In May 2020, in response to the rapidly expanding pandemic caused by the SARS-CoV-2 virus, the National Cancer Institute (NCI) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) together launched a program called “Digital Health Solutions for COVID-19.” The goal of this initiative was to support the development of digital health tools that could leverage multiple data sources, privacy-preserving technologies, and computational tools to assist in managing population health during the COVID-19 pandemic. Seven projects received National Institutes of Health (NIH) support to develop a variety of smartphone apps, wearable devices, and software tools capable of identifying infected individuals, tracking verified COVID-19 test results, and monitoring the health status of infected and potentially infected individuals. The NIH also supported the establishment of a data hub to facilitate the collection and curation of de-identified data generated by these projects, which will be made publicly available in late 2022. This will provide an opportunity for the broader research community to further analyze the data, refine predictive models, and continue to generate new learnings from the data sets. A major focus of the NCI/NIBIB initiative was to define the context of use for various digital health technologies to determine what could be learned from the data and how this could be leveraged to inform decision making during future pandemics. Among these technologies was an artificial intelligence, web-based decision aid, developed by Vibrent Health (Fairfax, Virginia), which can be used to evaluate COVID-19 symptoms of patients at home. Several articles in this issue describe how this system, and potentially others like it, can be extremely useful for both triage decisions and improving accuracy of in-home testing. While these testing improvements have been invaluable during the pandemic, such testing enhancements will become even more critical as COVID-19 shifts to an endemic. It will no longer be acceptable to triage patients based on general symptoms, such as fever and cough, as was done early in the pandemic. As patients return to normal activities and continue striving to make informed health care decisions, they will rightly expect more sophisticated methods to differentiate COVID-19 from other diseases with overlapping symptomatology. Accomplishing this goal will be tremendously complex and will require painstaking deconvolution of symptoms, but this can be achieved with the help of machine learning, as is shown in the Vibrent studies. Despite the fear and uncertainty throughout the COVID-19 pandemic, the public has demonstrated a remarkable willingness to participate in NIH-supported efforts aimed at improving COVID-19 outcomes. Projects supported under the Digital Health Solutions for COVID-19 initiative began in September 2020, just before the beta wave of the pandemic began, and study enrollment was generally high throughout the winter of 2020-2021. Study enrollment decreased somewhat in early 2021 with the rollout of COVID-19 vaccines and falling case numbers, and then steadily increased again during the spread of the delta variant in mid-2021. To successfully navigate these changing dynamics, it was especially important for the NIH-funded teams to establish clear and consistent communication with study participants. The team at Vibrent Health was able to successfully achieve this through targeted outreach campaigns and thoughtful community engagement. Particularly important were their studies aimed at engaging traditionally underserved and underrepresented populations, which showed that digital health tools play an important role in reaching populations that are not well served by current health systems. The Digital Health Solutions for COVID-19 initiative was just one component of a major coordinated effort by the NIH to combat COVID-19. Other programs such as the Rapid Acceleration of Diagnostics (RADx) Tech initiative also played a key role in speeding the development, validation, and commercializaiton of rapid, low-cost, point-of-care, and home-based tests. Importantly, large investments in next-generation testing approaches can only realize their full potential if these new technologies can be easily accessed and operated by the intended end users. The solutions developed by Vibrent Health and the other project teams have clearly demonstrated that digital health tools will play a crucial role in enhancing the use of various physical technologies going forward. In the future, public health officials, clinicians, employers, and the general community will rely heavily on these digital tools as they work to provide individuals with the necessary data to make informed choices about their health. The recent investment by the US government in digital health solutions and other COVID-19 technologies has not only provided clear and immediate benefits but also helped secure a solid foundation and infrastructure for the early detection, prevention, and monitoring of future pandemics. Hopefully, the recent COVID-19 experience will not soon be repeated, but the lessons learned will be invaluable when the next pandemic does occur. —Andrew Kurtz, PhD Program Director Center for Strategic Scientific Initiatives National Cancer Institute Bethesda, Maryland
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Aug 8, 2024
Maria Bishara + 8
Open Access
