Pediatric Price Transparency: Still Opaque With Opportunities for Improvement
Bibliographic record
Abstract
OBJECTIVES: Price transparency is gaining importance as families' portion of health care costs rise. We describe (1) online price transparency data for pediatric care on children's hospital Web sites and state-based price transparency Web sites, and (2) the consumer experience of obtaining an out-of-pocket estimate from children's hospitals for a common procedure. METHODS: From 2015 to 2016, we audited 45 children's hospital Web sites and 38 state-based price transparency Web sites, describing availability and characteristics of health care prices and personalized cost estimate tools. Using secret shopper methodology, we called children's hospitals and submitted online estimate requests posing as a self-paying family requesting an out-of-pocket estimate for a tonsillectomy-adenoidectomy. RESULTS: = 9). All 9 hospitals with a phone number for estimates provided the estimated patient liability for a tonsillectomy-adenoidectomy (mean $6008, range $2622-$9840). Of the remaining 36 hospitals without a dedicated price estimate phone number, 21 (58%) provided estimates (mean $7144, range $1200-$15 360). Two of 4 hospitals with online forms provided estimates. Fifteen (39%) state-based Web sites distinguished between prices for pediatric and adult care. One had a personalized cost estimate tool. CONCLUSIONS: Meaningful prices for pediatric care were not widely available online through children's hospital or state-based price transparency Web sites. A phone line or online form for price estimates were effective strategies for hospitals to provide out-of-pocket price information. Opportunities exist to improve pediatric price transparency.
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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 | 0.052 | 0.254 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".