Explore IQVIA’s transformative AI capabilities
Discovery
Clinical
Safety
Market Access
Med Affairs
Brand
Sales
Research & Development
Real World
Commercial
Create biomarker database
Increase clinical trial participation
Decrease risk liability
Predict treatment
response
Identify qualified patient leads
Measure HCP response to marketing
Click to read our customer success stories
Biomarker database created with IQVIA Natural Language Processing (NLP)
This led 1M facts to be extracted and integrated—including
12,000 genes
3,500 diseases, and 340k associations
Challenge: Diagnostic researchers from a top 10 pharma company needed to overcome barriers preventing ready access to disease-marker associations.
Solution: They developed an automated workflow for IQVIA Natural Language Processing (NLP) to extract facts from MEDLINE, NCBI GeneRIFs (reference-into-function), and in-house sources. This data was loaded into their knowledge base “DiMA” for search portal and dashboards.
Results: The dashboard graphic user interface (GUI) allows end users to filter information by marker or disease. It also offers visualisation of associations (expression, genetic variation, negative associations) to improve effectiveness.
1M
seamlessly extracted
and integrated—including:
12,000 genes
3,500
diseases
and
340k associations
1M facts
Research & Development
Discovery
Increasing early identification and recruitment of patients with Alzheimer’s Disease
This led 1M facts to be extracted and integrated—including
12,000 genes
3,500 diseases, and 340k associations
Challenge: More than 100+ Alzheimer’s disease (AD) clinical trials have been unsuccessfully completed over the past two decades due to difficulties identifying, recruiting and retaining qualified participants. As a result, AD treatments are limited.
Solution: Using predictive analysis and machine learning to quickly sort through data assets—including claims, electronic medical records, and prescription data—we identified patient populations with AD.
Results: These findings, which achieved 80% predictive algorithm precision, are now making a valuable contribution to trial referral networks to further development of new treatments.
1M
(76% through primary care settings)
for Alzheimer’s Disease
223K PATIENTS
Research & Development
Clinical
predicted as high risk
Improving the capture of adverse events in MedDRA
This led 1M facts to be extracted and integrated—including
12,000 genes
3,500 diseases, and 340k associations
Challenge: Adverse events (AE) are often captured in natural language that doesn’t readily fit into MedDRA classifications. This requires someone to manually code the AE to determine where it fits.
Solution: We built IQVIA Natural Language Processing (NLP) query strategies that work with Oracle Argus safety case management system to extract AEs described in verbatim language in individual case safety reports (ICSRs) and automatically translate them into the MedDRA classifications.
Results: This NLP solution doubled the amount of auto-coding possible (from only 30% auto-coded AEs to over 70%), while reducing the manual time required and improving coding consistently, thus ultimately decreasing risk liability.
1M
Eliminated the need to manually code
(saving 1-30 minutes each) for one pharma company
1k Adverse Events
Real World
Safety
monthly
Improving early diagnosis and prediction of treatment response in people with Alzheimer’s Disease (AD)
This led 1M facts to be extracted and integrated—including
12,000 genes
3,500 diseases, and 340k associations
Challenge: We needed to develop better ways to identify people with AD at an earlier stage and predict how they will respond to treatment.
Solution: Working with a large-scale electronic medical record (EMR) vendor and treating physicians, we developed a predictive tool that integrates into the EMR systems to support physician-patient engagement.
Results: This tool helps clinicians better determine optimal treatment type and dosage for people with AD. It also has potential to engage patients in helping develop effective treatment plans.
1M
Real-world data from
helps identify key clinical and demographic predictors of Alzheimer's Disease
40 specialist centers
Real World
Market Access & Medical Affairs
and
25k non-identified
patients
Using Artificial Intelligence & Machine Learning to expand patient pathways
This led 1M facts to be extracted and integrated—including
12,000 genes
3,500 diseases, and 340k associations
Challenge: A pharma company with a specialty biologic for severe asthma wanted to expand the reach of a rules-based trigger program to better identify eligible patients in a more time-effective way.
Solution: We helped the client implement a more flexible, dynamic, and predictive targeting model and worked closely with the client brand, medical, sales ops, analytics, and legal teams to ensure a seamless transition.
Results: This helped the company get better leads more efficiently to meet its targets over a 3-year deployment period.
1M
5x
Commercial
Brand
increased precision
increase in new patient initiation
20%
return
on
investment
~$5.8M
Tailoring multi-channel marketing to better engage healthcare professionals (HCPs)
This led 1M facts to be extracted and integrated—including
12,000 genes
3,500 diseases, and 340k associations
Challenge: Every clinician has different preferences for communication methods and channels, which can make it complicated to identify the best approach for the target audience.
Solution: We applied customized machine learning techniques to dynamically measure HCP responsiveness to promotions using a wealth of data, plus sensitivity scores by channel and time, to prioritize which physicians to reach, and the best channel to reach them.
Results: We identified who, when, and how to effectively promote across channels. This allowed us to optimize channel synergy and investment.
1M
Commercial
Sales
within 7 months of implementation
revenue increase at no additional cost
$20M+
