For decades, valuing a medical malpractice case meant relying on a combination of experience, instinct, and whatever comparable cases you could find in your memory or a spreadsheet. Experienced attorneys developed a feel for what a case was worth. Insurers relied on adjusters who had seen hundreds of similar situations.

But "a feel" and "similar situations" aren't the same as data. And that gap, between intuition and evidence, is exactly where AI changes the game.

Here's how it actually works.

The Foundation: Historical Cases

Every prediction the Hooper Engine makes starts with data. Specifically, it starts with more than 240,000 historical medical malpractice cases spanning 2004 through 2025.

These cases contain structured information about:

The type of allegation (surgical error, misdiagnosis, medication error, birth injury, and dozens more)
The severity of the patient's injury (from emotional injury only to permanent major impairment to death)
The jurisdiction where the case was filed
The type of licensed practitioner involved
Whether a payment was made, and if so, how much

This isn't a sample. It's one of the most comprehensive datasets of medical malpractice outcomes ever assembled for use in predictive modeling.

What the Hooper Engine Actually Learns

The Hooper Engine doesn't just average numbers. It learns relationships, the interaction effects between variables that determine how cases resolve.

For example:

Jurisdiction matters enormously. A surgical error resulting in permanent major injury doesn't have the same expected value in every state. Jury culture, tort reform laws, damages caps, and the local plaintiffs' bar all affect outcomes in ways that raw national averages obscure. The Hooper Engine learns these jurisdiction-specific patterns from thousands of cases per state.

Injury severity is the strongest signal. Across every allegation type and jurisdiction, injury severity is the single most predictive variable. The difference in expected payout between "temporary minor" and "permanent major" injury is measured in multiples, not percentages.

Allegation type interacts with practitioner type. A medication error by a nurse practitioner resolves differently than the same allegation against a hospital system. The Hooper Engine captures these interaction effects rather than treating variables in isolation.

History matters. Practitioners with prior reports behave differently in the dataset than first-time cases. The Hooper Engine accounts for this.

None of these insights are surprising to experienced litigators. What the Hooper Engine offers isn't new knowledge; it's those insights, applied consistently, at scale, across 20 years of outcomes.

The Three Outputs

When you run a prediction on MedMalPredict, you receive three pieces of intelligence:

1. Payment Probability

The likelihood that a case with these characteristics results in any payment, expressed as a percentage. This is the "is this case worth pursuing?" signal.

A case with a 15% payment probability doesn't mean you can't win; it means that historically, 85% of cases with similar characteristics did not result in payment. That's important context for a plaintiff attorney deciding whether to take a contingency case, or for a defense attorney advising a client whether to settle quickly.

2. Predicted Payout Range

For cases that do result in payment, the Hooper Engine generates a statistical estimate of the payout: low, expected, and high values.

These aren't random ranges. The expected value represents the median outcome for comparable cases. The low and high values represent the realistic spread; cases don't always land at the median, and understanding the distribution matters for negotiation strategy.

The values are adjusted to current dollars so you're comparing against today's landscape, not 2007 settlement amounts.

3. Outcome Severity Distribution

How similar cases distributed across severity outcomes: what percentage resulted in minor payments, significant payments, or major verdicts. This tells you something the single expected value can't: how volatile this case type is. A case type with a wide severity distribution carries more risk than one that clusters tightly around the median.

Why It Outperforms Intuition Alone

Human intuition is pattern recognition built from experience. For a veteran attorney who has tried 200 med mal cases, that intuition is real and valuable.

But intuition has limits.

It's biased toward recent cases and memorable outcomes. The case that settled for $4M last year looms larger in memory than the 15 cases that settled for $200K. Humans systematically overweight dramatic outcomes.

It's jurisdiction-limited. An attorney whose practice is primarily in New York has limited feel for how the same case plays out in rural Texas or suburban Ohio.

It's not available to everyone. Not every attorney has tried 200 med mal cases. Not every adjuster has decades of claims experience. AI democratizes access to pattern recognition that previously required years of experience to develop.

The Hooper Engine doesn't forget. It doesn't weight last year's high-profile verdict more heavily than the 500 cases before it. It processes the entire dataset every time.

What It Doesn't Do

It's worth being clear about the limits, because they matter.

It doesn't predict individual case outcomes. It predicts statistical distributions based on comparable cases. Your specific case will resolve based on facts, witnesses, experts, judges, and juries that no model can fully capture.

It doesn't replace legal judgment. The prediction is an input to decision-making, not a substitute for it. An attorney who sees a 40% payment probability still needs to evaluate the specific liability facts, the client's circumstances, and the strategic context.

It isn't legal advice. It's data. Your attorney provides legal advice.

The Practical Impact

Here's how attorneys and adjusters actually use it:

A plaintiff attorney receives a new med mal referral. Before spending 5 hours reviewing records, she runs a quick prediction. The case shows a 22% payment probability and an expected payout of $180K. That doesn't mean she won't take the case, but it recalibrates her initial optimism and helps her have an honest conversation with the client about realistic expectations.

A defense attorney is preparing for mediation. He runs the plaintiff's case through MedMalPredict to understand what the data says. The expected range comes back at $800K–$1.4M. His client has been anchored at $300K. That's a useful data point before he walks into a room with the other side.

An insurance adjuster is setting reserves on a new claim. Instead of relying on a comparable from three years ago, she runs the case characteristics and gets a statistically grounded range. The reserve is more accurate from day one.

None of these are decisions the Hooper Engine makes. The Hooper Engine provides the benchmark. The human makes the call.

Getting Started

MedMalPredict is available at medmalpredict.com. Single predictions, 5-packs, and annual subscriptions are available. Every prediction includes a downloadable PDF report formatted for case files and settlement discussions.

If you're an attorney, adjuster, or claims professional who has never tried it, run your next case through it. Compare what the data says to what your intuition says. You might be surprised how often they align. And when they don't, that gap is worth understanding.

MedMalPredict Predictions are statistical estimates based on historical case data and do not constitute legal advice.

How AI Predicts Medical Malpractice Outcomes