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Big Data Platform Processes Daily Healthcare Data for Clinic Use at Mayo Clinic
1.
©2015 MFMER |
slide-1 Big Data Platform Processes Daily Healthcare Data for Clinic Use at Mayo Clinic Dequan Chen, Ph.D. Mayo Clinic Big Data Core Team chen.dequan@mayo.edu; 507-250-7400 San Jose Convention Center June 9, 2015
2.
©2015 MFMER |
slide-2 Outlines • Healthcare Data Challenge at Mayo Clinic HL7 Data for Integrated Enterprise Usage Data Volumes, Types, and Processing Velocity Incapability of Existing RDBMS/Web Systems • Big Data Implementation for Enterprise-Level Clinical and Non-Clinical Usage • Big Data Implementation in Support of Colorectal Surgery Applications • On-going & Future Direction • Conclusion
3.
©2015 MFMER |
slide-3 Healthcare Data Challenge at Mayo Clinic
4.
©2015 MFMER |
slide-4 Healthcare Data Challenge at Mayo Clinic HL7 Data for Integrated Enterprise Usage • World’s largest integrated not-for-profit healthcare system – > 70 hospitals and clinics Enterprise Core Value: The Needs of the Patient Come First • Mayo Clinic Rochester, Minn. recognized as the top hospital in the nation for 2014-2015 by U.S. News & World Report • Provides care for > 1m (1,317,900 in 2014) patients from all 50 states & > 150 countries annually
5.
©2015 MFMER |
slide-5 Healthcare Data Challenge at Mayo Clinic HL7 Data for Integrated Enterprise Usage • Generates large amounts of EHR Data Structured Semi-Structured Unstructured • HL7 messages – mix of semi- and un-structured EHR data Enterprise-level clinical usage (diagnosis, treatment, prevention, or clinical reporting) Enterprise-level non-clinical usage (research, business intelligence, or health information exchange)
6.
©2015 MFMER |
slide-6 Healthcare Data Challenge at Mayo Clinic HL7 Data for Integrated Enterprise Usage • HL7 Message Example: (Source: http://www.priorityhealth.com) MSH|^~&|XXXX|C|PRIORITYHEALTH|PRIORITYHEALTH|20080511103530||ORU ^R01|Q335939501T337311002|P|2.3||| PID|1||94000000000^^^Priority Health||LASTNAME^FIRSTNAME^INIT||19460101|M||||| PD1|1|||1234567890^PCPLAST^PCPFIRST^M^^^^^NPI| OBR|1||185L29839X64489JLPF~X64489^ACC_NUM|JLPF^Lipid Panel - C||||||||||||1694^DOCLAST^DOCFIRST^^MD||||||20080511103529||| OBX|1|NM|JHDL^HDL Cholesterol (CAD)|1|62|CD:289^mg/dL|>40^>40|""||""|F|||20080511103500|||^^^""| OBX|2|NM|JTRIG^Triglyceride (CAD)|1|72|CD:289^mg/dL|35- 150^35^150|""||""|F|||20080511103500|||^^^""| OBX|3|NM|JVLDL^VLDL-C (calc - CAD)|1|14|CD:289^mg/dL||""||""|F|||20080511103500|||^^^""| OBX|4|NM|JLDL^LDL-C (calc - CAD)|1|134|CD:289^mg/dL|0- 100^0^100|H||""|F|||20080511103500|||^^^""| OBX|5|NM|JCHO^Cholesterol (CAD)|1|210|CD:289^mg/dL|90- 200^90^200|H||""|F|||20080511103500|||^^^""| …
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slide-7 Healthcare Data Challenge at Mayo Clinic Data Volumes • Daily enterprise-wide volume of real-time HL7 message data (msgs/day) • Large number (~1.83 billion prior to 12-31- 2014) of historical HL7 message data at Mayo Clinic
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slide-8 Healthcare Data Challenge at Mayo Clinic Data Types, and Processing Velocity • 60+ document types of HL7 messages Each document type generated by an individual healthcare source system Ex: Clinical Notes (cnote), Surgical Notes (opnote), Radiology, Pathology, Health_Quest, ECG/EKG… • Capability of fast processing (storing, analyzing, retrieving) of all types of HL7 data Real-time data and/or historical data Seconds - ER, ICU and Surgery Healthcare Minutes - Internal or Prevention Medicine
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slide-9 Healthcare Data Challenge at Mayo Clinic Challenges of Existing RDBMS/Web Systems • For enterprise-level clinical and non-clinical usage, the existing multiple RDBMS-based system implementations cannot achieve: All Real-time HL7 Messages – synchronously stored, analyzed and retrieved All Real-time and/or Historical HL7 Messages – quickly analyzed and retrieved Fast Free-Text Search on any medical terms Easy & Lower-cost scalability (scale-up & scale-out)
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slide-10 Big Data Implementation for Enterprise- Level Clinical and Non-Clinical Usage
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slide-11 Mayo Clinic Big Data Platform MC BigData Appliance (V1.0) • Started implementation in Jan 2014 • Purchased from Teradata • SUSE Linux Enterprise Server 11 (SLES 11) • Integration and Production Hadoop clusters • Each Hadoop cluster: 2 edge nodes, 2 master nodes, 6 data nodes Hadoop Stack – TDH1.3.2: Teradata-certified and modified HDP1.3.2 HDFS, MapReduce, Hive, Pig, HBase, Sqoop, Flume, Hcatalog, WebHcat, Zookeeper, Ganglia, Nagios, Oozie, Hue, and Ambari
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slide-12 Mayo Clinic Big Data Platform MC BigData Appliance (V1.0) • Each Hadoop cluster (cont’d): Built-in PostgreSQL Database Instance Apache Storm (version 0.9.1) ElasticSearch (ES) (version 1.0.0) Cluster Instances of Home-Developed Storm Topology – MayoTopology (One instance for one doc-type of HL7 messages) o1 spout o 2+ bolts
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slide-13 Data Flow Architecture Persisting Mayo Clinic Healthcare Data Into HDFS and ElasticSearch Index inside MC BigData Appliance(V1.0)
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slide-14 Testing (Measurement) Architecture Measurement of Data Persisting Capacity of MC BigData Appliance (V1.0)
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slide-15 HL7 Data Processing Capability HDFS and ES Index Data Persisting Capacity of MC BigData Appliance (V1.0) • Daily HL7-Persisting Capacity • 62 ± 4 million HL7 messages/day • No statistically significant change occurs when more types of HL7 Messages were included • ~20-50x more capacity than current daily max volume of all internal HL7 messages
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slide-16 HL7 Data Processing Capability Ultra-Fast Free-Text Search Capacity by ES Index of MC BigData Appliance (V1.0) • Data on ES Index HL7-V2-messages-derived-JSON-documents • Data Set size vs. Query speed (querying “Pain”) • ES Index: ~20000 – 30000x faster than NSE
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slide-17 Production Processing HL7 Data Reliability of MayoTopology Instances on BDProd Cluster on MC BigData Appliance (V1.0 & V2.0) • Production running started in May 2014 • Expanded to add additional edge nodes (3 more) and data nodes (4 more) • Upgraded from TDH1.3.2 to TDH 2.1 .. from ES1.0.0 to .. to ES1.5.2 • Successfully identified and fixed critical issues on the appliance • No Data Loss occurred up to now
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slide-18 Big Data Implementation in Support of Colorectal Surgery Applications
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slide-19 Goals for Support of Colorectal Surgery Applications • Optimize an existing NLP (Natural Language Processing) Pipeline • Move from thousands of HL7 documents to tens of thousands of documents processed daily • Replace existing free-text search facility used by Clinical Web Service supported applications • Move from minutes to milliseconds per search • Simplify overall architecture, increase data volume/velocity, and reduce costs
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slide-20 Output Parser Colorectal Surgical Applications HL7 HL7 HL7 UIMA Annotators 4-6 JMS queues, 250-300k HL7 msgs /day Elasticsearch (indexing & free text search) HL7 HL7 HL7 Storm (ID, Transform, and Parse) HDFS HL7 mgs for annotation To Annotation Facility Clinical Web Services REST API SQL NLP Discovery (MR, Hive, Pig, other) Existing Components Radiology Surgical ECG/EKG Pathology Clinical Notes Insurance Claims Flume RDBMS Persist Rules Engine New Components Services EnterpriseMessagingQueues(ESB) Solution Architecture In Support of Colorectal Surgery Applications
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slide-21 Enterprise Messaging Queues ClinNotes Surgery Radiology Rch Results Insurance ClinNotes OpNotes RadiolRpt ECG Rpt HDFS Big Data Platform Storm 1.Parse/Transform HL7 2.Persist JSON to Elasticsearch 3.Persist HL7 to HDFS 4.Route HL7 to NLP Queues ClinNotes OpNotes RadiolRpt ECG Rpt NLP Input Queues NLP Output Queues RBMS Structured Data Store NLP Evidence (annotations) Structured Data (from source systems) CRS Point of Care Tool User Interfaces External NLP Annotators Parse HL7 Setup UIMA Resources based on Message Type Run UIMA Pipeline Output 1:n Annotation Results (NLP evidence) A1. CRS_BLEED A2. CRS_ILEUS A3. NEURO_BLEED An. <expandable> … Big Data Input Queues Josh Pankratz – Apr 24, 2014 Elasticsearch SQL Solution Implementation
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slide-22 Data Flow Architecture
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slide-23 CRS HL7 Data Processing Capability MC BigData Appliance(Hadoop-ES)-NLPAnnotation(DS)- AmalgaRDB/Web for Colorectal Surgery (CRS) • Daily HL7-Persisting Capacity • 535k ± 31k messages/day • ~8-25x more capacity than current daily max volume of CRS HL7 messages
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slide-24 Production Processing CRS HL7 Data Reliability of MC BigData Appliance(Hadoop-ES)- NLPAnnotation(DS)-AmalgaRDB/Web for Colorectal Surgery (CRS) • Production running started in July 2014 • No Data Loss occurred up to now
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slide-25 On-going & Future Direction
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slide-26 On-going & Future Direction • Move current NLP Annotation Pipeline from DataStage production server environment to MC BigData appliance Hadoop cluster for CRS applications • Storm Topology • Dedicated edge nodes Faster & more reliable NLP annotation Higher Capacity of HL7 message processing
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slide-27 On-going & Future Direction • Build a unified data architecture – Unified Data Platform (UDP, an enterprise-integrated system) over the next few years: Enhance the Big Data platform Utilize existing RDBMS-based replication and data warehouse environment Create a variety of data endpoints (cubes, data services, advanced visualizations) for enterprise usage Integrate with non-Hadoop components for advanced Big Data analytics – R, Revolution R..
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slide-28 Conclusion
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slide-29 Conclusion Take-Home Messages • The implemented BigData platform coupled with DataStage (NLP) & RDBMS exceeds current Mayo Clinic patient-care needs: • Reliably handle ~20-50x more capacity than current daily volume of all HL7 messages • Provide ultra-fast Free-Text Search capabilities on medical terms • Reliably handle ~8-25x more capacity than current daily volume of HL7 messages for Colorectal Surgery Applications • Significantly outperform RDBMS-only-based systems
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slide-30 Conclusion Take-Home Messages • Big Data is a core component of Mayo Clinic UDP, which can utilize the power of Big Data technology at enterprise-level: Large data storage capability Structured, semi-structured and unstructured data Fast data exchange with RDBMS-based systems A variety of data-oriented Hadoop components – HDFS, Pig, Hive, HBase, Spark .. In-situ non-Hadoop data-processing components – R, Revolution R ..
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slide-31 Questions & Discussion
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slide-32 Reference Links • Mayo Clinic: http://www.mayoclinic.org/ • HL7: http://www.hl7.org/ • Hadoop Stack: http://hortonworks.com • Apache Storm: https://storm.apache.org/ • ElasticSearch: https://www.elastic.co/ • Teradata: http://www.teradata.com/