5 Things the Data Says About Medical Malpractice That Will Surprise You

Medical malpractice is a field built on instinct, experience, and pattern recognition. Attorneys develop gut feelings about what a case is worth. Adjusters calibrate reserves based on years of handling similar claims. Experts give opinions shaped by their own professional histories.

But when you analyze 220,000+ historical cases spanning two decades, some of those instincts hold up, and some of them fall apart completely.

Here are five findings from the data that surprised us, and that we think should change how you evaluate cases.

1. Death cases pay less than grave permanent injury cases

Median payout for cases resulting in death: $311,000.

Median payout for cases resulting in grave permanent injury: $807,000.

That is not a typo. Cases where a patient died settled for less than cases where a patient was left with severe, permanent disability, on average.

This surprises almost everyone. The intuitive assumption is that death equals maximum damages. But the data tells a different story. Grave permanent injury cases typically involve ongoing care costs, lost future earning capacity, and plaintiffs who can testify about their own suffering, all of which can push jury awards and settlements well above wrongful death claims, where damages are often capped or structurally limited.

If you are a plaintiff attorney, this is the kind of finding that reframes how you think about case selection and negotiation posture.

2. Where a case is filed matters more than almost anything else

California median payout: $73,000.

Pennsylvania median payout: $507,000.

Same allegation type. Same case characteristics. Seven times the payout depending on jurisdiction.

No single variable in the dataset produces a stronger effect than state. California's MICRA cap has historically compressed payouts dramatically. Pennsylvania has no comparable cap and is home to plaintiff-friendly venues in Philadelphia. When we say jurisdiction is a major predictor, we mean it in the most literal sense: the state you file in may matter more than the strength of your evidence.

This has practical implications for cases near state lines, for practitioners licensed across multiple jurisdictions, and for insurers setting national reserve policies.

3. Anesthesia cases are rare, but the payouts are severe

Anesthesia allegation cases have the highest median payout of any allegation category in the dataset at $507,000, and that includes allegations involving surgery, diagnosis errors, and medication management.

They are also among the least common cases filed. The combination makes them high-stakes outliers: infrequent enough that many firms do not have extensive historical benchmarks, but large enough that mispricing the risk is expensive.

If you handle or insure anesthesia-related claims, your gut feel is almost certainly calibrated on insufficient data.

4. Post-2018 payouts are rising, and older benchmarks are misleading you

Year-over-year, payouts in the dataset trend upward, but the inflection point is 2018. Post-2018 cases settle meaningfully higher than the historical average.

This matters because many practitioners, firms, and insurers still anchor to 5- or 10-year historical averages. If your benchmark includes 2010 to 2018 data and you are using it to value a 2025 case, you are systematically underpricing the exposure.

Historical data is valuable, but only if it is properly weighted toward the present.

5. Prior-history practitioners settle for significantly more

Practitioners with five or more prior adverse reports settle for 43% more than first-time reporters.

This is arguably the most actionable finding in the dataset. Practitioner history is something attorneys can research, adjusters can factor into reserves, and insurers can incorporate into pricing models. It is also something that changes the negotiation dynamic: when the opposing side has a paper trail, the range of reasonable outcomes shifts.

Prior history is not just a red flag. It is a quantifiable risk multiplier.

What this means

These are not edge cases or statistical anomalies. They are patterns that emerge consistently across 220,000+ cases and two decades of data.

The gap between conventional wisdom and what the data actually shows is where MedMalPredict operates. Our predictions incorporate jurisdiction, allegation type, injury severity, practitioner history, and patient demographics, because all of these factors matter, and some of them matter in ways that are genuinely counterintuitive.

If you want to see how these factors affect the prediction for your specific case type, try a prediction at medmalpredict.com.