Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Similar to Making obamacare work with Big Data
Similar to Making obamacare work with Big Data (20)
Recently uploaded
Recently uploaded (20)
Making obamacare work with Big Data
- 1. Making ObamaCare Work With Big Data Healthcare Use Cases
- 2. About Bob
- 3. About Vishnu
- 4. 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
- 5. Poll Are you a: A. Programmer B. Data scientist C. Manager D. Health IT technologist E. Other?
- 6. What Is Wrong With Healthcare? Fee-For-Service
- 7. Skyrocketing Cost 84 Million People Under- or Un-Insured
- 8. Healthcare Reform • Liberate Clinical Data • Care Coordination • Efficiency • Risk Adjustment
- 9. What Does a Patient Look Like To A Data Scientist?
- 10. Structured Data
- 11. Text
- 13. Decision Support Fails Without Access to Required Clinical Data How is Splenectomy documented? % with Pneumococcal Vaccine 54% Coded 29% 17% Non-coded 71% Coded History 11/1/2013 No Coded History in EHR 3x lower
- 14. Poll: What percent of the key clinical data to you think is missing from the coded layer? A. B. C. D. 10-25 % 25-50 % 50-75% 75+ %
- 15. >63% Missing
- 16. Real Patient Example Coded Data Free Text Scanned Documents Other Data Silos Jonathan Everett & Bob Rogers
- 17. Question your assumptions about data. 17
- 18. 18
- 19. Is it Heart Failure? “Heart Failure” in EHR problem list
- 20. … or Chart Failure? Heart Failure No Heart Failure
- 21. Where is the valuable data?
- 22. How Much Data Is There? Sources: EHR Structured, EHR Text, EHR Scanned, Claims, RAPS 200,000 Pts over 5 years 10 TB Structured: 13 M unique codes 4.8 M CPT, 4.8 M ICD9 Narrative: 338 M unique codes 98 M CPT, 120 M ICD9
- 23. Use Case: Risk Adjustment
- 24. How risk scores are used 1.01 1.20
- 25. Risk Assessment & Risk Scores CAD ICD-9 746.85 HCC 138 Score: 0.312 Total Score: 0.6 +0.312 ------------0.912
- 26. Risk Assessment & Risk Scores Type II Diabetes ICD-9 250.00 HCC 19 Score: 0.215 Decubitus Ulcer ICD-9 707.14 HCC 217 Score: 0.954 Total Score: 0.6 +0.215 +0.954 ------------1.769
- 28. Manual Chart Audit 1 hour per chart 100,000 patients =11.4 PERSON-YEARS!
- 29. Use Case: Care Network Referring MD represented in Green Consulting MD represented in Blue 29
- 30. Care Network- Referrals of Interest Referring MD represented in Green Consulting MD represented in Blue 30
- 31. How Do We Solve These Problems?
- 32. Apixio Architecture High Level Client Ingest Pipeline Clinical Knowledge Exchange Application General Event Stream Care Optimizer HCC Event Stream Quality Optimizer Quality Event Stream HCC Optimizer Provider files EHR coded data EHR text documents EHR scan documents Parse OCR Norm. Load Patient Object Model API Referral Event Stream Claims Eligibility 3rd Party Event Stream 3rd Party Event Stream
- 33. Apixio Platform Physical Architecture External Clients End Users Apixio Pipeline Receiver (HTTP) Web Tier Java/Python Metrics (Graphite) Apixio REST API Logging (Hive/Trace CF) Job Control Compute Pipeline Applications Experimental Infrastructure Audit (Trace CF) Logging Persistence Cassandra Hive/HDFS S3
- 34. Apixio Platform Logical Architecture • Append Only Model in Cassandra • Document Based L0 L1 • Event Based Append Only Model • Transient (Stored in HDFS) • Used for Inference • Application Specific Data Model • Optimized for Quick Retrieval L2
- 35. L0 – Document Level • Stored in cassandra • 2 Column Family / Customer • Append only Documents Column Family DOCID1 DOCID2 DOCID3 Partial Patient Object ApixioID Partial Patient Object Partial Patient Object Indices Column Family (2 types of data) DocID:<DOCID> ApixioID APIXIOID DocHash:<HASH> ApixioID APIXIOID
- 36. L1 – Event Streams Cassandra HIVE/HDFS An event is an assertion (fact) about a specific subject (patient) at a specific time
- 37. Event Extraction & Inference Mapper Reducer Cassandra HIVE/HDFS Converts Documents/Patients to Events Combines multiple events to create new events
- 38. Event Extraction & Inference Functional Composition of extractors/transformers gives us a scalable flexible inference engine.
- 39. Auditing • Access information stored in a tracing CF in cassandra • Append only • Keyed by document Audit Column Family Timestamp1 Timestamp2 Timestamp2 Activity Info DocID Activity Info Activity Info Parsing User Access Timeline User Access We can reconstruct the timeline of activity on any document once it hits our system.
- 40. 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.
Editor's Notes
- (Bob- 71% pie chart shrinks off to show 3x danger) Health information tech