi:10.1373/49.4.624
29 Stark M. A look at LOINC. J AHIMA. 2006;77(7):52,4–5; quiz 7–8.
30 Systematized Nomenclature of Medicine—ClinicalTerms (SNOMED–CT) [Internet]. International HealthTerminology Standards Development Organisation;[cited 2010 Nov 27]. Available from:http://www.ihtsdo.org.
31 Cornet R, de Keizer N. Forty years of SNOMED: aliterature review. BMC Med Inform Decis. 2008;8 Suppl1:S2. doi:10.1186/1472-6947-8-S1-S2
32 Donnelly K. SNOMED–CT: The advancedterminology and coding system for eHealth. St Heal T.2006;121:279–90.
33 SNOMED CT2 technical reference guide: January2010 international release [Internet]. International HealthTerminology Standards Development Organisation;c2002–2010 [cited 2010 Nov 28]. Available from:http://www.ihtsdo.org/fileadmin/user_upload/Docs_01/Publications/doc1_TechnicalReferenceGuide_Current-en-US_INT_20100131.pdf.
34 Massey KA, Ansermino JM, von Dadelszen P, MorrisTJ, Liston RM, Magee LA. What is SNOMED CT andwhy should the ISSHP care? Hypertens Pregnancy.2009;28(1):119–21. doi:10.1080/10641950802601294
35 Schulz S, Klein GO. SNOMED CT—advances inconcept mapping, retrieval, and ontological foundations;selected contributions to the Semantic Mining Conferenceon SNOMED CT (SMCS 2006). BMC Med Inform Decis.2008;8 Suppl 1:S1. doi:10.1186/1472-6947-8-S1-S1
36 Rector AL, Brandt S. Why do it the hard way? Thecase for an expressive description logic for SNOMED.J Am Med Inform Assoc. 2008;15:744–51.doi:10.1197/jamia.M2797
37 James AG, Spackman KA. SNOMED CT as theclinical terminology for the representation of the clinicalcare of the newborn infant. AMIA Annu Symp Proc.2007:989.
38 Giannangelo K, Fenton SH. SNOMED CT survey:an assessment of implementation in EMR/EHRapplications. Perspect Health Inf Manag. 2008;5:7.
39 Giannangelo K, Berkowitz L. SNOMED CT helpsdrive EHR success. J AHIMA. 2005;76(4):66–7.
40 Zimmerman KL, Wilcke JR, Robertson JL, FeldmanBF, Kaur T, Rees LR, et al. SNOMED representation ofexplanatory knowledge in veterinary clinical pathology.Vet Clin Pathol. 2005;34(1):7–16.doi:10.1111/j.1939-165X.2005.tb00002.x
41 SNOMED Clinical Terms [Internet]. Bethesda (MD):United States National Library of Medicine; 2009 [cited2010 Nov 27]. Available from: http://www.nlm.nih.gov/research/umls/Snomed/snomed_main.html.
42 Veterinary Terminology Services Laboratory[Internet]. Blacksburg (VA): Virginia-Maryland RegionalCollege of Veterinary Medicine; [cited 2010 Dec 1].Available from: http://vtsl.vetmed.vt.edu/.
43 National Veterinary Services Laboratory [Internet].Beltsville (MD): United States Department of Agriculture;[updated 2010 Mar 4; cited 2010 Nov 28]. Available from:http://www.aphis.usda.gov/animal_health/lab_info_services/about_nvsl.shtml.
44 National Animal Health Laboratory Network.[Internet]. Beltsville (MD): United States Department ofAgriculture; [updated 2010 Mar 4; cited 2010 Nov 28].Available from:http://www.aphis.usda.gov/animal_health/nahln/.
45 Prototype National Animal Health LaboratoryNetwork (NAHLN): Terminology Service [Internet].Blacksburg (VA): Virginia-Maryland Regional Collegeof Veterinary Medicine; [cited 2010 Nov 20]. Availablefrom: http://vtsl.vetmed.vt.edu/nahln/main.cfm.
46 Animal Health [Internet]. Beltsville (MD): UnitedStates Department of Agriculture; [updated 2010 Jul 14;cited 2010 Nov 28]. Available from:http://www.aphis.usda.gov/animal_health/.
47 Information technology roadmap: a new direction;paving the way for the future [Internet]. United StatesDepartment of Agriculture, Animal and Plant HealthInspection Service, Veterinary Services; 2009 [cited 2010Nov 28]. Available from: http://www.aphis.usda.gov/animal_health/vs_ocio/downloads/vs_it_roadmap.pdf.
48 Chapter 2, appendix B: subject taxonomy.Surveillance and data standards for USDA/APHIS/Veterinary Services. United States Department ofAgriculture, Animal and Plant Health Inspection Service,Veterinary Services; [cited 2010 Nov 28]. Available from:http://www.aphis.usda.gov/vs/nahss/docs/standards_appendixB_subject_taxonomy_v20100108.pdf.
108 JVME 38(2) 6 2011 AAVMC
49 AAHA history and mission statement. Lakewood(CO): American Animal Hospital Association; [2010 Nov27]. Available from: http://www.aahanet.org/about/mission.aspx.
50 Fung KW, McDonald C, Srinivasan S. The UMLS-CORE project: a study of the problem list terminologiesused in large healthcare institutions. J Am Med InformAssoc. 2010;17:675–80.
51 Alpi K. Exploring the state of veterinary informatics[Internet]. [cited 2010 Nov 27]. Available from:http://espace.library.uq.edu.au/eserv/UQ:179865/n5_3_Thurs_Alpi_199.pdf.
52 Burnett H. Everything’s eventual (even EHRs): aprogress report from AAHA’s Electronic Health RecordsTask Force; 2008.
53 AAHA diagnostic terms [Internet]. Lakewood (CO):American Animal Hospital Association; [2010 Nov 27].Available from: https://secure.aahanet.org/eweb/dynamicpage.aspx?site=resources&webcode=diagnosticterms.
54 Veterinary Terminology Services Laboratory Forum[Internet]. [cited 2010 Dec 9]. Available from:http://vtsl.vetmed.vt.edu/forums/index.php.
55 Smith-Akin KA, Bearden CF, Pittenger ST, BernstamEV. Toward a veterinary informatics research agenda:an analysis of the PubMed-indexed literature. Int J MedInform. 2007;76:306–12.
AUTHOR INFORMATION
Suzanne L. Santamaria, DVM, is Veterinary MedicalTerminologist at the Veterinary Medical InformaticsLaboratory, Virginia-Maryland Regional College ofVeterinary Medicine, Virginia Tech, 1880 Pratt Dr.,Room 1125 (#0493), Blacksburg, VA 24061.E-mail: [email protected]. She earned her DVM fromPurdue University and is currently an MS Candidate inVeterinary Medical Informatics at Virginia Tech.
Kurt L. Zimmerman, DVM, PhD, is Clinical and AnatomicDiplomat at the American College of VeterinaryPathologists and Associate Professor of Pathology andInformatics at the Virginia-Maryland Regional College ofVeterinary Medicine, Virginia Tech, 1880 Pratt Dr.,Blacksburg, VA 24061. E-mail: [email protected].
JVME 38(2) 6 2011 AAVMC 109
