See how researchers across specialties use MedTWIN to go from data to publication faster, with full confidence in their results.
Identify mortality predictors, analyze surgical outcomes, and publish findings with verified statistics that reviewers trust.
Cardiac surgery researchers spend months manually analyzing patient data, running statistical tests in SPSS, and formatting results for publication. When reviewers ask questions, reproducing analyses is painful.
Upload your cardiac surgery database. MedTWIN maps variables to standard schemas, runs multivariate logistic regression, and generates publication-ready text with inline statistics linked to their source computations.
Researchers publish faster with full confidence. When reviewers ask about a statistic, one click shows the exact dataset, code, and computation that produced it.
We went from data collection to submitted manuscript in 3 weeks instead of 6 months. The provenance tracking made reviewer responses trivial.
Analyze trial data with pre-specified analysis plans, generate CONSORT-compliant flow diagrams, and produce audit-ready documentation.
Clinical trial analysis requires strict adherence to pre-specified statistical analysis plans. Auditors need complete documentation of every analysis decision. Manual processes are error-prone.
Define your analysis plan in MedTWIN's declarative config. Lock it before unblinding. Every analysis run is versioned, timestamped, and linked to the exact data state used.
Auditors get one-click access to complete analysis provenance. No more scrambling to reconstruct what was done and when.
The version-controlled analysis specs saved us during an FDA audit. We could show exactly what was run, when, and on which data version.
Transform messy EHR exports into structured datasets, handle missing data intelligently, and generate publication-quality results.
EHR data exports are messy—inconsistent formats, missing values, duplicate records. Researchers spend more time cleaning data than analyzing it.
MedTWIN's AI-assisted data mapping recognizes common clinical variables and suggests standardized mappings. Quality checks flag issues before analysis. Missing data handling is documented and reproducible.
Go from raw EHR export to clean, analysis-ready dataset in hours. Every transformation is logged for methods section documentation.
I used to dread cleaning EHR exports. MedTWIN mapped 80% of my columns automatically and caught issues I would have missed.
Harmonize data from multiple institutions, maintain site-level privacy, and run federated analyses across distributed datasets.
Multi-site studies struggle with data harmonization—different EHR systems, variable definitions, and coding schemes. Sharing raw data raises privacy concerns.
Each site maps their data to MedTWIN's canonical schema. Analyses run on harmonized views without pooling raw data. Site-level statistics can be aggregated while preserving privacy.
Larger sample sizes, broader generalizability, and cleaner publications—without the data sharing headaches.
We pooled data from 5 institutions in weeks instead of months. Each site kept their data local while we ran unified analyses.
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