Glossary · Legal Concept
Outcome Severity Distribution
The spread showing how comparable medical malpractice cases divide across small, significant, and large payments, used to gauge how volatile a case profile is; distinct from insurance "severity," which is average cost per claim.
Also known as: severity distribution, outcome severity, severity spread
What it is
Outcome severity distribution shows how comparable cases spread across outcome sizes, from small payments to significant payments to large verdicts. It is one of the three core outputs of the Hooper Engine, alongside payment probability and payout range.
How to read it
Two case profiles can share the same expected payout yet differ sharply in their distributions: one clustering near the midpoint, the other carrying a long tail of large results. A narrow distribution means outcomes bunch close together; a wide one means the profile is volatile and can resolve well above or below its expected figure.
Why it matters
A single expected value cannot show volatility, but the severity distribution can. It tells a negotiator how often cases like this one resolve far from the midpoint, which is often the difference between a safe settlement posture and a risky one. It is distinct from insurance "severity," which measures the average dollar cost per claim across a book of business.
See Also
- Payment Probability — The likelihood, expressed as a percentage, that a medical malpractice case with a given profile results in any payment; the first of the Hooper Engine's three core outputs and the signal for whether a claim is worth pursuing.
- Payout Range — The Hooper Engine's low, expected, and high estimate of what a medical malpractice case will pay if it results in payment, adjusted to current dollars.
- Expected Value — The probability-weighted worth of a case before its outcome is known, found by multiplying payment probability by expected payout; the right figure for ranking cases at intake.
- Hooper Engine — MedMalPredict's proprietary AI prediction system, trained on more than 270,000 historical medical malpractice cases, that produces jurisdiction-aware predictions for payment probability, payout range, and outcome severity.