Tom Achoki of Mass Sciences explores the possibilities that better access to high-quality real-world data will create for accelerating novel drug discovery and ultimately improving patient outcomes.
Proactive decision makers within the biopharmaceutical industry need to find innovative ways to bridge the existing RWD gaps within their organization
The biopharmaceutical industry is charged with the responsibility of developing and delivering safe and high-quality medicines and other healthcare products needed to improve health. Access to high-quality real-world data (RWD) has become an integral part of this process, providing insights into the discovery, development and delivery of medicines. In addition to clinical trial data that is used to determine the safety and efficacy of medicines, access to RWD from patient interactions with the healthcare system could be useful in determining the effectiveness of medicines.
There is increasing evidence indicating that RWD could be vital in accelerating the discovery of novel medicines, maximizing the potential for the clinical and commercial success of products, and ensuring improved access to effective products, leading to better population health outcomes. Further, regulatory and market access considerations for safety, efficacy and cost-effectiveness could be satisfied by innovatively analyzing RWD from different sources within the healthcare system. This is particularly relevant given the recent trends where the focus is on delivering value to the patient with measurable outcomes, rather than on the volume and quantity of services provided.
The United States Government in its 21st Century Cures Act (Cures), recognizes the important role of data access as a driver of biomedical innovation. Further, the Pharmaceutical Research and Manufacturers of America (PhRMA), recommends the use of real-world evidence and observational data to establish the benefit of medicines as part of the push towards value-based healthcare delivery. Most recently, the US Food and Drug Administration (USFDA), launched a framework for real-world evidence programs to provide guidance for stakeholders on potential opportunities to leverage RWD.
All these trends point to the fact that proactive decision makers within the biopharmaceutical industry need to find innovative ways to bridge the existing RWD gaps within their organization. This can only be effectively accomplished by understanding the different stakeholders in the RWD landscape, existing models of data access and opportunities for innovation and disruption.
Understanding the Stakeholders
In the healthcare seeking process, patients may interact with various stakeholders, including healthcare providers, such as clinics and hospitals, pharmacies, diagnostic laboratories, insurance companies and in some cases employers and social workers, to name but a few. This process generates copious amounts of RWD that can be useful for decision making within the biopharmaceutical industry when analyzed jointly. This may include information into the channels patients use to access specific medicines, amounts paid, adherence and compliance patterns, as well as patient-reported and clinical health outcomes, among others. In addition, in their day to day living, patients also interact with other stakeholders beyond the healthcare value chain, such as retailers and transport systems, which can provide insights into behaviours and lifestyle choices that have an impact on health. The figure below provides an illustration on how this data could be brought together into a joint analytical framework to generate useful evidence for decision making.
Data Access Models
Given that high quality RWD has become a highly sought-after commodity within the biopharmaceutical industry, various business models have emerged to meet the need. The differences in structure of these models is not necessarily very clear cut and in some cases, there is some significant overlap in their attributes. The main models for RWD access are briefly described below:
Private Data Vendors
Some firms and institutions that have the capacity to curate data from multiple sources and generate useful insights are commercializing their capabilities and extending data access to biopharmaceutical companies. Many of these data vendors or data aggregators were structured in fee-for-service or subscription-based models, where they provide data driven informatics based on the integrated data sets.
Standalone Data Analytics Divisions
Companies that have privileged access to large amounts of health data such as large health insurance firms, medical and hospital groups have established separate data analytics divisions to leverage these data sets. These entities often enter into commercial arrangements, with biopharmaceutical companies to tap into this data resource.
Non-Profit Local Data Stores
These are mostly academic research institutions or non-profit agencies, including those in the public sector that are custodians of specific datasets based on the nature of their work or specific mandate in healthcare delivery. In order to access those data, external parties request permission to use datasets for specific analysis. Normally, the data requestor submits a research protocol that is reviewed by an independent panel and if approved, the requestor is allowed to view and analyze the data for a specified period of time.
Private Venture-Backed Data Aggregators and Analytics Companies
Recently, there has been an emergence of a new kind of data aggregator companies backed by major venture capital firms and often partnered with big pharma companies. Some of the conspicuous examples of this trend include companies like Flatiron Health, CanceIQ and Aetion.
Federated Data Access Models
Given that no single institution has all the data it needs to make a decision, there have been efforts towards collaboration. In a federated data access model, individual databases are linked in such a way that they can respond to queries from authorized users as if the data were hosted in a single database. Therefore, it is possible to access the collective power of all the data within the network, without submitting multiple queries, making this model attractive. In addition, the data owners, still retain full control of their data and only share the allowable features. The figure below provides an example of a federated data access model.
Opportunities to Push the Frontiers
With the rapid technological expansion in terms of data generation, storage and capacity to deploy analytical tools, the federated data access models present a unique opportunity for the biopharmaceutical industry and the broader healthcare space. With the right incentives to involve key stakeholders in the healthcare value chain, federated models could evolve into a sustainable data analytics market place, where specific datasets could be seamlessly accessed and analyzed by interested parties without incurring extensive infrastructure and setup costs.
To move towards this objective, it will be important to understand the incentives of different stakeholders and find alignment. Further, strong governance systems and ground rules from the outset are essential for long terms success of any data access initiative that involves a number of stakeholders. A “common language” across the federated network is essential and can be achieved through comprehensive efforts to standardize datasets among partners to facilitate ease of access, sharing and analysis. Lastly but not least, organizational and platform adaptability and leveraging technology to expand access is another attribute of successful data access model.
About the Author
Tom Achoki MD, PhD is a Co-founder and Chief Business Officer of Mass Sciences, an integrated data analytics company based in Boston US. He was previously a Sloan Fellow at M.I.T, where he focused on the role of data as a driver of innovation in the healthcare industry. He can be reached at: firstname.lastname@example.org