Usability-focused Clinical Decision Support with the Help of Semantic Technologies
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The document discusses usability-focused clinical decision support using semantic technologies, emphasizing the importance of integrating knowledge-based workflows into electronic health records (EHR). It identifies key usability requirements for practicing physicians and explores known semantic problems in medical terminology systems. Additionally, it proposes a unified semantic model leveraging upper ontologies and domain-specific ontologies to enhance clinical decision-making and workflow support.
Usability-focused Clinical Decision Support with the Help of Semantic Technologies
- 1. Usability-Focused Clinical Decision Support with the Help of SemanticTechnologies. Dr Silviu Braga MD , Paolo Ciccarese PhD Medicognos
- 2. Practicing Physicians Requirements Usability = Knowledge Based Workflow Support = “build in” Decision Support
- 3. Practicing Physicians USABILITY requirements 1. Clinical Workflow support Reengineer the EHR into a clinical WfMS 2. Medical Knowledge support Interface Terminology Code once use multiple times: Interface Terminology with transparent mapping to classifications and codifications Design for decision support at the point of care Multi-user, multilingual authoring by trained physicians Quality assurance by design (semantically constraint authoring + validation)
- 4. The Challenge The known semantic problems (B. Smith , A Rector , S. Schulz ,P. Elkin) • Snomed CT • HL7 v3-, OpenEHR- , EN 13606-, EN 13940 (ContSys) - RIMs The known RIM- Terminology interaction problems • The terminology binding “chaos” (A Rector) • The semantic “grey zone” problem (Markwell report for NHS) • The “system of coding vs system of meaning” problem ( A Rector ) Timing problems • Timing of Snomed redesign project (anatomy, organism, observable) • Timing and capacities of ICD 11 ?
- 6. Ontology based RIM & Terminologies 1. Use of an Upper Ontology 2. Every aspect in the system is built on orthogonal “domain ontologies” defined using a subset of OWL (proprietary solution at runtime) 3. More expressive OWL for analysts (knowledge modeling, model checking, maintenance, mining) 4. Reuse of existing ontologies such as FMA
- 7. Onthotypes® 1. Semantic data structures 2. Decoupling meaning from coding and presentation 3. Generate post-coordinated concepts through formal logical expressions 4. Lexico-syntactic annotations - Semantic User Interface Engine - Natural Language Generation for the EHR notes
- 8. Knowledge & Workflow based Architecture with build in CDS EPR EHR PHR NHR … Interface engine WF Engine Rules Engine Terminology Templates Executable Clinical Wf Executable Drug Wf Executable CDS logic “ontotypes” KB KB Care Plans 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 Situation Care Organisation Biomedical Care Plan Ontologies OBO BFO Organisation Time/Space Process Foundational layer
- 9. Knowledge Management Studio Build semantically constrained data structures “Onthotypes”
- 11. Knowledge Management Studio Knowledge based Clinical Activity Templates
- 12. Knowledge & Workflow based EHR
- 13. Knowledge & Workflow based EHR Workflow orientated user interface
- 14. Knowledge & Workflow based EHR Contextual knowledge access “pre-decision” support
- 15. Knowledge & Workflow based EHR Personalized Activities
- 16. Knowledge & Workflow based EHR Actionable DS inside the EPR Wf The recommendations are linked to the corresponding Activities
- 17. Knowledge & Workflow based EHR
- 18. Knowledge & Workflow based EHR Customizing the content and the workflow logic of the Actionable Alerts
- 19. Knowledge & Workflow based EHR Actionable Alerts