nSights — turning clinical research into a few minutes of work, not weeks.
A multimodal, agentic platform for clinical researchers: define cohorts in plain language, compare them with real statistics, read the evidence behind every claim, and quantify medical images — all over the industry's largest curated EMR dataset.
Real-world data holds the answers. Getting to them takes too long.
Most of what a clinical researcher needs is locked inside electronic medical records — and most of that sits in unstructured notes that no query can reach. Building a defensible cohort, comparing it against a control, and tracing every result back to source evidence is slow, manual work that can run for weeks before a single insight lands.
The goal was to collapse that loop. Let a researcher go from cohort to patient to evidence in minutes — with criteria that read like clinical language, statistics that hold up to scrutiny, and every claim deep-linked to the note it came from.
- 01Cohort BuilderGenerate regulatory-grade cohorts with flexible inclusion / exclusion criteria — either pre-curated clinical selectors or chat-based prompts.
- 02Comparative insightsCompare a test cohort against the full population or a control, with Chi-Squared, Rate Ratio, Cohen's D, survival probabilities and outlier methods.
- 03Advanced statisticsKaplan-Meier survival curves, propensity matching with custom covariates, and paired comparison to cut inter-subject variability.
- 04Augmented curationNeural networks, NER and LLMs turn raw EMR text into a structured, labelled dataset of billions of clinical notes.
- 05Patient AI-ExpertAsk questions about a patient in natural language and get answers grounded in deep-linked evidence from their unstructured notes.
- 06Imaging biomarkersComputable anatomy from medical images — classification, entity recognition, segmentation and annotation for model development.
Make unstructured notes structured, traceable, and answerable.
The hard part of the experience is upstream of any screen: a clinical note is free text, and a researcher needs it as discrete, addressable facts. The curation pipeline reads a note, finds its section headers, predicts where each sentence belongs, and resolves the ambiguous cases — so a later question like "what was the tumour size, and did it change?" can be answered against labelled structure rather than a wall of prose.
That structure is what makes the rest of the product honest: every highlighted entity, every statistic, every AI answer traces back to a specific sentence in a specific note.
One workspace: cohort, patient, statistics and imaging.
The platform pulls cohort definition, statistical analysis, evidence curation, AI Q&A and medical imaging into a single workspace — so the researcher never has to leave one tool to verify what another claimed.
