Data Needs of Health Care and Life Sciences

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Data Needs of Health Data Needs of Health Care and Life Care and Life Sciences Sciences Chimezie Ogbuji

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Data Needs of Health Care and Life Sciences. Chimezie Ogbuji. The HCLS data crisis. U.S. Clinical research productivity is facing a crisis Healthcare delivery is under-informed Recruitment of clinicians and patients is low - PowerPoint PPT Presentation

Transcript of Data Needs of Health Care and Life Sciences

Page 1: Data Needs of Health Care and Life Sciences

Data Needs of Health Data Needs of Health Care and Life SciencesCare and Life Sciences

Chimezie Ogbuji

Page 2: Data Needs of Health Care and Life Sciences

The HCLS data crisisThe HCLS data crisis• U.S. Clinical research productivity is facing a

crisis• Healthcare delivery is under-informed• Recruitment of clinicians and patients is low• Today’s ad hoc medical informatics methods are

inadequate for current demands for drugs, personalized medicine, and diagnostic tools

• A fundamental restructuring is required and will impact basic research institutions, pharmaceutical companies, and care delivery organizations.

_____________________________

PricewaterhouseCoopers’ Health Research Institute 2008

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SW vision for HCLSSW vision for HCLS• Reconstitution of biological

research knowledge ecosystem• Retooling the flow from clinical

trial to the provision of care• Demand high fidelity, proactive

medical records and information-based medicine

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HCLS domain topologyHCLS domain topology

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Information-based medicineInformation-based medicine• Online access of a patients

complete Computer-based Patient Record (CPR)

• Personalized prescription of medication and treatment

• Non-redundant, structured data entry collection

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A working exampleA working example• Patient / medical records are a

key component of the ecosystem • Focus on the use of Semantic

Web representation for patient record content

• Demonstrate the ability to meet the requirements for information-based medicine

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Representing CABGRepresenting CABG• Coronary Artery Bypass Grafting• Represent a medical record of a

patient who is a candidate for CABG

• Re-use existing medical ontologies

• Demonstrate useful entailments, identification of trial candidates

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Candidate ontologiesCandidate ontologies• Basic Formal Ontology (OWL)• CPR ontology (OWL)• GALEN ontology (OWL)• Foundational Model of Anatomy

(OWL)

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Key conceptsKey concepts• Anatomy• Pathological body structures• Procedure methods• Pre-operative risk factors for

CABG• Patient demographics• Disease findings• Disease finding severity

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AnatomyAnatomy• CABG is a procedure that acts

on a stricture located in a coronary artery

• Graft method involves reusing anatomical parts as conduits for blood flow around the constriction

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Anatomy (continued)Anatomy (continued)

• Formal anatomy definitions burrowed from FMA

• Concept of a coronary stricture burrowed from GALEN

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Procedure methodsProcedure methods• GALEN has a rich framework for

surgical procedures we can re-use• Model grafting as an operation

involving a specific method (SNOMED-CT also classifies procedures in this manner)

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Grafting in an ontologyGrafting in an ontology

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Adding CABG classAdding CABG class

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Modeling “Modeling “co-morbidities”co-morbidities”• The simultaneous presence of 2+ morbid conditions or diseases

in the same [patient], which may complicate a [patient’s] hospital stay

_____________________________McGraw-Hill Concise Dictionary of Modern Medicine

• Morbid conditions -> clinical findings• Presence of a disease -> clinical diagnosis

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Modeling morbiditiesModeling morbidities

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How can this be used?How can this be used?• In a CPR system where the records are

RDF graphs that conform to this ontology ….

• You can incorporate patient-specific genetic markers to personalize the (already) rich overview

• You can have medical terms that resolve to informative literature

• You can incorporate additional risk factors

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How can this be used?How can this be used?• You can present entailments to a

clinician to facilitate their clinical problem solving process

• You can identify candidates for CABG research by (SPARQL) querying for risk factors against all patients

• You can extend the domain easily and in a controlled way

• You can “apply” general guidelines against a specific patient context