Clinical Coding 2

1. Clinical Coding 2 Dr JL FisteinMA MB BChir BarristerE-mail: jfistein@hiconsultants.com March 2003

2. Aims • Some learning objectives & quick recap of part 1 • Hands-on-coding exercise • Tools and examples

3. Aims • Some learning objectives & quick recap of part 1 • Hands-on-coding exercise • Tools and examples

4. Learning objectives 1 • Know what a medical terminology is • Be aware that many medical terminologies exist Source: RCSEd

5. Learning objectives 2 • Know where medical terminologies fit in with other technologies • Integrating with other medical knowledge bases • Integrating with data models Source: RCSEd

6. Learning objectives 3 • Know what the component parts of medical terminologies are: • Concept • Link • Term • Code • Organisation Source: RCSEd

7. Learning objectives 4 • Know how to classify medical terminologies • By domain type (or content) • Scope • Coverage • By use or intended purpose • Fitness for purpose • By technical properties Source: RCSEd

8. Learning objectives 5 • Understand the problems in creating / using medical terminologies • Scaling • Usability • Analytic capability Source: RCSEd

9. Learning objectives 1 • Know what a medical terminology is • Be aware that many medical terminologies exist Source: RCSEd

10. What is a medical terminology? • NB Strictly terminologies are just collections of terms (see later) without any kind of link to concepts!

11. What is a medical terminology? • Controlled vocabularies • Collections of words (terms) usually assembled for a specific purpose • Hard to reuse • Coding schemes • Some kind of term list where each term has a code • Often arranged hierarchically according to the meaning of the terms but may be flat (unordered)

12. What is a medical terminology? • Classifications / taxonomies / ontologies • Term list + codes + hierarchy • Hierarchy should be “is-a” (this is not the case in many real-world examples – expedience) • Concepts usually richly represented • Usually creates a model of the domain that aims to allow maximum reuse

13. Learning objectives 1 • Know what a medical terminology is • Be aware that many medical terminologies exist Source: RCSEd

14. How many different terminologies exist? • Remember there are hundreds! • We’ll recap why this is later • E.g. UMLS contains cross-references between 79 different schemes

15. How many different terminologies exist? • Graph of Primary Care schemes used in different countries (1995) Source: Coding and nomenclatures: a snapshot from around the world (Wilson & Purves)

16. Learning objectives 2 • Know where medical terminologies fit in with other technologies • Integrating with other medical knowledge bases • Integrating with data models Source: RCSEd

17. Medical Terminologies and Clinical Information Systems Source: the OpenGALEN organisation www.opengalen.org

18. Terminologies and CISs • Terminologies must integrate with CISs to be useful – cannot exist in isolation • Terminologies become bits of software not static resources (like books) • Requires good “interfaces” between terminology and the CIS

19. Integrating Terminologies with other knowledge sources • Particularly • post hoc analysis • Prediction (decision support) • Typically using IF…THEN reasoning (although remember other approaches)

20. Integrating terminologies with other data models • Different data models for different purposes • Rich codes & simple data model vs. simple codes & rich data model • Encapsulation • (A slight aside: interface layers to present related concepts e.g. cough / smoking)

21. Learning objectives 3 • Know what the component parts of medical terminologies are: • Concept • Link • Term • Code • Organisation Source: RCSEd

22. Concept • NB Semantic triangle • Further refinement • Primitive concept – not composed of other concepts • Composed concept – composed of a (sensible) combination of other concepts

23. Link • NB Object-Attribute-Value triples • A relationship between two concepts • Types: • Is-A • Symmetrical (e.g. is parallel to) • Asymmetrical (e.g. has part)

24. Term • NB Semantic Triangle • A text label for something • Also • Synonyms • Homonyms • Eponyms

25. Code • A (usually abstract) identifier for a concept or link

26. Putting it all together A classification (ontology / taxonomy) fragment Femoral pathology Fracture (P-23) Fractured Femur (MS-1100) Fractured Shaft of Femur (MS-1233) Fractured NOF (MS-1234) NOF (A-99) Fracture (P-23) hasLocation (L-2)

27. Putting it all together An is-A relationship Femoral pathology Fracture (P-23) Fractured Femur (MS-1100) Fractured Shaft of Femur (MS-1233) A composed concept Fractured NOF (MS-1234) NOF (A-99) Fracture (P-23) hasLocation (L-2) A primitiveconcept A link Codes

28. Putting it all together Femoral pathology Fracture (P-23) Fractured Femur (MS-1100) Fractured Shaft of Femur (MS-1233) Fractured NOF (MS-1234) NOF (A-99) Fracture (P-23) hasLocation (L-2) “NOF” “Femoral neck” “neck of femur” Terms

29. Learning objectives 4 • Know how to classify medical terminologies • By domain type (or content) • Scope • Coverage / depth • By use or intended purpose • Fitness for purpose • By technical properties Source: RCSEd

30. Specialist topic • Nurses: ICNP, NANDA, NIC, OMAHA, HHCC • Surgery only: OCPS-4, CPT • Diseases: ICD • Cancer: ICD-O • Impairment, disability & handicap: ICIDH • All of medicine: READ, SNOMED

31. Intended Application • Billing: CPT4, ICD9-CM, CCAM • Epidemiology & Statistics: ICD • Healthcare record: READ • Reference: ? SNOMED-CT, UMLS

32. Technical Properties • Enumerated vs compositional schemes • Lexical schemes

33. Learning objectives 5 • Understand the problems in creating / using medical terminologies • Scaling • Usability • Analytic capability Source: RCSEd

34. Scale • (Particularly enumerative schemes) tend to become very large 150-200,000 terms • Hard to remember what’s in there • Hard to organise • Hard to analyse • Different people use the same code to mean different things

35. Organisation • Needs organisation for different purposes • Navigation • Statistical analysis • Underlying collection of terms and concepts is large and complex • Hard to guess every possible use for the concepts

36. Technical • Assigning meanings to codes (may mislead the user) • Having limited code lengths (READ2)

37. Other problems • Compositional nonsense • Redundancy • Post-hoc classification • Computational intractability

38. Aims • Some learning objectives & quick recap of part 1 • Hands-on coding exercise • Tools and examples

39. Objective • See how hard it is to devise clinical terminologies! • Pity the poor coding clerks

40. The Task • Come up with the one true, correct, complete classification for children’s party food, etc. (Output: a single OHP sheet) • Time limit: • 15 minutes (in groups) • 15 minutes debrief / comparisons (argument!)

41. Perspectives • Caterers • Nutritionists • Parents • Children

42. Approaches • Use any that we have discussed so far • (hint: you will need to devise a classification so don’t adopt a purely enumerative approach) • Don’t worry that you can’t classify everything perfectly – that’s what computers are for!

43. A starter for 10… • Some concepts you might consider… • Hula hoops • Jammie dodgers • Paper plates • Orange squash • Gluten-free birthday cake • Candles • Barbie

44. Results • I predict: completely different classifications between the groups, argument within the groups re: the best approach and where concepts should fit.

45. Results • But: don’t be discouraged • Even professionals disagree (that’s why there are many different coding schemes) • Different purposes • Different individuals • Limited time

46. Results of another coding exercise • Students asked to describe Magritte’s “The Heart of the Matter” using the Art & Architecture Thesaurus (AAT) from the Getty Museum Thanks to Jeremy Rogers, MIG, University of Manchester

47. Coding Confusion: An example Suitcase Luggage Attaché case Model Person Woman Adults Headcloth Cloth Scarf Standing Background Brown Blue Chemise Dress Tunics Clothes Brass Instrument French Horn Horn Tuba X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X X

48. Real-life examples • Clue (Read 3) • KnoME (GALEN) • Clinergy – a clinical application!

49. Recommended Web Links and Papers • Bechhofer SK, Goble CA, Rector AL, Solomon WD, and Nowlan WA. Terminologies and Terminology Servers for Information Environments. In: Proceedings of STEP '97 Software Technology and Engineering Practice, 1997. URI: http://citeseer.nj.nec.com/354766.html • Chute CG, Elkin PL, Sheretz DD and Tuttle MS. Desiderata for a Clinical Terminology Server. In: Proceedings of AMIA'99 Annual Symposium, 1999. URI: http://www.amia.org/pubs/symposia/D005782.PDF • Rector AL. Clinical Terminology: Why Is it so Hard? Methods Inf Med. 1999;38(4-5):239-52 • The British Association of Clinical Terminology Specialists: http://www.bacts.org.uk/ • OpenGALEN: http://www.opengalen.org/and the Medical Informatics Group of Manchester Universitywww.cs.man.ac.uk/migparticularly www.cs.man.ac.uk/mig/links/RCSEd/terminology.htm Jeremy Roger’s excellent set of teaching materials; has links to other sources & more exercises • Read Codes Engines: http://www.cams.co.uk/and http://www.visualread.com • See also “Related Web Links” section at:https://wwws.soi.city.ac.uk/intranet/students/courses/mim/mi/lect2_2.htm