Demand connected medical devices to improve military EHRs Change procurement of medical devices to help fill EHRs with clinically useful data for comparative effectiveness research and data interoperability

www.netspective.com 2 @ShahidNShah Who is Shahid? • 25+ years of software engineering and multi-site healthcare system deployment experience • 20+ years of technology management experience (government, non-profit, commercial) • 15+ years of digital health, healthcare IT and medical devices experience (blog at http://healthcareguy.com) Author of Chapter 13, “You’re the CIO of your Own Office”

@ShahidNShah www.netspective.com 3 What’s this talk about? Miliary Health IT / MedTech Landscape • Data has potential to solve some hard healthcare problems and change how medical science is done. • The government & military are paying for the manual collection and clerk- like entry of clinical data. • Current data collection is unreliable, slow, and error prone. Key Takeaways • Medical devices are the best sources of quantifiable, analyzable, and reportable clinical data. • New devices must be designed and deployed to support inherent connectivity. • Government and military buyers have the procurement muscle to force vendors to become more connected.

www.netspective.com 4 @ShahidNShah 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

@ShahidNShah www.netspective.com 5 Patient populations need different solutions • Obesity Management • Wellness Management • Assessment – HRA • Stratification • Dietary • Physical Activity • Physician Coordination • Social Network • Behavior Modification • Education • Health Promotions • Healthy Lifestyle Choices • Health Risk Assessment • Diabetes • COPD • CHF • Stratification & Enrollment • Disease Management • Care Coordination • MD Pay-for-Performance • Patient Coaching • Physicians Office • Hospital • Other sites • Pharmacology • Catastrophic Case Management • Utilization Management • Care Coordination • Co-morbidities Prevention Management 26 % of Population 4 % of Medical Costs 35 % of Population 22 % of Medical Costs 35 % of Population 37 % of Medical Costs 4% of Population 36 % of Medical Costs Source: Amir Jafri, PrescribeWell

www.netspective.com 6 The digital enterprise is revolving rapidly based on key trends, health systems’ evolution, and IT’s evolution Key Industry Trends . Health System Evolution IT Systems Evolution IT Role Evolution Fee For Service, Shared Risk, Bundled Payments, etc. Value, Care Management, Population Management PRESENT Full Risk, Integrated Health Plan & Care Delivery System Personalized Medicine, Wellness Management FUTURE Fee For Service Volume/Episodic Care PAST Integrated EHR Health Information Exchange (HIE) Enterprise Data Warehouse (EDW) Patient Engagement Mobile Health Connected Care Wellness Management Advanced Informatics Personalized Medicine Ancillary Systems (Lab/Rad/Pharmacy, etc.) Enterprise Resource Planning (HR, GL, SCM) Revenue Cycle Ambulatory EHR Hospital EHR Patient Access Enabler Innovator Installer Integrator Integrated Healthcare Network Integrated Healthcare Ecosystem Multi-Specialty Care Clinic & Hospital Integrated Healthcare System Source: Bruce Metz, CIO, Lahey Health

@ShahidNShah www.netspective.com 7 We’ve seemingly accepted lack of cures… The Shift The clinical model is shifting away from treatment of chronic conditions and focusing more on prevention, wellness, obesity intervention, behavior and lifestyle modification. Objectives • Keep people out of the hospital ($$$) • Keep people from their docs ($$) • Keep people off drugs ($) • Keep people at home Implications Clinical operations are shifting to hospital and physician ‘centered’ services that will rely heavily on health information technologies to monitor, coordinate, and manage care. • Successful Transition in Care resulting in Reduced Hospital Readmission Rates • Proactive population management • Patient engagement and collaboration • Disease prevention through wellness and obesity management • Chronic disease management • Care coordination and collaboration • Metrics and analytics

@ShahidNShah www.netspective.com 8 Data changes the questions we ask Simple visual facts Complex visual facts Complex computable facts

@ShahidNShah www.netspective.com 9 Data can change medical science The old way Identify problem Ask questions Collect data Answer questions The new way Identify data Generate questions Mine data Answer questions

www.netspective.com 10 @ShahidNShah Data Comprehension is hard What does it mean? How do I use it? • Must be continuously recomputed • Difficult today, easier tomorrow • Super-personalized • Prospective • Predictive Bio IT and Genomics Secondary Aggregation • Can be collected infrequently • Personalized • Prospective • Potentially predictive • Digital • Family history is easier Phenotypics Primary Data Collection • Continuously collected • Mostly Retrospective • Useful for population health • Part digital, mostly analog • Family History is hard Admin Data Collection • Business focused data • Retrospective • Built on fee for service models • Inward looking and not focused on clinical benefits Biosensors Social Interactions

www.netspective.com 11 Data Medical Hardware Consumer Hardware Health Records Patient IT Social Media Health Literacy Retail purchases Behaviors IoT Sensors Hardware Software Pharma / Clinical Trials Labs / Imaging Payments Bioinformatics Health Info Exchg Provider Engagement Care Coordination Compliance Marketing IT Retrospective Prospective Med Devices Integration Comprehension Tools, Storage, Services Science, Discovery Research Consent Digital Chemistry Provider Stature Ratings/reviews

www.netspective.com 12 @ShahidNShah Unstructured phenotypic 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

www.netspective.com 13 @ShahidNShah Structured phenotypic 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

www.netspective.com 14 @ShahidNShah Demand medical device integration/connectivity Most obvious benefit Least attention Most promising capability This talk focuses on connected devices

www.netspective.com 15 @ShahidNShah Procure poly-connectable devices Device Hospital Network Corporate Gateway External Cloud Hospital Systems Option 1 (no cellular access or hospital IT integration required) Device External Cloud Option 2 (cellular access and no hospital IT integration required) DDS REST HL7 X.12 DDS REST MPEG-21 MPEG-21 Could be a Home Network, too Wired Wireless Bluetooth, WiFi, Zibee, etc. Wireless, Cellular MQTT MQTT XMPP XMPP

www.netspective.com 16 @ShahidNShah Demand better manageability in devices Security • Is the device authorized? Inventory • Where is the device? Presence • Is a device connected? Teaming • Device grouping

www.netspective.com 17 @ShahidNShah Demand extensibility Legacy Devices Future Devices

@ShahidNShah www.netspective.com 18 Start procuring consumer-grade physiologics

@ShahidNShah www.netspective.com 19 Invest in digital biology and digital chemistry Labs on chips Personal Genomics

@ShahidNShah www.netspective.com 20 Start procuring consumer grade monitoring

www.netspective.com 21 @ShahidNShah Invest in predictive analytics and CEP

@ShahidNShah www.netspective.com 22 Device Components 3rd Party Plugins App #1 App #2 Security and Management Layer Device OS (QNX, Linux, Windows) Sensors Storage Display Plugins Web Server, IM Client Connectivity Layer (DDS, HTTP, XMPP) • Presence • Messaging • Registration • JDBC, Query Cloud Services Management Dashboards Data Transformation (ESB, HL7) Device Gateway (DDS, ESB) Healthcare Enterprise Enterprise Data Demand Connected Architecture Plugin Container Event Architecture Inventory Workflow Notifications Patient Context Location Aware 1 2 3 4 5 6 7 8 9 SSL VPN

@ShahidNShah www.netspective.com 23 Ultimate Architecture Core Device Components Security and Management Layer Device OS (QNX, Linux, Windows) Connectivity Layer (DDS, HTTP, XMPP) Plugin Container Don’t create your own OS! Security isn’t added later Think about Plugins from day 1 Connectivity is built-in, not added Build on Open Source Create code as a last resort

@ShahidNShah www.netspective.com 24 Connectivity components Device Components Security and Management Layer Device OS (QNX, Linux, Windows) Web Server, IM Client Connectivity Layer (DDS, HTTP, XMPP) • Presence • Messaging • Registration • JDBC, Query Plugin Container Surveillance & “remote display” Remote Access Alarms Event Viewer Design all functions as plugins

@ShahidNShah www.netspective.com 25 Demand enterprise integration Cloud Services Management Dashboards Data Transformation (ESB, HL7) Device Gateway (DDS, XMPP, ESB) Enterprise Data Inventory Cross Device App Workflows Alarm Notifications Patient Context Monitoring Device Teaming Device Management Report Generation HIT Integration Remote Surveillance Device Data SSL VPN

Conclusion and Questions @ShahidNShah [email protected] www.ShahidShah.com