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Source A Source B Source C Source D

FHIR representation

FHIR is key to solving semantic data normalization challenge- needed for reproducible AI

FHIR Resources examples:Patient, Encounter, Practitioner, Procedure, Condition, Observation, Procedure Request, Medication Request, Medication Administration.

Time

BQ FHIR Analytics StoreDe-id services

AI Training/Testing/Validations

Discovery/Experiments, Dashboards, Reports, Analysis Notebooks

De-Id Analytics Store, Features, Measures.

AI Inference Models

Inference Output (FHIR) Engagement Applications

Synthetic data for AI training

MODEL ARTIFACT and LINEAGE TRACKING. MODEL PERF. MONITORING. DATA VALIDATION.

Streaming data FHIR API StoreCloud Function

Feature Definitions

Orchestrated experimentation

Development datasets

Source RepositoryData

ExtractionData Valid.

Data Prep.

Model Training

Model Eval.

Training pipeline CI/CD

Build components &

pipelines

Run automated tests

Tag and store artifacts

Deploy to target

environment

Artifact Store

Trainingdatasets

Model Registry

Continuous training

ML Metadata Store

Source code

ML pipeline Artifacts

TrainedModels

Model Valid.

Data Extraction

Data Valid.

Data Prep.

Model Training

Model Eval.

Model Valid.

Exploratory Data Analysis

Feature Store

Patient’s longitudinal records in FHIR format data warehouse

Confidential & Proprietary

Data Distribution in source systems

Human Bias

Human Bias

Human Bias

Human Bias

Human Bias

Classification Bias

Selection Bias

Information Bias

Selection/Classification Bias

Selection Bias

Patient has symptoms, acute illness and seeks

care

Enter data in EMR vs. Scanned PDF

Coding and submission of

claims

Adjudication and Payment of Claims

Exams, Diagnostics, Prescription

Verify across multiple resources Combine multiple sources Maintain Provenance and Lineage of Data

Multiple mentions of diagnostic code to confirm.

Occurrence of disease specific procedure.

Medication specific to the disease.

Lab tests specific to the disease.

Validate structured data with in Clinical notes, images, and scanned documents.

Combine Inpatient, Outpatient, and Telehealth encounters - longitudinal patient record in FHIR representation.

Use catalog for datasets and models - document datasets and model metadata.

Document source of data, source type, last updated, user/process.

Document how a certain feature is created, which models use the feature.

Organize by Patient Timeline / Time Window.

Define Prediction Task.

Find / Derive Labels.

Select Cohort.

Create new features (if needed).

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What are we predicting? (task)For whom are we predicting? (cohort)When are we predicting? (prediction time)What’s the outcome of the prediction? (label)

What are we predicting? (task)

For whom are we predicting? (cohort)

When are we predicting? (prediction time)

All in-patients who’ve been in the hospital for 24 hrs

When will this patient leave the hospital

24 hours after admission

What’s the outcome of the prediction? (label)

Length of stay in days: 1-3, 3-7, 7-14, or 14+ days

SELECTId,subject.patientid,class.code as enc_class,period.start as periodStart,period.end as periodEnd,date_diff(cast(substr(period.end,0,10) as date),cast(substr(period.start,0,10) as date),day)as LOSFROM Encounter WHERE class.code='IMP'

Define length of stay from FHIR - Encounter table.

Creating “Length of Stay” Label from FHIR data

A set of patients that satisfies some inclusion

criterion, typically an exposure /outcome of some sort.

Exposure Outcome

A condition of interest that could happen after the exposure

Something that could happen to the patient.

Diabetic

HbA1c > 6.5 Count of Glucose test

Knowledgebase: Diabetes → GlucoseDiabetes → HbA1C

https://phekb.org/

https://rdrr.io/github/OHDSI/Aphrodite/

WITH diabetes as (

SELECT

dx.patientId as patientId,

dx.codeCodingCode as diagnosis_code,

dx.codeCodingDisplay as diagnosis_desc,

obs.codeText as obs_desc

FROM `gcp-hcls-demo.fhir_batch_analytics.condition` as dx

JOIN `gcp-hcls-demo.fhir_batch_analytics.observation` as obs

ON obs.patientId=dx.patientId

WHERE lower(dx.codeCodingDisplay) like '%diabe%'

and lower(obs.codeText) ='glucose'

),

medication_counts as (

SELECT

rx.patientId as patientId,

COUNT(DISTINCT rx.medicationCodeableConceptCodingCode) AS med_count

FROM `gcp-hcls-demo.fhir_batch_analytics.medication_request` as rx

WHERE status = 'active'

GROUP BY 1

)

SELECT

diabetes.patientId,

diabetes.diagnosis_code,

diabetes.diagnosis_desc,

medication_counts.med_count

FROM diabetes,medication_counts

where diabetes.patientId = medication_counts.patientId

ORDER BY med_count descDefine diabetic patients -using Condition, Observation, and Medications table.

Cohort Selection Example

Aggregation of Codes Scoring Fields

Use BMI as a measure.

Comorbidity Burden - account for overall illness and avoid comparing sick people to healthy people.

Severity of Illness Scores: SAPS, SOFA, OASIS, APACHE

Use medication class instead of medication code.

Use condition category or procedure modality.

Adding Knowledge/Context

Socio Economic and social vulnerability status from public data - census etc.

SOFA score calculation from EHR data example: https://github.com/MIT-LCP/mimic-code/blob/master/concepts/severityscores/sofa.sql

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Utility: Purpose of the model. What action can be taken on the model outcome to improve “X” for “Y”.

Feasibility: Workflow and IT integration. Implementation and maintenance cost.

Impact: On clinical care, patient outcomes, and operational efficiency.

Timeline

Inpatient admission Discharge Readmission Renal Failure

Window of ObservationWindow of Prediction and Action:Hours, Days, Months, Years

Past Events

ActionBeneficiary Incentives and Authority

Economic Validity

Regulation/FDA approval

Patient Privacy and Consent

Resource Capacity

Logistics and Cost

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Time

BQ FHIR Analytics StoreDe-id services

AI Training/Testing/Validations

Discovery/Experiments, Dashboards, Reports, Analysis Notebooks

De-Id Analytics Store, Features, Measures.

AI Inference Models

Inference Output (FHIR) Engagement Applications

Synthetic data for AI training

MODEL ARTIFACT and LINEAGE TRACKING. MODEL PERF. MONITORING. DATA VALIDATION.

Streaming data FHIR APICloud Function

Feature Definitions

Model Deployment CI/CDSource Repository

Serving infrastructure

Build Prediction Service

Run Automated Tests

Deploy to Target Environment

Log StoreLive Data

Model Registry

Explain Evaluate MonitorPredict

Serving Logs

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Google EHR Deep Learning Paper

Emory Sepsis Deep Learning

Artificial Intelligence in intensive care

Example of a Sequence Deep Learning Architecture

Tensorflow Extended (TFX)

Google Cloud AI Platform Pipeline

FHIR API Specification

FHIR Risk Assessment Resource

Google FHIR Example

Tensorflow Word Embeddings

HIPAA Aligned Cloud Architecture

GCP Healthcare Analytics Platform

GCP Healthcare API

Healthcare Data Harmonization

Healthcare Data Protection Suite

Various GCP Healthcare Examples

● Leveraging Synthetic Data to Benchmark a Cloud-Based FHIR API

Onix

Friday, Nov 20th, 2:00 pm-2:45 pm ET

Related Presentations

● Let’s Build! Google Cloud FHIR APIs Thursday, Nov 19th, 5:15 pm-6:00 pm CET

● FHIR Analytics using OHDSI Tools on CloudThursday, Nov 19th, 5:40 pm-6:00 pm CET

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