My presentation at the World Drug Safety Congress Europe held on September 10th-11th, 2019 in Amsterdam

1. Using Artificial Intelligence
to optimize PV workload
World Drug Safety Congress Europe
Amsterdam 2019
Omar AIMER – September 10th, 2019
Canada

2. Contents of this presentation is either personal opinion or compilation
from information available in public domain.

3. Outlines
AI to improve speed and security of adverse
event case process
01
How AI will allow early detection of potential
drug-related side effects
02
How can we guarantee regulatory compliance
using these tools?
03
Using AI to facilitate organizational design and
standardize data
04
Omar AIMER – September 10th, 2019

4. Pharmaceutical industry, and more particularly
pharmacovigilance, has seen the amount of data from
individual safety reports grow exponentially due to the
evolution of regulation with more requirements from health
authorities for a better understanding of the safety of
pharmaceutical products and ultimately better patient care
Challenge

5. Compliance
Evolving and un-harmonized regulations
Costs
High cost of managing AEs in-house
Workload
Increased reporting of adverse events in social
media and literature
Resources
Lack of internal resources to manage the huge AE
workload
Why AI?
The growing number of ADRs
and chronic diseases will
increase the global PV market
size, which is expected to
reach $8.2 billion by 2022

6. Artificial intelligence in PV Machine
Learning
There are some extraordinary examples of AI being
developed within PV, such as auto-narrative
generation; narrative analysis (including case
extraction and creation); QC assessment; causality
assessment; and ‘touchless’ case processing,
where non-serious cases are received, verified,
coded, processed and submitted without any
human intervention

7. Drug Safety Application of AI
AE extraction from
varied data sources
• Scientific literature
• Healthcare records
• Product monographs
• Social media
Signal detection
• Multi data source combination
• AE prediction
• Severity progression
Signal validation/assessment
• Real world evidence
• PK-PD / Pharmacogenomics
Intake
• Extraction of data elements
• Triage
Processing
• Duplicate/initial/FU detection
• End-to-End workflow automation
• Narrative generation.
Assessments
• Seriousness
• Severity
• Expectedness
• Causality
• Significant FU
• Reportability
Case
processing
Signal
detection

8. Benefits of Automation
HUMAN CAN INVOLVE
INTO MORE
CREATIVE TASK
INSTEAD OF DOING
DATA ENTRY
REDUCED TRAINING
COST- TRAINED
ONCE AND USED FOR
EVER IN LIFETIME
PROCESS LIFE
CYCLE (RPA WILL RUN
24*7)
50%
Machine
Learning
Natural Language
Processing
Robotic Process
Automation API
Optical character
recognition (OCR)
INCREASED SPEED OF
SIGNAL DETECTION
IMPROVISED QUALITY – IT
WORKS WITHOUTANY ERROR

9. to improve speed and
security of adverse event
case process

10. Evaluation: differentiated between the capabilities
of vendors to identify the best candidate.
AI-based technology feasible for extracting data from
AE source documents and evaluating AE case validity.
AI and RPA to create AE reports for 3 vendors,
compared with the established Pfizer AI Center
of Excellence. ~50 000 case SD.
AI to create AE case reports feasible and
potentially cost saving to PV budgets.1
AI& Case Processing
"The ML algorithms used were able to successfully train
based solely in AE database content . . . and the multiple
combined accuracy measures allowed adjudication of the
different vendor algorithms“
1. Schmider J. Innovation in Pharmacovigilance: Use of Artificial Intelligence in Adverse Event Case Processing. Clinical Pharmacology and Therapeutics 2018.

11. will allow early detection
of potential drug-related
side effects

12. detection of potential D-R ADRs
Signal-management tools of the future will scrape the
web for meaningful data related to a particular drug.
Data and discovering resources (omics data, social
network, electronic medical records) opened great
opportunities for detecting ADRs. 2
ML & DM also quickly created various powerful
methods successful in analyzing those data
resources.
data features of historical safety issues with
other drugs will help predict future safety issues
associated with new drugs.
.
system will automatically identify analogues based
on logical constructs like drug class and
therapeutic area, as well as less intuitive factors
2. Ho TB, Le L, Thai DT. Data-driven Approach to Detect and Predict Adverse Drug Reactions. Current Pharmaceutical Design. 2016;22(23):3498-526.

13. How can we guarantee
regulatory compliance
using these tools?

14. & Regulatory Compliance
Productivity
Quality
Compliance
INCREASE QUALITY – IT WORKS
WITHOUTANY ERROR
INCREASE COMPLIANCE BY IMPACTING
TIME REQUIRED AND RESOURCES
HUMAN CAN INVOLVE INTO MORE CREATIVE TASKS
(SM – RMM) INSTEAD OF DOING DATA ENTRY and
focusing on signal detection
.
AI

15. to facilitate organizational
design and standardize
data

16. organizational design & standardize data
INCREASE RETURN ON INVESTMENT BY REDUCING
TRAINING COSTS, PROCESSING LARGE VOLUMES
OF DATA…
IMPROVE QUALITY AND ACCURACY OF PV DATA
USING STANDARDIZED INBOUNDS AND AUTOMATED
CASE PROCESSING
ALLOW HUMAN TO INVOLVE INTO MORE CREATIVE
TASK FACILITATING A SIGNIFICANT DECREASE IN
CYCLE TIMES DUE TO ACCELERATED WORKFLOWS.
AI

17. THANK YOU