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November 20-22, Amsterdam | @HL7 @FirelyTeam | #fhirdevdays | www.devdays.com
IoMT - High Frequency Data in FHIR
Dustin Burson
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Dustin Burson
• Software Engineer at Microsoft
• Health Cloud and Data Team
• Focus on IoMT devices
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IoMT FHIR Connector for Azure https://github.com/microsoft/iomt-fhir
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Quick Demo
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Demo - Continued
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Session Outline
• Define IoMT
• Key Scenarios
• Building for IoMT
• Challenges Encountered
• Lessons Learned
• Existing Support in FHIR
• What we have built
• Future Improvements
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What is IoMT?
Internet of Medical Things
• Subset of IoT devices
• Patient health data
• Devices can be…
• Worn
• Ambient
• Used
Internet
of
Medical
Things
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Key Scenarios
Use sensors and PROs to
monitor patients
Use sensors and PROs to
monitor patients
Streamline hospital processes
with automation
• Remote / In-patient • Home Care / Telemedicine
• Prevent Patient Readmission
• Early Intervention
Patient Monitoring Research Smart Hospitals
• Clinical Trials • Drug Discovery
• Model Development
• Augment Care teams • Improve workflow safety
and efficiency
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Why is Microsoft releasing IoMT solutions? 1. Simplify sharing IoMT data with
physicians and researchers
2. Promote and bootstrap experimentation and learning
3. Support and Evangelize FHIR
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Building for IoMT
• Ingest high frequency data
• Low latency
• Associate data to patient
• Support a multitude of devices
• Interoperability
• Privacy and Security
• Support data analytics & ML
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Challenges Encountered
• Late arriving data
• Out of order data
• Duplicates
• Error handling
• Linking data to patient
• Balancing latency and load on the FHIR server
• Each device represents data differently
• Keeping number of FHIR resources created reasonable
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Lessons Learned
• Single value observations are not sufficient
• Collecting data on device presents problems
• Device complexity
• Introduces latency
• Code portability
• FHIR on devices can be hard
• FHIR is evolving
• FHIR payloads can be large
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Storing in FHIR – Sampled Data
• Sequence of values
• Period in milliseconds
• All values for the observation period should be represented
• Missing data represented by “E”
“valueSampledData” : { “origin” : { … }, “dimensions” : 1, “period” : 1000, “data” : “88 89 E E E 77 75 79 81”, }
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IoMT FHIR Connector for Azure
Normalize Group Transform Persist
Transforms data into FHIR Observations
Persists Observations to a FHIR Server
Groups data by device, type and time
Normalizes an intermediate to a format
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Key Benefits
Scalability
Azure
Interoperability
FHIR
Development Time and
Infrastructure Cost
SECURITY AND COMPLIANCE (Designed for HIPAA, HITRUST, GDPR)
Cost & Time Savings
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IoMT FHIR Connector for Azure
• High frequency ingestion
• Can scale with your workload
• Design for compliance
• Configurable end to end latency
• Seamlessly connect to Azure IoT
• Extend with additional analytics
• IoMT data stored in FHIR
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Requirements and Setup
• R4 FHIR server
• OAuth 2.0 identity provider
• An existing device resource and patient resource on the FHIR server
• Device content and FHIR mapping templates
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Not Covered
• Individual Device Security
• Device Management
• Device Provisioning
• Patient Provisioning
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Normalization
• Match and Extract
• Templates use JSONPath
• Configuration evaluated per message batch
• Projection
Normalized
heartrate : 59
name : heartrate value : 65 name : heartrate value : 59 name : heartrate value : 88
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Normalization - Projection
Normalized name : heartrate value : 59 name : steps value : 5 name : hrv value : 67
“steps” : 59
“heartrate” : 59
“hrv” : 67
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Template Example
{
“Body” : { “hr” : “88”, “date” : “2019-02-12T22:01:55”, “deviceId” : “device123”, }
}
{
“typename” : “heartrate”, “typeMatchExpression” : “$..[?@hr]”, “deviceIdExpression” : “$.deviceId”, …
}
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Template Example Continued
{
“Body” : { “hr” : “88”, “date” : “2019-02-12T22:01:55”, “deviceId” : “device123”, }
}
{ …
“timestampExpression” : “$.date”, “values” : [ {
“valueExpression” : “$.hr”, “valueName” : “heartrate”
}
]
}
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Grouping & Buffering
• Leverages Azure Stream Analytics
• Grouped according to type, window date, and device
• Controls end to end latency
• Job partitioned by device id
• Should be scaled with Event Hubs
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FHIR Conversion
• Define Period
• Define Codings
• Set data type
• Maps data to correct observation
• Identifier handled by system
• Linking to patient & device
• Component support
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Persistence
• Data in connector is transient
• Final destination is a FHIR server
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Getting Started
Clone the Repo Deploy the ARM Template
Upload Configurations
Open, Extensible, Easy to Deploy
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How can we improve?
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Limit History Creation
• As E2E latency is reduced the number of history records increase
• Append to observation while in preliminary status
• While in preliminary status history records won’t be created
• History created normally once the observation is final
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Sparse Data Streams
• Can be high frequency but short duration
• SampledData type usually results many “E” values in the data stream
• New type with defined time dimension or offset
• Avoid loss of accuracy
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Summary
• Defined IoMT
• Covered Challenges and Lessons Learned
• Overview of IoMT FHIR Connector for Azure
• Future improvements
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Thank you https://github.com/microsoft/iomt-fhir
Star the repository Contribute to the project See us after
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