In the digital age, your pulse, your lab results, and even your sleep may be worth more than your money. The problem is—you rarely control them.
Healthcare has quietly entered the data economy. Every blood test, radiology scan, prescription refill, insurance claim, smartwatch step count, and voice note in an electronic medical record generates information. Individually, these data points seem mundane. Aggregated, they are extraordinarily valuable.
Health data doesn’t just describe illness anymore. It predicts behavior, guides markets, trains algorithms, and shapes public policy. In many ways, it has become a new currency. But unlike money, most people have little idea where it goes—or who ultimately owns it.
Health data sits at the intersection of three powerful forces: scale, intimacy, and scarcity.
Unlike browsing history or purchase data, health data is deeply personal and biologically anchored. It reveals not just what you do, but what you are: your risks, your vulnerabilities, your future probabilities. For pharmaceutical companies, it accelerates drug discovery. For insurers, it sharpens risk stratification. For AI companies, it is the raw material that enables prediction.
Much of the health data economy hides behind a comforting phrase: de-identified data. Remove names, addresses, and IDs, and the data is declared safe for trade.
But true anonymity is fragile. With enough variables—age, location, diagnosis, timestamp—re-identification becomes disturbingly feasible. Studies have shown that a handful of data points can uniquely identify individuals, especially in rare diseases or small populations.
Anonymization often protects institutions more than patients.
Data is sold, licensed, or exchanged for analytics tools, discounts, or research partnerships. Patients rarely see financial benefit, even when their data underpins billion-dollar products.
This raises an uncomfortable question: if health data is currency, why are the people generating it unpaid?
As AI enters healthcare, health data acquires a new role: training intelligence. Algorithms learn patterns from millions of patient records, often without explicit patient awareness.
The benefits are real—earlier diagnoses, better triage, and personalized treatment. But the ethical imbalance remains. Patients contribute to systems they may never access. Bias in data becomes bias in care.
The future of medicine may well be data-driven.
The real test is whether it will also be patient-owned.
MBH/PS