Claim Denials: Low-Income Patients From Disadvantaged Racial And Ethnic Groups Experienced The Largest Burdens
Bibliographic record
Abstract
Insurance claim denials are a common source of administrative burden, especially for patients with private health insurance. Contesting denied claims requires considerable investment from physicians and patients or caregivers, including both institutional knowledge of health policies and billing practices and the means to engage in reconciliation. We used a novel national data set comprising remittance data and patient demographics to describe disparities in the rates of seeking and receiving claim denial corrections across demographic and socioeconomic dimensions. We found that patients from historically disadvantaged racial and ethnic groups or with low household incomes experienced the largest burdens from claim denials. Patients with household incomes less than $50,000 annually were least likely to have denied claims contested and, conditionally, have cost-sharing obligations reduced. Racial minority patients were more likely than non-Hispanic White patients to have cost-sharing obligations reduced but achieved lower mean savings per successfully contested denial. Policy makers working to promote equitable health care access should make available more resources for contesting and rectifying administrative errors and enact policies to prevent billing errors and consequent claim denials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".