Real-World Evidence is based on Real-World Data, so to understand Real-World Evidence, we need to cover data first!
Real-world data is defined as any data collected in the context of routine care delivery, as opposed to data collected in a clinical trial where the study design controls variability in ways that are not representative of real-world care and outcomes.
The U.S. Food and Drug Administration (FDA) defines Real-World Evidence as “the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of Real-World Data.”
Unlike traditional clinical trials, where required data elements can be curated and collection mandated, the development of Real-World Evidence necessitates assessing, validating, and aggregating various, often disparate, sources of data available through routine clinical practice.
Real-World Evidence creation necessitates a combination of powerful analytics, a validated approach, and a thorough understanding of available Real-World Data sources (e.g. what data is captured within existing quality registries, what data can be captured through an EHR or claims, which patient organisations capture data on relevant patient cohorts).
This procedure consists of several steps:
- Creating a study protocol that addresses relevant clinical questions.
- Specifying which data elements can be gathered from which RWD sources.
- Creating data capture agreements and protocols with existing RWD sources.
- Using probabilistic record matching algorithms to combine disparate data sources.
- Using editable eCRFs to validate and supplement blended data.
- Defining and calculating clinically meaningful outcomes and measures.
- Assessing and controlling for variability in data quality, availability, and confounding patient factors influencing measured outcomes.
RWE can provide a comprehensive view of patients that traditional clinical trials cannot in many cases.
Types of RWD
There are numerous types and sources of RWD. Let’s take a closer look at each type, its origin, and its applications.
Clinical information is derived from electronic health records (EHRs) and case report forms (eCRFs). This data contains information about the patient’s demographics, family history, comorbidities, procedure and treatment history, and outcomes.
Patient-generated information derived from patient-reported outcome (PRO) surveys. These data provide direct patient insights and help researchers understand what happens outside of clinic visits, procedures, and hospital stays.
How does Real-World Evidence and Real-World Data help?
The drug development process takes 8 to 15 years, costs up to $11 billion, and is based on an expensive and frequently inefficient clinical trials process, as well as expensive, sparse data (often only claims data) that does not provide a complete picture of patient health. For example, claims data will show that a patient fills a prescription but will not provide information on outcomes, side effects, or other factors.
Life science companies can improve this process and save money by collaborating with a healthcare transformation company to gain access to and leverage extended RWD and RWE to better understand the populations that use their drugs and their outcomes. The following knowledge areas will enable a fair, outcomes-driven healthcare system as the entire system (regulators, payers, manufacturers, and providers) aligns around outcomes:
• Real-life patient care (e.g., care variability between and within health systems).
• Measuring outcomes in real-time across a range of provider types, shapes, sizes, and locations.
• Understanding the clinical processes that lead to those results.
