Semantics is not a luxury
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The document discusses building new clinical information systems that enable intra- and inter-operability. It provides background on Paolo Ciccarese, including his experience developing ontologies and semantic technologies to improve clinical decision support, workflows, and data integration across healthcare organizations. Ciccarese advocates for knowledge-based clinical systems that leverage formal medical terminologies and ontologies to optimize processes and support continuity of care.
Semantics is not a luxury
- 1. Seman/cs is not a luxury Building the new genera/on of Clinical Informa/on Systems “Intra‐ and Inter‐operability” Paolo Ciccarese*, PhD Co‐founder and knowledge architect *Assistant in Neurology @MassachuseHs General Hospital, Instructor in Bioinforma/cs @Harvard Medical School, Health Care Life Science Interest Group @W3C, Consultant in SoOware innova/on
- 2. Timeline Mass General/Harvard Medical School Visitor at Stanford Univ.* * Decision Support/ Workflow and Ontology Based SoOware Dev. (Constant innova/on) Online Scien/fic Communi/es (Ontologies and Interoperability) HCLS IG W3C (Seman/c Web Technologies) Simile Project – MIT** HMS Faculty (Seman/c Interoperability) University of Pavia and Consultant Clinical Workflow, Decision Support and Knowledge Management (Guide Project) Temporal Abstrac/ons in Medicine Regional and Na/onal Networks 2000 2004 2005 2006 2007 2008 2009 *Visi/ng Samson Tu (SAGE author) **Volunteer (coding)
- 3. • I am a developer/technologist/scien/st • I define ontologies I don't define myself as ontologist • I work on interoperability with seman/c technologies in different fields • I believe that soOware engineering is easier than social engineering • I have been lucky because at one point of my life I could start from scratch
- 4. Medicognos Research background P.Ciccarese Phd ( Medicognos architect) Mass General/Harvard Medical School Online Scien/fic Communi/es (Ontologies and Interoperability) HCLS IG W3C (Seman<c Web Technologies) Simile Project – MIT** HMS Faculty (Seman<c Interoperability) University of Pavia and Consultant Clinical Workflow, Decision Support and Knowledge Management (Guide Project) Temporal Abstrac<ons in Medicine Regional and Na<onal Networks New Genera/on Knowledge & Process Based EHR 2000 2004 2005 2006 2007 2008 2009 Decision Support/ Workflow and Ontology Based So:ware Dev.
- 5. Original GPs requirements 2005 1. Usability 2. Process Management/Op/miza/on/Improvement 3. Collabora/on ‐ care networks 4. Medical Knowledge Management ‐ formal mul/lingual terminologies, evidence‐based recommenda/ons 5. Communica/on ‐ interoperability with labs and hospitals Transform the problem oriented EPR in a distributed clinical process management system with embedded clinical decision support Con/nuity of Care and Disease Management
- 6. My problem is • Represent the pa/ent • Document the care process enough to pursue con/nuity of care and disease management
- 7. Why it is so hard? Diminishing the seman/c impedance physician mental model soOware Usability Evidence‐based Workflow op/miza/on medicine Cost Containment Economies by Outcome (Disease Management) Improvement Interoperability Quality of data informa/on knowledge encoded in the plaporm and exchanged with the external world
- 9. eHealth for Safety 2007* New genera/on of advanced, user‐friendly and ubiquitous tools for: • Integra/on of decision support and workflow support systems with pa/ent record • Knowledge representa/on • Advanced terminology‐driven eHealth tools for data entry and retrieval • Clinical informa/on systems integra/on of pa/ent data across the con/nuum of care * eHealth for Safety – Impact of ICT on Pa/ent Safety and Risk Management, October 2007 European commission, Informa/on Society and Media
- 11. New organiza/onal architecture Health Insurance Data 2x anonym P4P Quality Indicators Primes (global) Feedback GP Data anonymized P4P Primes Cl Experts QI PH Experts SP Coaches MD Pharmacy Pa/ent Nurse Hospital
- 12. New informa/on architecture Knowledge based Clinical Workflow Management with Decision Support Patient Status Patient Status Patient Status Data Data Data Data Data Data KPI KPI KPI Clinical Guideline CDS Operational Process Management ex: lab tests Care flow execution Alerts Agreed Care Plan Quality /Safety /Efficiency Indicators Ac/vity 1 D Ac/vity 2 Ac/vity n Process Reenginering Process Mining Strategic Process Management CDS :clinical decision support
- 13. The key role of clinical decision support • All currently known large studies concluded that: EPR/ EHR are not enough for quality improvement! • As EHR use broadens, one should not assume an automa/c diffusion of improved quality of care • In selec/ng an EHR, physician prac/ces should carefully consider the inclusion of: clinical decision support to facilitate quality care 1.EMR SophisBcaBon Correlates to Hospital Quality Data, Comparing EMR AdopBon to Care Outcomes, HIMSS 2007 2.Electronic Medical Records and Diabetes Quality of Care: Results From a Sample of Family Medicine PracBces Jesse C. Crosson, PhD ANNALS OF FAMILY MEDICINE MAY/JUNE 2007 3.Electronic Health Record Use and the Quality of Ambulatory Care in the United States, Jeffrey A. Linder ARCH INTERN MED JULY 2007 4.The Value of InformaBon Technology‐Enabled Diabetes Management Davis Bu,Center for InformaBon Technology Leadership. 2007
- 15. Knowledge modeling challenge rela/onship Biological class Nosological is about is about syndrome
- 17. Ontology‐based plaporm • Every aspect in the system is built on ontologies defined using a subset of OWL (proprietary solu/on at run/me)* • More expressive OWL for analysts (knowledge modeling, model checking, maintenance, mining) with enterprise tools • Referent tracking** (unique IDs) * Ontology‐Based Integra/on of Medical Coding Systems and Electronic Pa/ent Records Ceusters W, Smith B, De Moor G ** Strategies for referent tracking in electronic health records. Ceusters W, Smith B. J Biomed Inform. 2006 Jun;39(3):362‐78. Epub 2005 Sep 9.
- 21. Unified Seman/c Model Occurrence How it occurred When? Who reported it? What were circumstances? … Process (Workflow) How it was acquired Who? Biomedical Disease When? Sign What Data Within which Symptom careplan task? … … Classifiers ‐ Ac/ve problems/cau/ons/… Journal ‐ Encounter/sub‐encounter Medicognos ‐ Observa/on/Query templates Onthotypes® How it organized for the users Clinical Info Management
- 22. Medicognos onthotypes® • Defined through expressions • Decoupling meaning from presenta/on • Extensive usage of classifica/on • Mul/ple presenta/on forms can have the same meaning
- 29. Medicognos architecture 20% 80% Onto‐Terminology Info. Models Executable Drug Wf Executable CDS logic “ontotypes” KB Templates KB Drug Therapy Wf KB Rules KB Concept Template Workflow Drug KB Queries &Rules Builder Builder Builder Builder Builder Clinical Domain Language Clinical Knowledge Management Studio Domain Clinical SituaBon Care OrganisaBon Biomedical Ontologies Care Plan OBO? BFO? OrganisaBon Time/Space Process FoundaBonal layer
- 30. 2 cents on interoperability • Need for formally defined seman/cs • Intra‐operability and Inter‐operability go together • Being able to communicate with other systems depends on the expressiveness of the “internals” first and of the communica/on protocols later on • The seman/c models have to be produced incrementally and tested con/nuously in seman/cally oriented soOware (full stack) • The seman/c is not limited to pa/ent data but is a mix‐up of several clinical aspects • All the aspects have to evolve in lockstep