150 likes | 171 Views
Medical Informatics Lessons Learned. Experiences MHS AHLTA interoperability Private sector SNOMED CT implementation and legacy migration CPOE implementation. Medical Informatics Lessons Learned. Lack of definitions Divergent information models Ontologic weakness of reference terminology
E N D
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
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
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
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
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
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
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
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
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
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
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
Semantic Interoperability in Health Informatics: Lessons Learned Medical Informatics Lesson Learned • Limitations of 1:1 map • Equivalent • Narrower than • Broader than • Other relationship
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
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
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