Out‐of‐Pocket Costs and Surprise Billing in Otolaryngology: A National Database Analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate out-of-pocket (OOP) costs and surprise billing (unexpected charges from out-of-network providers) in otolaryngology. STUDY DESIGN: Retrospective cohort study. SETTING: National commercial claims database. METHODS: Merative MarketScan database was queried for commercially insured patients aged 18 to 64 who underwent any of six otolaryngology procedures (thyroidectomy, parotidectomy, hypoglossal nerve stimulator implantation, drug-induced sleep endoscopy, septoplasty, or tonsillectomy) from 2014 to 2022. OOP costs were defined as the sum of deductibles, copays, and coinsurance for each 30-day surgical episode. A potential surprise bill was defined as an out-of-network claim within an episode where both the primary surgeon and facility were in-network. Relationships between OOP costs, potential surprise bills, and patient- and system-level exposures were analyzed. RESULTS: Of 58,772 procedures meeting inclusion criteria, 52,131 (89%) procedures were outpatient and 6641 (11%) were inpatient. Median (interquartile range [IQR]) total OOP costs were $1207 ($183-$2594), and coinsurance accounted for 66% of OOP costs. OOP costs were higher for patients with insurance plans that were fee-for-service-based (2.9 times; P < .001) or high-deductible (4.7 times; P < .001) versus managed care plans. A potential surprise bill accompanied 4.8% of surgical encounters, which was associated with significantly higher OOP costs (median $1739 vs $1269; P < .001). The odds of having a potential surprise bill were lower in 2022 versus 2014 to 2021 (odds ratio [OR] 0.29, 95% CI 0.21-0.40; P < .001). CONCLUSION: Common elective otolaryngology procedures were associated with high OOP expenditures, potentially exceeding the reserve funds of many patients. Potential surprise bills decreased after the passage of the No Surprises Act but were not eliminated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".