Creating a Data-Driven Research Ecosystem with Patients at the Center

Editor’s Note: The article was first published on January 23, 2020 on Pharma Boardroom.

research ecosystemHealthcare data is seen by many as the ‘new goldmine,’ with many businesses in this field evaluated and sold at a significant value. The underlying premise is that once the data is fully de-identified it can be used by third parties for purposes other than the standard of care, empowering discoveries, etc. This has led most pharma companies to invest heavily in ‘Real World Data’ departments, which purchase, aggregate and analyse data to derive insights and better inform value-based models. Last but not least, de-identification offers a way to collaborate across provider organisations effectively, as Health Catalyst already does for its customer base that is comprised of hospitals in the US and around the world.

And yet, when we talk about healthcare data, we are ultimately talking about patient data, their lives, their disease, their treatments, their deaths. And often they are kept out of the loop, not benefitting or engaging with how their data is being used. Patients are increasingly demanding a more active involvement in their health, which is difficult to adhere to when the patient data is de-identified. As individuals become increasingly aware of how their data is handled, we will need to evolve towards models that engage the patients and ensure that, wherever possible, they are informed about projects involving their data, and can receive some value back. Many companies are now moving to provide services in this area, including those with the ability to retrieve a patient’s medical records or engage patients through peer-to-peer communities.

As the model has taken ground, healthcare data has evolved to become its own asset class. When surveyed, most patients are willing to donate their data to research, but they are more enthusiastic about donating when they can learn 1) how their data has benefitted others affected by the same disease or 2) the future of research. Through the de-identification process, it is difficult to communicate back to the patient any of the findings and to conduct longitudinal studies. Also, there are several uses of de-identified data that are driving highly commercial use cases (e.g., analyzing market share across geographies or demographics for a specific product) which patients would be less likely to support especially if it is associated with a brand or company that has been in the news for the wrong reasons.

This situation often leaves many healthcare providers with highly debated questions on how to extract value from data, commercial models, user permissioning, and more.

Serving Life Sciences Through Novel Insights

At Health Catalyst, our focus is to work with industry on specific projects that will have an impact on patient outcomes and/or advance Research and Development of new therapies where use can be tracked. Many digital therapeutics companies use data and digital means to improve patients’ lives, one example is Omada Health. As Lucia Savage, their Chief Privacy and Regulatory Officer pointed out in her testimony to the US Senate HELP committee recently, there is a growing consensus that a clinical fact (e.g., your blood glucose reading from this morning) cannot be purchased and sold, as it is a biological reality that belongs to the patient that provided the blood sample. Companies are creating revenue by organizing or analysing those facts to improve outcomes, but this practice raises the emerging idea that the underlying data ought not to be purchased and sold.

Clearly ethical and legal positions differ from one country and state to another, but I believe we are moving toward a next-generation revenue model that will revolve less around the licensing of the data, and more on how the data can be used to derive value, as healthcare is pushed toward value-based models. Life sciences organizations are increasingly interested in improving the quality of care to help improve the system at large.

Integrated Patient Insights Through Diverse Data Sets

When a pharma company is using data to understand the patients’ pathway and outcomes, they need to see the full richness and diversity, especially the differences existing across states, countries, rural and urban environments and race, ethnicity, age, gender and socioeconomic status. As we move towards a data-centric world and utilize more analytics and artificial intelligence, the richness of the data used becomes crucial. Health Catalyst brings together data from more than 100 million patient records. This also benefits providers as they can leverage much broader populations for benchmarking or for research studies.

This approach most importantly benefits patients. As an example, many biotech companies are developing new curative gene therapies for rare diseases. They often struggle to find enough patients for participation. With global access to digital health information, these companies could have a worldwide reach, in real-time, giving hope to many that would otherwise die. Similarly, by comparing and learning from some of the most advanced academic centres, smaller, less sophisticated health systems can benefit from improved diagnosis methods and treatment pathways for their patients. Unfortunately, there is wide disparity in how patients are diagnosed and treated. Digital technologies could significantly bridge this divide.

As more and more tools are created to advance our understanding of the disease and patient experience, Health Catalyst is striving to translate these tools and experiences into the world of care delivery to further define therapeutic value in an outcomes-focused world.

Additional Reading

Would you like to learn more about this topic? Here are some articles we suggest:

  1. Extended Real-World Data: The Life Science Industry’s Number One Asset
  2. Data-Driven Precision Medicine: A Must-Have for the Next-Generation of Personalized Care
  3. Healthcare Data: Creating a Learning Healthcare Ecosystem
  4. A New Era of Personalized Medicine: The Power of Analytics and AI
  5. Bridging the Data and Trust Gaps: Why Health Catalyst Entered the Life Sciences Market
Loading next article...