The value of data collaboration

A year of COVID-19 has taught us many data lessons. Tableau Advisory Board member Francis X. Campion discusses his new perspectives on data collaboration.
“Going forward, I expect that the health delivery system and public health systems will be more closely tied together by rapid cycle data initiatives on a grand scale.” - Francis X. Campion

Recently we asked Amanda Makulec, Rabah Kamal, Francis X. Campion, members of the Tableau Advisory Board, what they felt were the biggest data lessons they’ve learned since the beginning of the pandemic. The learnings were both practical and provocative; from the necessity for trust, to the power of multi-disciplinary collaboration, to addressing the limitations of data that lead to misinformation and inequality. The value of data collaboration is part three of a three-part series: Data in the time of COVID-19: What have we learned?


As the principal lead in digital health at the MITRE Corporation, Dr. Francis X. Campion has seen first-hand how data can impact patient care and treatment. “During the pandemic, the scientific and public health community were forced to make decisions using ‘the best available data’. As a physician, this is a process we use everyday in the care of individual patients, making treatment decisions on the use of medicines and surgery to treat people, always weighing the potential benefit and harm. And during the pandemic, this process has been compressed on a grand scale.” In this crisis, Dr. Campion has observed new attitudes and practices around data. More from Dr. Campion: 
Collaboration and trust is essential. We’ve been forced to compress the scientific process into a very short time frame and accelerate change in the way we do science and public policy. This takes collaboration and trust. We rapidly built new coalitions such as the C19HCC coalition (COVID-19 Healthcare Coalition) to harness the good will and talent of people and institutions to solve problems together, based on the best available data.

Existing data can be used in new ways to improve results. In traditional claims analysis we’ve had to wait six months or more for “closed claims.” During the pandemic, using early claims, we have been able to report in near-real time on the expansion and distribution of telehealth in all 50 states in record time. We collected and analyzed teleheath claims data (in collaboration with Change Healthcare, Inc.) between the time the claim leaves the physician’s office, and before it arrives at the insurance company. 

Federated research initiatives can drive faster insights. We collected “real world data” from electronic health records from hospitals across the country in record time and established collaboration among the largest electronic health record companies in the nation to establish data definitions and create “federated research protocols” to rapidly gain knowledge about the effectiveness of treatments for COVID-19.

The future includes larger data initiatives and deeper coordination across public and private health systems. Going forward, I expect that the health delivery system and public health systems will be more closely tied together by rapid cycle data initiatives on a grand scale. The use of AI and ML on very large data sets will be possible through these collaborations; I also expect the discovery of new treatments and creation of new drugs, vaccines, and the re-use of existing therapies for novel purposes to be accelerated by these advances in data science.


This is part one of a three-part series. Find the other blogs in the series here, and visit the Tableau COVID-19 Data Hub for more data insights, resources, and thought leadership to help you navigate the pandemic.