Bringing Clarity to Malpractice Negotiations

Our Mission

Medical malpractice litigation affects every stakeholder differently: attorneys must evaluate case viability, clinicians face professional and financial uncertainty, and patients seek fair compensation. MedMalPredict AI was founded to bring objective, statistically grounded analysis to all parties, reducing the time, cost, and guesswork involved in settlement negotiations.

The Technology

Historical Malpractice Outcomes

Our Hooper Engine™ is trained on over 220,000 malpractice case outcomes spanning 20 years, the most comprehensive analysis of its kind.

Human Factors Analysis

Go beyond the data with optional human factors scoring. Pain and suffering, loss of lifestyle, family impact, and jury appeal are weighted and combined into a multiplier that adjusts the base prediction to reflect the non-economic damages that influence real jury deliberations.

Jurisdiction-Aware

Predictions account for state-level variations in malpractice law, caps on damages, and historical settlement patterns across all 50 states and U.S. territories.

Calibrated Estimates

All predictions include confidence ranges and are calibrated against held-out test data of 54,000+ cases to ensure statistical reliability.

The Hooper Engine™

Our AI engine applies a proprietary ensemble of advanced gradient intelligence models to predict payment probability, expected payout range, outcome severity distribution, and human factors adjustment.

Our Hooper Engine™ is named for the 1932 T.J. Hooper case: the landmark negligence case in which Judge Learned Hand held that a tugboat operator's failure to use available radio technology, which would have provided storm warnings, was itself negligent, regardless of industry custom. The ruling established a foundational principle: when the means to prevent foreseeable harm exist, the failure to use them is no longer defensible. Our Hooper Engine applies that same logic to legal outcomes.

Methodology

Our advanced proprietary AI uses a multi-model approach. A payment probability model estimates the likelihood of any payout based on published litigation research (approximately 22% base rate), adjusted for injury severity, license type, and jurisdiction. A payment amount model produces low, expected, and high estimates adjusted to current dollars. An outcome severity model generates probability distributions across nine standardized injury categories.

A fourth dimension, human factors analysis, allows users to score non-economic elements such as pain and suffering, loss of lifestyle, family impact, and jury appeal. These scores are weighted and combined into a composite multiplier (1.0x to 2.5x) that adjusts the base payment prediction to reflect jury-driven dynamics that historical data alone cannot capture.

All models are regularly retrained as new historical case data becomes available, ensuring predictions reflect current trends in malpractice litigation.