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Panel: Problems with Existing EHR Paradigms and How Ontology Can Solve Them

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

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Panel: Problems with Existing EHR Paradigms and How Ontology Can Solve Them

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  1. Panel: Problems with Existing EHR Paradigms and How OntologyCan Solve Them Roberto A. Rocha, MD, PhD, FACMI Sr. Corporate ManagerClinical Knowledge Management and Decision Support,Clinical Informatics Research and Development, Partners Healthcare System Lecturer in MedicineDivision of General Internal Medicine and Primary Care, Department ofMedicine, Brigham and Women’s Hospital, Harvard Medical School International Conference on Biomedical Ontology July 28-30, 2011 Buffalo, New York, USA

  2. Panel: Problems with Existing EHR Paradigms and How OntologyCan Solve Them Roberto A. Rocha, MD, PhD, FACMI Sr. Corporate ManagerClinical Knowledge Management and Decision Support,Clinical Informatics Research and Development, Partners Healthcare System Lecturer in MedicineDivision of General Internal Medicine and Primary Care, Department ofMedicine, Brigham and Women’s Hospital, Harvard Medical School International Conference on Biomedical Ontology July 28-30, 2011 Buffalo, New York, USA

  3. 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

  4. Example: Simple ‘If - Then’ rule

  5. Example: Simple ‘If - Then’ rule Lab results? Problem list? Bedside measurements? Medications? Classifications? Coded values? Formulas? Rules? LOINC? SNOMED CT? Patient data Concepts Knowledge

  6. 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) Natural language processing? Voice recognition? Health IT Data Standards! Mobile devices? Knowledge-driven documentation? Semantic expressivity (adaptive)?

  7. Efficient dissemination strategy Similar model for a Personal Health Records (individuals) Stead WW and Lin HS, editors. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. National Research Council, 2009.

  8. Current dissemination barriers Large scale CDS What will differentiate clinical systems? Process automation?Ease of use?Advanced CDS functions?

  9. 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

  10. Thank you! Roberto A. Rocha, MD, PhD rarocha@partners.org

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