HEOR, Pharmacoepidemiology & Drug Safety

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IMSHealthRWES

IMS Health Real-World Evidence Brochure. Contact us at RWEinfo@imshealth.com to receive the complete brochure.

HEOR, Pharmacoepidemiology & Drug Safety
IMS Health Real-World Evidence Solutions
An RWE Ecosystem
for Real-World Success
RWES MedSci Broc ORB01080M v2_Layout 1 03/07/2015 15:11 Page 1

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IMS Health is a global healthcare company that provides real-world evidence solutions to solve healthcare challenges. They use large datasets, advanced technologies and methodologies, and strategic expertise. Their Real-World Evidence Solutions team has opportunities for scientists, researchers, data analysts, product developers, consultants, and marketers to help clients improve patient outcomes. Employees can gain global experience, see the impact of their work, and be rewarded in a meritocratic environment. In one example, they created an algorithm to help diagnose a rare disease faster by identifying high-risk patients.

AccessPoint - 9th Issue - November 2014

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This document summarizes an article from IMS Health Real-World Evidence Solutions & HEOR. It discusses three main topics: 1) How new research using real-world evidence is revealing insights into diabetes treatment outcomes and understanding patient subgroups. This is helping to improve care provision. 2) Trends in real-world evidence, including new data strategies, greater involvement of commercial teams, and increased collaboration with external stakeholders. 3) Advancements in real-world evidence, such as improved data sourcing, predictive modeling techniques, and leveraging data from Scandinavia to enable disease-level insights. This is helping to demonstrate treatment value and support care management.

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This document provides an agenda for a workshop titled "Using real-world data to find undiagnosed patients with rare diseases" hosted by IMS Health at the World Orphan Drug Congress from April 20-22 in Washington D.C. The workshop will explore how real-world data can help address underdiagnosis of rare diseases. Speakers will discuss using large-scale real-world data to transform understanding of rare diseases, and present case studies of companies leveraging real-world data. The workshop will also cover using predictive modeling of real-world data to identify undiagnosed rare disease patients and new engagement models with healthcare providers and payers to increase rare disease treatments.

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This document discusses how predictive analytics using real-world data can help identify undiagnosed rare disease patients. It describes two case studies: 1) A screening algorithm identified potentially undiagnosed patients for a rare multi-system disease with a high risk prevalence of 20.5% compared to 0.7% of the population. 2) An analysis of a rare cardiac disease identified health system barriers like variability between diagnostic centers that could cause under diagnosis. While initial results are promising, challenges remain around data privacy, sample size, and clinician adoption of screening algorithms.

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Facts Addressing Migraine Market Misperceptions

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- The document analyzes misperceptions about migraine and its treatment. It finds that while migraine affects more women, over half of diagnosed migraine patients are over age 40, contradicting the idea that it only impacts young women. - It also finds that the majority (76%) of diagnosed adult migraine patients have cardiovascular risk factors that caution against the use of triptan medications, much higher than generally believed. Having a migraine diagnosis increases the risk of future cardiovascular events. - Current treatment patterns show triptan use is lower in high-risk patients than others, and off-label opioid use is higher, suggesting this population is underserved by existing options.

Enabling clinical trial expansion

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This document describes how real-world data was used to help expand a clinical trial for a rare disease. Specifically: 1. Claims data was analyzed to identify patients diagnosed with the rare disease and their physicians, applying inclusion criteria like diagnostic tests and drug treatments. This sized the national patient population and located diagnosing/treating doctors. 2. Physicians were mapped to local hospitals to identify areas with high concentrations of relevant patients and doctors. 3. A density analysis identified the top 20 hospitals that could serve as potential new clinical trial sites based on clusters of physicians and their patient counts. This innovative use of real-world data provided new insights to strengthen the company's rare disease research program.

EMR as a highly powerful European RWD source

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This document discusses leveraging electronic medical record (EMR) data from multiple countries in Europe to improve clinical trial design and patient recruitment for a cardiovascular drug trial. Specifically: EMR data from France, UK, Italy, Germany and Spain was used to 1) characterize and quantify the number of potential patients meeting the inclusion criteria, and 2) identify and pre-select high-potential clinical trial sites. This approach helped clarify the definition of "statin-intolerant patients" and ensured the number of required trial sites would be sufficient. Using real-world data in this way enhanced the clinical trial strategy and avoided potential delays or additional costs associated with insufficient patient recruitment.

RWE: A new way to engage IDNs

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As pharmaceutical manufacturers look for ways to build stronger relationships with their Integrated Delivery Network (IDN) clients, RWE is emerging as a desired infrastructure capability, presenting a window of opportunity to support and collaborate on IDN efforts. If done well, these RWE-related partnerships should provide value for both parties involved but require pharma to expand its mindset beyond product-specific approaches.

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Welcome to the IMS Health Real-World Evidence (RWE) Solutions program of activities at the forthcoming ISPOR 18th Annual European Congress in Milan. We invite you to join us at more than 70 presentations that spotlight exciting innovations and applications of outcomes research and RWE. And please visit us at our booth to learn more about our pioneering efforts including the e360TM technology suite, RWD Catalogue with 1,800+ data sources identified, and the newly launched RWE Dictionary. Full details of our ISPOR schedule can be found in the brochure.

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1) Researchers developed the DIAREG registry in Germany using a mixed methods approach to overcome limitations of electronic medical record databases which often lack critical variables. 2) The registry links retrospective data from electronic medical records with prospective clinical data collected through electronic case report forms and patient-reported outcomes to create a rich data resource. 3) As of September 2014, the DIAREG registry contained data on 1,071 diabetes patients in Germany, enabling granular observational research on relationships and outcomes that were previously difficult to study.

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