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

NETSPECTIVE www.netspective.com 2 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”

NETSPECTIVE www.netspective.com 3 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.

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

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

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

NETSPECTIVE www.netspective.com 7 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

NETSPECTIVE www.netspective.com 8 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

NETSPECTIVE www.netspective.com 9 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

NETSPECTIVE www.netspective.com 10 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

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

NETSPECTIVE www.netspective.com 12 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

NETSPECTIVE www.netspective.com 13 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

NETSPECTIVE www.netspective.com 14 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

NETSPECTIVE www.netspective.com 15 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

NETSPECTIVE www.netspective.com 16 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

NETSPECTIVE www.netspective.com 17 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

NETSPECTIVE www.netspective.com 18 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

The Do’s and Don’ts of Data Storage

NETSPECTIVE www.netspective.com 20 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.

NETSPECTIVE www.netspective.com 21 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

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

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

NETSPECTIVE www.netspective.com 24 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.

NETSPECTIVE www.netspective.com 25 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

NETSPECTIVE www.netspective.com 26 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

NETSPECTIVE www.netspective.com 27 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

NETSPECTIVE www.netspective.com 28 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

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

NETSPECTIVE www.netspective.com 30 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

NETSPECTIVE www.netspective.com 31 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?

Thank You Visit http://www.netspective.com http://www.healthcareguy.com E-mail [email protected] Follow @ShahidNShah Call 202-713-5409