The U.S. democratic system relies on embedded expectations that elected leaders can be trusted to represent and protect the goals, needs, values, and preferences of constituent voters. Inherent in these relationships is recognition that leaders failing to serve the public may fail in getting re-elected. In essence, the ability to continue leading depends on the ability to deliver on promises – for different people with different problems.
Delivering on the promise of quality health care in a country as geographically expansive and demographically diverse as the U.S. is especially challenging. Determining how best to deliver quality care for different people in different places requires first seeing the full picture of difference: using complete, comprehensive public health data to understand the needs and nuances of the many people accessing health care in the U.S. However, recent Trump administration executive orders limit access to data sources that best support strategic decision-making to improve health system efficiency and resiliency.
As we have highlighted in other posts in this series, modifying or purging public health surveillance data results in a lack of insights that inform strategic decisions regarding, for example, hospital utilization and resource allocation. By posing challenges for designing and deploying the solutions Americans need, these changes will undermine impact and efficiency throughout the health ecosystem.
The research community is responding to these changes with innovative crowdsourcing initiatives. Crowdsourcing, which aggregates data provided directly by many individual contributors, proved especially valuable for informing effective COVID-era initiatives. DataLumos archives public and other social science data, and the Data Rescue Project offers a “clearinghouse for data rescue-related efforts and access points” of information sources currently at risk. Other examples include:
- Flu Near You captures citizens’ own reports of flu-like symptoms, which helps account for those mis- or underrepresented in traditional surveillance (e.g., rural or uninsured groups). This helped fill gaps in CDC data and improved public health responsiveness when traditional surveillance was limited.
- Project Tycho crowdsources and digitizes historical U.S. disease data, making them more accessible and usable by community groups and researchers. This effort, which helps avoid duplicative government spending, has been used in over 100 countries to model disease trends and inform health policies.
- Zoe Global’s COVID Symptom Study data helped identify COVID-related health disparities and monitor symptom severity by race, income, and geography. The supporting public-private partnership played a vital role in informing effective COVID responses, without requiring heavy federal oversight.
- MIT’s Senseable City Laboratory anticipated structural insecurities in local suspension bridges, using vibrational frequency data from car passengers’ smartphones.
- Salud Activa gamified activities like scanning nutrition labels, to afford more granular health behavior and non-communicable disease (NCD) tracking than is often available.
As demonstrated by these and other initiatives, crowdsourcing health data readily aligns with ideals embraced across the political spectrum – including those espoused by the predominant political parties in the U.S. For instance, public engagement strengthens personal responsibility and accountability in health, while building digital and health literacy promotes trust and transparency, particularly among marginalized groups.
While the benefits of crowdsourcing health data are clear, it is important to recognize that this complements rather than replaces the essential role of government-collected data. Government data sources provide systematic, comprehensive, and standardized insights that are crucial for maintaining a complete, longitudinal understanding of national health needs and outcomes. These data, gathered through established infrastructures with rigorous methodologies, are indispensable for ensuring the consistency and continuity of public health research.
Integrating crowdsourced data with government datasets can promote innovation, enhance time and cost savings, and fill gaps in epidemiological models: enhancing the depth and applicability of health analyses, and creating a richer and more accurate picture of public health trends and needs. In advocating for broader use of crowdsourced data, we must also support the maintenance and enhancement of open access to government data. This dual approach ensures that health policies are both informed by grassroots, real-time insights and grounded in authoritative, long-term data, thereby equipping policymakers to make decisions that truly reflect the diverse needs of the population.