How it works
From raw case to defensible consensus
The entire mechanism, step by step — the same pipeline behind every DataLaps product. No black box: you can download the exact delivery format below.
Book a demoCases come in, de-identified
Your cases — images, clinical notes, model outputs, transcriptions — enter the pipeline stripped of PII. We scope the clinical question with you before any physician sees a case.
Verified physicians are assigned
Reviewers are licensed MDs whose credentials passed multi-stage screening (document review, identity checks, human verification). Where the task calls for it, profiles are matched to the specialty.
Each one answers blind
Multiple physicians review the same case independently. Nobody sees anyone else's verdict — no anchoring, no groupthink. Each verdict carries its own reasoning.
Verdicts become a consensus
Independent verdicts are reconciled statistically into a consensus label. Agreement is measured per item — not assumed. Chance-corrected agreement, not raw match rates.
Disagreement is adjudicated, not hidden
When physicians disagree, the case is flagged and escalated for adjudication. The final dataset records that it happened — disagreement is information, not noise to average away.
You receive an audit-ready dataset
Delivery includes every independent verdict, the consensus label, the agreement level and the adjudication flag, per row — the evidence trail a regulator or clinical advisor will ask about.
The deliverable
Open the exact format we deliver
This is a synthetic, PII-free sample — no real patients, no real physicians. The structure is the real thing.
| case_id | Stable identifier for each case |
| md1_verdict · md2_verdict · md3_verdict | Each physician's independent, blind verdict |
| consensus_label | The reconciled verdict — not a blind average |
| agreement_pct | How strongly the panel agreed on this item |
| confidence | Delivery confidence derived from agreement |
| adjudicated | Whether disagreement was escalated and resolved |
Or get it in your inbox with the format walkthrough:
Why blind matters
Independence is the whole point
Single annotators can't be defended
When a regulator asks "how do you know this label is correct?", one opinion — however expert — has no error bar. Independent replication does.
Discussion contaminates judgment
Panels that deliberate converge on the most confident voice, not the most correct one. Blind review keeps every verdict independent by construction.
Agreement becomes a metric
Because verdicts are independent, inter-rater agreement is statistically meaningful — a number you can put in a regulatory file.
See it run on your cases
Send a sample sprint through the pipeline — you keep the data and the results.
Book a demo