As a health economist, researcher in evidence-based medicine, and consultant to biopharma executives, I am no stranger to data. We all want more of it, to make sense of it, and to use it to drive our decisions — business and clinical. Every health industry leader knows the massive value of data and the equally large headaches that can be involved to source it, analyze it, and earn a return on investments.
In my consulting years, I regularly encountered life sciences execs who bemoaned their spend (several million dollars annually) on dataset subscriptions yet had little understanding of whether or not their businesses had derived value. Only a handful of players in the sector have been able to treat data as a product, according to McKinsey.
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Jennifer Hinkel Jennifer Hinkel is a Managing Director of the Data Economics Company and a DPhil researcher in Evidence Based Health Care at the University of Oxford.
Today that landscape is shifting, however.
Healthcare providers and care delivery organizations, particularly major institutions and integrated delivery networks, or IDNs, have now realized that their data is extremely valuable, and they want in on the mix. Patients, given the right to full, free, digital access to their data via the 21st Century Cures Act, are beginning to notice that companies and institutions are aggregating and re-selling their data for profit. Life sciences leaders are eager to find ways to connect directly with both patients and providers to obtain high-quality, consented, proven-provenance data without aggregators acting as middlemen. None of these parties will remain satisfied with the status quo for long.
Data economics
An emerging scientific field, data economics, can inform how we generate health data, decide who should own and control it, assign value to it, and create rational and fair systems for data utilization, sharing, and compensation. Data economics can reduce data silos and drive not only more efficient care delivery but also the incentivization of better health outcomes.
A Deep-dive Into Specialty Pharma
A specialty drug is a class of prescription medications used to treat complex, chronic or rare medical conditions. Although this classification was originally intended to define the treatment of rare, also termed “orphan” diseases, affecting fewer than 200,000 people in the US, more recently, specialty drugs have emerged as the cornerstone of treatment for chronic and complex diseases such as cancer, autoimmune conditions, diabetes, hepatitis C, and HIV/AIDS.
Until recently, healthcare providers, and probably many patients, have thought of the data they generate more like “exhaust,” a byproduct of their operations, rather than as a valuable digital confirmation of their work and activities. Now many are shifting their perspective towards a data economics mode of thinking. If healthcare delivery tasks generate billing codes, for example, doesn’t that piece of data represent the valuable input of a clinician’s labor along with real-world patient care and patient health outcomes? Patients, also, can be data generators, whether reporting on adherence to therapy or sharing a biospecimen for sequencing. The resulting data has been productized by aggregator companies. Why can’t providers, institutions, and patients do the same?
Previous wisdom would tell us that the data of any one physician or patient isn’t valuable enough to market, or that doing so would make it impossible to preserve privacy, hence the value-add of data aggregation companies and brokers. The science of data economics demonstrates the fallacy in this assumption. New technologies can enable ownership, access control, and privacy while still allowing that data to participate in networks for analysis, whether by humans or AI-driven machines. Data aggregation becomes less interesting when analysis tools are more powerful; data curation becomes its own discipline.
In this new landscape, providers don’t have to choose between ringfencing their data or sharing it to drive new discoveries or better care efficiencies. They can have both. Data economics treats data like a product, or asset, that affords future benefits, by informing methods to package and safeguard it. Mechanisms to verify provenance, consent, ownership, licensing, and more contextual features can label, preserve and track data assets. While these mechanisms are primarily technological in nature, they can also be legal, or a combination of both embedded in “smart contracts” that implement legal agreements into computer code.
The result is a world where providers, patients, payers, life sciences companies, biotech investors, and others can assign values to various data assets in a data market using the same regulatory and ethical principles that govern other asset values. These prices and values may be highly contextual. Oncology patient and provider data is valuable to a company leading discovery on cancer drugs with a platform for CAR-T or bispecific antibodies; it is likely less valuable to a company developing Alzheimer’s therapies.
The upside of data utility
Importantly, the mechanisms and frameworks enabled by data economics can ensure that data generators participate in the upside of data utility. If your data plays even a small part in another party’s profitability, you can receive credit. In the past, patients have had little incentive to share data with providers before or after care, for example. If they benefited more, they would likely supply more. Likewise, current incentive structures often put payers, providers, and patients at odds, with each group hesitant at times to share data with the others, as PwC recently explained. Data economics can help align incentives by appropriately valuing data exchange.
Once multiple healthcare providers, patients, or other stakeholders create a market or community whose members use the same mechanisms to manage their data, these “data economic networks” and their members can share, trade, or use data much more easily and effectively. The mechanisms that make these networks possible are comparable to shipping containers that allow for the easy transfer of diverse goods within an agnostic, uniform infrastructure. Ships and ports handle the containers with appropriate paperwork, but aren’t permitted to open them; likewise, in a data economic network, data containers can signal their contents without betraying privacy or transferring ownership. Rather than depending on a single arbiter of price, decentralized networks that leverage distributed ledger and blockchain technologies can help ensure trust and generate consensus among participants.
Both providers and patients benefit when containerized data and data economic networks are implemented. They streamline data management and offer potentially exponential value opportunities, boosting revenues. Previously unconnected datasets can be “crawled” by AI algorithms without that data ever leaving its point of generation and without containers needing to be opened or transferred, potentially leading to new scientific discoveries that improve care. Patients and stakeholders are more engaged, in contrast to, for example, social media sites where they provide data for free but are at risk of privacy loss and other downsides. Data economic networks, furthermore, allow for massive scalability as many parties can participate in data generation and utilization, all while maintaining their own “vaults” without having to rely on any single source of data protection or centralized system or format.
Valuable digital data is a relatively new phenomenon in the history of healthcare. Providers need to think of it in a new way — not as a byproduct, but as another valuable outcome they are generating as they do their jobs and care for patients. Their data assets can generate enormous value for their practices, their institutions, for patients, and for science.
Photo: from2015, Getty Images
Jennifer Hinkel is a Managing Director of the Data Economics Company and a DPhil researcher in Evidence Based Health Care at the University of Oxford.
