1. Synthetic Data Working Group

2. Agenda Quick Review of Kickoff material
Example of the value of synthetic data
Group exercise: what does success look like for this working group

3. Principles Why Synthetic Data Synthetic yet realistic High fidelity
Demographic fidelity
Clinical fidelity
Temporal fidelity
Realistic population distribution
Standards based, easily-ingestible records
Highly configurable, profile based
Repeatable process

4. Synthetic Data Framework
Use Cases
Intended purpose, users and high-level requirements
Target data scope
Profiles
Demographic distribution
Disease or other clinical probabilities
Generation
Synthetic records based on data profiles
Clinical fidelity (no 100 pound babies)
Ingestion
Ingestion mechanism and target database
Retain clinical fidelity
Utilization
Static snapshots for viewers and dashboards
Temporal distribution for workflow and dynamic processes

5. Example

6. Group Activity: What does success look like?
Objectives Metrics Characteristics Milestones High-level requirements

7. Intended purpose, users and high-level requirements Target data scope
Use Cases
Intended purpose, users and high-level requirements
Target data scope
Define detailed data and usage requirements
What data makes the Veteran (or other target groups) population unique (e.g., Agent Orange exposure and its relevant to comorbidities) – defining use cases and requirements for specific patient populations like Veterans or people from other parts of the world
Need aging algorithms
Cohorts by war area, geographic specific info (ebola outbreaks), geospatial theatre operations
Occupational concerns (e.g., military working near jet fuels)
Need additional data elements that are not always accounted for in an EMR – relationship status, financial status (e.g., determinants of health) – as they have impact on conditions like PTSD, depression and mental health and suicide prevention
Appropriate terminology code sets (e.g., DSM 5 for mental health) – DSM 5 currently linked to ICD-10 codes for VA
Non-clinical data: e.g., service connection,
Mapping to the data sets and data models of target systems (e.g., VistA and Cerner)
Pandemics

8. Demographic distribution Disease or other clinical probabilities
Profiles
Demographic distribution
Disease or other clinical probabilities
Profile reference sources:
US Census bureau - demographics
CDC – disease statistics
NIH – disease statistics
Peer reviewed publications – disease statistics
Combine disease statistics with “standards of care” to drive the application of care to the data probabilities
Demographic distributions for age, gender, race, income, education that mirror the real-world distributions
For target populations (e.g., Veterans), need to expand the data set to include items on the other slide – data.gov has good data sets for this type of information; search for “public health government” to get war specific morbidities
Semantics – need to model relationships between morbidities or between morbidities and related information such as meds, procedures, findings, etc.
Synthea: disease models based on extensive research (not easy) and then captured in statistical models
Mining ontologies for relationships: value set authority from NLM (e.g., trauma value set), SOLOR, agency maps between terminologies, UMLS – meta-mapping between ontologies and terminology code sets, SNOMED browser/CSIRO, others: Medcin, 3M HDD, FDB
Look for statistics for the data used in major scientific studies
VA aggregated data sets (CDW) available to ask questions about morbidity percentages and the like

9. Synthetic records based on data profiles
Generation
Synthetic records based on data profiles
Clinical fidelity (no 100 pound babies)

10. Ingestion mechanism and target database Retain clinical fidelity

11. Static snapshots for viewers and dashboards
Utilization
Static snapshots for viewers and dashboards
Temporal distribution for workflow and dynamic processes