Reimagining data: Building trustworthy systems to power prevention
According to the Centers for Disease Control and Prevention (CDC), 90% of the nation’s $4.9 trillion in annual healthcare expenditures go toward treating people with chronic and mental health conditions. Yet the 2023 Lancet Global Burden of Disease Study found that nearly half of all health burden in the United States is attributable to modifiable risk factors, meaning a significant portion of this spending could be avoided through prevention. The barrier isn’t a lack of data—health care and medical information comprises roughly 30% of the world’s data—but rather the fragmentation of that data across systems, which can become complicated due to policy and legal restrictions.
Deloitte’s 2021 report, Breaking the cost curve, offers a compelling vision for this transformation. The report projects that by 2040, a shift toward prevention and well-being could generate a $3.5 trillion “well-being dividend”—spending that would otherwise go toward treatment but could be redirected by empowering consumers to monitor their health through technologies that detect early disease signals in asymptomatic people. This would represent a fundamental reorientation: In 2019, roughly 80% of health spending went toward care and treatment, but by 2040, the report envisions 60% going toward improving health and well-being instead.
A recent panel discussion convened by Deloitte and the Milken Center for Advancing the American Dream brought together leaders from technology, health care, and retail to explore how integrated data systems could transform preventive care and build healthier tomorrows. The conversation revealed both the immense promise and critical challenges of leveraging data to stop disease before it starts.
Social determinants of health
Panelists emphasized that social determinants of health represent an area where investment has lagged behind recognition. One highlighted how understanding whether someone is housing or food insecure before a crisis occurs can fundamentally change outcomes. Another noted that social risk factors drive approximately 70% of health care costs, making grocery stores and pharmacies unexpected but crucial players in prevention.
Building trust through community
Trust emerged as the foundational challenge for any data-driven health initiative. One leader shared an example in which engagement rates more than doubled when community health workers conducted home visits. Similarly, a retail pharmacy initiative achieved a more than 50% response rate by leveraging the trusted relationships people have with their local pharmacists—relationships reinforced through monthly, and often more frequent, in-person interactions each year.
The message was clear: A patient’s participation in prevention requires trust, and trust requires messengers from within the community. Technology alone cannot solve this equation.
Responsible AI and federated data models
As artificial intelligence (AI) becomes more prevalent in health care, panelists stressed the need for leaders across industries to understand how AI works, how to apply it responsibly, and what role humans should play in oversight. One executive pointed to federated machine learning initiatives where data from multiple cancer centers can power collective research without any data leaving its original location or losing its anonymization.
This federated approach aligns with the vision in Breaking the cost curve, which emphasizes that interoperable data systems could replace disconnected health care components with secure data-sharing that creates highly personalized pictures of consumers’ well-being. The report notes that real-world evidence based on interoperable data could lead to more personalized and effective treatments for cancer and other high-cost diseases.
The potential to impact patients in real time is significant but requires new frameworks for data governance.
A vision for the next decade
The panelists offered bold visions for prevention-focused health care within 10 years: personalized health portals that patients own and control; AI-powered grocery recommendations that automatically fill carts with foods aligned to individual health needs and cultural preferences; health plans that reimburse personalized preventions through programs like Medicaid waivers; and integrated genetic information available from birth.
Yet achieving this vision requires confronting institutional inertia, rethinking policies around sharing personal health information, and finding a better balance between overly cautious protections that harm people and accessibility that puts privacy at risk. The path forward demands collaboration across major technology companies, retailers, and health care organizations to build aligned incentive models.
As one panelist noted: “Low-cost prevention costs a lot less than high-cost hospitalization.” The challenge now is building the trust, infrastructure, and policies to make prevention-powered-by-data a reality and build healthier tomorrows for everyone.
Endnotes:
- Centers for Disease Control and Prevention (CDC), Chronic Disease, August 8, 2025.
- The Lancet, Global Burden of Disease Resource Center, accessed January 2026.
- Greg Wiederrecht, Sasson Darwish, and Andrew Callaway, “The convergence of healthcare and technology,” RBC Capital Markets, accessed January 2026.
- Kullen Gebreyes et al., Breaking the cost curve, Deloitte Insights, February 9, 2021.
- Vijay Raaghavan and Kanchan Raghuwanshi, Time to act: Investing in addressing social determinants to improve health, World Economic Forum, October 2021.
- Gebreyes et al., Breaking the cost curve.
Beth Meagher
