The COVID-19 pandemic has amplified broad societal inequities and trained a spotlight on the strengths and weaknesses of the U.S. public health system. Chief among these revealed weaknesses is a lack of real-time information that could provide a precise and representative picture of the risks different people face. Tragically, our public health data is still not up to the task.

Decentralized and fractured public health data collection impeded the U.S.’s initial ability to anticipate spikes in COVID cases and deaths and limit the disease’s continued spread among vulnerable groups. The disease’s unequal impacts were obscured by inconsistent data reporting and testing gaps in many under-resourced communities. These blind spots in our data largely reflect societal inattention to long-standing inequities in health, economic opportunity, and community wellbeing.

Tech companies that collect and analyze vast amounts of population data have done much to help fight this pandemic, such as sharing data about people’s movements. But to really make a difference, they should focus on a critical need: to make public health data more equity-centered and tech-engaged. That is, public health data needs to be a resource that counteracts rather than amplifies inequity, drawing its power from technology assets.

What would more equity-centered, tech-engaged public health data look like?

First, the U.S. could leverage the ubiquity of technology applications to increase coverage across different groups of people. Public health data relies heavily on clinical sources like medical records or insurance claims, missing people that cannot or do not seek medical care, such as uninsured or undocumented people. Further, Americans – particularly those in minority or marginalized groups – can be reluctant to share information with governmental entities. Many of these Americans are far less reluctant to share data with companies that provide valued services such as social media.

Second, a new approach to public health data could improve timeliness, using technology to quickly identify and respond to public health needs. Many companies already possess real-time behavioral data, and can quickly generate more.

Third, tech-engaged public health data could integrate data to leverage sensemaking capabilities that uncover patterns in the data or evaluate the impact of policy changes. Fusing complementary data sources allows for more accurate insight. Tech companies could deploy powerful analytic capabilities, using personalization and identity management to help identify solutions to unaddressed health needs.

Improving equity-centered public health data could also benefit tech companies themselves. Higher quality data about larger swaths of the population could improve fairness (e.g., addressing bias) in analytic models that depend on accurately representing people’s attitudes, behaviors, and movements. Echoing calls from their workforces and across broader society, tech companies would be extending their existing efforts to remedy racial and other injustices. Addressing health inequities would also ultimately improve the well-being of the communities they serve.

Yet fitting such advanced capabilities – which largely reside in private companies – into the public health system is undoubtedly challenging. For one, some might assume this is a job for the government rather than the private sector. But the “public health system” in the U.S. has always included both governmental and non-governmental entities.

An appropriate balance must be struck to leverage private capabilities for public goals. Disruption can be good. But any underinvestment in public health must be corrected and not replaced by an overreliance on tech companies. Alongside government and academia, large companies could – rather than building potentially redundant data sets – release application programming interfaces (APIs) to share their data with public health entities.  Smaller companies could lend expertise to improve their local health department’s tech capabilities, rather than developing a competing app. Resolving tensions that may arise between the public good and profit motives, such as around data privacy or access, would seem to be key.

Incorporating tech data in decision-making must not exacerbate existing inequity by presenting a skewed view of the population. Health policy decisions based on biased historical information or data collected through tools (e.g., fitness trackers, expensive smartphones) that primarily serve certain groups could reproduce or even perpetuate inequity. Rigorous assessments of tech data collection and subsequent policy actions could examine their impact on vulnerable groups.

Private tech companies possess resources and capabilities that could supplement the public health system’s current reliance on over-stretched and fragmented government data. But we may also need to keep in mind the “public” part of public health. True partnership and collaboration might magnify our view of public health needs that are currently tucked away in the crevasses of our data, advancing our ability to improve health and well-being for all.