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NLP Data Factory
Mobilize your textual data with enterprise NLP extraction at scale
At the intersection of data science and healthcare
With data volume increasing exponentially, AI and machine learning capabilities are more powerful than ever. There is unprecedented potential for healthcare.
To realize that potential, you need to answer increasingly complex questions that require not only cutting-edge data and technology, but also the right expertise.
By bringing these capabilities together for you, we help make intelligent connections and deliver healthcare-grade solutions – enabling you to uncover deeper insights, meet growing demands, and make better decisions.
IQVIA uses AI to help customers from clinical to commercial to enhance precision, increase speed, and scale to meet evolving challenges.
- Improve design and execution of clinical trials
- Find correlations that enable early disease detection
- Transform sales and marketing outcomes
- Turn unstructured text into structured, actionable data
- Increase automation and operational efficiencies
- Access AI insights via scalable, secure platforms
CASE STUDIES
Improving patient outcomes and closing care gaps
IQVIA is working with life sciences companies, governments, and non-profit organizations to power smarter healthcare with advanced AI capabilities.
Closing care gaps with Social Determinants of Health (SDoH)
Challenge: Social factors such as employment status, financial situation, or stress are useful predictors of health outcomes but are often only captured in unstructured physician notes. For example, NorthShore University HealthSystem found only 0.1% of patient records had these fields completed.
Solution: IQVIA assisted NorthShore with the use of Natural Language Processing (NLP) to assess that unstructured data across multiple sources.
Results: NorthShore is now able to help close care gaps by identifying and screening 56% more at-risk patients. In one example, they were able to identify at least one SDoH risk factor in 30% of their patient population (up from only 0.1%).
Solution: IQVIA assisted NorthShore with the use of Natural Language Processing (NLP) to assess that unstructured data across multiple sources.
Results: NorthShore is now able to help close care gaps by identifying and screening 56% more at-risk patients. In one example, they were able to identify at least one SDoH risk factor in 30% of their patient population (up from only 0.1%).
Reducing risk of stroke for AFib patients
Challenge: Atrial Fibrillation (AFib) patients are 5x more likely to have a stroke. IQVIA partnered with the UK National Health Service to reduce AFib related strokes through identifying at-risk patients.
Solution: The risk of stroke for AFib patients was predicted using EMR data including age, gender, and clinical risk factors (e.g. Congestive Heart Failure, Hypertension, Stroke/Transient Ischemic Attack, Diabetes, Vascular Disease).
Results: Annul strokes reduced by approximately 22% during the implementation phase compared to the prior period. This also led to an estimated reduction in healthcare costs amounting to an annual savings of approximately $2m.
Solution: The risk of stroke for AFib patients was predicted using EMR data including age, gender, and clinical risk factors (e.g. Congestive Heart Failure, Hypertension, Stroke/Transient Ischemic Attack, Diabetes, Vascular Disease).
Results: Annul strokes reduced by approximately 22% during the implementation phase compared to the prior period. This also led to an estimated reduction in healthcare costs amounting to an annual savings of approximately $2m.
Improving care for patients with Type 1 Diabetes
Challenge: Nearly 40% of adults with Type 1 Diabetes are initially misdiagnosed with Type 2. IQVIA worked with the Juvenile Diabetes Research Foundation (JDRF) to identify Type 1 patients most likely to be misdiagnosed.
Solution: IQVIA developed an AI algorithm on EMR data, to identify misdiagnosed Type 1 among patients with Type 2 at different points in the patient’s treatment journey.
Results: Inputs into the AI algorithm are routinely collected in EMR data – thus available within any Healthcare Organization (HCO) providing opportunity to deploy the program at scale. The algorithm is currently being prospectively validated at 4 US HCOs testing 2 different scaling paths.
Solution: IQVIA developed an AI algorithm on EMR data, to identify misdiagnosed Type 1 among patients with Type 2 at different points in the patient’s treatment journey.
Results: Inputs into the AI algorithm are routinely collected in EMR data – thus available within any Healthcare Organization (HCO) providing opportunity to deploy the program at scale. The algorithm is currently being prospectively validated at 4 US HCOs testing 2 different scaling paths.
Enhancing routine care for diabetes and CVD
Challenge: The government of one Middle East country is investing in AI to make better healthcare decisions in the diagnosis and treatment of both diabetes and cardiovascular disease.
Solution: IQVIA developed 14 decision support tools that can be used by physicians to help guide screening, improve preventative care and optimise disease management.
Results: The program is being delivered by a cross-functional team of local and global IQVIA experts over a three year period.
Solution: IQVIA developed 14 decision support tools that can be used by physicians to help guide screening, improve preventative care and optimise disease management.
Results: The program is being delivered by a cross-functional team of local and global IQVIA experts over a three year period.
Identifying patients at risk for respiratory disease
Challenge: A client launched a new product to treat respiratory disease. They needed help identifying patient cohorts with this specific unmet need to then reach physicians most likely to have these patients.
Solution: IQVIA deployed the AI/ML Platform capability to identify and profile eligible patient populations with unmet needs. The patients were profiled based on sub-national concentration, demographics and other clinical drivers, with focus on physicians treating them.
Results: Patients predicted for high likelihood to have respiratory disease with 86% precision, while removing approximately 30% of non-target patients from the target population data. Furthermore, a 6-month analysis of patients on this new treatment showed a 17% reduction in uncontrolled disease status.
Solution: IQVIA deployed the AI/ML Platform capability to identify and profile eligible patient populations with unmet needs. The patients were profiled based on sub-national concentration, demographics and other clinical drivers, with focus on physicians treating them.
Results: Patients predicted for high likelihood to have respiratory disease with 86% precision, while removing approximately 30% of non-target patients from the target population data. Furthermore, a 6-month analysis of patients on this new treatment showed a 17% reduction in uncontrolled disease status.
AI Solutions Suite Animated Infographic
IQVIA’s latest infographic shows how our platform of interconnected apps can enhance your commercial effectiveness.
Improving Decision-Making through Connected Intelligence
This report examines the fundamental elements of connected intelligence that feed into decision-making at life sciences companies: building meaningful connections in data and technology, elevating skills to generate insights, and shaping the organizational structure and culture to share, link and utilize those insights.
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