© 2016 TM Forum Live! 2016 | 1 Anywhere Point-of-Care Diagnostics Vodafone, Cepheid, Guavus, InSTEDD, FIND

© 2016 TM Forum Live! 2016 | 2 Outline  Business Challenge  The Team  The Catalyst  Potential Patient Value  Next Steps

© 2016 TM Forum Live! 2016 | 3 Business Challenge Many people with serious infectious diseases like TB are not aware that they are infected, and thus do not seek effective treatment, and may infect others In developing countries (with high incidence of such diseases), access to healthcare may be limited – by cost of tests in hospitals, and distance to clinics/hospitals It is difficult to ensure that the correct treatments are stocked locally due to lack of timely and contextual disease information

© 2016 TM Forum Live! 2016 | 4 IoT-Enabled Molecular Diagnostics

© 2016 TM Forum Live! 2016 | 5 The Team  M2M/IoT platform & connectivity provider  Value Proposition: A new & high profile use of IoT technology to benefit society by improving people’s lives VodafoneCepheid  Medical diagnostics instrument and test manufacturer  Value Proposition: A network of partners that allows effective use of their new POC instruments Guavus  Big data analytics company  Value Proposition: Includes additional sources of data for analysis, provides rich insights into disease trend prediction & management FIND  Non-profit enabling development/delivery of diagnostic tests for poverty-related diseases  Value Proposition: New and better epidemiology information InSTEDD  Non-profit using technology to improve health, safety and sustainable development  Value Proposition: New and better epidemiology information

© 2016 TM Forum Live! 2016 | 6 Solution Architecture InSTEDD and FIND’s Platform Access DxAPI to consume diagnostic data CDX - Receive and Store Diagnos tic Data Diagnostic Data Cepheid Diagnostic Data Feed FIND Diagnostic Data Feed Guavus Analytics Open External Data Guavus Data Mediation and Validation Collector Dashboard & Analytics Incidence Index Map Exploration and Correlation Health Trends & Insights Alert Notification Disease Trend Prediction Contextual Drill Down Data from multiple sources like diagnostic equipment, telecom network data, and publically-available demographic and economic data can be analysed using big data technology for finding insights Vodafone(M2M/IoT platform / connectivity provider ) Molecular diagnostics data from point-of-care instruments – IoT /smartphone-enabled transfer Collect medical test, location and date/time from both the medical instrument and the smartphone/network data transmission

© 2016 TM Forum Live! 2016 | 7 Delivering Timely and Automated Insights Analytics Framework External Solution Context Data Location DataDiagnostic Data Guavus Reflex™ Analytics Fusion & Aggregatio n Continuous Collection Location characteristics Economy Health Solutions Service Optimization Better Knowledge of Epidemiology Patterns Rapid results and Correct Treatment Antibiotic stock Management Continuous Monitoring Proactive Service Monitoring Anomaly Detection & Predictive Modeling Targeted Action Exploration & Discovery Healthcare Operational Intelligence Save Patients Lives Health Resource Planning Business Drivers

© 2016 TM Forum Live! 2016 | 8 Solution Snapshot Real-time system-generated alerts, triggered by simple thresholds or complex scenarios Identify characteristics associated with affected regions Compare trends and forecast over a selected period of time See whether external factors are associated with disease spread

© 2016 TM Forum Live! 2016 | 9 Real-Time Alerts for Decision Support  User can view daily alerts and explore trends  Prescriptive analytics for decision support and automation  Alerts for appropriate stocking of antibiotics  Alerts for increase in MDR-TB cases – suggest re-ordering appropriate drugs  Alerts to provide relevant insights from the data

© 2016 TM Forum Live! 2016 | 10 External Data Correlation  External data overlay  Food Hygiene, Population density, Unsafe Drinking Water, HIV+ Prevalence, Economy are a few examples we have used to demonstrate how we can correlate external data with a specific disease prevalence to get insights

© 2016 TM Forum Live! 2016 | 11 HIV+ Prevalence Correlation  HIV+ Prevalence rate overlay  Food Hygiene, Population density, Unsafe Drinking Water, HIV+ Prevalence, Economy are a few examples we have used to demonstrate how we can correlate external data with a specific disease prevalence to get insights

© 2016 TM Forum Live! 2016 | 12 Region Characteristics Exploration & Correlation  Relevant publically available external information can be incorporated for additional insights  For example: 85% people in Namibia carry mobile phones. Educational programs aimed at reducing disease spread could be designed to suit available devices and literacy rate information

© 2016 TM Forum Live! 2016 | 13 Tuberculosis Trend Prediction  See trend and forecast over a selected period of time.  Advance data science to predict future trends. This information can be used to stock relevant medication  May be used to provide early warning for spread/increase of disease incidents  Flexible time range selections

© 2016 TM Forum Live! 2016 | 14 Potential Patient Value  The correct antibiotics can be given as early as possible, leading to better medical outcome for the patient (increased cure rate) and reduced spread of the infection to other people  More appropriate stocking of antibiotics due to knowledge of changing local drug-resistance patterns (cost savings, reduced risk of stock-outs)  Better knowledge of local disease and drug-resistance patterns, analysed together with other factors (economic, demographic, other diseases, local events) can improve disease prediction, allocation of medical resources, and education initiatives

© 2016 TM Forum Live! 2016 | 15 Possible Future Work Next Steps  Other disease data can also be analysed to understand their impact on local population and correlation with TB  Refine patient and drug stock forecasting/prediction model  Additional alerts for decision support and automation  Education campaign design could be better targeted if consumer phone type/connectivity data would be available  If patients opt in to provide location information, this can be used to understand movement of people, and therefore spread of a disease

© 2016 TM Forum Live! 2016 | 16 Thank you!