Healthcare systems today generate enormous volumes of data from electronic records, registries, sensors, and administrative platforms. Yet for artificial intelligence to deliver reliable value, the challenge is not volume. The challenge is fidelity. What AI models need is verified reality. This means an accurate, provenance anchored record of real world interactions among patients, clinicians and devices.

Real World Data Is Not Automatically Real World Evidence

Real world data is abundant but often incomplete or inaccurate. Missing context, inconsistent coding, and limited capture of clinician decisions are common issues that reduce suitability for AI or regulatory analysis. A recent review lists data quality, validity, and transparency as the primary barriers to real world evidence.
Source: https://www.mdpi.com/2306 5354/11/8/784

Flattened records do not capture the true flow of care. They miss device events, handoffs, decision overrides, and many other interactions that determine outcomes. As a result, models trained on these datasets may perform well in retrospective analyses but fail in real clinical environments.

Interactions Are the Missing Layer

Healthcare is driven by interactions. Moments such as a nurse escalating a triage priority, a device alert being silenced, a physician overriding a suggestion, or a patient ignoring an alert are rarely recorded in structured form. Yet these moments shape the entire clinical journey.

What is needed is a layer that captures interactions in real time without replacing existing health record systems. This layer should convert micro events into verifiable signals while maintaining privacy and institutional control.

Zero Knowledge as the Privacy Foundation

The main barrier to interaction capture is privacy. How can a system verify that an event occurred without exposing sensitive information
Zero Knowledge proofs provide an answer. These cryptographic methods allow a party to prove the correctness of a statement without revealing the underlying data.
Source: https://sedicii.com/news/zkp transform healthcare data privacy

In healthcare, Zero Knowledge proofs can verify that

• A device alert occurred and was acknowledged
• A safety protocol was followed
• A dataset used for training came from authorised systems within the correct jurisdiction

All of this can be done without revealing protected health information.

Verified Interactions Create Stronger AI

When an infrastructure captures interactions with cryptographic provenance, AI systems benefit from

• Higher quality features
• Better context for clinical decision flows
• Transparent data lineage
• Resilience to drift
• Evidence suitable for regulatory review

Verified interaction data offers the fidelity required for modern clinical AI. The winners in this space will not be organisations with the most data but organisations with the most trustworthy and verifiable data.

References

Real World Data and Real World Evidence: https://www.mdpi.com/2306 5354/11/8/784
Real World Data Challenges: https://pmc.ncbi.nlm.nih.gov/articles/PMC8339486
Ethical Use of Real World Data for AI: https://academic.oup.com/jamia/advancearticle/doi/10.1093/jamia/ocaf133/8264332
Zero Knowledge Privacy in Healthcare: https://sedicii.com/news/zkp transform healthcare data privacy
Foundations of Zero Knowledge: https://link.springer.com/chapter/10.1007/978 3 031 51063 2 8

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