Real World Data in Adaptive Biomedical Innovation: A Framework for Generating Evidence Fit for Decision Making: Healthcare Databases with Rapid Cycle Analytics to Support Adaptive Biomedical Innovation.

Analyses of healthcare database (claims, EHR) 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, HTA, 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 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. This article is protected by copyright. All rights reserved.

Investigators
Abbreviation
Clin. Pharmacol. Ther.
Publication Date
2016-09-14
Pubmed ID
27627027
Medium
Print-Electronic
Full Title
Real World Data in Adaptive Biomedical Innovation: A Framework for Generating Evidence Fit for Decision Making: Healthcare Databases with Rapid Cycle Analytics to Support Adaptive Biomedical Innovation.
Authors
Schneeweiss S, Eichler HG, Garcia-Altes A, Chinn C, Eggimann AV, Garner S, Goettsch W, Lim R, Löbker W, Martin D, Müller T, Park BJ, Platt R, Priddy S, Ruhl M, Spooner A, Vannieuwenhuyse B, Willke RJ