New Desiderata for Biomedical Terminologies

1. New Desiderata for Biomedical Terminologies Discussant: James J. Cimino Columbia University

2. Dilemmas of Biomedical Terminologies

3. Dilemmata of Biomedical Terminologies • Abstract concepts vs. real world entities • Knowledge about concepts vs. knowledge about extensions in reality • Terms with context-specific meanings • Changes in knowledge vs. changes in meaning

4. Abstract Concepts vs. Real World Entities Philosophy Terminology Ontology Ontology Realism Conceptualism GO FMA Concept

5. Desiderata for Controlled Medical Data I - Capture what is known about the patient II - No information loss III - No false implications IV - Support retrieval V - Support reuse VI - Support aggregation VII - Support inference

6. Desiderata forControlled Medical Terminologies • Provide identifiers for meanings we want to apply to the patient • Make sure the semantics are universally understood, separate from linguistics • Make sure that, as our understanding changes, original meaning is not forgotten • Provide a bridge between what we record and how we reason

7. Patient Data as Extensions of Concepts • Everything we record is an abstraction • Context tells us how the abstractions are interpreted • Example: contexts of diagnoses in medical records • Example: events • Example: evolution of patient problems

8. Everything We Record is an Abstraction • We don’t say that concepts cause concepts or terms cause terms • Nor do we say that we are storing bits of pneumonia in the EMR • Pneumococcal pneumonia is something we “know” about that we associate with the patient through interpretation of what we “know” about the patient • So is the infiltrate seen on the chest x-ray • So is the chest x-ray

9. Context in the EMR: What We See • Lab results: Sodium 130 • Problem list: Hyponatremia • Medication list: Hypertonic Saline • Medication administration: Hypertonic Saline

10. Context in the EMR: What We Record • At <timestamp> lab reports <procedure> with id <ID> and value <value> for <patient> • At 10AM EST May 1, 2005 lab reports Stat Serum Sodium with id #123123 and value 130 for Mr. Jones • At <timestamp> <clinician> interprets <ID> as indicating <condition> for <patient> • At 10:01AM EST May 1, 2005, Dr. Brown interprets #123123 as indicating Hyponatremia for Mr. Jones

11. Context in the EMR: What We Record • At <timestamp> <clinician> orders pharmacy item <formulary item> with order id <ID> for <patient> • At 10:02AM EST, May 1, 2005 Dr. Brown orders pharmacy time <Hypertonic Saline> with order id #3233 for Mr. Jones • At <timestamp> pharmacy delivers <inventory item> with inventory id <ID> for order id <ID> for <patient> • At 10:03AM EST, May 1, 2005 pharmacy delivers Lilly Product 5505 with inventory id #223232 for Mr. Jones

12. Context in the EMR: What We Record • At <timestamp> decision support system suggests <condition> for <patient> • At 10:04AM EST, May 1, 2005 decision support system suggests Factitious Hyponatremia for Mr. Jones

13. Concepts or Entities? • Concepts included in EMR: • Stat Serum Sodium • Hyponatremia • Hypertonic Saline • Lilly Product 5505 • Factitious Hyponatremia • Entities in Real World • Mr. Jones • Mr. Jones’s Blood • Serum Specimen #55555 • Lab • Test ID #123123 • Analyzer #114 • 130 (reading on analyzer) • Dr. Brown • Pharmacy • Lilly Product 5505 #223232 • Decision Support System

14. What Do Knowledge Do We Need? • Stat Serum Sodium measures Sodium • Hyponatremia is defined as Sodium below normal range • Normal Range of Sodium is 135-145 • Lilly Product 5505 is a Hypertonic Saline • #223232 is an available instance of Lilly Product 5505 • Analyzer #114 uses Flame Photometry Method • Factitious Hyponatremia occurs when Sodium is low, method is flame photometry, and triglycerides are high

15. How Do We Cope with Changes in Our Knowledge About the Patient? Barrrows RC, Johnson SB: A data model that captures clinical reasoning about patient problems. SCAMC 1995:402-405. • Chest pain  R/O CAD  Costochondritis  Resolved (S/P Costochondritis) • Thread of assessments at points in time • What do you think the patient has vs. (please tell the court) What did you think the patient had?

16. How Do We Cope with Changes in Our General Knowledge? • Change in: • what tests measure • formulary • normal ranges • definition of disease • The test: if we change update the knowledge, do we change the old data?

17. Referent Tracking • We do this now (or we should) • Implicit vs. explicit interpretation • Each referent still has to correspond to some meaning • Solution must be practical, usable, and maintainable

18. The Desiderata Revisited • Concept orientation - what is the alternative? • Concept permanence and graceful evolution - version control • Formal definitions - add to knowledge vs. recognize change • Reject NEC - store what the patient has and classify later • Multiple granularities - patient level vs. reuse • Representing context - the implicit meaning in the EMR design

19. General Philosophy of Concepts and Data Concept plus Context Meaning plus Stamp Ontology Recapitulates Philately