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

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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

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


Panel problems with existing ehr paradigms and how ontology can solve them1

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


Opportunity

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

Example: Simple ‘If - Then’ rule


Example simple if then rule1

Example: Simple ‘If - Then’ rule

Lab results?

Problem list?

Bedside measurements?

Medications?

Classifications?

Coded values?

Formulas?

Rules?

LOINC?

SNOMED CT?

Patient data

Concepts

Knowledge


Availability of data

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)?


Efficient dissemination strategy

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.


Current dissemination barriers

Current dissemination barriers

Large scale CDS

What will differentiate clinical systems? Process automation?Ease of use?Advanced CDS functions?


How ontologies can help

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

Thank you!

Roberto A. Rocha, MD, PhD

[email protected]


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