Enhancing Healthcare Interoperability: Key Lessons & Solutions

1. Semantic Interoperability in Health Informatics: Lessons Learned Medical Informatics Lessons Learned • Experiences • MHS AHLTA interoperability • Private sector SNOMED CT implementation and legacy migration • CPOE implementation

2. Semantic Interoperability in Health Informatics: Lessons Learned Medical Informatics Lessons Learned • Lack of definitions • Divergent information models • Ontologic weakness of reference terminology • Quality assurance • Communication across team

3. Semantic Interoperability in Health Informatics: Lessons Learned Medical Informatics Lesson Learned • Importance of definitions • SNOMED CT and most terminologies in use lack explicit definitions of concepts and relationships • Meaning may be implied by common usage or position in hierarchy • Domain experts often disagree on meaning

4. Semantic Interoperability in Health Informatics: Lessons Learned Medical Informatics Lesson Learned • Description Logic in SNOMED CT • Appendicitis (Fully defined by)‏ • Associated morphology • Inflammation • Finding site • Appendix structure • Fully defined concepts may reduce need for text definition • Up to half of disorders in SNOMED CT lack full Description Logic definition

5. Semantic Interoperability in Health Informatics: Lessons Learned Medical Informatics Lesson Learned • Divergent information models • SNOMED CT disorder context model • Subject • Temporality • Assertion/negation • Modal status • Laterality • Severity • Acuity • Course/stages

6. Semantic Interoperability in Health Informatics: Lessons Learned Medical Informatics Lesson Learned • Divergent information models • Precoordinated • Postcoordinated • SNOMED CT implements context model inconsistently • Similar inconsistencies in other terminologies

7. Semantic Interoperability in Health Informatics: Lessons Learned Medical Informatics Lesson Learned • Ontologic weakness of SNOMED CT • Confusion in Hierarchy • Semantic type confusion • IS-A overload • SNOMED CT SEP anatomy model

8. Semantic Interoperability in Health Informatics: Lessons Learned Medical Informatics Lesson Learned • Confusion in Hierarchy • Many use cases rely on subsumption of clinical disorders • Medications are more complicated • Ontologic hierarchy may not reflect clinical view • Numerous exceptions to hierarchy complicate implementation and maintenance

9. Semantic Interoperability in Health Informatics: Lessons Learned Medical Informatics Lesson Learned • SNOMED CT Semantic type confusion • Disorder vs. morphology • Finding vs. observable • Confusion in understanding of intent by implementers

10. Semantic Interoperability in Health Informatics: Lessons Learned Medical Informatics Lesson Learned • Is-a Overload • Confusion of • Is a subclass of • Is a part of • Is contained in

11. Semantic Interoperability in Health Informatics: Lessons Learned Medical Informatics Lesson Learned • SNOMED CT SEP anatomy model • Entire hand • Hand structure • Hand part • Structure of left hand • Structure of right hand • Anatomic region vs. anatomic surface area

12. Semantic Interoperability in Health Informatics: Lessons Learned Medical Informatics Lesson Learned • Limitations of 1:1 map • Equivalent • Narrower than • Broader than • Other relationship

13. Semantic Interoperability in Health Informatics: Lessons Learned Medical Informatics Lesson Learned • Quality Assurance burden • Difficult without automated tools • Full definition in Description Logic enables automated error checking

14. Semantic Interoperability in Health Informatics: Lessons Learned Medical Informatics Lesson Learned • Communication issues • Medical informaticist • Ontologist • Domain expert • Software developer • Use and design of tools

15. Semantic Interoperability in Health Informatics: Lessons Learned Solutions • Improve the ontological basis of standard terminologies • Educate informaticists and domain experts about ontology practices • Address information model issues • Create mapping tools to support interoperability • Semantic type subsets • Medical specialty subsets