Reasons why health data is poorly integrated today and what we can do about it


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Presented at StrataRX 2012:
While the entire healthcare community, for decades, has been clamoring for, cajoling, and demanding integration of its IT systems, we’re actually in a pretty elementary stage when it comes to useful, practical, health IT systems integration beyond on-premise and in-building hospital software. Our problem in the industry is not that engineers don’t know how to create the right technology solutions or that somehow we have a big governance problem; while those are certainly issues in certain settings, the real cross-industry issue is much bigger – our approach to integration is decades old, opaque, and rewards closed systems.

For decades, starting in the 50’s through the mid 90’s before the web / Internet came along, systems integration meant that every system had to know about each other in advance, decide on what data they would share, engage in governance meetings, have memoranda of understanding or contracts in place, etc. After the web came along, most of that was thrown out the window because the approach changed to one that said the owner of the data provides whatever they decide (e.g. through a web server) and whoever wants it will be provided secure access and they can come get it (e.g. through a browser or HTTP client). This kind of revolutionary approach in systems integration is what the health IT and medical device sectors are sorely lacking and something that ONC can help promote.

Specifically, the following things are holding us back when it comes to poor integration in healthcare and what future EHRs can do about it:

• We don’t support shared identities, single sign on (SSO), and industry-neutral authentication and authorization. Most health IT systems create their own custom logins and identities for its users including roles, permissions, access controls, etc. stored in an opaque part of their own proprietary database. ONC should mandate that all future EHRs use industry-neutral and well supported identity management technologies so that each system has a least the ability to share identities. Without identity sharing and exchange there can be no easy and secure application integration capabilities no matter how good the formats are. I’m continually surprised how little attention is paid to this cornerstone of application integration. There are very nice open identity exchange protocols, such as SAML, OpenID, and oAuth as well as open roles and permissions management protocols such as XACML that make identity and permission sharing possible. Free open source tools such as OpenAM, Apache Directory, OpenLDAP, Shibboleth, and many commercial vendors have drop-in tools to make it almost trivial to do identity sharing, SSO, and RBAC.

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Reasons why health data is poorly integrated today and what we can do about it

  1. 1. The Myth of Health Data Integration Complexity An opinionated look at why current health IT systems integrate poorly and what we can do about it 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 you’ll learn today Let’s stop the hand waving and relying on the government to take care of integration Background • • • • A deluge of healthcare data is being created as we digitize biology, chemistry, and physics. Data changes the questions we ask and it can actually democratize and improve the science of medicine, if we let it. While cures are the only real miracles of medicine, big data can help solve intractable problems and lead to more cures. Healthcare-focused software engineering is going to do more harm than good (industry-neutral is better). Key takeaways • • • • Applications come and go, data lives forever. He who owns, integrates, and uses data wins in the end. Never leave your data in the hands of an application/system vendor. There’s nothing special about health IT data that justifies complex, expensive, or special technology. Spend freely on multiple systems and integration-friendly solutions. 3
  4. 4. NETSPECTIVE NEJM believes doctors are trapped It is a widely accepted myth that medicine requires complex, highly specialized information-technology (IT) systems. This myth continues to justify soaring IT costs, burdensome physician workloads, and stagnation in innovation — while doctors become increasingly bound to documentation and communication products that are functionally decades behind those they use in their “civilian” life. New England Journal of Medicine “Escaping the EHR Trap - The Future of Health IT”, June 2012 4
  5. 5. What’s creating the “data deluge”?
  6. 6. NETSPECTIVE We’re digitizing biology Last and past decades Digitize mathematics Digitize literature Digitize social behavior Predict human behavior Gigabytes and petabytes This and future decades Digitize biology Digitize chemistry Digitize physics Predict fundamental behaviors Petabytes and exabytes 6
  7. 7. NETSPECTIVE What’s creating “data deluge”? 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 7
  8. 8. NETSPECTIVE Data changes the questions we ask Simple visual facts Complex visual facts Complex computable facts 8
  9. 9. NETSPECTIVE Implications for scientific discovery The old way Identify problem Identify data Ask questions Generate questions Collect data Mine data Answer questions The new way Answer questions 9
  10. 10. NETSPECTIVE We’re in the integration age We’re not in an app-driven future but an integrationdriven future. He who integrates the best, wins. Source: Geoffrey Raines, MITRE 10
  11. 11. Where is all the data coming from? Recognizable Data Sources
  12. 12. NETSPECTIVE Data is hidden everywhere 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 12
  13. 13. NETSPECTIVE More hidden sources of data 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 13
  14. 14. 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 14
  15. 15. 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 15
  16. 16. What are we doing wrong? What’s the problem?
  17. 17. NETSPECTIVE Why you can’t just “buy interoperability” Interoperability of data is an emergent property of your IT environment Myth Truth • 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 • My vendor already knows how all this works and will solve my problems • 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 • Vendors aren’t incentivized to integrate data 17
  18. 18. 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 18
  19. 19. 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 19
  20. 20. How do we modernize integration?
  21. 21. NETSPECTIVE Why health IT systems integrate poorly Technology “Culture” • • • • • Permissions-oriented culture prevents tinkering and “hacking” We don’t let patients drive data decisions. No scripting or customizing EHRs, lab systems, etc. Interoperability isn’t required for transactions to be completed (ecommerce) We have “Inside out” architecture, not “Outside in” Actual Technology • • • • We don't support shared identities, single sign on (SSO), and industryneutral authentication and authorization We're too focused on "structured data integration" instead of "practical app integration“ We focus more on "pushing" versus "pulling" data than is warranted early in projects We're too focused on heavyweight industry-specific formats instead of lightweight or micro formats 21
  22. 22. NETSPECTIVE Promote “Outside-in” architecture The IT department inside your organization cannot possibly do everything you’d like Process and people consolidation won’t work in the future Defining and coordinating interactions across a multitude of organizations is the new way “For decades, businesses typically have been rewarded for consolidation around standard processes and stockpiling assets through people, technology and goods. Companies are discovering they need a new kind of leverage – capability leverage – to mobilize third parties that can add value.” • Outside-in architecture asks you to think about your operations and processes as a collection of business capabilities or services. • Each individual service must be analyzed and packaged to see who can deliver them best. According to Deloitte, “this architectural transition requires new skills from the CIO and the IT organization. CIOs who anticipate and understand the opportunity are likely to become much more effective business partners with other executive leaders.” Source: Deloitte “Outside-in Architecture” 22
  23. 23. NETSPECTIVE Implement industry-neutral ICAM Implement shared identities, single sign on (SSO), neutral authentication and authorization Proprietary identity is hurting us • • Most health IT systems create their own custom identity, credentialing, and access management (ICAM) in an opaque part of a proprietary database. We’re waiting for solutions from health IT vendors but free or commercial industryneutral solutions are much better and future proof. Identity exchange is possible • Follow National Strategy for Trusted Identities in Cyberspace (NSTIC) • Use open identity exchange protocols such as SAML, OpenID, and Oauth • Use open roles and permissions-management protocols, such as XACML • Consider open source tools such as OpenAM, Apache Directory, OpenLDAP Shibboleth, or , commercial vendors. • Externalize attribute-based access control (ABAC) and role-based access control (RBAC) from clinical systems into enterprise systems like Active Directory or LDAP . 23
  24. 24. NETSPECTIVE App-focused integration is better than nothing Structured data dogma gets in the way of faster decision support real solutions Dogma is preventing integration App-centric sharing is possible Many think that we shouldn’t integrate until structured data at detailed machinecomputable levels is available. The thinking is that because mistakes can be made with semi-structured or hard to map data, we should rely on paper, make users live with missing data, or just make educated guesses instead. Instead of waiting for HL7 or other structured data about patients, we can use simple techniques like HTML widgets to share "snippets" of our apps. • Allow applications immediate access to portions of data they don't already manage. • Widgets are portions of apps that can be embedded or "mashed up" in other apps without tight coupling. • Blue Button has demonstrated the power of app integration versus structured data integration. It provides immediate benefit to users while the data geeks figure out what they need for analytics, computations, etc. 24
  25. 25. NETSPECTIVE Pushing data is more expensive than pulling it We focus more on "pushing" versus "pulling" data than is warranted early in projects Old way to architect: “What data can you send me?” (push) Better way to architect: “What data can I publish safely?” (pull) The "push" model, where the system that contains the data is responsible for sending the data to all those that are interested (or to some central provider, such as a health information exchange or HL7 router) shouldn’t be the only model used for data integration. • Implement syndicated Atom-like feeds (which could contain HL7 or other formats). • Data holders should allow secure authenticated subscriptions to their data and not worry about direct coupling with other apps. • Consider the Open Data Protocol (oData). • Enable auditing of protected health information by logging data transfers through use of syslog and other reliable methods. • Enable proper access control rules expressed in standards like XACML. 25
  26. 26. NETSPECTIVE Industry-specific formats aren’t always necessary Reliance on heavyweight industry-specific formats instead of lightweight micro formats is bad HL7 and X.12 aren’t the only formats Consider industry-neutral protocols The general assumption is that formats like HL7, CCD, and X.12 are the only ways to do data integration in healthcare but of course that’s not quite true. Microsoft Excel & Access, Google Docs, etc. don’t have live access to our data in transactional systems such as EHRs. • • • • Consider identity exchange protocols like SAML for integration of user profile data and even for exchange of patient demographics and related profile information. Consider iCalendar/ICS publishing and subscribing for schedule data. Consider microformats like FOAF and similar formats from Consider semantic data formats like RDF, RDFa, and related family. 26
  27. 27. NETSPECTIVE Tag all app data using semantic markup When data is not tagged using semantic markup, it's not securable or shareable by default Legacy systems trap valuable data Semantic markup and tagging is easy In many existing contracts, the vendors of systems that house the data also ‘own’ the data and it can’t be easily liberated because the vendors of the systems actively prevent it from being shared or are just too busy to liberate the data. • One easy way to create semantically meaningful and easier to share and secure patient data is to have all HTML tags be generated with companion RDFa or HTML5 Data Attributes using industry-neutral schemas and microformats similar to the ones defined at • Google's recent implementation of its Knowledge Graph is a great example of the utility of this semantic mapping approach. 27
  28. 28. NETSPECTIVE Produce data in search-friendly manner Produce HTML, JavaScript and other data in a security- and integration-friendly approach Proprietary data formats limit findability Search engines are great integrators • Legacy applications only present through text or windowed interfaces that can be “scraped”. • Web-based applications present HTML, JavaScript, images, and other assets but aren’t search engine friendly. • Most users need access to information trapped in existing applications but sometimes they don’t need must more than access that a search engine could easily provide. • Assume that all pages in an application, especial web applications, will be “ingested” by a securable, protectable, search engine that can act as the first method of integration. 28
  29. 29. NETSPECTIVE Rely first on open source, then proprietary “Free” is not as important as open source, you should pay for software but require openness Healthcare fears open source Open source can save health IT • Only the government spends more per user on antiquated software than we do in healthcare. • There is a general fear that open source means unsupported software or lower quality solutions or unwanted security breaches. • Other industries save billions by using open source. • Commercial vendors give better pricing, service, and support when they know they are competing with open source. • Open source is sometimes more secure, higher quality, and better supported than commercial equivalents. • Don’t dismiss open source, consider it the default choice and select commercial alternatives when they are known to be better. 29
  30. 30. 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? 30
  31. 31. Visit E-mail Follow @ShahidNShah Call 202-713-5409 Thank You