Understanding Consensus-Based Scoring

At the heart of VACScore is a scoring architecture unlike anything else in healthcare analytics. Rather than relying on a single fixed algorithm, VACScore employs a panel of independent AI evaluators that must reach consensus on every score. Here is how it works and why it matters.

The Problem with Single-Algorithm Scoring

Most scoring systems use a fixed formula: assign weights to inputs, apply the formula, produce a number. This approach is fast and reproducible, but it carries inherent risks. A single algorithm embeds the biases and assumptions of its designer. If the formula underweights a critical factor — say, post-market surveillance data — every score it produces will reflect that blind spot. There is no internal check, no second opinion.

How Consensus Scoring Works

VACScore assembles a panel of specialized scoring agents, each focused on a specific domain of evidence. These agents independently analyze the same body of evidence for a given device and produce individual domain scores. The agents do not share intermediate reasoning — they work in isolation to prevent groupthink.

Once initial scores are produced, the panel enters a convergence protocol. Agents compare their assessments, identify areas of disagreement, and refine their evaluations through structured rounds. The process continues until scores converge within an acceptable threshold. An independent auditor agent monitors the entire process, verifying that convergence was achieved through genuine agreement rather than artificial compromise.

Why This Matters

Consensus-based scoring provides several advantages over single-algorithm approaches. First, it reduces single-point bias — no one agent's assumptions dominate the final score. Second, it surfaces disagreement: when agents diverge significantly on a domain score, that divergence itself is informative and is captured in the scoring record. Third, the multi-agent architecture produces a natural audit trail. Every intermediate assessment, every convergence round, and every auditor decision is logged and traceable.

Evidence Is Ruled In or Ruled Out

An important principle underlies the entire process: evidence is either "ruled in" to the scoring panel's consideration or "ruled out" based on transparent criteria. There is no subjective judgment — every inclusion or exclusion decision follows documented, reproducible rules. Ruled-out evidence is recorded with reasoning, and that reasoning is available for review. This ensures that the scoring process remains defensible and auditable.

We believe that the most trustworthy scores come not from the most complex algorithm, but from a process that mirrors the rigor of expert panel review — structured, independent, and transparent. Consensus-based scoring is that process, scaled through AI.