Government Turns to Non-Traditional Players to Harness the Power of Big Data in Healthcare Against COVID-19

The onslaught of the COVID-19 pandemic caused a rapid mobilization of the country’s healthcare resources. Operation Warp Speed shattered previous vaccine development deadlines and led to the creation of a vaccine in record time.

The government has spent $1.7 trillion combatting the virus, including $171 billion spent by the Department of Health and Human Services.

The HHS organizations most directly responsible for fighting the virus, such as the NIH and the CDC, have mobilized a critical resource to accelerate the response to the pandemic: data.

To quickly and effectively use big data in healthcare to respond, federal healthcare agencies have turned to non-traditional partners such as commercial software providers, universities and non-profits.

Turning to Non-Traditional Partners To Harness Big Data in Healthcare

NIH has emerged as a leader in using commercial software players for data aggregation and analysis.

One big partner of the government on this front has been Oracle, which is active in both commercial and government spaces. The company developed the COVID-19 Prevention Network (CoVPN) for the NIH in order to identify and screen volunteers who want to participate in COVID-19 clinical trials for vaccines and antibodies.

Launched in July 2020, the cloud-based network enrolls volunteers, which healthcare networks nationwide can then draw from to support clinical trials.

At the beginning of the pandemic, Oracle also rapidly designed and launched the Therapeutic Learning System, which allowed physicians and patients to submit data on the effects of promising but little tested drug therapies and allowed government to access aggregated versions of this data.

NIH also contracted with Indica Labs and Octo to build a cloud-based repository of COVID-related human tissue images, the COVID-DPR; the images can be digitally annotated and easily shared.

Because not all organizations are equipped with the facilities to perform autopsies from patients who died of COVID-19, the COVID-DPR fills a gap by providing a centralized, cloud-based database accessible to all physicians.

By tapping this unique pair – a more traditional government IT provider teamed with a commercial healthcare software company – NIH hopes to build a resource that gives researchers more timely access to clinical and imaging data and accelerate progress towards a cure.

Finally, federal healthcare agencies have tapped Palantir, a big data software analytics firm, for help with COVID detection. Between April and May 2020, it won $30 million in contracts from HHS and the VA. Its software, which can intake data in a variety of forms and make it usable and analyzable, helped officials at both agencies to track and study COVID outbreaks; the software also fed into HHS Protect, a public data hub that helped inform the White House Coronavirus Task Force.

Government healthcare agencies are also drawing on universities and non-profits, which have long been government partners in the R&D space, to support with data analytics and product development.

For example, in August 2020 the University of Chicago was awarded a $50 million contract for the Medical Imaging and Data Resource Center (MIDRC). The tool, funded by NIH’s National Institute of Biomedical Imaging and Bioengineering, collects and processes medical images from COVID-19 patients, with the goal of creating a large, curated dataset for AI algorithm development – essentially a test bed within which new COVID detection algorithms can be trialed.

Through collaboration with two leading radiology organizations (the American College of Radiology and the Radiological Society of North America), MIDRC will have access to a uniquely large data set. This big data in healthcare tool went live in October 2020.

Another tool that uses data for disease detection is ESSENCE, a system that tracks various data types nationwide (ranging from emergency room and urgent care visits to calls to poison control centers) to predict the rise and spread of infectious diseases.

It was developed by the Applied Physics Lab at Johns Hopkins University in the late 1990s and has been continuously updated since, being used to track diseases such as SARS and Ebola. Two CDC awards in April 2020 and February 2021 provided additional support for its work to combat the coronavirus.

A Lasting Shift in Big Data in Healthcare Requires New Approaches

Given its effectiveness in combatting the COVID-19 pandemic, it seems that large-scale big data in healthcare collection and analysis is entering a new era of accelerated adoption among government health agencies.

Although it was developed for COVID-19, the researchers intend for MIDRC to become a resource for chronic disease research at other NIH institutes as well as for future infectious pandemics. Furthermore, HHS released a strategy for AI in healthcare just last month, which called for accelerating the adoption of AI technologies at scale across a variety of health-based use cases.

The CDC is also looking for lasting ways to integrate data and other forms of emerging IT, including turning to more traditional industry providers for support integrating technology in order to help it achieve better help outcomes.

Of course, there will be hurdles. Cost is a barrier for widespread implementation of these technologies at local hospitals, which are critical data collection points for federal datasets and enablers of big data in healthcare.

Federal government-funded tools that are free for these users can help solve this challenge (for example, both TLS and the COVID-DPR are free of cost), but this solution is less feasible outside the context of COVID-19.

The government also faces the logistical challenge of collecting usable data from disparate non-government sources, and in honoring data terms that may restrict usage to certain research, require deidentification, or other nuances; at the same time, data protection remains essential, and the government needs the right safeguards in place when handing off data for control by other users. Finally, it will need to modernize infrastructure and apps to better manage data – for example, to more efficiently sort and tag it – and fully harness its power.

In light of these challenges, industry must consider how to best harness its offerings for government to successfully use big data in healthcare. For example:

• For those serving both government and commercial customers, how can you help improve data collection in hospitals and data sharing with government?
• If you are a commercial software provider, what products do you have or could build that would serve the government mission?
• If you are a more traditional federal contractor, what non-traditional players could you partner with to bring something unique to your customers?
• How else can you serve the government – for example, as an integrator or prime for those less experienced in government work – as it turns to non-traditional players to solve these challenges?

Though we will eventually be past the COVID-19 pandemic, the use of data science within the federal healthcare space is here to stay. As the government continues to look for innovative, non-traditional ways to harness it, industry players of all types need to establish their strategies for the next wave of adoption.