panel: problems with existing ehr paradigms and how ontology can solve them
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Panel: Problems with Existing EHR Paradigms and How Ontology Can Solve Them. Roberto A. Rocha, MD, PhD, FACMI Sr. Corporate Manager Clinical Knowledge Management and Decision Support, Clinical Informatics Research and Development, Partners Healthcare System - PowerPoint PPT PresentationTRANSCRIPT
Panel: Problems with Existing EHR Paradigms
and How OntologyCan Solve Them
Roberto A. Rocha, MD, PhD, FACMISr. Corporate Manager
Clinical Knowledge Management and Decision Support,Clinical Informatics Research and Development, Partners Healthcare
SystemLecturer in Medicine
Division of General Internal Medicine and Primary Care, Department ofMedicine, Brigham and Women’s Hospital, Harvard Medical School
International Conference on Biomedical OntologyJuly 28-30, 2011
Buffalo, New York, USA
Panel: Problems with Existing EHR Paradigms
and How OntologyCan Solve Them
Roberto A. Rocha, MD, PhD, FACMISr. Corporate Manager
Clinical Knowledge Management and Decision Support,Clinical Informatics Research and Development, Partners Healthcare
SystemLecturer in Medicine
Division of General Internal Medicine and Primary Care, Department ofMedicine, Brigham and Women’s Hospital, Harvard Medical School
International Conference on Biomedical OntologyJuly 28-30, 2011
Buffalo, New York, USA
Opportunity
• New generation of clinical systems beyond efficient record storage and communication– New paradigm with pervasive computerized data analysis
and decision support– Widespread use of interoperable services and data, with
advanced functions that enable team-based care
Example: Simple ‘If - Then’ rule
4
Example: Simple ‘If - Then’ rule
Patient data
Concepts Knowledge
LOINC?
Problem list?
Coded values?
SNOMED CT?
Bedside measurements?
Lab results?
Medications?
Rules? Formulas?
Classifications?
Availability of data
• Availability of structured and coded clinical data determines the feasibility of CDS interventions– Data is expensive to generate at the point-of-care (systematically)– Benefits frequently not tangible to data “producers” (extra incentives)
• Dissemination and exchange of knowledge assets depends on data standardization (structure & semantics)
Health IT Data Standards!
Natural language processing?
Voice recognition?
Mobile devices?
Knowledge-driven documentation?
Semantic expressivity (adaptive)?
Efficient dissemination strategy
Stead WW and Lin HS, editors. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. National Research
Council, 2009.
Similar model for a Personal Health Records
(individuals)
Current dissemination barriers
Large scale CDS
What will differentiate clinical systems? Process automation?
Ease of use?Advanced CDS functions?
How ontologies can help?
• Shared concepts and logical models (data & knowledge)– Proper domain coverage, but without compromising
extensibility and innovation– More accessible methods and tools to enable widespread
adoption– Training and demonstration projects
• Cost-effective semantic interoperability– Lower the cost and overhead of the data & knowledge
‘translation’ every time exchange is necessary• Clinical systems that can seamlessly represent and
process a complete electronic patient care record– Move beyond interoperability space and start
influencing/guiding transactional data and knowledge representation models
Thank you!
Roberto A. Rocha, MD, [email protected]