ADVANCED PATIENT INFORMATION SYSTEMS AND MEDICAL CONCEPT REPRESENTATION MEDINFO’95 WORKSHOP

1. ADVANCED PATIENT INFORMATION SYSTEMSAND MEDICAL CONCEPT REPRESENTATIONMEDINFO’95 WORKSHOP

2. CHRISTOPHER G. CHUTE MAYO CLINIC, ROCHESTER, MINNESOTA, USA JAMES J. CIMINO COLUMBIA UNIVERSITY, N.Y., N.Y., USA EIKE H.-W. KLUGE VICTORIA UNIVERSITY, VICTORIA, B.C., CANADA YVES LUSSIER UNIVERSITY OF SHERBROOKE, SHERBROOKE, QUEBEC, CANADA JOCHEN R. MOEHR VICTORIA UNIVERSITY, VICTORIA, B.C., CANADA VIMLA L. PATEL MCGILL UNIVERSITY, MONTREAL, QUEBEC, CANADA ANGELO ROSSI MORI NATIONAL RESEARCH COUNCIL, ROME, ITALY

3. ADVANCED PATIENT INFORMATION SYSTEMS AND MEDICAL CONCEPT REPRESENTATION MEDINFO’95 WORKSHOP • History of the Workshop • Purpose of the Workshop • Goal: • Explore characteristics and requirements of advanced patient records (APR). • Identify need for improved medical concept representation in APR. • Review current status of medical concept representation and identify challenges and necessary improvements

4. ADVANCED PATIENT INFORMATION SYSTEMS AND MEDICAL CONCEPT REPRESENTATION MEDINFO’95 WORKSHOP I Issues: 9:00-10:00 Advanced Patient Records: Characteristics (Jochen R. Moehr) Clinical Perspective (James Cimino) Ethico Legal Perspective (Eike H.-W. Kluge) Cognitive Science Perspective (Vimla L. Patel) II Current Status and Limitations 10:00-11:00 of Medical Concept Representation: IMIA WG 2 Initiatives/Activities (Christopher G. Chute) Semantic Concept Representation Work (James Cimino, C. G. Chute) Controlled Vocabularies (Yves Lussier) Galen Project (Angelo Rossi Mori) Standardization Initiatives (Angelo Rossi Mori) Break 11:00-11:30 III Discussion: Requirements for Improvement: 11:30-12:30

5. Introduction • Advanced Patient Records • Characteristics • Implications • Proposal for Solution

6. Advanced Patient Records • Characteristics • Ubiquitous • Multi-Institutional • Networked • Multi Medial • Voluminous • Virtual Patient Record • Implications: • Information Problems • Recall & Precision • Ethical Problems (Kluge)

7. Recall and precision problems are compounded in the networked virtual health record.

8. Recall Problem • The location of relevant data is not necessarily known, making it hard to retrieve them • Increasing recall decreases precision • Increasing precision decreases recall

9. Precision Problem • Data relevant for a defined decision are usually mixed with data that do not contribute to that decision • Caused by: • Volume • Time • Source

10. Volume • medical records are voluminous • no upper boundary exists, such as a criterion for ‘completeness’ • hence it is difficult to find relevant data among the masses of data • the need to find relevant data and the size of the record increase in problem patients • where precision matters most precision is least

11. Time • medical data become quickly obsolete for medical care decisions • relevance of data, i.e., precision, decreases over time • the older the data the less relevant (precise)

12. Source • medical data quality depends on their source, e.g.: • laboratory • clinical • subjective impression • the more valid the source, the higher the precision • data validity is not source specific but context specific

13. Semantic Indexing as a Solution -Alternatives • a) Retrieval of “complete” virtual record • b) Retrieval by imposed structure, e.g., SOAP, Time and Source • c) Retrieval by automated semantic indexing

14. Semantic Indexing as a Solution -Alternatives • a) Retrieval of “complete” virtual record “all data available on John Doe” • potentially boundless • costly (time, $$) • very low precision • high recall • little value • b) Retrieval by imposed structure, e.g., SOAP, Time and Source • c) Retrieval by automated semantic indexing

15. Semantic Indexing as a Solution -Alternatives • a) Retrieval of “complete” virtual record • b) Retrieval by imposed structure, e.g., SOAP, Time and Source: “all radiographs and lab data obtained March and April ‘95 at xyz hospital” • improved precision • reduced recall • P&R limited by original structure • feasible • improved benefit/cost ratio • c) Retrieval by automated semantic indexing

16. Semantic Indexing as a Solution -Alternatives • a) Retrieval of “complete” virtual record • b) Retrieval by imposed structure, e.g., SOAP, Time and Source • c) Retrieval by automated semantic indexing “all data of John Doe pertinent to chronic pulmonary hypertension” • instantiation meta data base • need for representation of deep knowledge • potentially high precision and recall • P&R not limited by imposed structure • potential for solution also of ethical problems • cost benefit ratio and feasibility unknown

17. Clinical PerspectiveJames J. Cimino, M.D.Department of Medical InformaticsColumbia UniversityNew York, New York, USA

18. Clinical Perspective Data Capture Clinical Computing Perspective

19. Clinical Computing Perspective • Record Structure • Represented by Data Dictionary • Record Content • Represented by Content Dictionary

20. Same Meaning, Different Structure "Family History of Cancer" Finding + Modifier: "Cancer (Family History of)" Finding + Modifier: "Family History (Cancer)" Family History Table: "Cancer"

21. Content Dictionary - Limitations • Limited by coding structure • Limited by strict hierarchy • Limitations on freedom of expression • Must avoid redundant forms of expression • Meaning of terms must be clear • No one level of granularity is appropriate • Can't be independent of data dictionary

22. Cognitive Science PerspectiveVimla L. Patel, Ph.D.Centre for Medical EducationMcGill UniversityMontreal, Quebec, Canada

23. Levels of Meaning in Textand Discourse • Text-based Model • Representation of Textual Material (Syntax/Semantics) • Generation of Local Inferences • Propositional Representation • Situational Model • Representation of Events, Actions and Persons in Context • Conceptual Representation: Semantic Network • Generation of High-Level Inferences • Pragmatics: Context-bound Inferences

24. CLINICAL DATA Data-driven Process Generalization Representation of Clinical Information Interpretation (in Context) New Knowledge PRIOR KNOWLEDGE Comprehension Instantiation Conceptually Driven Process Interactive Process of Understanding Clinical Problems

25. Semantic Network (relationships between propositions) CONCEPTUAL REPRESENTATION situational model PROPOSITIONAL REPRESENTATION text-based model Propositional Analysis (a form of representation of a semantic network in memory) NATURALLANGUAGE THOUGHTS AND IDEAS expressed through Semantic Representation of Natural Language Analysis

26. F9 FA5 O6 F8 O12 D3 + O5 F7 FA4 O11 F6 O4 C2 O10 FA3 F5 D2 O3 Schematic Representation of Hierarchical Structure of Medical Knowledge as Used for Problem-Solving O9 F4 + O2 FA2 F3 C1 D1 O8 O1 F2 + FA1 O7 F1 5. COMPLEXES LEVEL 4. DIAGNOSTIC LEVEL 2. FINDING LEVEL 3. FACET LEVEL 1. OBSERVATION LEVEL

27. Concept Representation Work • Semantic network approach • Unified Medical Language System (UMLS) • Galen • Canon Group • Electronic Medical Record Project • SNOMED International

28. Content Dictionary - Implications • Not limits by coding structure • Multiple hierarchies permitted • Semantic attributes increase expressiveness • Redundant forms of expression recognized • Semantic attributes define meaning • Multiple levels of granularity • Can also model data dictionary

29. Same Meaning, Equivalent Structures "Family History of Cancer" Cancer - has modifier - Family History of Family History - has modifier - Cancer Family History - has element - Cancer

30. US Vocabulary Development • Unified Medical Language System • SNOMED International • Canon • Electronic Medical Record Project

31. Unified Medical Language System • National Library of Medicine 10-year effort • Metathesaurus • Semantic Network • Information Sources Map • Specialist Lexicon

32. Canon • Independent researchers with different needs • Experiment in collaborative vocabulary development • Representation of chest xray reports • Development of a "merged model"

33. Electronic Medical Record Project • National Library of Medicine • Controlled vocabulary of clinical medicine • Cooperative agreement • Arden workshop on Patient Problems