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1
Putting it all together: unlocking the potential of tomorrow with ML
and the CloudSession INT6, February 11, 2019
Aashima Gupta, Director, Global Healthcare Solutions, Google Cloud
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2
Aashima Gupta
Has no real or apparent conflicts of interest to report.
Conflict of Interest
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3
• Describe the realities of evolving technology changing the interoperability landscape.
• Assess the role of use cases in driving policies and technologies to support value-based care.
• Evaluate the anticipated benefits and challenges of TEFCA implementation across stakeholder groups
• Analyze the remaining gaps as data exchange is expanded to broader stakeholder groups in support of innovation.
• Describe the value of payer and provider data exchange within the healthcare ecosystem.
Learning Objectives
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1 in 20 searches are health related
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An example: mental health
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An example: public health
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Make health information universally useful and accessible
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Organize the world’s healthcare and life sciences data, make it accessible , secure and useful .
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Organization
Make it easy to ingest, normalize, and join data
Accessible
Support open standards, APIs, interoperability, and discovery through search
Useful
Insights to action through analytics and machine learning
Secure
Lead the industry in security, privacy and compliance from endpoint to data center
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What if
Query for all females, age 45 -60 with a specific biomarker, with history of breast cancer that haven’t been screened in the last 24 months
01
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Three pillars to democratize innovation for healthcare
01Democratizing Technology Foundation
Google Cloud Infrastructure, scale security and compliance
0 2Democratized Machine Learning
Cloud AutoML, TensorFlow
0 3Democratizing Patient Health Records
Interoperability and Standardization
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Democratizing Infrastructure
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Eight cloud products with over one billion users each,all powered by the c loud.
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Google is a Data Company
uploads per minute
500hrsusers
1B+search index
100PB+query response time
0.25s
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Compliance for Healthcare
Global Scale 18 regions by end of 2018
Google Compute Engine guarantees 99.95% uptime
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Health DataInteroperability
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Fostering data interoperability and nurturing FHIR Community
“The HL7 FHIR Foundation is thrilled by this generous contribution of Google Cloud Platform services to support the ongoing activities of the FHIR community to help advance our goal of global health data interoperability,” said Grahame Grieve, HL7 FHIR Foundation Board Member and HL7 FHIR Product Director. “The future of health computing is clearly in the cloud, and FHIR will serve to accelerate this transition.”
Grahame Grieve , FHIR Principal
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Healthcare Enterprise Landscape
Cloud and AI Enabled Digital Health Platform
Payer/Health Plans/Pharma
EMR, Legacy Systems
Genomics Data , Imaging PACS
On-prem Data warehouse
Data/Analytics Engine
Remote Monitoring Devices
Wellness and Fitness Devices
Patient Mobile and Tablet Apps
External Data
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OLD will not get you the NEW
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ChallengeBridging Chile’s healthcare
Chile’s 1,400 connected health facilities and 1,000 remote medical facilities lacked connectivity – while many of its healthcare systems couldn’t easily, or securely, interoperate.
Previous attempts to resolve failed due to the overwhelming costs and complexity of the solution.
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New ApproachDigital Architecture
Nationwide API-based architecture
● Data and applications are easily, yet securely, available
● Enables Public Alerts, Population Health Management Programs, etc
● Connects HC Centers – large and small
● Reinforces and leverages digitizing all clinical and administrative processes
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Google Cloud Healthcare API
EHR ImagingHealthcare data
Genomics
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Google Cloud Healthcare API
EHR ImagingHealthcare data Genomics
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Google Cloud Healthcare API
EHR ImagingHealthcare data Genomics
Healthcare datasetCloud Healthcare API
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Google Cloud Healthcare API
EHR ImagingHealthcare data Genomics
Healthcare dataset
HL7v2 FHIR GA4GH APIsDICOM
Cloud Healthcare API
Per Mobility
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Google Cloud Healthcare API
EHR ImagingHealthcare data Genomics
Healthcare dataset
HL7v2 FHIR GA4GH APIsDICOM
Cloud Healthcare API
Per Mobility
More modalities in the future More modalities...IoT
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Google Cloud Healthcare API
EHR ImagingHealthcare data Genomics
Healthcare dataset
HL7v2 FHIR GA4GH APIsDICOM
Cloud Healthcare API
Support standard protocols GeonomicsReads
VariantsPipelines
DICOMwebWADO-RSSTOW-RSQIDO-RS
FHIRCreate UpdateRead
HL7v2MLLP
Msg storeAPI
Per Mobility
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Google Cloud Healthcare API
EHR ImagingHealthcare data Genomics
Healthcare dataset
HL7v2 FHIR GA4GH APIsDICOM
Cloud Healthcare API
Transformations/conversions
Per Mobility
Convert to FHIR
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Google Cloud Healthcare API
EHR ImagingHealthcare data Genomics
Healthcare dataset
HL7v2 FHIR GA4GH APIsDICOM
Cloud Healthcare API
De- identification
Per Mobility
De- identified datasetDe- id
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Google Cloud Healthcare API
EHR ImagingHealthcare data Genomics
Healthcare dataset
HL7v2 FHIR GA4GH APIsDICOM
Cloud Healthcare API
Data joining and export to analytics engine exploration, processing and machine learning
De- identified datasetDe- id
Join, Export
CloudDataproc
CloudDatalab
Cloud MLBigQuery
Per Mobility
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Google Cloud Healthcare API
EHR ImagingHealthcare data Genomics
Healthcare dataset
HL7v2 FHIR GA4GH APIsDICOM
Cloud Healthcare API
Model development and testing De- identified datasetDe- id
Join, Export
Per Mobility
ML ModelsCloudDataproc
CloudDatalab
Cloud MLBigQuery
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Google Cloud Healthcare API
EHR ImagingHealthcare data Genomics
Healthcare dataset
HL7v2 FHIR GA4GH APIsDICOM
Cloud Healthcare API
Model inference workflow integration
De- identified datasetDe- id
Join, Export
Per Mobility
ML ModelsCloudDataproc
CloudDatalab
Cloud MLBigQuery
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Google Cloud Healthcare API
EHR ImagingHealthcare data Genomics
Healthcare dataset
HL7v2 FHIR GA4GH APIsDICOM
Cloud Healthcare API
Advanced operations and transformations
Per Mobility
Search
NLP
MPI
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Google Cloud Healthcare API
EHR ImagingHealthcare data Genomics
Healthcare dataset
HL7v2 FHIR GA4GH APIsDICOM
Cloud Healthcare API
Serving to applications
Per Mobility
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Digital data exchange with Cloud Healthcare APIs
Medical RecordsEMRGenomicsMedicationsImagesEnvironmentSocial determinants
Clo
ud H
ealth
API
sH
L7, F
HIR
, DIC
OM
, Gen
omic
s
Data warehouse, analytics and
machine learning
Research and clinical processing
and workflows
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Health APIx: Developer portal
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Machine Learning for All
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Machine Learning with Google Cloud
orUse our models
● Leverage Google’s domain expertise
● No tools or expertise required
Train your own models
● Build on your own specialized domain expertise
● Use Google tools for building and training models
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Machine Learning with Google Cloud
orUse our models
● Leverage Google’s domain expertise
● No tools or expertise required
Train your own models
● Build on your own specialized domain expertise
● Use Google tools for building and training models
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Diabetic retinopathyDiabetic retinopathy:Fastest growing cause of blindness
415M people with diabetes
North America and Caribbean
2015 44.3 million2040 60.5 million
Europe2015 59.8 million2040 71.1 million
Middle East and North Africa
2015 35.4 million2040 72.1 million
Western Pacific2015 153.2 million2040 214.8 millionSouth east asia
2015 78.3 million2040 140.2 million
Africa2015 14.2 million2040 34.2 million
South and Central America2015 29.6 million2040 48.8 million
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INDIAShortage of 127,000 eye doctors45% of patients suffer vision loss before diagnosis
Diabetic retinopathy
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Diabetic retinopathy
How DR is diagnosed:Retinal fundus Images
No DR
Mild DR
Moderate DR
Severe DR
Proliferative DRHealthy
Hemorrhages
Diseased
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Diabetic retinopathy
Adapt a Google deep neural network to read fundus images
130k images
Deep convolutionalneural network
54 ophthalmologists
880k diagnoses
No DR
Mild DR
Moderate DR
Severe DR
Proliferative DR
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Diabetic retinopathy
Adapt deep neural network to read fundus images
No DR
Mild DR
Moderate DR
Severe DR
Proliferative DRConv Network - 26 layers
Image Quality
L/R eye
Fie ld of View
130k images
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Cloud AutoML Vision
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ML starts with getting hold of your data
CaptureCloud Health APIs
Pub/Sub
Storage Transfer Service
Data Transfer Appliance
ProcessCloud Health APIs
Dataprep
Dataflow
Dataproc
StoreCloud Health APIs
Cloud Storage
BigQuery
Cloud SQL
Datastore
BigTable
AnalyzeBigQuery
Dataflow
Datalab
Insight
Cloud ML Engine
Cloud AutoML
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Step 1: Training images from a Dermatology Clinic.
IMPORT: User uploads CSV file with image names and labels into the app
Most popular image file formats are supported (jpg, gif, png, etc.)
LABEL: Images are tagged with “labels” (“Nevus”, “Lentigo NOS”, “Healthy”, etc.)
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Step 2: Automatically train ML model
TRAIN: user initiates training of the model
Training takes between 10 min to 24 hours (weeks/months of DIY project)
EVALUATE: Results of the training are shown (Precision, Recall, etc.)
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Step 3: Time for predictions
PREDICT: model assigns class probabilities for new images
Prediction only takes seconds
Access: model via UI, command line, or API and integration in Clinical Workflows
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Skin disease classification using AutoML
Input:Images with associated “classes”
Model Evaluation Result:Confusion matrix
Prediction:Image classification
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Machine learning: from big data to meaningful insights
Raw Health Data
Clinical Impact
data mining
machine learningvisualization
analysis
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Three pillars to democratize innovation for healthcare
01Democratizing Technology Foundation
Google Cloud Infrastructure, scale security and compliance
0 2Democratized Machine Learning
Cloud AutoML, TensorFlow
0 3Democratizing Patient Health Records
Interoperability and Standardization
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We’re not just thinking about now. We’re thinking about, what is next.And how we can build a healthier future...
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Thank you