March 23, 2021 - 422 views|
Like the Pandemic Response Challenge, the VA is utilizing AI to optimize care strategies for COVID and other urgent healthcare needs.
With large numbers of people suffering and dying from COVID every day, there’s no time to waste. As a result, we’ve been driven to meet this challenge with the most advanced care strategies and latest technological tools available. Key factors for success that we’ve identified include collaboration and the ability to understand complex interrelated factors to guide data-informed decision making to enhance care.
In this sense, our approach is similar to that of the Pandemic Response Challenge, to which I’ve been invited as a senior advisor. Cognizant and XPRIZE launched the challenge to bring experts from around the world together to combine data, artificial intelligence (AI), collaborative partnerships and human-led innovation to understand and predict how policy interventions will impact the trajectory of COVID-19. (Take a look at the finalists, the winners and the technology behind the Pandemic Response Challenge.)
The VA National Center for Collaborative Healthcare Innovation (NCCHI) collaborates not only with the U.S. Department of Veterans Affairs but also with other experts from around the world. In this catalytic environment, we’re accelerating our combined understanding of advanced technologies such as AI, virtual reality, augmented reality, sensor technologies and 5G.
One of these important areas involves big data. At VA, we are fortunate to have a robust longitudinal electronic health record (EHR) database, which we’ve been utilizing to better understand and inform optimal care strategies for our Veterans. Importantly, the insights from VA have also serendipitously been beneficial to others who are working to solve similar challenges.
Predicting needs and healthcare resources
Prior to the pandemic, NCCHI tested and verified a way to use advanced analytics and data in our EHR dataset to predict risk and the likelihood of different postsurgical outcomes. The goal was to see if routinely collected historical data could help guide the best care options for individual patients. In our published peer reviewed research, we found our EHR system could do this better than other tools available and do it automatically because the structured data is gathered routinely. We’ve collaborated with our VA colleagues as we leverage big data to address critical issues such as hospital readmission risk.
Once COVID hit, our NCCHI team switched gears and quickly utilized the same analytics strategy to understand and improve COVID-19 care. One of our early objectives was to identify the patients at greatest risk for serious COVID-19 outcomes. These projects were developed with the intention to help both front-line staff with direct care decisions and executive leaders working to tactically distribute resources. In one of our published peer reviewed publications, we demonstrated that demographic factors, comorbidities and other data in our EHR could predict the likelihood of COVID-19 mortality, hospital admission, long hospital admission, ICU stay, long ICU stay and intubation.
As we worked to optimize our COVID-19 predictive models, one of our data analysts uncovered an unexpected association related to COVID-19 mortality. Specifically, our recently published paper reveals that taking aspirin prior to COVID-19 diagnosis was correlated with a dramatic decrease in mortality. Although prospective assessment is required before incorporating this finding into care, the potential association between aspirin’s anti-inflammatory and anti-coagulation properties could have a significant impact on COVID-19 treatment strategies in the near future. In parallel, we are also closely assessing the impact of COVID-19 in relationship to social determinants of health.
We’ve also collaborated on a COVID-19 wearables research project with the Department of Defense (DoD). This DoD study utilizes wearable sensor data and AI to detect changes in vital signs that can predict the likelihood of early COVID-19 infection. The goal is that early detection can prevent the unintended spread of the virus before symptoms arise.
We are also utilizing AI and other advanced technologies to enable remote care, which has become more important during COVID, and to provide insights that offer the potential to improve outcomes. Here’s a quick look at just three of these initiatives:
The human component
Although advanced tools such as AI are integral to innovations that improve outcomes, there are three decidedly non-technology-oriented factors that are also critically important:
Unfortunately, the challenges from COVID-19 will not be over when the virus is eradicated. For example, “long haulers” and others with longer term health problems caused by the virus will need to be better understood, and specific customized treatments developed for those fighting prolonged illness. In addition, we will also be faced with the needs of those who have avoided medical treatment or did not have access to typical routine care as a result of COVID. We will need to develop and deploy advanced solutions that can predict gaps and develop advanced ways to avoid and address long-term challenges.
With large numbers of people suffering and dying from COVID every day, there’s no time to waste. The fact that XPRIZE and its partners have come together with such agility is inspirational – there’s no doubt we can do more, more quickly, by working together with the best tools at our disposal at a critical time.
This article also appears on the XPRIZE website.
Learn more about our Pandemic Response Challenge with XPRIZE, a $500k, four-month challenge that focuses on the development of data-driven AI systems to predict COVID-19 infection rates and to prescribe intervention plans.