Achieving a multi-angle view of the patient

Seeing the same patient in different data sets is a major challenge in RWR. How can you do it, with less time, expense and risk?
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lulu k lee
Lulu K
Lee, Ph.D.

Vice President Health Outcomes Research

Tom
Haskell

Global Head of Innovation, Real-World Data & Analytics

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To fully demonstrate the value proposition of their products to various constituents, life sciences and pharmaceutical companies need highly accurate and timely information from both the physician and patient’s perspectives. With patient input playing an increasingly important role in pharma decision-making, today there’s an increased focus on gaining a clearer picture of the patient from multiple angles.

However, achieving this multi-angle view of the patient is highly complex. In the past, producing a comprehensive view of the patient often required companies to conduct large linked prospective studies. The problem here is that these types of studies are not only time consuming – often taking one to three years to complete – but also very costly. On top of that, there’s significant risk involved because companies really don’t know whether the evidence generated by the studies will be favourable until the end of long engagement periods.

For many years, the goal of life sciences companies has been to find a way to secure the information they need without having to deal with the time commitment, expense or risk involved with the previous collection of studies. Kantar sought to solve this challenge by linking its syndicated patient reported outcomes (PRO) data with clinical data. We were successful, recently introducing ClaritisTM, a breakthrough approach to real world data analytics.

This linked data method delivers significant benefits – allowing for better evidence generation, greater clarity and a more complete view of the patient. It offers a more holistic understanding of the patient, giving life sciences companies a longitudinal view of the patient’s journey and a more comprehensive view of the patient due to the richness of the dataset. Also, by linking data, companies can conduct a single, unified study instead of two to three disparate analyses.

The ability to look at the same individual in different data sets represents a major advancement in evidence generation for real world research and supports a host of evidence generation scenarios, including: providing payer agencies with the required evidence to support reimbursement decisions, supporting regulatory bodies that require evidence of real world safety and effectiveness, as well as generating critical information that’s used by other healthcare stakeholders, including healthcare professionals, patients and caregivers.

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