As one of the most important medical research hubs in the country the National Institutes of Health (NIH) has been leading the fight against COVID-19 since the earliest days of the pandemic. With a fast-paced roll-out of funding and programs, streamlining operations at the NIH was imperative to avoid duplication to both save budgets and optimize research and results. In doing so the NIH would be able to help deliver necessities – whether that was PPE or targeted data for clinical trials – at the right moment to the right place.
Andrew Kelly, Budget Officer at the National Center for Advancing Translational Sciences (NCATS) at the NIH, where he also serves as the chief of the Center’s Financial Management Branch, explained the situation the NIH found themselves in in early April 2020 at recent event hosted by Qlik. “When COVID came along, all of a sudden everyone started standing up research programs,” shared Kelly. “We realized very quickly that there was lots of redundancy, lots of people doing a lot of the same things or searching for the same types of resources.”
What Kelly quickly realized was that he needed to help the NIH streamline operations to ensure that duplication was minimized and that the organization was able to direct resources to the right places to fuel research and help scientists and clinicians understand the virus, identify treatments, and get to work on therapies and a vaccine. As well as the rush to research that created multiple projects in a short period of time the NIH lacked visibility courtesy of its own inventory and tracking cycles.
“We really had no way of knowing what was being researched and no way to inventory to see what all these projects were,” shared Kelly. “We normally report our intramural research projects on an annual cycle. So, in April when there was this explosion in COVID-19 research projects we wouldn’t even have found out what all of the institutes were doing until October.” With the seriousness of the pandemic and the mounting case load and death toll it was imperative that the NIH have this visibility to ensure that research was being directed in the right areas immediately and that researchers were receiving the support they needed to be successful.
Kelly and his team asked researchers at the Institutes to submit projects and share information on what they needed to complete their research and what they could to do support other research teams via a SharePoint form. From there users could extract the data using Qlik’s web connector to create a comprehensive inventory of information about research projects and resources that could be used across the board to identify efficiencies and points of synergy to accelerate research and outcomes. For example, this process enabled Kelly to understand the PPE needs and coordinate purchases and distribution. “We were able to use the Qlik Sense dashboard to actively communicate and coordinate between the logistics team and the leadership team,” shared Kelly. “We could see who received the PPE at each location, when it was received, how much was delivered, so that we could be sure that there was enough at all the different locations to support and protect our teams.”
But managing essential supplies was not the only data-driven advantage that Kelly was able to facilitate. The public’s desire to participate in stopping the spread of the virus and learning more about it to support the development of therapies and a vaccine was also supported by the investment in data literacy at the NIH. A seropositivity study that began in April 2020 was initially searching for 1,000 volunteers who were not COVID positive to donate blood samples to help determine what percentage of the U.S. population was positive but did not know.
“When we released this call to action, we actually got 400,000 volunteers – that level of engagement is unheard of in medical research – and it was particularly fortuitous in this instance because it would give us a pretty representative sample of the US populations,” explained Kelly. “Being able to use our Qlik platform to identify where we needed more data to build that broadly reflective sample. On a daily basis we were able to direct the call center using the data from Qlik to say: ‘focus on Montana’ or ‘focus on people over the age of 60’.” The end result of this seropositivity study was that there were 4.8 undiagnosed cases for every diagnosed case between April 2020 and July 2020 giving researchers and clinicians a much better perspective on the seriousness of the pandemic and enabling frontline workers to be better prepared.
The NIH’s ability to take data and make it actionable in near real-time was vital to their ability to support researchers and clinicians in the fight against COVID-19. Whether it was in getting PPE to the right place at the right time or being able to identify gaps in research, streamlining operations with data made all the difference in the NIH’s capabilities at a defining moment. With these pandemic experiences almost behind them, the lessons learned during a most interesting year will surely help the NIH as it continues to drive clinical research to solve today’s biggest medical challenges and the next virus that might come our way.
