EMERGING TECHNOLOGIES FOR HEALTHCARE AND LIFE SCIENCES MARK WEILER ETD SOLUTIONS ARCHITECT FOR HEALTHCARE 1
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AGENDA Healthcare IT Challenges Current ETD Solutions Scale-Out Data Lake Isilon ViPR/ECS Isilon in Healthcare Isilon for NGS Analytics Driving Healthcare Innovation
ETD HEALTHCARE SOLUTIONS MAKE HEALTHCARE DATA AN ASSET, NOT A BURDEN 4
HEALTHCARE DATA GROWING AT 48% PER YEAR THROUGH 2020 Industry Drivers: Clinical applications Compliance Requirements HITECH, EMR MU HIPAA, EU Data Directives FutureCare-enabling technologies for cloud, Big Data, mobile, & social Source: IDC /EMC Digital Universe Healthcare Industry Brief http://www.emc.com/analyst-report/digital-universe-healthcare-vertical-report-ar.pdf
2015 HEALTHCARE CHALLENGES EMR MEANINGFUL USE JUST THE BEGINNING M&A Traditional M&A BUDGET PRESSURES DEMAND FOR HIT REGULATION & COMPLIANCE REVENUE SHIFT Joint Operating Agreements Divestments Maintaining SLAs While Growing Reduce Budgets To Match Reduced Reimbursements Fixed Budgets Despite Variable Hospital Mix Data Growth BYOD MU Stage 2 Analytics HITECH HIPAA ICD-10 Joint Commission State Requirements Reimbursement Decreasing Aging Patient Population Outcome-based Payments Bundled Payments
THE HEALTHCARE DATA IT OPEX PROBLEM HEALTHCARE DATA STORED DOUBLES EVERY TWO YEARS Healthcare Data 2,500 2,000 Exabytes (EB) 1,500 1,000 But OPEX must be held constant IT simplicity and automation closes this gap 500 0 2013 2014 2015 2016 2017 2018 2019 2020 Enterprise Strategy Group 2011, Research report: North American Healthcare Provider Information includes hospitals & Ambulatory Health Care Provider Market Size & Forecast
TRADITIONAL WORKLOADS NEXT-GEN WORKLOADS NAS DAS File Shares Analytics SAN CLOUD HPC Mobile TAPE OBJECT Backup/Archive Cloud Apps 8
TRADITIONAL WORKLOADS NEXT-GEN WORKLOADS NAS DAS File Shares HPC SAN EMC Data Lake Foundation CLOUD Analytics Mobile TAPE OBJECT Backup/Archive Cloud Apps 9
Next-Gen Access Methods File Shares FILE Analytics FILE HPC/EDW Mobile Backup/Archive Cloud Apps 10
EMC DATA LAKE FOUNDATION ISILON ECS EMC Data Lake Foundation 11
ISILON ONEFS: SCALE-OUT ARCHITECTURE Single Volume/ File System Simplicity & Ease of Use High Performance Linear Scalability Unmatched Efficiency Cloud Tiering Ready Easy Growth Hadoop Enabled 12
ISILON ONEFS: BUILT FOR BIG DATA Massive Scalability automates activities unfit for humans Symmetric scale-out architecture Fully distributed, fine-grained services Unified IP storage (NFS, SMB, Object, HDFS)
GROWTH MADE EASY Isilon AutoBalance Cluster Expansion BALANCED EMPTY NEAR FULL BALANCED EMPTY NEAR FULL BALANCED EMPTY NEAR FULL BALANCED EMPTY NEAR FULL BALANCED Seconds Cluster Expansion Add A Node In 60 Seconds No Downtime Clients see the Added Capacity With a Simple Refresh Unlocks Extra Usable Space On Existing Nodes As Well AutoBalance Automated Data Balancing Across Nodes While The Cluster Is Online And In-production Eliminates Hot Spots EMPTY
FUTURE-PROOF ARCHITECTURE Isilon AutoBalance - Push-button Retire BALANCED BALANCED BALANCED Push-button Retire No Fork-lift Migrations No Downtime No Manual Pre-configuration No Side-By-Side Hardware Requirements Migrate Between Hardware Generations BALANCED BALANCED AutoBalance Automated Data Balancing Across Nodes While The Cluster Is Online And In-production Eliminates Hot Spots
ONEFS PERFORMANCE AND CAPACITY TIERS S-Series Nodes High IOPs X-Series Nodes High Throughput NL-Series Nodes Nearline Archive HD-Series Nodes Dense NL Archive CloudPool Tier Off-Prem Archive CloudPools Private Cloud Service Provider Public Cloud Key Features Stub to Cloud of choice Extending workflow of SmartPools to CloudPools Encryptions & Key management Compression for efficient transport Simple management from familiar interfaces Benefits Seamless placement and availability of data per policy Enable offsite DP & archive Transparent integration with offsite stores One Accessible namespace
THE ISILON ADVANTAGE FOR HADOOP SCALE-OUT STORAGE WITH NATIVE HADOOP INTEGRATION In-place analytics Native integration speeds time to insight Enterprise data protection Fast snapshots, backup, and data recovery Simple, efficient data replication for disaster recovery Lower costs Eliminates the need for dedicated Hadoop infrastructure Much more efficient than DAS-based approach Increase flexibility Simultaneous support for any Apache-compliant Hadoop distribution Ambari integration for management, monitoring, and provisioning
HADOOP ARCHITECTURE DAS VS ISILON Data Node + Compute Node Data Node + Compute Node Data Node + Compute Node Ethernet NameNode Data Node + Compute Node Data Node + Compute Node Data Node + Compute Node Compute Node Compute Node Compute Node Ethernet name node name node name node data node Compute Node Compute Node Compute Node
OPTIMIZE YOUR INFRASTRUCTURE EMC SOFTWARE-DEFINED STORAGE SOLUTIONS New and Basic Workloads Performance & Mission-Critical Workloads ViPR Controller Provisioning Self-Service Automation Reporting ECS Software Optional ViPR Data Services (File Arrays) Commodity ECS Appliance EMC Arrays 3 rd Party Arrays
ECS SOFTWARE ARCHITECTURE CLOUD-SCALE STORAGE SERVICES ON COMMODITY ECS Software OBJECT STORAGE HDFS STORAGE Geo-Replicated Data Protection Active-Active read/write support with strong consistency No single point of failure Performance and efficiency for small and large objects SITE 1 SITE 2 SITE 3
EMC LEADERSHIP IN HEALTHCARE EMC HP IBM Dell Not Reported NetApp HDS Philips Alliance Cisco Over 6,000 Healthcare Providers Worldwide Deploy EMC Infrastructure 3,000+ Customers in North America #14 Healthcare Informatics Top Vendors 100 (2014) HIMSS Member 16+ Years CHIME Member 11+ Years 0 20 40 60 Storage Market Penetration % Source: HIMSS Analytics, 12/2014
EMC ISILON TRUSTED IN HEALTHCARE Over 600 healthcare customers worldwide, including: 7 out of top 10 US News Best Hospitals Honor Roll 2014-2015 6 out of top 10 US News Best Children s Hospitals Honor Roll 2014-2015 9 out of top 15 Truven Top Health Systems use Isilon
EMC ISILON HEALTHCARE APPLICATION PARTNERS Delivering jointly engineered, certified, and pre-integrated solutions for healthcare providers worldwide
ISILON SOLUTIONS IN HEALTHCARE Partners Certifications Shared Workloads Mobility Analytics Future Diagnostics Clinical NGS & Digital Pathology
ENTERPRISE IMAGING SOLUTION DICOM VNA ETC.. RADIOLOGY Radiology PACS CARDIOLOGY ECHO LAB CARDIOLOGY CATH LAB EKG ENDOSCOPY PACS Vendor Neutral Viewing & Enterprise Worklist PET X-Ray Cardiovascular Information System (CVIS) Patient-centric Viewing MRI CT Echo Systems Cath X-ray EKG HIS/EMR Outside Images Ultrasound Intra-Op Echo Systems Hemodynamic Monitoring Stress VIRTUAL INFORMATION REPOSITORY VENDOR NEUTRAL ARCHIVE Enterprise Image Viewing
What Is Next Generation Sequencing (NGS)? DNA determines the genetic traits of an organism DNA consists of 4 nucleotide bases A, T, G, C, paired in a double helix DNA sequencing identifies the order of nucleotide bases to create a genetic map A Genome is the entire DNA sequence for an organism Next Generation Sequencing (NGS) Automates the sequencing genomes Leveraged as research and clinical tool Makes accurate genetic information available rapidly ~150 GB for each WGS
NGS RESEARCH DATA WORKFLOW RAW Image Analysis Image Processing & Base Calling Whole Genome Mapping Alignment To Reference Genome Variant Calling Annotation Secondary Analysis Genetic Variation Detection (SNP, SV, CNV) Link Variants To Biological Information Downstream Analysis And Interpretation - Iterative (E.G., Case Vs. Control) SHORT READS A TAATGTCTAATGT CTAATGT A TAATGT CTAATGT TAATGT CTAATGTCTAATGT GATTACAGATTACA G ATTACAGATTACAGATTAC REFERENCE GENOME
NGS Data in a Clinical Setting The clinical objective is different not looking for how a variant manifests itself or changes within a population but what it can say about the prognosis and treatment options for an individual Major clinical use areas for NGS: Congenital conditions Early detection prenatal tests are now common Management plans Cancer Identification Observation Translational medicine developing
NGS DATA INTERPRETATION Medical Imaging: Lab Results: TESTS RESULT FLAG UNITS REFERENCE INTERVAL WBC 7.2 x10e3/ul 4.0-10.5 RBC 5.34 * x10e3/ul 3.8-5.1 Hemoglobin (Hg) 10.8 * g/dl 11.5-15.0 Hematocrit (HCT) 38.9 % 34.0-44.0 MCV 90 fl 80-98 NGS Data:
Patient Reports Companies and research institutions are now beginning to offer Patient Reports which look at data derived from sequencing to help determine treatment paths NextBio (owned by Illumina) Ingenuity (owned by Qiagen) Because it is drawn from a constantly evolving knowledge base, these reports will always need to be obtained from outside the clinic Ideally, some information would be drawn from the EMR Again, ideally the EMR would link to this report Key takeaways There is a long term archiving need There is a need to make the data resident on the Isilon available to external SaaS providers Appliances are key Isilon will deploy with VCE and others in appliance model
NGS Data Architecture Clinical
Advantages of EMC Isilon in Life Sciences On-site data acquisition Off-site data acquisition Instrument/workstations can feed data directly into an Isilon cluster using SMB or NFS. Isilon supports a Global Name Space (GNS) which minimizes the need to move data around after it is copied to the cluster Data produced by other institutions or service venders can be collected onto Isilon clusters using the fully integrated Aspera service or other standard transfer methods Repository Isilon uses a Global Name Space (GNS) and expands easily to accommodate and manage large (multi-petabyte) data repositories SmartPools provides a rule based system for data management HPC & Hadoop Isilon supports quick access to data from multiple nodes via NFS/SMB and SmartConnect over multiple 10GbE connections The Hadoop filesystem (HDFS) is natively supported
Advantages of EMC Isilon in Life Sciences Workspaces User home directories can be staged within the same cluster giving users a space to arrange data for study and manage study metadata Collaboration Archival Users within the same institution can leverage SyncIQ to replicate data between different clusters. Isilon clusters can tie into institution-wide directory services (LDAP, AD, etc.) to provide a common user/permission framework SnapshotIQ and SmartPools can both be used to manage the movement of data into an archive. Isilon supports both near line and offline/tape-out archives. Shared Storage Institutions can add nodes to a cluster to store sequencing data alongside clinical data Co-location of research and clinical data allows for in-place analytics via Hadoop Ability to partition cluster resources ensures adequate resources for both workloads
ANALYTICS DRIVING INNOVATION IT S ALL ABOUT THE DATA 34
HEALTHCARE IS CHANGING http://www.forbes.com/sites/brucejapsen/2015/01/26/medicares-bolt-from-fee-for-service-means-50-percent-value-based-pay-by-2018/
DISTRIBUTION OF PERSONAL HEALTH SPENDING NIHCM Foundation Analysis of data from the 2009 Medical Expenditure Panel Survey
What are they asking for? Can analytics provide the answer? CEO How do we analyze our patient populations to effectively share risk with physician groups and payers? CNO How do we predict patient volumes to staff accordingly with peak times? CMO How can I hold physicians accountable to quality of care measures and to reduce costs? Researcher How can we look across all of the many clinical images we have stored for patterns of a diagnosis? The Value Proposition: Analytics can Convert Data into a Strategic Asset Single source of truth for Clinical & Financial Look across time & populations to Improve Quality of Care
PATIENT DATA INTELLIGENCE SOLUTIONS ENABLE THE PREDICTIVE PROVIDER ARCHITECTURE Deploy infrastructure for delivery flexibility, efficiency and compliance 3 1 INITIATIVES 2 Identify use cases to optimize value propositions INSIGHTS Accelerate application development to become a predictive enterprise
EMC CONSULTING SERVICES Step 1: Big Data Vision Workshop Intended to demonstrate the value and feasibility of Big Data analytics; this is achieved by using client data to generate real business insights for a use case that is important to the client. Result is a selection of prioritized use cases that can be further explored through an Analytic POV. Engagement helps to build organizational alignment for potential of Big Data through stakeholder interviews, group dynamics and envisioning exercises to identify business opportunities. Step 2: Analytics Proof of Value Demonstrates the business value and technical feasibility of a Big Data analytic use case identified in Big Data Envision workshop. Results in business insights based on available big data, a corresponding business case and a deployment roadmap. Step 3 Analytics Implementation Move model developed during an Analytics POV into production. POV analytics model is migrated to the production data platform, source system data integration / transformation are established, data refresh cycle is established, and insights are provided to visualization layer. Client team is enabled to maintain the model and processes.
ANALYTICS ENGAGEMENT EXAMPLE Customer Profile: 8 hospital health system Multi-million dollar Epic deployment Frustrated: constituents could not access right data at the right time Challenges included: Epic ETL challenges Customer Satisfaction low Lack of access to data Lack of Data Catalogs Philosophical differences around centralized reporting Lack of Enterprise Data Governance IT Complexity Multiple BI cloud solutions Multiple local data warehouses Data Quality Issues Regulatory Risk
EPIC CURRENT STATE Nightly ETLs run long impacting Reports I can t get what I need so I am going to buy another SaaS solution Or build my own solution I can t get the data I need I have no input into what gets built Operational Challenges Typical Information Consumer Nightly ETL runs long or has issues, impacting downstream report accuracy Seems like system office gets what they need but not the care sites Cogito provides numerous reports but on only what is in EPIC Report backlog has stabilized, but some (many?) have just stopped asking
Current Analytics Landscape Crimson Market Adv Crow Horwath Surgery Compass Point SaaS solutions Replicated data/subject areas No central architectural/governance oversight Multiple versions of the truth Lack of a single view Lack of Governance Multiple Feeds/ETL ~400 Brittle Costly to Maintain ETL performance impacts report accuracy Decentralized approach will not scale
Hi Business Alignment Lo Analytic Use Case Prioritization J I H G A C B D-F Implementation Feasibility Hi Business Initiatives (A) Unplanned Readmissions Analysis (B) HAI / HAC Analysis (C) Service Variance Analysis (D) Staffing, Cost, Outcomes Effectiveness (E) Staff Retention (F) Procedures Cost Analysis (G) Supply Chain (H) Pharmacy Analysis (I) Volume Forecasting (J) Population Health
Potential Use Case #1 Population Health (Accountable Care Reporting) Description Enable Population Health Self Service Reporting. Patient population risk stratification and development of Business Object Reports Business Value/ROI Short Term Enhanced ACO Self Service Reporting o Ad Hoc query capability Long term allow retirement of existing population health data platform ROI Set stage to scale back current point solution implementation Self contained minimal dependencies Data Requirements Use existing ACO/Health Plan Feeds o Cigna Extracts Kaiser Extracts ~14 Extracts Deliverables Population Health requirements document Land existing extracts for Epic, Cigna & Kaiser claims in Data Lake Import data into Hawq Create Metadata layer on top of Hawq in form of BOE Universe Enable ad-hoc reporting in BOE Web Intelligence Produce standard Population Health Reports Assumptions Leverage Existing ACO feeds from Epic Leverage Cigna & Kaiser claims data All source feeds are documents with column-level descriptions Dependencies Need BAA with health plans Need Kaiser extract analysis Duration 10 weeks PM/Business Objects Lead Business Analyst (Part Time) Data Architect (Part Time) Hadoop Developer Information Quality SME / Data Governance (Part-Time) Health Science SME (Part Time)
Potential Use Case #2 HAI Monitoring and Analysis Description Support the analysis of HAI incidents with a goal of tracking ongoing metrics, early identification of negative trends and outliers, risk factor monitoring, and driving prompt initiation of corrective and preventive actions. Business Value/ROI Board Approved Quality Goal Support HAI reporting across care centers and self-service analysis Integration of external data sets to support peer/competitor benchmarking and quality improvement goals. Track against CMS rankings Foundation for predictive modelling, supporting HAI-related risk assessment, early detection, and targeted intervention programs Leverages data lake to archive all Ensemble HL7 data for future analysis - both for HAI and other use cases Data Requirements Use existing Vigilanz HL7 extracts CMS Hospital comparison data Assumptions Requirements workshops are held to explore the business requirements for HAI analysis Existing Vigilanz feeds can be used as a primary source of data All source data is comprehensively documented, with field-level descriptions available for input into analysis and design Deliverables Requirements workshop HAI reporting requirements document Land all Ensemble HL7 message as-is in Data Lake Import subset of Vigilanz data into Hawq as per requirements developed during workshop Create Metadata layer on top of Hawq in form of BOE Universe Enable ad-hoc reporting in BOE Web Intelligence Dependencies Need to perform Vigilanz Extract analysis Clinical SME and Champion Duration 14 weeks PM/Business Objects Lead Business Analyst (Part Time) Data Architect (Part Time) Hadoop Developer Information Quality SME / Data Governance (Part Time) Health Science SME (Part Time)
EMC HEALTHCARE DATA LAKE WHAT DOES IT LOOK LIKE? 46
Hospital Radiology PACS Clinics, Claims, Public Health Cardiology PACS Mobile Analytics Clinical Documents File Shares Pathology Acquired Hospitals Endoscopy, Surgical, Sleep Video Surveillance Dermatology, Ophthalmology Social Networks Clinical NGS