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Comparison of Hospital Online Price and Telephone Price for Shoppable Services

2023· article· en· W4386818446 on OpenAlexaff
Merina Thomas, James D. Flaherty, Jiefei Wang, Morgan Henderson, Vivian Ho, Mark Cuban, Peter Cram

Bibliographic record

VenueJAMA Internal Medicine · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineChildbirthCashTelephone callPhoneTelephone linePhone callMedical emergencyFinanceBusinessTelecommunicationsTelephone networkPregnancy

Abstract

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Importance: US hospitals are required to publicly post their prices for specified shoppable services online. However, the extent to which a hospital's prices posted online correlate with the prices they give to a telephone caller is unknown. Objective: To compare hospitals' online cash prices for vaginal childbirth and brain magnetic resonance imaging (MRI) with prices offered to secret shopper callers requesting price estimates by telephone. Design, Setting, and Participants: This cross-sectional study included cash online prices from each hospital's website for vaginal childbirth and brain MRI collected from representative US hospitals between August and October 2022. Thereafter, again between August and October 2022, simulated secret shopper patients called each hospital requesting their lowest cash price for these procedures. Main Outcomes and Measures: We calculated the difference between each hospital's online and phone prices for vaginal childbirth and brain MRI, and the Pearson correlation coefficient (r) between the online and phone prices for each procedure, among hospitals able to provide both prices. Results: A total of 60 representative US hospitals (20 top-ranked, 20 safety-net, and 20 non-top-ranked, non-safety-net hospitals) were included in the analysis. For vaginal childbirth, 63% (12 of 19) of top-ranked hospitals, 30% (6 of 20) of safety-net hospitals, and 21% (4 of 19) of non-top-ranked, non-safety-net hospitals provided both online and telephone prices. For brain MRI, 85% (17 of 20) of top-ranked hospitals, 50% (10 of 20) of safety-net hospitals, and 100% (20 of 20) of non-top-ranked, non-safety-net hospitals provided prices both online and via telephone. Online prices and telephone prices for both procedures varied widely. For example, online prices for vaginal childbirth posted by top-ranked hospitals ranged from $0 to $55 221 (mean, $23 040), from $4361 to $14 377 (mean $10 925) for safety-net hospitals, and from $1183 to $30 299 (mean $15 861) for non-top-ranked, non-safety-net hospitals. Among the 22 hospitals providing prices both online and by telephone for vaginal childbirth, prices were within 25% of each other for 45% (10) of hospitals, while 41% (9) of hospitals had differences of 50% or more (Pearson r = 0.118). Among the 47 hospitals providing both online and phone prices for brain MRI, prices were within 25% of each other for 66% (31) of hospitals), while 26% (n = 12) had differences of 50% or more (Pearson r = -0.169). Among hospitals that provided prices both online and via telephone, there was a complete match between the online and telephone prices for vaginal childbirth in 14% (3 of 22) of hospitals and for brain MRI in 19% (9 of 47) of hospitals. Conclusions and Relevance: Findings of this cross-sectional study suggest that there was poor correlation between hospitals' self-posted online prices and prices they offered by telephone to secret shoppers. These results demonstrate hospitals' continued problems in knowing and communicating their prices for specific services. The findings also highlight the continued challenges for uninsured patients and others who attempt to comparison shop for health care.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.061
GPT teacher head0.342
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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