Transcript Vodafone Cepheid

Anywhere Point-of-Care
Diagnostics
Vodafone, Cepheid, Guavus, InSTEDD, FIND
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Outline
 Business Challenge
 The Team
 The Catalyst
 Potential Patient Value
 Next Steps
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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
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IoT-Enabled Molecular Diagnostics
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The Team
Vodafone
Cepheid
 M2M/IoT platform &
connectivity provider
Guavus
 Big data analytics
company
 Medical diagnostics
instrument and test
manufacturer
 Value Proposition: A
new & high profile use of
IoT technology to benefit
society by improving
people’s lives
 Value Proposition: A
network of partners that
allows effective use of their
new POC instruments
FIND
 Non-profit enabling
development/delivery of
diagnostic tests for
poverty-related diseases
 Value Proposition: New
and better epidemiology
information
 Value Proposition:
Includes additional
sources of data for
analysis, provides rich
insights into disease trend
prediction & management
InSTEDD
 Non-profit using
technology to improve
health, safety and
sustainable development
 Value Proposition: New
and better epidemiology
information
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Solution Architecture
InSTEDD and FIND’s Platform
Guavus Analytics
Diagnostic Data
Cepheid
Diagnostic Data
Feed
Collect medical test,
location and date/time
from both the medical
instrument
and the
FIND Diagnostic
smartphone/network
Data Feed
data transmission
Vodafone(M2M/IoT
platform/connectivity
provider )
Collector
CDX Receive
and
Store
Diagnos
tic Data
Access
DxAPI to
consume
diagnostic
data
Dashboard & Analytics
Incidence Index
Map
Exploration and
Correlation
Guavus
Data
Mediation
and
Validation
Health Trends &
Insights
Alert Notification
Disease Trend
Prediction
Data from multiple sources like diagnostic equipment,
telecom network data, and publically-available demographic
Contextual
Drill
and economic data can be analysed
using big
Down
data technology for finding insights
Open External Data
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Analytics Framework
Delivering Timely and Automated Insights
Business Drivers
Solutions
Healthcare Operational
Intelligence
Health Resource
Planning
Save Patients Lives
Rapid results and
Correct Treatment
Antibiotic stock
Management
Better Knowledge of
Epidemiology Patterns
Proactive Service
Monitoring
Continuous Monitoring
Service Optimization
Guavus Reflex™ Analytics
Continuous
Collection
Diagnostic Data
Fusion &
Aggregatio
n
Anomaly Detection &
Predictive Modeling
Location Data
Targeted Action
Location
characteristics
Exploration &
Discovery
Economy
Health
External Solution Context Data
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Solution Snapshot
Real-time system-generated
alerts, triggered by simple
thresholds or complex scenarios
Compare trends and forecast over a
selected period of time
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See whether external factors are
associated with disease spread
Identify characteristics associated with
affected regions
Real-Time Alerts for Decision Support
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
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
External Data Correlation
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
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
HIV+ Prevalence Correlation
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
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
Region Characteristics Exploration & Correlation
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
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
Tuberculosis Trend Prediction
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
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
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
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Next Steps
Possible Future Work
 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
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Thank you!
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