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(HLS305) Transforming Cancer Treatment: Integrating Data to Deliver on the Promise of Precision Medicine | AWS re:Invent 2014

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In the past ten years, the cost of sequencing a human genome has fallen from $3 billion dollars to $1,000, unlocking the ability for clinicians to use genomics in routine care. As the volume of genomic data used in the clinic begins to grow, healthcare providers are facing a number of new IT challenges, such as how to integrate this data with clinical data stored in electronic medical records, and how to make both available in real time to inform clinical decisions. In this session, find out how UCSF Medical Center and Syapse met these challenges head-on and solved them using AWS, all while remaining compliant with privacy and security requirements. Learn how Syapse's precision medicine platform uses Amazon VPC, Dedicated Instances, Amazon EC2, and Amazon EBS to build a high performance, scalable, and HIPAA-compliant data platform that enables UCSF to deliver on the promise of precision medicine by dramatically reducing time and increasing the accuracy and utility of genomic profiling in cancer treatment.

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(HLS305) Transforming Cancer Treatment: Integrating Data to Deliver on the Promise of Precision Medicine | AWS re:Invent 2014

  1. 1. © 2014 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc. November 13, 2014 | Las Vegas, NV HLS305 Transforming Cancer Treatment: Integrating Data to Deliver on the Promise of Precision Medicine Jonathan Hirsch, Syapse Kristen McCaleb, UCSF Nate Slater, AWS
  2. 2. 75% OF CANCER PATIENTS GET A DRUG THAT IS INEFFECTIVE FOR THEM
  3. 3. Legacy oncology practice “Nuclear bomb” therapies
  4. 4. Making cancer care more effective through precision medicine
  5. 5. Precision cancer care “Smart bomb” therapies
  6. 6. UCSF’s challenge Providing rich genetic data and actionable information to physicians while overcoming legacy software infrastructure
  7. 7. Best of the 1980s: EMR, PACS, LIS, CPOE, eMAR Legacy software
  8. 8. The precision medicine workflow… …and barriers to adoption Order test Clinical workup & Review clinical history Lab generates MDx test report View clinical & MDx data Receive decision support based on guidelines, clinical, molecular data Order therapy or enroll patient in clinical trial Process drug procurement Monitor patient outcome & revise care strategy Track cost & adherence Obtain pre-authorization Molecular Tumor Board reviews clinical & MDx data; delivers guidance to physician Obtain off-label reimbursement authorization Assess health outcomes & modify care pathways decision support for MDx test orders pre-authorization support systematic decision support for therapy or clinical trials mechanism for sharing patient records systematic capture of physician decisions & patient outcomes systematic capture of treatment costs systematic update of care pathways No No No No No No No data integration and visualizationNo
  9. 9. The Precision Medicine Workflow… …and barriers to adoption. Order test Clinical workup & Review clinical history Lab generates MDx test report View clinical & MDx data Receive decision support based on guidelines, clinical, molecular data Order therapy or enroll patient in clinical trial Process drug procurement Monitor patient outcome & revise care strategy Track cost & adherence Obtain pre-authorization Molecular Tumor Board reviews clinical & MDx data; delivers guidance to physician Obtain off-label reimbursement authorization Assess health outcomes & modify care pathways decision support for MDx test orders pre-authorization support systematic decision support for therapy or clinical trials mechanism for sharing patient records systematic capture of physician decisions & patient outcomes systematic capture of treatment costs systematic update of care pathways No No No No No No No data integration and visualizationNo EMR tabs EMR records Paper reports Emails Phone calls XLS, PPT, DOC files Mental steps 8 ~50 9 4 5 12 4 Conservative estimate by users
  10. 10. UCSF “APeX” Electronic Medical Record
  11. 11. UCSF “APeX” Electronic Medical Record Can’t handle complex genomic data No data mining, visualization Built for billing & compliance
  12. 12. UCSF “APeX” Electronic Medical Record But the EMR is central to clinical workflow, so we must figure out how to get information in & out with minimal disruption to physicians.
  13. 13. Genomics InfoSec today
  14. 14. “Obtaining the information needed to develop treatment plans and summaries…required 23 separate steps including visits to various screens, tabs and other sources within the EMR.” ASCO, The State of Cancer Care in America: 2014
  15. 15. Genomic data: EMR “Import”
  16. 16. How do we fix this?
  17. 17. A modern-day Tower of Babel No standard schemas No standard terminology Unstructured or semi-structured Thousands of record types Millions of property types
  18. 18. API for healthcare
  19. 19. The Syapse solution
  20. 20. Data integration Physician Sequencing & Analytics Sendout Labs One-Time Migration Data Ingestion PDF Excel PowerPoint Filemaker Pro LIS Data Warehouse CPOE EMRPACS Drug Administration Interfaced Systems
  21. 21. Oncologist dashboard 5 4 3 2 1 2 Omics data 3 Drug procurement 5 Imaging metadata 1 Structured clinical data 4 Longitudinal data * All data included in this chart is for informational purposes only and does not include actual patient data
  22. 22. Cancer genomics workflow enabled by Syapse Clinical Workup Patient Consent Test Order in EMR Specimen Procurement Sequencing & Processing Filtering Searchable Database Report Delivery Clinical Data Review Molecular Tumor Board Syapse Clinical Decision
  23. 23. Integrate molecular data into clinical workflow Tailor decision support to organization best practices Extend expertise to affiliate network Introducing Syapse: Enterprise software to enable precision medicine
  24. 24. Adopted by large healthcare providers
  25. 25. DATA SOURCES ?
  26. 26. Modern data architecture enabling innovative features DATA SOURCES
  27. 27. Industry-leading security & HIPAA compliance ARCHITECTURE Controlled Vocabs ETL Process Semantic Store Data Model Config Expressive, flexible data integration DATA SOURCES
  28. 28. Industry-leading security & HIPAA compliance TOOLS Semantic Query Rules Engine Knowledge Base Workflows ARCHITECTURE Controlled Vocabs ETL Process Semantic Store Data Model Config Interfaces with and augments existing systems DATA SOURCES
  29. 29. Industry-leading security & HIPAA compliance TOOLS Semantic Query Rules Engine Knowledge Base Workflows ARCHITECTURE Controlled Vocabs ETL Process Semantic Store Data Model Config APPLICATIONS Analyst Application Patient Dashboard Decision Support Interactive Reports Intuitive user interfaces supporting complex processes DATA SOURCES
  30. 30. Industry-leading security & HIPAA compliance TOOLS Semantic Query Rules Engine Knowledge Base Workflows ARCHITECTURE Controlled Vocabs ETL Process Semantic Store Data Model Config APPLICATIONS Analyst Application Patient Dashboard Decision Support Interactive Reports INTEGRATION REST API Interface Engine External Connectors Interfaces with and augments existing systems DATA SOURCES
  31. 31. Syapse AWS architecture
  32. 32. Syapse technology stack Interface Engine Semantic Layer Components Web Application Components Application Data Stores Application RDBMS RDF Biomedical Data Store Customized Components Gateway Web Server Client Browser Applications IPsec Tunnel to Customer Firewall REST API Application Data Access Layer SPARQL Layer Syapse Customer Deployment Custom Apps S3 File Storage Snapshot • AWS enables creation of enterprise-grade web application architectures • AWS does not constrain choice of database, application components, services, etc. HL7
  33. 33. Syapse technology stack • Single-page JavaScript applications • RESTful HTTPS API • Python / Django application server • MySQL database • “Bigdata” triple store • Open-source (SYSTAP) • AWS: Amazon EC2, Amazon S3, Amazon EBS • Linux: Ubuntu Server • nginx, Memcached, etc. Web Application Stack Semantic Data Layer Deployment • Custom server: Loosely coupled Python component • Integration messages (API) • Workflows (API, Syapse UIs) • Report configuration • Open-source interface engine (Mirth Connect) • Standard message implementations Configuration Facility HL7 Messaging
  34. 34. Syapse semantic data platform Complexity Flexibility Customizability Data Integration Precision Sufficient expressivity for medicine Ontologies can be changed dynamically Composable, extendable, reusable customer ontologies Hub for integrating disparate data types Expressive, exact queries
  35. 35. High availability Amazon EC2 enables loose-coupled N-tier architecture Tiers can scale independently and on demand Elastic Load Balancing distributes traffic across EC2 instances / zones
  36. 36. Geographically distributed 3 main campuses, 19 other locations, 5 affiliates UCLA Clinical Exome Sequencing UCSF Major MDx laboratories
  37. 37. Segregation of Production, Dev, QC Resource Tier Client Layer Service Tier Customer 1 Production Server Resource Tier Client Layer Service Tier Customer 1 Dev Server Resource Tier Client Layer Service Tier Customer 1 QC Server • Easy deployment in cloud • Templated instances • Pipes to mirror configuration • Necessary for integrating with hospital systems
  38. 38. Web Server HTTPS Secure integration with customer systems Interface Engine Gateway Syapse Customer Deployment Client VPN Syapse Application SyMessage Facility Interface Engine Customer Firewall Application Components HL7 HL7 EMR CPOE Path. IPsec Tunnel to Customer Firewall SSLHTTPS
  39. 39. Real-time computation Physicians Need: • Point-of-care access to large-scale data • Surfacing of relevant data, information, decisions • Fast, complex, under-the- hood queries • Semantic rules engine for real-time decision support AWS Enables: • Memory- / IO-intensive application • Indexing of data in real-time & loading in memory • Flexible node size, number • Moving indexes between nodes
  40. 40. AWS & HIPAA compliance
  41. 41. Backup, recovery, redundancy Nightly Snapshots Customer Instance Backup tapes not required Test recovery procedure through AWS admin tools Failover AZ #1 Region #1 AZ #2 AZ #3 Region #2 AZ #4
  42. 42. Security: standard • Auth integration: SSO (SAML, OAuth), LDAP • Best-in-class AWS security • Regular audits against SAS70, ISO 27001, and others • FedRAMP Compliant Cloud Service Provider (HHS) Web Application Security Data Center Security
  43. 43. Security: healthcare-grade • Single-tenant deployment • Dedicated hardware • AWS CloudFormation templating • Facilitated by AWS Management Console • All data encrypted at rest and in transit • Encryption of data in transit between each layer of Syapse application stack Customer Data Segregation Data Encryption • AWS Virtual Private Cloud • Gateway to customer firewall Firewall
  44. 44. HIPAA compliance EC2 S3 EBS Glacier Application deployment File storage Snapshots Long-term data retention BAA-Covered Services AWS Platform • Access logging • Data integrity monitoring • Flexibility to implement additional security provisions (e.g., encryption of data in transit between layers)
  45. 45. HIPAA compliance Data can be segregated within an AWS region AWS does not move customer content outside of the customer’s chosen AWS region
  46. 46. Syapse and AWS have signed a BAA Syapse has signed BAAs and passed InfoSec reviews with several of the largest healthcare providers in the United States.
  47. 47. AWS & healthcare Nate Slater AWS Solutions Architect
  48. 48. The AWS philosophy Eliminate “Undifferentiated Heavy Lifting” • Syapse and UCSF are able to focus on improving cancer treatment “Shared Responsibility” Security Model • Provides a foundation that allows Syapse to meet the most stringent security requirements in their technology stack Customer Obsession • AWS is excited to partner with Syapse and UCSF to help them deliver on the promise of precision medicine
  49. 49. Please give us your feedback on this presentation © 2014 Amazon.com, Inc. and its affiliates. All rights reserved. May not be copied, modified, or distributed in whole or in part without the express consent of Amazon.com, Inc. Join the conversation on Twitter with #reinvent HLS305 Join us outside for Q&A with Syapse CTO Tony Loeser, PhD and VP of Customer Solutions Andreas Heid. www.syapse.com gmi.ucsf.edu jon@syapse.com mccalebk@humgen.ucsf.edu Jonathan Hirsch Kristen McCaleb

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