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Applying Big Data Superpowers to Healthcare

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When I see a data analyst quickly transform and drill through a new pile of data to uncover a keen insight, I feel like I'm watching a new movie from the Marvel universe. If you haven't explored and learned to apply cloud, big data, streaming data, and rapid analytics techniques, then you haven't uncovered your superpowers, yet. Here's how you can get started.

Veröffentlicht in: Daten & Analysen
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Applying Big Data Superpowers to Healthcare

  1. 1. OCTOBER 2017
  2. 2. • Introduction • Why do we need super powers? • What are big data super powers? • Cloud infrastructure • Hadoop and distributed databases • Stream data processing • Rapid analytics • How do you get your super powers?
  3. 3. PAUL BOAL Mild-mannered VP Delivery
  4. 4. • Complex coordination of services • Care management, behavioral health, provider networks, community services, telemedicine, transportation, health neighborhood • More varieties and larger volume of data • Clinical notes, lab results, social media • Wearable fitness and health trackers • Faster turn-around time on business questions • Missed opportunities, interactive discovery and refinement of needs • Population Health and Care Management • Complex groups of people with complex needs • Value-based payment models • Complex revenue models including cost management
  5. 5. 1. Cloud infrastructure 2. Hadoop and distributed databases 3. Stream data processing 4. Rapid analytics Also (for another time) • Machine learning • Streaming analytics • Interactive data visualization
  6. 6. Definition: The provisioning and use of compute, data management, and analysis resources through an external technology vendor who hosts those services. For example: AWS, MS Azure, Google Cloud • Provision server, storage, and network infrastructure more quickly. • Reduce development time by using higher-level services like database, messaging, software solutions, and machine learning as a service. • Align infrastructure investment with business solutions and value. • Learning curve and adoption • Security management • Development of skills and new processes
  7. 7. • New infrastructure on same day it’s requested • Build, test, and deploy features in days • Convert from fixed to variable cost • 12 million messages per week • 20% annual growth We’re a much more responsive and agile organization using AWS, and that helps us grow our organization.
  8. 8. 1. Identify primary business goals 2. Evaluate vendors and partners 3. Align roadmap and milestones with strategic priorities 4. Track and report value realization 1. Identify a cross-team cloud-migration team 2. Train in alignment with vendor and technology direction 3. Establish new cloud-first IT processes 4. Test and refine processes through roadmap execution
  9. 9. Definition: Highly scalable, distributed data storage, processing, and query solutions including relational databases, Hadoop, and NoSQL databases. For example: Hortonworks, MS HDInsight, AWS Redshift, Cloudera, MS SQL PDW, Teradata, Netezza, Vertica, Google Bigtable, IBM Big Insights • Ability to cost-effectively scale to billions of rows and many terabytes+ of data • Specialized data structure and query/search tools for text, images, relationships, and documents • Storage and transformation on common platform • Apply best tools for each data processing need • Many, diverse vendors crowding the space • Learning curve and adoption • New requirements in data architecture, modeling, and metadata • Duplication of data for different needs
  10. 10. New patient data in the US would create a stack of paper 1,000 miles high every year. 250 miles International Space Station 340 miles Hubble Telescope 35,000 ft (6.6 miles) Commercial Airplane 1,000 miles
  11. 11. • 15 million members • IL, MT, NM, OK, TX • Single view of membership • ACA reporting • Understand cross-channel member interactions “improve customer service by understanding what our customers are experiencing and enabling us to have a real-time view of what’s going on in our business”
  12. 12. SQLInterface Key-Value Store (NoSQL) Column- Optimized Relational Files Text- Indexed Search Engine Simple File Storage ODBC JDBC WebServices
  13. 13. 1. Identify primary business goals and quick wins 2. Leverage road-shows to demonstrate possibilities 3. Establish executive support and departmental sponsorship 4. Track and report value realization 5. Scale into enterprise strategy 1. Identify energetic learners and partner to get started 2. Use known data to create a proof of concept and innovative business insights 3. Create flashy demonstrations, videos, and roadshows to create excitement 4. Train core team and expand big data impact with best practices
  14. 14. • Support intra-business cycle management decisions • Enable active front-line decision making • Personal rather than aggregate decisions • New data processing paradigm • Leads to increased volume of data • Managing out-of-order and incomplete transactions • Learning curve and adoption Definition: The movement and processing of data for decision making and management on a timeframe that enables the business to rapidly adapt to customer interactions and changing needs. For example: Internet of Things, Lambda Architecture, AWS Kinesis, Azure Event Hubs, Spark, Storm, Flume, and Kafka.
  15. 15. • Ability to classify and adjust patient risk through stay • Reduced heart-failure readmissions from 26% to 21% • Redirected resources to highest risk patients “It also allows companies to almost instantaneously detect fraud and intrusions, rather than waiting to collect all the data and processing it after it is too late.”
  16. 16. 1. Identify missed business opportunities 2. Establish executive support and departmental sponsorship 3. Track and report value realization 1. Identify team with mix of batch and application expertise 2. Consider a new real-time source or conversion of existing batch process 3. Evaluate technology options between ETL vendor, Open Source, and Cloud 4. Implement POC to lay ground work for enterprise best practices 5. Don’t expect one technology for everything
  17. 17. • Quickly prototype and test hypotheses • Intuitiveness of visual exploration • Reduced investment from developers / IT teams • Encourages intimate understanding of operational data and processes • Potential duplication of effort across teams or initiatives • Most effective with the adoption of formal processes by analysts Definition: Processes and tools that provide ways for analysts to quickly moving from raw data to analytical datasets to insights without the establishment of a large scale project. For example: Agile methods, Data Lake, self-service desktop analytics, Tableau, Qlikview, PowerBI 100101010100 100101011001 010100100010 100010101001 101001010100
  18. 18. • Wellmark • Text analytics, Call center and web logs, Out of state claims • North Carolina BCBS • Extract Factory • Cleveland Clinic • Agile processes, Visual exploration 1. Agile analysis lets people analyze data the way they actually think. 2. Quick iterations. It’s like agile development in this way. 3. Data granular enough to answer unanticipated questions. 4. New importance of personal skill, knowledge of the subject, and skill managing data. 5. It thrives in organizations that encourage it.
  19. 19. 135 million prescription claims 500 GB of data in text files Insights within days No existing infrastructure  Setup time: 2 days • Create AWS Redshift database • Upload files to S3 • Load data to Redshift • Profile and check data quality • Cleanse and transform with SQL • Building reports in Tableau and R  1 month collaboratively running analysis and gaining insights  Total infrastructure cost: $300
  20. 20. 1. Evaluate organization-wide analytics culture and needs 2. Identify opportunities and gain executive buy-in for future 3. Create roadmap and implement change program 1. Understand how analysts and end-users actually do their work 2. Evaluate technologies, processes, and education to support analysts and users 3. Identify processes that are impeding analysts and users without adding value 4. Execute plan in alignment with strategic direction
  21. 21. Super Power Getting Started Guides Cloud infrastructure AWS, MS Azure Hadoop and distributed databases Hortonworks Data Platform Sandbox Stream data processing Hortonworks Data Flow Rapid analytics Tableau • Do online tutorials. • Take a problem you already know how to solve well and resolve is using these technologies. • Ask Amitech team members for help, advice, and tutorials.

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