Data Rich, Information Poor: Can We Use Electronic Health Records to Create a Learning Healthcare System for Pharmaceuticals?

Abstract

Judicious use of real-world data (RWD) is expected to make all steps in the development and use of pharmaceuticals more effective and efficient, including research and development, regulatory decision making, health technology assessment, pricing, and reimbursement decisions and treatment. A "learning healthcare system" based on electronic health records and other routinely collected data will be required to harness the full potential of RWD to complement evidence based on randomized controlled trials. We describe and illustrate with examples the growing demand for a learning healthcare system; we contrast the exigencies of an efficient pharmaceutical ecosystem in the future with current deficiencies highlighted in recently published Organisation for Economic Co-operation and Development (OECD) reports; and we reflect on the steps necessary to enable the transition from healthcare data to actionable information. A coordinated effort from all stakeholders and international cooperation will be required to increase the speed of implementation of the learning healthcare system, to everybody's benefit.

Report on the current status of the use of real‐world data (RWD) and real‐world evidence (RWE) in drug development and regulation

“…Life sciences have become fertile ground for employment of RWD to improve many procedures in the delivery of health care, including research and development of pharmaceuticals, regulatory decision making, health technology assessment (HTA), pricing and reimbursement decisions, and treatment. The potential for RWD to contribute to a “Learning Healthcare System” has been cogently articulated by Eichler and his colleagues …”

“…The potential for RWD to contribute to a "Learning Healthcare System" has been cogently articulated by Eichler and his colleagues. 1 RWD can be defined as "data related to healthcare status, routinely collected from a variety of sources, outside of randomised clinical trials (RCTs)." 2 These sources include primary and secondary patient care records such as those in electronic health records (EHRs), insurance claims data, routinely collected administrative data, product and disease registries, and emerging observational sources such as social media and data collected from mobile devices and apps.…”

Are Novel, Nonrandomized Analytic Methods Fit for Decision Making? The Need for Prospective, Controlled, and Transparent Validation

“…However, the need for quality assurance and control procedures has been recognized and a range of initiatives are aiming to bring RWD quality to a level of regulatory acceptability. Collaborations among stakeholders and opportunities for data processing and quality improvement are constantly growing . Progress will likely happen in fits and starts but we foresee a future where healthcare data from different sources and of sufficient quantity and, eventually, quality will be available for rapid secondary analysis by researchers.…”

“…Collaborations among stakeholders and opportunities for data processing and quality improvement are constantly growing. 3,4 Progress will likely happen in fits and starts but we foresee a future where healthcare data from different sources and of sufficient quantity and, eventually, quality will be available for rapid secondary analysis by researchers. Some secondary use of RWD is well established 5 (e.g., drug utilization, disease epidemiology, or safety evaluation).…”

“…The broad range of research questions that might be addressed with the help of new data sources have been described elsewhere. 4,8…”