Revenue opportunities in the management of healthcare data deluge


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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.

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Revenue opportunities in the management of healthcare data deluge

  1. 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
  2. 2. NETSPECTIVE Who is Shahid? • • • • 20+ years of software engineering and multidiscipline complex IT implementations (Gov., defense, health, finance, insurance) 12+ years of healthcare IT and medical devices experience (blog at 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 nonprofit) Author of Chapter 13, “You’re the CIO of your Own Office” 2
  3. 3. NETSPECTIVE 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. 3
  4. 4. Users’ expectations about the availability of data are increasing Without data, users can’t do their jobs
  5. 5. NETSPECTIVE Data is in the news for good reason Data matters more than ever Providers have lots of it 5
  6. 6. NETSPECTIVE What users want vs. what they’re offered Data visualization requires integration and aggregation What’s being offered to users What users really want 6
  7. 7. NETSPECTIVE Data is key for move from FFS to ACOs Integrated and aggregated data is the only way to get to ACOs and PCMHs The business needs The technology strategy • Quality and performance metrics • Patient stratification • Care coordination • Population management • Surveys and other directfrom-patient data collection • Evidence-based surveillance • • • • • • • • Aggregated patient registries Data warehouse / repository Rules engines Expert systems Reporting tools Dashboarding engines Remote monitoring Social engagement portal for patient/family 7
  8. 8. NETSPECTIVE Data is getting more sophisticated Social Interactions Biosensors Economics Phenotypics Since 1970, pennies per patient Since 1980s, pennies per patient • Business focused data • Retrospective • Built on fee for service models • Inward looking and not focused on clinical benefits • Must be continuously collected • Mostly Retrospective • Useful for population health • Part digital, mostly analog • Family History is hard Genomics Since 2000s, started at $100k per patient, <$1k soon • Can be collected infrequently • Personalized • Prospective • Potentially predictive • Digital • Family history is easy Proteomics Emerging • Must be continuously collected • Difficult today, easier tomorrow • Super-personalized • Prospective • Predictive 8
  9. 9. NETSPECTIVE 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 Operational Systems Systems Data 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) 9
  10. 10. NETSPECTIVE Data accessibility issues Lack of Financial Data Interchange Lack of Clinical Data Interchange • Extended days sales outstanding • Difficulty in following up with rejected claims • Reduced collections • 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 10
  11. 11. Storing data long-term and keeping it accessible is not easy Health data management is tough
  12. 12. NETSPECTIVE 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 12
  13. 13. NETSPECTIVE Data is hidden everywhere Patient Education, Calculators, Widgets, Content Management Clinical trials data (failed or successful) Secure Social Patient Relationship Management (PRM) Patient Communications, SMS, IM, E-mail, Voice, and Telehealth 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 Excel files, Word documents, and Access database Patient Consent, Permissions, and Disclosure Management 13
  14. 14. NETSPECTIVE 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 14
  15. 15. NETSPECTIVE Unstructured patient data sources Patient Source Self reported by patient Health Professional Observations by HCP Labs & Diagnostics Computed from specimens Errors High Medium Slow Slow Low Medium Megabytes Megabytes Megabytes Data type PDFs, images PDFs, images PDFs, images Availability Common Common Common Computed from specimens High Data size Computed realtime from patient Medium Reliability Biomarkers / Genetics Low Time Medical Devices Uncommon Uncommon 15
  16. 16. NETSPECTIVE Structured patient data sources Patient Source Self reported by patient Health Professional Observations by HCP Labs & Diagnostics Specimens Medical Devices Real-time from patient Biomarkers / Genetics Specimens Errors High Medium Low Low Low Time Slow Slow Medium Fast Slow Reliability Low Medium High High High Kilobytes Kilobytes Kilobytes Megabytes Gigabytes Gigabytes Gigabytes Uncommon Uncommon Discrete size Streaming size Availability Uncommon Common Somewhat Common 16
  17. 17. NETSPECTIVE Application focus is biggest mistake Application-focused IT instead of Data-focused IT is causing business problems. Silos of information exist across groups (duplication, little sharing) Clinical Apps Billing Apps Lab Apps Other Apps Healthcare Provider Systems Patient Apps Partner Systems Poor data integration across application bases 17
  18. 18. NETSPECTIVE The Strategy: Modernize Integration Need to get existing applications to share data through modern integration techniques Clinical Apps NCI App Billing Apps Lab Other Apps Apps NEI App Healthcare Provider Systems Patient Apps NHLBI App Partner Systems Master Data Management, Entity Resolution, and Data Integration Improved integration by services that can communicate between applications 18
  19. 19. The Do’s and Don’ts of Data Storage
  20. 20. NETSPECTIVE Don’t try to do it all in one step Utilize and Enhance Analyze & Predict Match & Link Transform Transport Once we have predictive and analytics available we can use the information back within our applications or just for dashboards/reports. As soon as data has been matched and linked we can start using it for analytics and prediction. 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. 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. Getting the data from one application to another is the first problem to solve. SOA, ETL, huband-spoke and other mechanisms can be a good start. 20
  21. 21. NETSPECTIVE Ensure transport flexibility Hospital or Cloud Development TCP, HTTPS, SOAP, REST HTTP, SFTP, SCP, MLLP SMTP, XMPP Vendors & Partners VPN Services Remote Center Apps Apps Registry MQs Services HTTPS, REST, SOAP SFTP, SCP, MLLP SMTP, XMPP, TCP Embeddable Integration Backbone Central DB Security Service DB Management Services Firewall App DB 21
  22. 22. NETSPECTIVE Don’t limit the format types HL7 HL7 RIM CDISC Excel, CSV Access, SQL SEND CCD CCR RDF, RDFa ATOM Pub X.12 22
  23. 23. NETSPECTIVE Choose tools that can do it all Connect Collect & Cleanse Exchange Standardize (Map & Link) Federate Store Analyze Report Secure Audit Guarantee HIPAA Compliance 23
  24. 24. NETSPECTIVE Don’t start without a plan Outcome 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 Allows you to reduce costs, increase revenues, & improve care by having faster and more comprehensive access to data. 24
  25. 25. NETSPECTIVE Don’t move without success criteria Goals Senior executives finalize the definition of the success criteria and list of target financial and clinical systems that need to be integrated. Integration engineers analyze, gather, and document the technical connection points. Requirements Business analysts catalog the data origination sources and destination sinks. Result PLAN Create an executable data integration plan 25
  26. 26. NETSPECTIVE Choosing the right tool is the key Senior integration engineers install tools and experiment with external systems. Result Experiment Senior integration engineers install tools and experiment with internal systems. Goals Senior architect uses the data integration plan to select a vendor and create a deployment strategy. Tool Ready to Use Begin using tool for financial data and when successful move to clinical data 26
  27. 27. NETSPECTIVE Decouple your systems Formats Senior architect uses data sources catalog to decide on adapters, protocols, and formats for data exchange Result Programmers write custom adapters for non-standard protocols and formats Code Programmers start wiring up near-, medium-, and long-term connection points (following goals set by executives) Data Interoperability A/R should improve, care coordination should improve, etc. 27
  28. 28. NETSPECTIVE 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-topeer network (co-op model) Centralized clinical database or data warehouse Health data claims bank Clinical data exchange cooperative 28
  29. 29. NETSPECTIVE Build vs. Buy? Build (or use Open Source) Buy (commercial) Start Immediately Capabilities Engineering Costs License Costs 29
  30. 30. NETSPECTIVE Build vs. Buy Elaborated Buy (Commercial) Build (or use Open Source) • 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 • 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 Best choice if you’re not creating your own interface engine Recommended if you want to build and sell your own interface engine 30
  31. 31. NETSPECTIVE 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? 31
  32. 32. Visit E-mail Follow @ShahidNShah Call 202-713-5409 Thank You