Multiple Ontologies for Medical Informatics (discussant)

1. Multiple Ontologies for Medical Informatics (discussant) Stanley M. Huff, M.D. Intermountain Health Care University of Utah Salt Lake City, Utah

2. Biases • Very practical • Homer Warner and the HELP system • Design and manage interfaces (mostly HL7) • Maintain vocabulary server for decision support • UMLS contractor, involved in SNOMED, co-chair of the LOINC committee • But… • Truth is stranger than fiction – the real problems present more of a challenge than the theoretical/philosophical ones

3. All relationships in terminologies/ontologies/classifications reflect a particular purpose

4. Often the purpose is not stated • SNOMED CT – mostly reflects a hierarchy for easy maintenance of the terminology • I have never found a single hierarchy in SNOMED (or any other published terminology/classification/ontology) that met my clinical need without modification • But, all of the published sources are VERY useful as a starting point

5. IHC Diabetic protocol: • “All diabetics should get a Hgb A1c test every 6 months.” • Diabetes Mellitus • Diabetes mellitus type 1 (disorder) • Diabetes mellitus type 2 (disorder) • Brittle diabetes mellitus (disorder) • Maternal diabetes mellitus (disorder) • Diabetes mellitus during pregnancy, childbirth and the puerperium (disorder) • Gestational diabetes mellitus (disorder)

6. SAGE Immunization Protocol: • “If the patient has an acute illness, then don’t immunize.” • General body state finding (finding) • Illness • (No term for “acute illness”, no children of illness) • If there was a term for “acute illness” then it would not be the set of things I want in the context of immunizations

7. 3376-1 BARBITURATES:ACNC:PT: SER/PLAS/BLD:ORD: 82205 Barbiturates, not elsewhere classified 3924-8 PENTOBARBITAL:MCNC:PT: SER/PLAS:QN: 82205 Barbiturates, not elsewhere classified All mappings between terminologies are use specific • LOINC to CPT mapping for billing

8. Context in the next version of IHC’s vocabulary database • Concept1, Relationship, Concept2, Context, Owner (person or organization) • “Type I Diabetes is-a Diabetes Mellitus in the context of the HgbA1C protocol, Beatriz Rocha”

9. Some context can only be captured in the a complete clinical record • Heart rate in Bruce Stage 3 treadmill protocol • “The blood pressure that was 47 minutes after a 60 milligram dose of gentamicin given I.V. piggy back, 32 minutes after the sponge bath, 14 minutes after a family visit, with patient supine in ICU bed 6.”

10. We must prioritize what relationships we want to represent • The most useful knowledge may not tie directly to verifiable science • Mappings for billing codes • It is not exciting scientifically • It is ultimately the choice of a payer

11. Homer Warner – Knowledge engineering sessions • What decision are you going to make that requires that data? • Only collect data that you are going to use to make a specific decision or answer a specific research question • Corrollary: We should only make ontologies (and cross mappings) where we know they will be used and for what purpose

12. What sorts of knowledge will really be used? • IHC – • Drugs (structural relationships) • Drugs (functional relationships) • Microorganisms (gram stain morphology) • Limited anatomy • Other things are short enough to put in a list in line in the decision logic • Antibiotic Assistant (Scott Evans) – “Drugs metabolized in the kidney.”

13. Why do we choose particular things to put in our terminologies • An innate interest • “My father has hypertension.” • “I just always liked dinosaurs.” • Funding • Publications • Fame • Improved patient care • (These all represent biases)

14. Even if we narrow the scope to verifiable things • There are an infinite number of things that are true in the real world • We cannot represent all knowledge at once • When we make the selection of what to do first, we are demonstrating a bias

15. Ontologies must be connected to the real world if they are to be useful • The only way they can be connected is by people through words • (Connecting them to an arbitrary cohort/instance ID is useless) • Words change meaning over time • People have been unsuccessful in enforcing usage • Homer Warner: Let’s get people to use the words we want them to use • It is a great theory, and we should try to support proper use, but it will never be perfect • You must be able to handle semantic drift in the words and concepts that people use

16. All names (terms) are not created equal • Just a handle - The name of a “gene” (BRCA1) denotes a particular sequence of nucleic acid bases, much of the meaning and computability is contained in an associated database • Sequence, protein(s), disease risk, correlations in animals • Semantically rich - Lung, heart, baby, infant, blood, have a rich context from daily experience • We may need different ways of creating and displaying ontologies based on the kinds of names with which we are dealing

17. Controlled combinatorial explosion is unavoidable • Combinations are not random, they serve a purpose • Stage IIb adenosquamous cell carcinoma of the rectum with K-ras oncogene expression • Combinations come from sources that you cannot control • National Registry of Myocardial Infarctions • Society of Thoracic Surgeons • Cancer registries • Post coordination causes complexity in addressing a concept as it participates in decision logic or as clinicians think about it • Carcinoma with body_site = rectum, with stage = Iib, with oncogene expression = K-ras