Revenue opportunities in the management of healthcare data deluge

Healthcare data is hard to deal with and getting even harder and more expensive. In this presentation, Shahid Shah covers why:

* Healthcare data is going from hard to nearly impossible to manage.
* Applications come and go, data lives forever.
* Data integration is notoriously difficult, even in the best of circumstances, and requires sophisticated tools and attention to detail.

And, then talks about how new techniques are needed to store and manage healthcare data.

Transcript

1. Revenue opportunities in the
   management of healthcare data deluge
   Healthcare data is hard to deal with and
   getting even harder and more expensive
   By Shahid N. Shah, CEO
   

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2. NETSPECTIVE
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   Who is Shahid?
   • 20+ years of software engineering and multi-
   discipline complex IT implementations (Gov.,
   defense, health, finance, insurance)
   • 12+ years of healthcare IT and medical
   devices experience (blog at
   http://healthcareguy.com)
   • 15+ years of technology management
   experience (government, non-profit,
   commercial)
   • 10+ years as architect, engineer, and
   implementation manager on various EMR
   and EHR initiatives (commercial and non-
   profit)
   Author of Chapter 13, “You’re
   the CIO of your Own Office”
   

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3. NETSPECTIVE
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   What’s this talk about?
   Background
   • Healthcare data is going from hard
   to nearly impossible to manage.
   • Applications come and go, data lives
   forever.
   • Data integration is notoriously
   difficult, even in the best of
   circumstances, and requires
   sophisticated tools and attention to
   detail.
   Key takeaways
   • New techniques are needed to store
   and manage healthcare data.
   • He who has, integrates, and uses
   data wins in the end.
   

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4. Without data, users can’t do their jobs
   Users’ expectations about the availability of data are increasing
   

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5. NETSPECTIVE
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   Data is in the news for good reason
   Data matters more than ever Providers have lots of it
   

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6. NETSPECTIVE
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   What’s being offered to users What users really want
   What users want vs. what they’re offered
   Data visualization requires integration and aggregation
   

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7. NETSPECTIVE
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   The business needs
   • Quality and performance
   metrics
   • Patient stratification
   • Care coordination
   • Population management
   • Surveys and other direct-
   from-patient data collection
   • Evidence-based surveillance
   The technology strategy
   • Aggregated patient registries
   • Data warehouse / repository
   • Rules engines
   • Expert systems
   • Reporting tools
   • Dashboarding engines
   • Remote monitoring
   • Social engagement portal for
   patient/family
   Data is key for move from FFS to ACOs
   Integrated and aggregated data is the only way to get to ACOs and PCMHs
   

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8. NETSPECTIVE
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   Data is getting more sophisticated
   Proteomics
   Emerging
   •Must be continuously collected
   •Difficult today, easier tomorrow
   •Super-personalized
   •Prospective
   •Predictive
   Genomics
   Since 2000s,
   started at $100k
   per patient, <$1k
   soon
   •Can be collected infrequently
   •Personalized
   •Prospective
   •Potentially predictive
   •Digital
   •Family history is easy
   Phenotypics
   Since 1980s,
   pennies per
   patient
   •Must be continuously collected
   •Mostly Retrospective
   •Useful for population health
   •Part digital, mostly analog
   •Family History is hard
   Economics
   Since 1970,
   pennies per
   patient
   •Business focused data
   •Retrospective
   •Built on fee for service models
   •Inward looking and not focused
   on clinical benefits
   Biosensors
   Social Interactions
   

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9. NETSPECTIVE
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   Data needs to be highly available
   • Simplify & Unify: Create innovative techniques to capture clinical
   data as a byproduct of care instead of specific documentation
   entered by practitioners.
   • Embrace, Adopt, Extend: Take data being created by vendors
   systems (medical devices, labs, etc.), add value by repurposing
   and aggregating it.
   Operational
   Systems
   Analytical
   Systems
   Feedback Loop:
   Analytics must create new insight (such as patient value and safety prediction)
   and feed it back to the operational systems (the applications)
   Data
   Operational
   Systems
   

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10. NETSPECTIVE
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    Data accessibility issues
    Lack of Financial Data
    Interchange
    • Extended days sales outstanding
    • Difficulty in following up with rejected
    claims
    • Reduced collections
    Lack of Clinical Data
    Interchange
    • Inability to use data for patient care
    improvements
    • Difficulty using data for marketing
    • Lag in regulatory or MU reporting
    Lack of Document
    Interchange
    • Requires fax or other document
    sharing
    • Adds costs, reduces operational
    efficiency
    

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11. Health data management is tough
    Storing data long-term and keeping it accessible is not easy
    

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12. NETSPECTIVE
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    Debunking data myths
    Myth
    • I already know how to acquire the data
    I need
    • Extracting, transforming, and loading
    (ETLing) data is a “solved” problem
    • I only have a few systems to integrate
    • I know all my data formats
    • I know where all my data is and most
    of it is valid
    Truth
    • Data acquisition protocols are wide
    and varied
    • ETL grows more and more difficult as
    the number of systems to integrate
    increases
    • There are actually hundreds of systems
    • There are dozens of formats you’re
    not aware of
    • Lots of data is missing and data quality
    is poor
    • Tons of undocumented databases and
    sources
    

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13. NETSPECTIVE
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    Data is hidden everywhere
    Excel files, Word
    documents, and
    Access database
    Clinical trials data
    (failed or successful)
    Secure Social Patient
    Relationship
    Management (PRM)
    Patient
    Communications,
    SMS, IM, E-mail,
    Voice, and Telehealth
    Patient Education,
    Calculators, Widgets,
    Content
    Management
    Blue Button, HL7,
    X.12, HIEs, EHR, and
    HealthVault
    Integration
    E-commerce, Ads,
    Subscriptions, and
    Activity-based Billing
    Accountable Care,
    Patient Care
    Continuity and
    Coordination
    Patient Family and
    Community
    Engagement
    Patient Consent,
    Permissions, and
    Disclosure
    Management
    

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14. NETSPECTIVE
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    System have different storage needs
    Clinical systems
    Consumer and
    patient health
    systems
    Core transaction
    systems
    Decision
    support systems
    (DSS and CPOE)
    Electronic
    medical record
    (EMR)
    Managed care
    systems
    Medical
    management
    systems
    Materials
    management
    systems
    Clinical data
    repository
    Patient
    relationship
    management
    Imaging
    Integrated
    medical devices
    Clinical trials
    systems
    Telemedicine
    systems
    Workflow
    technologies
    Work force
    enabling
    technologies
    

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15. NETSPECTIVE
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    Unstructured patient data sources
    Patient Health
    Professional
    Labs &
    Diagnostics
    Medical Devices Biomarkers /
    Genetics
    Source Self reported by
    patient
    Observations by
    HCP
    Computed from
    specimens
    Computed real-
    time from patient
    Computed from
    specimens
    Errors High Medium Low
    Time Slow Slow Medium
    Reliability Low Medium High
    Data size Megabytes Megabytes Megabytes
    Data type PDFs, images PDFs, images PDFs, images
    Availability Common Common Common Uncommon Uncommon
    

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16. NETSPECTIVE
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    Structured patient data sources
    Patient Health
    Professional
    Labs &
    Diagnostics
    Medical Devices Biomarkers /
    Genetics
    Source Self reported by
    patient
    Observations by
    HCP
    Specimens Real-time from
    patient
    Specimens
    Errors High Medium Low Low Low
    Time Slow Slow Medium Fast Slow
    Reliability Low Medium High High High
    Discrete size Kilobytes Kilobytes Kilobytes Megabytes Gigabytes
    Streaming size Gigabytes Gigabytes
    Availability Uncommon Common Somewhat
    Common
    Uncommon Uncommon
    

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17. NETSPECTIVE
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    Application focus is biggest mistake
    Application-focused IT instead of Data-focused IT is causing business problems.
    Healthcare Provider Systems
    Clinical
    Apps
    Patient
    Apps
    Billing
    Apps
    Lab
    Apps
    Other
    Apps
    Partner Systems
    Silos of information exist across
    groups (duplication, little sharing)
    Poor data integration across
    application bases
    

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18. NETSPECTIVE
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    NCI
    App
    NEI
    App NHLBI
    App
    Healthcare Provider Systems
    Clinical
    Apps
    Patient
    Apps
    Billing
    Apps Lab
    Apps Other
    Apps
    Master Data Management, Entity Resolution, and Data Integration
    Partner Systems
    Improved integration by services
    that can communicate between applications
    The Strategy: Modernize Integration
    Need to get existing applications to share data through modern integration
    techniques
    

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19. The Do’s and Don’ts of Data Storage
    

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20. NETSPECTIVE
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    Don’t try to do it all in one step
    Transport
    Transform
    Match & Link
    Analyze & Predict
    Utilize and Enhance
    Getting the data from one application to another is the first problem to solve. SOA, ETL, hub-
    and-spoke and other mechanisms can be a good start.
    Once an application can send and receive information , it needs to transform it into a
    manner it can understand. This means structural, format, and units may need to be
    translated.
    Depending on the complexity of information identifiers and other important
    data may need to be matched and linked across applications. This is where
    we manage data quality.
    As soon as data has been matched and linked we can start using it
    for analytics and prediction.
    Once we have predictive and analytics available we can use
    the information back within our applications or just for
    dashboards/reports.
    

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21. NETSPECTIVE
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    Ensure transport flexibility
    Embeddable Integration Backbone
    Service
    DB
    Management
    Services
    Security
    Firewall
    HTTPS, REST, SOAP
    SFTP, SCP, MLLP
    SMTP, XMPP, TCP
    TCP, HTTPS, SOAP, REST
    HTTP, SFTP, SCP, MLLP
    SMTP, XMPP
    Vendors & Partners
    Apps MQs Services
    Apps Services
    Hospital or Cloud
    Development
    App
    DB
    Central
    DB
    Registry
    Remote
    Center
    VPN
    

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22. NETSPECTIVE
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    Don’t limit the format types
    HL7 HL7 RIM CDISC Excel, CSV
    Access,
    SQL
    SEND CCD CCR
    RDF, RDFa ATOM Pub X.12
    

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23. NETSPECTIVE
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    Choose tools that can do it all
    Connect
    Collect &
    Cleanse
    Exchange
    Standardize
    (Map & Link)
    Federate Store Analyze Report
    Secure Audit
    Guarantee
    HIPAA
    Compliance
    

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24. NETSPECTIVE
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    Don’t start without a plan
    Gather Data
    Interchange
    Requirements
    Select and
    Deploy the
    right tools
    Create Data
    Interchange
    Connection
    Points
    Ability to connect
    multiple systems
    without each
    system knowing
    about each other
    Outcome
    Allows you to reduce costs, increase
    revenues, & improve care by having
    faster and more comprehensive
    access to data.
    

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25. NETSPECTIVE
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    PLAN
    Don’t move without success criteria
    Senior executives finalize the definition of
    the success criteria and list of target
    financial and clinical systems that need to
    be integrated.
    Business analysts catalog the data
    origination sources and destination sinks.
    Integration engineers analyze, gather, and
    document the technical connection points.
    Goals
    Requirements
    Create an
    executable data
    integration plan
    Result
    25
    

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26. NETSPECTIVE
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    Tool Ready to Use
    Choosing the right tool is the key
    Senior architect uses the data
    integration plan to select a vendor
    and create a deployment strategy.
    Senior integration engineers
    install tools and experiment with
    internal systems.
    Senior integration engineers
    install tools and experiment with
    external systems.
    Goals
    Experiment
    Begin using tool
    for financial data
    and when
    successful move
    to clinical data
    Result
    

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27. NETSPECTIVE
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    Data Interoperability
    Decouple your systems
    Senior architect uses data sources
    catalog to decide on adapters, protocols,
    and formats for data exchange
    Programmers write custom adapters for
    non-standard protocols and formats
    Programmers start wiring up near-,
    medium-, and long-term connection
    points (following goals set by executives)
    Formats
    Code
    A/R should
    improve, care
    coordination
    should improve,
    etc.
    Result
    

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28. NETSPECTIVE
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    Don’t limit your exchange models
    Federated model with
    shared repositories
    Federated model with
    peer-to-peer network +
    real-time, request/delivery
    of clinical data
    Federated model with
    peer-to-peer network +
    clinical data pushed from
    sending organization
    Federated model with
    peer-to-peer network–no
    real-time clinical data
    sharing
    Non-federated peer-to-
    peer network (co-op
    model)
    Centralized clinical
    database or data
    warehouse
    Health data claims bank
    Clinical data exchange
    cooperative
    

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29. NETSPECTIVE
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    Build vs. Buy?
    Build (or use
    Open Source)
    Buy
    (commercial)
    License Costs
    Engineering Costs
    Capabilities
    Start Immediately
    

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30. NETSPECTIVE
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    Build vs. Buy Elaborated
    • Reasonable purchase cost, low maintenance cost
    • Low engineering resources cost (less expertise
    required)
    • Easy to acquire and deploy
    • High Performance, Reliable, Stable
    • Excellent documentation and support
    Buy
    (Commercial)
    • No purchase cost, no license maintenance cost
    • Low engineering resources cost (less expertise
    required)
    • Effort required to get high performance and stability
    • Adequate documentation and paid support
    Build (or use
    Open Source)
    Best choice if
    you’re not
    creating your own
    interface engine
    Recommended if
    you want to build
    and sell your own
    interface engine
    

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31. NETSPECTIVE
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    Primary challenges
    • Tooling strategy must be comprehensive. What hardware and
    software tools are available to non-technical personnel to encourage
    sharing?
    • Formats matter. Are you using entity resolution, master data and
    metadata schemas, documenting your data formats, and access
    protocols?
    • Incentivize data sharing. What are the rewards for sharing or penalties
    for not sharing healthcare data?
    • Distribute costs. How are you going to allow data users to contribute
    to the storage, archiving, analysis, and management costs?
    • Determine utilization. What metrics will you use determine what’s
    working and what’s not?
    

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32. Thank You
    Visit
    http://www.netspective.com
    http://www.healthcareguy.com
    E-mail shahid.shah@netspective.com
    Follow @ShahidNShah
    Call 202-713-5409
    

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