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Bob Rogers, PhD, Chief Scientist and Co-founder at Apixio, and Vishnu Vyas, Principal Scientist at Apixio will be presenting on October 30, 2013. They will describe use cases in which Apixio is using NoSQL and Hadoop to deliver powerful risk assessment results based on unstructured data in electronic health record systems.
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Making ObamaCare Work With Big Data
Healthcare Use Cases
About Bob
About Vishnu
Overview
• What is wrong with healthcare?• What is ObamaCare?• What does patient data look like?• Risk Adjustment use case• Care Network use case• Apixio’s Big Data solutions
Poll
Are you a:A. ProgrammerB. Data scientistC. ManagerD. Health IT technologistE. Other?
What Is Wrong With Healthcare?
Fee-For-Service
Skyrocketing Cost
84 Million People Under- or Un-Insured
Healthcare Reform
• Liberate Clinical Data• Care Coordination• Efficiency• Risk Adjustment
What Does a Patient Look Like To A Data Scientist?
Structured Data
Text
Scanned Documents
% with Pneumococcal
Vaccine
54%
17%
No CodedHistory in EHR
Coded History
Decision Support Fails Without Access to Required Clinical Data
3 x lower
Coded29%
Non-coded71%
How is Splenectomy documented?
04/11/2023
Poll:
What percent of the key clinical data to you think is missing from the coded layer?
A. 10-25 %B. 25-50 %C. 50-75%D. 75+ %
>63%Missing
Jonathan Everett & Bob Rogers
Real Patient Example
Coded Data
Free Text
Scanned Documents
Other Data Silos
17
Question your assumptions about data.
18
“Heart Failure”in EHR problem list
Is it Heart Failure?
Heart Failure No Heart Failure
… or Chart Failure?
Where is the valuable data?
How Much Data Is There?
Sources: EHR Structured, EHR Text, EHR Scanned, Claims, RAPS
200,000 Pts over 5 years 10 TBStructured: 13 M unique codes
4.8 M CPT, 4.8 M ICD9
Narrati ve: 338 M unique codes98 M CPT, 120 M ICD9
Use Case: Risk Adjustment
How risk scores are used1.01 1.20
Risk Assessment & Risk Scores
CADICD-9 746.85
HCC 138Score: 0.312
Total Score: 0.6
+0.312-------------0.912
Risk Assessment & Risk Scores
Decubitus UlcerICD-9 707.14
HCC 217Score: 0.954
Type II DiabetesICD-9 250.00
HCC 19Score: 0.215
Total Score: 0.6
+0.215+0.954-------------1.769
Where’s the beef?
AssessMonitor Evaluate Treat
MEAT
Manual Chart Audit
1 hour per chart100,000 patients=11.4 PERSON-YEARS!
Use Case: Care Network
Referring MD represented in GreenConsulting MD represented in Blue
29
Referring MD represented in GreenConsulting MD represented in Blue
30
Care Network- Referrals of Interest
How Do We Solve These Problems?
Apixio Architecture High Level
EHR coded data
EHR text documents
EHR scan documents
Claims
ParseOCR
Norm.Load
Client Ingest Pipeline
Patient ObjectModel
GeneralEvent Stream
HCCEvent Stream
QualityEvent Stream
ReferralEvent Stream
3rd PartyEvent Stream
API
Clinical Knowledge Exchange
CareOptimizer
Quality Optimizer
HCCOptimizer
3rd PartyEvent Stream
Application
Eligibility
Provider files
Apixio Platform Physical Architecture
Audit(Trace CF)
Logging(Hive/Trace CF)
Metrics (Graphite)
Apixio Pipeline Receiver (HTTP)
Cassandra Hive/HDFS S3
Apixio REST API
Web TierJava/Python
External Clients End Users
Persistence
ComputeJob Control Pipeline
ApplicationsExperimental Infrastructure
Logging
Apixio Platform Logical Architecture
• Append Only Model in Cassandra• Document Based
L0
L0 – Document Level• Stored in cassandra• 2 Column Family / Customer• Append only
ApixioID DOCID1 DOCID2 DOCID3
Partial Patient Object
Partial Patient Object
Partial Patient Object
Documents Column Family
DocID:<DOCID> ApixioID
APIXIOID
Indices Column Family (2 types of data)
DocHash:<HASH> ApixioID
APIXIOID
L1 – Event Streams
An event is an assertion (fact) about a specific subject (patient) at a specific time
Cassandra
Event Extraction&
Inference
HIVE/HDFS
Event Extraction & Inference
Cassandra
Event Extractors
Event Transformer
HIVE/HDFS
Mapper Reducer
Event Extractors
Event Transformer
Converts Documents/Patients to Events
Combines multiple events to create new events
Event Extraction & Inference
Stacking Composition
Event Extractors
Event Extractors
Event Extractors
Event Extractors
Event Extractors
Event Extractors
Sequencing Composition
Functional Composition of extractors/transformers gives us a scalable flexible inference engine.
Auditing• Access information stored in a tracing CF in cassandra• Append only• Keyed by document
DocID Timestamp1 Timestamp2 Timestamp2
Activity Info Activity Info Activity Info
Audit Column Family
Parsing User Access Timeline
We can reconstruct the timeline of activity on any document once it hits our system.
What happens when something goes wrong?
• Comprehensive Logging through custom appenders (log4j)• All pipeline level events are logged to a trace column family• Real-time metrics logged through graphite.
