Datavant’s vision is to organize the world’s health data. We believe that this is one of the most important data challenges of this era: if we are successful over the next 20 years, we are confident that our work will improve patient outcomes, bring medicines and medical solutions to market faster, reduce costs, inspire entrepreneurship, fight disease, and improve lives. Here’s how.
Part 1: Why Data Must Be Organized
We believe that data has the power to transform healthcare. In the coming decades, the advanced use of data and analytics has the ability to transform everything from how scientists discover drugs to how pharma runs clinical trials, regulators approve drugs, doctors select treatments for patients, and payers measure value and reimburse. The advanced use of data can unlock:
- Faster & lower-risk drug development. Clinicians, scientists, and data scientists around the world still struggle with basic questions. Will this drug work? Why did this drug fail? Was it biology, trial logistics, dose, or patient selections? Seamlessly linking new data types with traditional data in a real-time and fluid manner will bring new insights and more advanced study design.
- Better understanding of diseases, leading to new treatments. Many genetic and socioeconomic causes of disease progression are poorly understood, and deep understanding of the underlying data modalities is still nascent. Novel data sets coupled with hypothesis-driven and hypothesis-free technological approaches offer the opportunity to derive clinical benefits from these data.
- Better patient care. Medical practice remains highly experiential and anecdotal, while advances in disease understanding continue to stratify patients and treatment options into an almost endless variety of possibilities. Clinical support tools can be greatly enhanced through the improved timeliness, completeness, and comprehensiveness of real-time data.
- A cheaper health system that ties cost to value. It is difficult to measure the value that a treatment actually has in the real world. With better data, economic incentives could be tied to the real value of a treatment for a given patient.
Unfortunately, data siloing holds back these possibilities. Against the backdrop of what we could accomplish with a data-driven approach, there is the stark reality that most data is extremely siloed.
There are many types of silos holding progress back, such as:
- Technology silos between databases, analytics tools, and even mobile phone platforms
- Institutional silos — healthcare delivery is fragmented but novel clinical insights often require a comprehensive view of the individual and of populations.
- Competitive business silos when multiple drug companies are developing similar compounds against the same targets and pulling from the same patient pool
- Incentive silos where internal competition, fear of failure, and human nature gets in the way of collaboration
- Federal silos that keep healthcare data from flowing across agencies with complementary missions
- State silos that confound demographic epidemiology at an aggregate level across states
Consider the domain of “real world data” (i.e. data about what happens to patients outside the context of clinical trials). The average person has data split across dozens of databases over the course of their lives: 5+ different primary care physicians, 10+ different specialists’ or hospitals’ EHR systems, 5+ insurers’ databases, 5+ pharmacies’ databases, and many additional emerging sources such as wearables and genetic profiles.
The parties that control each source of data are often unwilling or unable to directly share data, making any data-driven analysis extremely difficult to conduct. Patient privacy, data security, and misaligned commercial incentives have created significant bottlenecks to combining more comprehensive data sets, and each must be addressed for data to reach its potential in medicine.
Part 2: How Datavant will Address this Challenge: Organizing The World’s Health Data
Our focus is on removing the silos that have held back analytical research in the past. Our products reduce the friction involved in connecting data between different data sources, with technology focused on patient linking, data permission management, and data discovery — each of which serve to unlock more data sharing. We aim to become the trusted steward that is ultimately used by all healthcare data owners and data consumers to safely combine datasets for research purposes.
As we build our business, we are aligned with healthcare data owners and the value we help them create with their data. We make money by creating products that make it easier to for healthcare institutions to combine, share, and unlock the value of data sets.
There are five principles that drive our efforts.
1. We put the patient first: patient privacy and patient benefit are at the center of every decision we make. We believe that sophisticated use of healthcare data should serve patients. Better data can yield better treatments, better drugs, and a better healthcare system overall. We also believe that patients have the right to control how their data is used, and that patients have a right to their data being handled securely. We have built and will continue to build de-identification tools and data visibility tools to ensure organizations can honor patient choices, and we will always put patient benefit at the center of our business.
2. We are seeking to organize all forms of healthcare data. We believe that the combination of data sets is greater than the sum of its parts. In healthcare, there is no one dataset that answers all questions; different parties hold different keys to understanding how the human body works. As a simple example, analyzing genomic data alone and medical history alone are much less valuable than analyzing the combination of genomic and phenotypic data. Safe data sharing unlocks value, and our value as an enterprise lies in eliminating the friction that prevents the combination of data.
3. We serve the healthcare institutions that hold data: we believe that institutions should be able to control and restrict the use of their data. The healthcare data business today is largely an arbitrage business; data aggregators purchase data at the lowest price they can, sell it for the highest price they can, and use it for whatever purposes they see fit. This prevents data liquidity, as most institutions are understandably uncomfortable with their data being used this way. We believe that healthcare institutions should have more control than this: they should be able to determine what other institutions access data from them, how that data is used, and how that data is monetized. Our goal is to be an escrow service where data holders retain control.
4. Datavant’s role is to organize data. We power an ecosystem of data and analytics companies who can understand the data that we organize. Datavant will be focused on making safe data sharing simple, which will require us to power an ecosystem of partners who can use the data effectively. To take advantage of the power of data across healthcare, we expect hundreds (if not thousands) of healthcare analytics companies will emerge over the next decade, and we expect many to be successful at reimagining key parts of healthcare. Our strategy is to build an open tent: in the long run, we aspire to power every healthcare analytics company in the world.
5. We power entrepreneurship around new models of medicine. We are building an open ecosystem around health data. The availability of more extensive and fluid data will take medicine beyond applied analytics and linear clinical support tools. We encourage experimentation and will partner generously with those that seek to explore and expand the power of data.
We are on a quest to organize the world’s healthcare data. It is the biggest data problem of our generation and an opportunity to save lives. We look forward to unlocking a data-driven future of healthcare.
PS — if this resonates with you, we’re hiring! Visit us at www.datavant.com.
The Datavant Vision: Organizing the World’s Health Data was originally published in Datavant on Medium, where people are continuing the conversation by highlighting and responding to this story.