Real World Data in Adaptive Biomedical Innovation: A Framework for Generating Evidence Fit for Decision‐Making

Abstract

Analyses of healthcare databases (claims, electronic health records [EHRs]) are useful supplements to clinical trials for generating evidence on the effectiveness, harm, use, and value of medical products in routine care. A constant stream of data from the routine operation of modern healthcare systems, which can be analyzed in rapid cycles, enables incremental evidence development to support accelerated and appropriate access to innovative medicines. Evidentiary needs by regulators, Health Technology Assessment, payers, clinicians, and patients after marketing authorization comprise (1) monitoring of medication performance in routine care, including the materialized effectiveness, harm, and value; (2) identifying new patient strata with added value or unacceptable harms; and (3) monitoring targeted utilization. Adaptive biomedical innovation (ABI) with rapid cycle database analytics is successfully enabled if evidence is meaningful, valid, expedited, and transparent. These principles will bring rigor and credibility to current efforts to increase research efficiency while upholding evidentiary standards required for effective decision-making in healthcare.

Choosing Among Common Data Models for Real‐World Data Analyses Fit for Making Decisions About the Effectiveness of Medical Products

“…Of course, there is the assumption that the underlying data are detailed and complete enough to answer the question at hand. 10 Adaptability to fit the needs of a specific study question Investigators producing evidence for decision making require CDMs to be adaptable for two main reasons:…”

“…We identify three generic types of data models and assess how they may affect the efficiency (ease of use), transparency, and adaptability to fit a specific research question. Our intention is to present factors that improve the confidence decision makers have in real‐world evidence . Others have written more comprehensive and more technical reviews of data models in health care …”

“…Our intention is to present factors that improve the confidence decision makers have in real-world evidence. 10,11 Others have written more comprehensive and more technical reviews of data models in health care. 6,12,13…”

The ENCePP Code of Conduct: A best practise for scientific independence and transparency in noninterventional postauthorisation studies

“…The ENCePP network has also helped to facilitate the introduction of amended regulatory concepts and amended frameworks specifically in the field of epidemiology for the conduct of postauthorisation safety studies (PASS), and especially following also regulatory requirements coming into force with the EU pharmacovigilance legislation in 2012 . Evolution of the ENCePP network continues to support the work of EU regulatory agencies mirroring also the increased demand for generation of post‐approval data . ENCePP guidance documents are seen as important resource and form part of recommended references and resources of regulatory guidance such as GVP Modules.…”

The Life Cycle of Health Technologies. Challenges and Ways Forward

“…As previously mentioned, the value of health technologies in routine care differs from that promised in theory and according to trials in ideal conditions. That's why HTA bodies, regulators, payers, health professionals, and patients are claiming for the establishment of mechanisms that headed to the generation of evidence in real practice what has been called “real world data” (Schneeweiss et al, 2016). Real world data generation supports other actions such as: “Manage Entry Agreements” (Klemp et al, 2011), adaptive licensing for innovations market authorization (Schneeweiss et al, 2016), and public procurement and prices negotiation, based on comparative effectiveness, when demonstrated value.…”