Evidence map›Paper›PMID 28874404›Full record

ArticleHospital pediatrics2017

Pediatric Price Transparency: Still Opaque With Opportunities for Improvement.

Laura J Faherty, Charlene A Wong, Jordyn Feingold, Joan Li, Robert Town, Evan Fieldston, Rachel M Werner

Abstract readMulticenter Study
In one paragraph

Article in Hospital pediatrics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Laura J FahertyRobert Wood Johnson Foundation Clinical Scholars Program.
Charlene A WongLeonard Davis Institute of Health Economics, charlene.wong@duke.edu.
Jordyn FeingoldDivision of General Internal Medicine.
Joan LiPerelman School of Medicine, and.
Robert TownLeonard Davis Institute of Health Economics.
Evan FieldstonLeonard Davis Institute of Health Economics.
Rachel M WernerLeonard Davis Institute of Health Economics.

Funding

Scientific & Technical CoreP2CHD042849 · NICHD · UNIVERSITY OF TEXAS AT AUSTIN · PI Catherine A Calder · 2017 to 2026
$5.2M
Midcareer Mentoring Award for Patient-Oriented Research in AgingK24AG047908 · NIA · UNIVERSITY OF PENNSYLVANIA · PI WERNER, RACHEL M · 2014 to 2024
$2.0M
NIA NIH HHS K24 AG047908NICHD NIH HHS P2C HD042849
6 · The paper itself

Abstract

objectivesPrice 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.

methodsFrom 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.

resultsEight children's hospital Web sites (18%) listed prices. Twelve (27%) provided personalized cost estimate tool (online form

conclusionsMeaningful 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.

Indexed as

CommerceHealth Care CostsHealth ExpendituresChildDisclosureHospitals, PediatricHumansInternetUnited States

Identifiers

PMID28874404
PMCPMC6345274

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.