Transcript Forest plot - University of Colorado Denver

OBSERVATIONAL
MEDICAL
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Welcome to the OMOP CDMv3 Webinar
• To join the web event:
https://cc.readytalk.com/r/wmeqn1g60847
• You will be connected to broadcast audio after
joining the meeting.
• As an alternative, you may connect via telephone:
– U.S. & Canada Toll-Free: 800-893-0176
– UK: 08005280985
– Sweden: 020799844
• Please use the web Q/A to submit questions during
the presentation and there will be time allotted after
the presentation for Q/A
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OMOP Common Data Model v3
Overview
Patrick Ryan, OMOP
Lisa Schilling, U Colorado, SOM
2 December 2011
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Today’s Agenda
Purpose: To introduce you to the draft CDMv3 and tell you how
to submit feedback during the open comment period through
January 9, 2012
• Review of where we’ve been: OMOP’s research using CDMv2
– Original design
– Applications of CDMv2
– Lessons learned
• Why we need CDMv3: Additional use cases across the OMOP
community
– SAFTINeT
– SCANNER
• Proposed specifications for CDMv3
– What’s new?
– Standard conventions
– Other key highlights
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OMOP common data model (CDMv2)
*RxNorm
*SNOMED-CT
*LOINC
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Developed with broad stakeholder input
Designed to accommodate disparate types of data (claims and EHRs)
Optimized to use case of standardized large-scale analytics
Applied successfully across OMOP data community
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Standards-based, conforming to ONC Meaningful Use Stage 2 recommendations
http://omop.fnih.org/CDMandTerminologies
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Applications of CDMv2
• Data Community
OMOP Extended Consortium
OMOP Research Core
Research Lab &
Coordinating Center
Centralized data
OSIM2
Distributed Network
• Standardized analytics
– Data characterization and quality assessment
– Outcome definition implementation
– Analytical methods for active drug safety surveillance
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Lessons Learned
• CDM was feasible to implement both centrally and in a
distributed network, across both administrative claims and
electronic health records data
• Disparate coding systems can be harmonized, with minimal
information loss, to a standardized vocabulary
• CDM enabled a wide array of efficient, scalable analyses for
active drug safety surveillance
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Why we need CDMv3: Additional use cases
across the OMOP community
Lisa Schilling, MD, MSPH
University of Colorado School of Medicine
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Comparative Effectiveness Research
• CER is the generation and synthesis of evidence that
compares the benefits and harms of alternative
methods to prevent, diagnose, treat and monitor a
clinical condition, or to improve the delivery of care.
• The purpose of CER is to assist consumers, clinicians,
purchasers, and policy makers to make informed
decisions that will improve health care at both the
individual and population levels.
Ref- Institute of Medicine, Initial Priorities for Comparative
Effectiveness Research, June 2009
(http://www.iom.edu/Reports/2009/ComparativeEffectivenessResearchPri
orities.aspx)
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AHRQ-funded
Scalable Distributed Research Networks
• Scalable Architecture for Federated Translational
Inquiries Network (SAFTINet) (R01 HS19908-01)
• Scalable National Network for Effectiveness Research
(SCANNER ) (R01 HS19913-01)
• Both projects:
– infrastructure building grants to create scalable,
distributed networks to support CER
– Multi-state
– Include clinical and claims data, PROs
– Selected the OMOP CDM V2 as their baseline DM
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SCANNER CER Hypotheses
• Diabetes patients receiving care from a team
consisting of a primary care provider and a clinical
pharmacist show better HbA1c control than patients
only receiving care from a primary care provider.
• Clinical decision support with design based on
principles of behavioral economics can costeffectively reduce inappropriate antibiotic prescribing
• Dabigatran is a safe and effective alternative to
Warfarin
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SAFTINet Use Case: CER Question
• Health care delivery system factors, such as the patientcentered medical home…
DELIVERY SYSTEM
FACTORS
+
COVARIATES
→
OUTCOMES
(chronic disease
control)
are important determinants of the control of asthma
(adults and children), high blood pressure and
hypercholesterolemia.
***Additionally, we wanted to create an intermediary
databases that partners could use to support QI, PM
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SAFTINet Use Case: 2ndary
• Partner intermediary databases with PHI could
support other initiatives
– Quality improvement
– Benchmarking/ performance measurement
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Data Model Requirements
Patient-level data equivalent to a limited data set
Identify/track cohorts of interest
Socioeconomic/demographic data (race, ethnicity)
Link patients & outcomes to health care systems
– Place of Care and affiliated Organization
– Care Provider
– Geographical locations: patients, places of care
• Link patients & outcomes to insurance/payer
coverage data
– payers and coverage plan, benefits, enrollment periods
• Link patients & outcomes to cost of care data
– Drugs, procedures (procedure, visit)
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SCANNER - Primary Data Modeling Team
• Daniella Meeker, PhD, Information Scientist, Rand
Corporation, SCANNER co-investigator
• Lola Ogunyemi, PhD, Charles Drew University –
Director of Biomedical Informatics
• Aziz Boxwala, MD PhD, Associate Professor,
University of California San Diego, SCANNER co-PI
• Lucila Ohno-Machado, MD, PhD, University
of California San Diego, Professor; Division Chief
UCSD Bioinformatics, SCANNER PI
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SAFTINet – Primary Data Modeling Team
• Michael Kahn, MD, PhD, University of ColoradoSchool of Medicine, Co-Director, Colorado Clinical
and Translational Sciences Institute
• Elias Brandt, BS, Research Systems Analyst, American
Academy of Family Physicians, National Research
Network
• Patrick Hosokawa, MS , Colorado Health Outcomes
Program
• Lisa Schilling, MD, MSPH, University of ColoradoSchool of Medicine, SAFTINet PI
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OMOP Common Data Model Version 3 (CDMv3)
Open For Review
Download at: http://omop.fnih.org/CDMV3
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OMOP CDM v3
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Person
Observation_period
Payer_plan_period
Provider
Location
Care_site
Visit_occurrence
Organization
Drug_exposure
Drug_era
Drug_cost
Condition_occurrence
Condition_era
Procedure_occurrence
Procedure_cost
Observation
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•
•
Health Outcomes of Interest
Drugs of Interest
Interventions
Cohort
Death
Standardized
Vocabulary
Illustrative version:
Refer to CDMv3
document for formal
specification
OMOP CDM v3 - What’s new?
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Person
Observation_period
Payer_plan_period
Provider
Location
Care_site
Visit_occurrence
Organization
Drug_exposure
Drug_era
Drug_cost
Condition_occurrence
Condition_era
Procedure_occurrence
Procedure_cost
Observation
•
•
•
Health Outcomes of Interest
Drugs of Interest
Interventions
Cohort
Death
Standardized
Vocabulary
Illustrative version:
Refer to CDMv3
document for formal
specification
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What’s new: health care delivery
Person
Observation_period
Payer_plan_period
Provider
Location
Care_site
Visit_occurrence
Organization
Drug_exposure
Drug_era
Drug_cost
Condition_occurrence
Condition_era
Procedure occurrence
_
Procedure_cost
Observation
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•
•
Health Outcomes of
Interest
Drugs of Interest
Interventions
Cohort
Death
Standardized
Vocabulary
• Captures information about
health care system
administration
• Visits to care sites allow
linkage between patients and
providers
• Increased specification for
location to allow geospatial
analysis where data available
• Linkages to services rendered
by provider throughout
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What’s new: financial information
Person
Observation_period
Payer_plan_period
Provider
Location
Care_site
Visit_occurrence
Organization
Drug_exposure
Drug_era
Drug_cost
Condition_occurrence
Condition_era
Procedure occurrence
_
Procedure_cost
Observation
•
•
•
Health Outcomes of
Interest
Drugs of Interest
Interventions
Cohort
Death
• Captures payments
associated with drugs and
procedures to facilitate
health economic analyses
• Associating costs with payers
and health plans can enable
exploration of impact of
health policies and benefit
design
Standardized
Vocabulary
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What’s new: Cohort
Person
Observation_period
Payer_plan_period
Provider
Location
Care_site
Visit_occurrence
Organization
Drug_exposure
Drug_era
Drug_cost
Condition_occurrence
Condition_era
Procedure occurrence
_
Procedure_cost
Observation
•
•
•
Health Outcomes of
Interest
Drugs of Interest
Interventions
Cohort
Death
Standardized
Vocabulary
• Cohort allows standardized
structure for storing groups
that share a common
element, ex:
– Persons taking ‘ACE Inhibitors’
– Persons with ‘Angioedema’
– Providers who receive a
behavioral intervention
• Provides common place to
store Health Outcomes of
Interest and Drugs of Interest
to facilitate drug safety
analyses of custom-defined
concepts
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What’s new: Death
Person
Observation_period
Payer_plan_period
Provider
Location
Care_site
Visit_occurrence
Organization
Drug_exposure
Drug_era
Drug_cost
Condition_occurrence
Condition_era
Procedure occurrence
_
Procedure_cost
Observation
•
•
•
Health Outcomes of
Interest
Drugs of Interest
Interventions
Cohort
Death
Standardized
Vocabulary
• Standardizes capture of
death information from
across disparate sources
– Discharge records
– Diagnosis codes
– Death registries
• Enables capture of cause of
death
• Enforces consistent rule:
persons can only die once
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OMOP CDMv3: Entity-relationship diagram
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•
Standard Variable Name Conventions
across CDMv3
<entity>_CONCEPT_ID:
– Foreign key into the Standard Vocabulary, which serves as the primary basis for all
standardized analytics
– Ex: CONDITION_CONCEPT_ID = 31967 contains reference value for SNOMED concept of
‘Nausea’
•
<entity>_SOURCE_VALUE:
– Verbatim information from the source data, typically used in ETL to map to CONCEPT_ID,
and not to be used by any standard analytics
– Ex: CONDITION_SOURCE_VALUE = ‘787.02’ was the ICD-9 code captured as a diagnosis
from the administrative claim
•
<entity>_TYPE_CONCEPT_ID:
– Delineates the origin of the source information, standardized within the Vocabulary
– Ex: DRUG_TYPE_CONCEPT_ID can allow analysts to discriminate between ‘Pharmacy
dispensing’ and ‘Prescription written’
•
<entity>_ID:
– Unique identifiers for key entities, which can serve as foreign keys to establish
relationships across entities
– Ex: PERSON_ID uniquely identifies each individual. VISIT_OCCURRENCE_ID uniquely
identifies a PERSON encounter at a point of care.
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CDMv3 continues to be a person-centric model
• Uniquely identifies all
individuals with person-level
information in all entities
• Captures standardized
demographics
• Allows maintenance of
current location and primary
provider and care site
• PERSON_ID is a foreign key
for all person-level
information
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Linkage to visits and providers
• CONDITION_OCCURRENCE,
PROCEDURE_OCCURRENCE,
DRUG_EXPOSURE,
OBSERVATION now
optionally allow linkage to a
corresponding visit and
associated provider
• VISIT_OCCURRENCE_ID
allows linkage to specific
CARE_SITE through the
VISIT_OCCURRENCE table
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Incorporating financial information
• Each DRUG_EXPOSURE can
have one or more
DRUG_COST entries
• Each
PROCEDURE_OCCURRENCE
can have one or more
PROCEDURE_COST entries
• Focus on amount paid, and
can broken down by
typically available
categories
• Cost records can optionally
link to person’s payer/plan
information, as available
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CDMv3 Vocabulary
• Same general
organization as CDMv2
• Added
‘valid_start_date’,
‘valid_end_date’, and
‘invalid_reason’ fields to
accommodate
deprecated/changing
concepts
• Expanded set of source
codes and standard
terminologies
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Acknowledgements
• OMOP research and technical team
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Christian Reich
Mark Khayter
Don Torok
Marc Overhage
Paul Stang
Bram Hartzema
David Madigan
Martijn Schuemie
Emily Welebob
Tom Scarnecchia
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How to submit your CDMv3 feedback
• Download the CDMv3 specification document at:
http://omop.fnih.org/CDMV3
• All comments must be submitted by January 9, 2011. Submit
comments two ways:
– Follow and reply to the CDM v3 discussion or post your
comments (public) at http://omop.fnih.org/CDMV3
– Provide feedback via email to Emily Welebob at
[email protected]
• Once the CDM is finalized:
– Creation of new ETL specifications for data in the central
research lab
– Release open-source tools that are CDMv3-compatible
• Thank you in advance for your time and expertise!
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