1. Revolutionizing Point of Care with Remote Healthcare Solutions Lance Myers, PhD

2. How can wireless medical devices enable remote medical care and lower healthcare cost?

3. Confidential and Proprietary, Sentrian 2015. Distribution of healthcare spending Expenditure as % of GDP

4. Confidential and Proprietary, Sentrian 2015. Distribution of healthcare spending $0 $2,000 >$3,200 Average Monthly Cost

5. Confidential and Proprietary, Sentrian 2015. Cost breakdown of healthcare expenditure

6. Confidential and Proprietary, Sentrian 2015. Millions of Hospital Admissions are Preventable

7. Confidential and Proprietary, Sentrian 2015. To Combat Costs, Healthcare is Shifting to Fee-for-Value 0% * * * Department of Health and Human Services (HHS) targets Government Alternative Payment Targets

8. Confidential and Proprietary, Sentrian 2015. ACOs Are Being Created to Capture Shared Savings Health Affairs, March 31, 2015.

9. Confidential and Proprietary, Sentrian 2015. Disease Process Starts in Advance of Symptoms Patient Deterioration Time Target Detection Hospitalization Change in Weight Hemodynamic Parameter Changes Change in Fluid Levels Infection, Pollution, Malaise & Diminished Exercise Tolerance Increased Resting Heart Rate Decreased Peak Flow Reduced O2, Shortness of Breath COPD CHF 1 st Gen Detection

10. Confidential and Proprietary, Sentrian 2015. Remote Patient Intelligence Solution

11. Confidential and Proprietary, Sentrian 2015. Wide Array of Available Wireless Monitoring Devices

12. Confidential and Proprietary, Sentrian 2015. Collecting Symptomatic Data

13. Confidential and Proprietary, Sentrian 2015. CHF Hospitalizations by Age 75% of patients hospitalized for CHF are over the age of 65

14. Confidential and Proprietary, Sentrian 2015. Gateway Options

15. Confidential and Proprietary, Sentrian 2015. Traditional Remote Patient Monitoring Care team receives alerts Care team virtually manages condition Patient Hospitalized Patient Improves or Home hub uploads data Binary rules from single sensor Patient uses single sensor at home

16. Confidential and Proprietary, Sentrian 2015. 180

17. Confidential and Proprietary, Sentrian 2015. The problem of false alarms

18. Confidential and Proprietary, Sentrian 2015. The value of context Systolic BP = 172mmHg SpO2 = 93%

19. Confidential and Proprietary, Sentrian 2015. The value of context Systolic BP = 172mmHg Heart Rate is trending up

20. Confidential and Proprietary, Sentrian 2015. The value of context Systolic BP = 172mmHg PEF is trending down

21. Repeat measurements Actual Systolic BP distribution of repeat measurements on 6,314 patients First measurement > 180mmHg Difference between 1 st and 2 nd measurement Count

22. Confidential and Proprietary, Sentrian 2015. Machine Learning Actions Remote Patient Intelligence

23. Confidential and Proprietary, Sentrian 2015. Machine learning can identify predictive patterns CHFCOPDDMAFIBHTNBehavioral Heart Rate BP SP02 Weight Stroke Volume Activity Intrathoracic Fluid Peak Flow Glucose Sleep Respiratory Rate Self Report

24. Confidential and Proprietary, Sentrian 2015. Patterns may not easily be communicated or understood CHFCOPDDMAFIBHTNBehavioral Heart Rate BP O2 Sat Weight Stroke Volume Activity Intrathoracic Fluid Peak Flow Glucose Sleep Respiratory Rate Self Report Traditional machine Learning detects abstract patterns clinicians may not easily understand

25. Confidential and Proprietary, Sentrian 2015. Building a validated data driven model Identify Problem Source Datasets Transform Data Build Model Test Model Deploy Model 1 – 2 years

26. Confidential and Proprietary, Sentrian 2015. Models Required Chronic Disease Patients By Disease By Stage By Comorbidity By Exacerbation

27. Confidential and Proprietary, Sentrian 2015. Patterns can be expressed in a natural grammar Long term trend Short term trend Spike Shift High variability Low variability

28. Confidential and Proprietary, Sentrian 2015. Using an advanced rules-based system Care team receives alerts Care team virtually manages condition Patient Hospitalized Patient Improves or Home hub uploads data Binary rules from single sensor Patient uses single sensor at home

29. Confidential and Proprietary, Sentrian 2015. Using an advanced rules-based system Patient uses multiple sensors at home Care team sent notifications Care team virtually manages condition Patient Hospitalized Patient Improves Sentrian RPI evaluates data against rules expressed in a natural grammar and entered by physicians Data uploaded to secure cloud or

30. Confidential and Proprietary, Sentrian 2015. Sentrian system expresses rules in a natural grammar

31. Confidential and Proprietary, Sentrian 2015. Sentrian Remote Patient Intelligence Patient uses multiple sensors at home Care team sent notifications Care team virtually manages condition Patient Hospitalized Patient Improves Sentrian RPI evaluates data against rules Data uploaded to secure cloud Sentrian RPI learns from actual patient outcomes to improve rules

32. Confidential and Proprietary, Sentrian 2015. Sentrian’s Rule Recommendation Engine

33. Confidential and Proprietary, Sentrian 2015. WEAR-HeFT study conducted at Cedars Sinai Medical Center Sentrian results – Heart Failure Clinical Trial Physician rules 5 data streams Machine learning rules 5 data streams Machine learning rules 3 data streams