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Connected medical devices
 

Connected medical devices

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How medical devices help fill EHRs with clinically useful data for comparative effectiveness research and data interoperability. This talk was given at the IEEE Baltimore Section EMB Society

How medical devices help fill EHRs with clinically useful data for comparative effectiveness research and data interoperability. This talk was given at the IEEE Baltimore Section EMB Society

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    Connected medical devices Connected medical devices Presentation Transcript

    • Connected Medical Devices How medical devices help fill EHRs with clinically useful data for comparative effectiveness research and data interoperability Shahid N. Shah, CEO
    • Who is Shahid? • 20+ years of software engineering and multi-site healthcare system deployment experience • 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 nonprofit) www.netspective.com Author of Chapter 13, “You’re the CIO of your Own 2 Office”
    • What’s this talk about? Health IT / MedTech Landscape Key Takeaways • Data has potential to solve some hard healthcare problems and change how medical science is done. • The government is paying for the collection of clinical data (Meaningful Use or “MU”). • All the existing MU incentives promote the wrong kinds of data collection: unreliable, slow, and error prone. • Medical devices are the best sources of quantifiable, analyzable, and reportable clinical data. • New devices must be designed and deployed to support inherent connectivity. www.netspective.com www.netspective.com 3
    • What problems can data help solve? Cost per patient per procedure / treatment going up but without ability to explain why Cost for same procedure / treatment plan highly variable across localities Unable to compare drug efficacy across patient populations Unable to compare health treatment effectiveness across patients Variability in fees and treatments promotes fraud Lack of visibility of entire patient record causes medical errors www.netspective.com 4
    • Data changes the questions we ask Simple visual facts Complex visual facts www.netspective.com www.netspective.com Complex computable facts 5
    • Data can change medical science The old way The new way Identify problem Identify data Ask questions Generate questions Collect data Mine data Answer questions Answer questions www.netspective.com www.netspective.com 6
    • Evidence-based medicine is our goal www.netspective.com 7
    • Evidence-based Medicine and Comparative Effectiveness Research Early 1970s 1978 1990’s Today Medical Technology Assessment (MTA) National Center for Health Technology Assessment Agency for Healthcare Research and Quality (AHRQ) Comparative Effective Research (CER) Success factor: large well-designed effectiveness studies with mountains of data www.netspective.com 8
    • AHRQ’s definition of CER process Identify new and emerging clinical interventions. Review and synthesize current medical research. Identify gaps between existing medical research and the needs of clinical practice. Translate and disseminate research findings to diverse stakeholders. Train and develop clinical researchers. Promote and generate new scientific evidence and analytic tools. Reach out to stakeholders via a citizens forum. www.netspective.com Source: http://effectivehealthcare.ahrq.gov/index.cfm/what-is-comparative-effectiveness-research1/ 9
    • CER is about the patient • CER sounds like it’s all about the government and evidence-based medicine to contain healthcare costs but ultimately it’s about providing treatment comparison choices to help make informed decisions. • Healthcare professionals must deliver tools to the patient that can help the patient and their families select the right treatment options. www.netspective.com 10
    • Healthcare landscape background • The government (through Meaningful Use & ACO incentives) is paying for the collection of clinical data. • Medical devices are the best sources of quantifiable, analyzable, and reportable clinical data. • Most medical devices today are not connected so you do not have access to the best data. • New devices are being design and deployed to support connectivity. www.netspective.com 11
    • What if we had access to all this data? Source: Jan Whittenber, Philips Medical Systems www.netspective.com www.netspective.com 12
    • Unstructured patient data sources Patient Source Self reported by patient Health Professional Observation s by HCP Labs & Diagnostics Computed from specimens Errors High Medium Slow Slow Low Medium Megabytes Megabytes PDFs, images PDFs, images Common Common Common Uncommon PDFs, images Availability Uncommon Megabytes Data type Computed from specimens High Data size Computed real-time from patient Medium Reliability Biomarkers / Genetics Low Time Medical Devices www.netspective.com 13
    • 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 Discrete size Streaming size Availability Uncommon www.netspective.com Common Somewhat Common Uncommon Uncommon 14
    • The need for connected devices • Meaningful Use and CER advocates are promoting (structured) data collection for reduction of medical errors, analysis of treatments and procedures, and research for new methods. • All the existing MU incentives promote the wrong kinds of collection: unreliable, slow, and error prone. • Accurate, real-time, data is only available from connected medical devices www.netspective.com 15
    • Medical Device Connectivity is a must Most obvious benefit Least attention Most promising capability www.netspective.com This talk focuses on 16 connected devices
    • It’s not as hard as we think… • Modern real-time operating systems (open source and commercial) are reliable for safety-critical medical-grade requirements. • Open standards such as TCP/IP DDS, HTTP , , and XMPP can pull vendors out of the 1980’s and into the 1990’s.  • Open source and open standards that promote enterprise IT connectivity can pull vendors into the 2010’s and beyond. www.netspective.com 17
    • Sampling of OSS / open standards Comments Project / Standard Subject area D G Linux or Android Operating system   OMG DDS (data distribution service) Publish and subscribe messaging   AppWeb, Apache Web/app server   OpenTSDB Time series database  Open source project Mirth HL7 messaging engine  Built on Mule ESB Alembic Aurion HIE, message exchange  Successor to CONNECT HTML5, XMPP, JSON Various areas   Don’t reinvent the wheel SAML, XACML Security and privacy   DynObj, OSGi, JPF Plugin frameworks   www.netspective.com Open standard with open source implementations Build for extensibility 18
    • Ask for device connectivity Physical • Wired, wireless (WiFi, cellular, etc.) Logical • Device  Gateway  Data Routers  Systems Structural • Security, Numbers, Units of Measure, etc. Semantic • Presence, Vitals, Glucose, Heartbeats, etc. www.netspective.com 19
    • Ask for better manageability Security • Is the device authorized? Teaming Inventory • Device grouping • Where is the device? Presence • Is a device connected? www.netspective.com 20
    • Ask for extensible devices Legacy Devices www.netspective.com Future Devices 21
    • Appreciate tradeoffs Integrationfriendliness The more connectionfriendly a device, the harder it is to validate it Ease of validation Lesson: Demand Testability www.netspective.com 22
    • Ultimate Connectivity Architecture 5 Device Components Sensors Storage Display 3rd Party Plugins Web Server, IM Client • Presence 6 • Messaging App #1 • Registration • JDBC, Query Plugins App #2 4 Connectivity Layer (DDS, HTTP, XMPP) 3 Plugin Container 2 Security and Management Layer 1 Device OS Event Architecture Location Aware 7 (QNX, Linux, Windows) SSL VPN Healthcare Enterprise 8 Patient Context Device Gateway (DDS, ESB) Inventory Workflow Notifications Cloud Services Data Transformation (ESB, HL7) Management Dashboards www.netspective.com www.netspective.com 9 Enterprise Data 23
    • Ultimate Architecture Core Connectivity is built-in, not added Device Components Think about Plugins from day 1 Connectivity Layer (DDS, HTTP, XMPP) Plugin Container Security and Management Layer Device OS (QNX, Linux, Windows) Don’t create your own OS! Build on Open Source www.netspective.com www.netspective.com Security isn’t added later Create code as a last resort 24
    • Connectivity components Surveillance & “remote display” Remote Access Alarms Device Components Event Viewer Design all functions as plugins Web Server, IM Client • Presence • Messaging • Registration • JDBC, Query Connectivity Layer (DDS, HTTP, XMPP) Plugin Container Device OS (QNX, Linux, Windows) Security and Management Layer www.netspective.com www.netspective.com 25
    • OSS enables enterprise integration Device Teaming Cloud Services SSL VPN Device Gateway (DDS, XMPP, ESB) Patient Context Monitoring Device Data Cross Device App Workflows Data Transformation (ESB, HL7) Remote Surveillance Management Dashboards HIT Integration Report Generation www.netspective.com www.netspective.com Enterprise Data Alarm Notifications Device Management Inventory 26
    • Ultimate Connectivity Architecture Device Components Sensors Storage Event Architecture Location Aware Display 3rd Party Plugins Web Server, IM Client • Presence • Messaging • Registration • JDBC, Query Plugins App #1 App #2 Connectivity Layer (DDS, HTTP, XMPP) Plugin Container Security and Management Layer Device OS (QNX, Linux, Windows) SSL VPN Healthcare Enterprise Device Gateway (DDS, ESB) Patient Context Inventory Workflow Notifications Cloud Services Data Transformation (ESB, HL7) Management Dashboards www.netspective.com www.netspective.com Enterprise Data 27
    • Conclusion and Questions