When a California HMO denies care and the patient appeals to the state, an independent doctor decides, and the written reasoning is published. A model read all 42,746 decisions since 2001 in about eight minutes for $2.55. Insurers now lose most of these appeals, and the cases show what separates the patients who win from the ones who don’t.
Insurers deny tens of millions of claims a year, and almost nobody pushes back. People don’t know they can appeal, don’t know whether they would win, and don’t know what to send. The best evidence about what wins sits in public, in the written findings of independent reviewers, as free text no patient, doctor or regulator has time to read.
Source: KFF, Claims Denials and Appeals in ACA Marketplace Plans in 2024. Marketplace data only; employer plans don’t report.
The cost is care people give up on, bills they pay themselves, and denials that keep coming because challenging them is rare. A denial nobody appeals is a denial that works.
It’s the flip side of rising healthcare costs that ordinary people can actually act on. The data is public, de-identified and already judged, so every insight comes with an answer key. And the finding is practical: it can tell a real person whether to fight and what to bring.
The ideaAsk a small decision model six typed questions about every reviewer’s findings, join its answers to the state’s outcome, and turn the patterns into odds and a checklist a patient can use.
Sources: California Department of Managed Health Care, Independent Medical Review Determinations (42,746 decisions, 2001 to 2026); TypeSafe jev-1.13.0 answers, run September 27, 2026.
Each dot is one decision. Green means the reviewer overturned the insurer and the patient got the care. Regroup the dots to see which kinds of disputes patients win. Tap any dot to read the case.
Tap any dot to read that case.
Describe your denial. You’ll see how similar appeals turned out, what decided them, a checklist built from the winning cases, and how to file.
These are historical results from California independent medical reviews, not a prediction for your case and not medical or legal advice. They include only people who appealed all the way to the state.
The share of appeals reviewers decided for the patient, by year. Pick a dispute type to compare it with all appeals.
Treatments ranked by how often reviewers overturned the insurer. When a denial loses nine times in ten, the denial itself is the pattern worth examining.
Say a patient advocacy group, a health system’s denials team or a regulator wants these six answers for every decision. Here is the cost by hand against the cost with the model and a person reviewing what it’s unsure about.
In practice nobody reads 42,746 decisions by hand. They count the state’s outcome field, which says who won but never why. The hand cost is what the “why” would take.
Today the people who fight denials work from memory and anecdote, one case at a time. Here is how the work shifts, and who it helps.
Reading thousands of decisions to find a pattern disappears. Deciding to appeal, writing the letter, and the medical judgment itself stay with people. The model reads what reviewers wrote, not the medical record, so it can say what tends to win, never whether a specific patient should get a specific treatment.
This covers California plans regulated by the Department of Managed Health Care, mostly HMOs. It leaves out PPOs regulated by the Department of Insurance, self-funded employer plans and Medicare. It includes only people who appealed all the way to the state, who are not a random sample of everyone denied. Findings are the reviewer’s summary, not the medical record, and they don’t name insurers. Associations here aren’t causes: patients who document failed treatments may differ in other ways. I haven’t built an independent hand-labeled set; accuracy rests on the two field checks above and spot reads. The model’s severity answer reflects how reviewers wrote each case, so I use it only in case cards. Results are pinned to jev-1.13.0.