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Public reporting on quality, waiting times and patient experience in 11 high-income countries

2016· article· en· W2285638239 on OpenAlexaffabout
Bernd Rechel, Martin McKee, Marion Haas, Gregory P. Marchildon, Frédéric Bousquet, Miriam Blümel, Alexander Geißler, Ewout van Ginneken, Toni Ashton, Ingrid Sperre Saunes, Anders Anell, Wilm Quentin, Richard B. Saltman, Steven D. Culler, Andrew J. Barnes, Willy Palm, Ellen Nolte

Bibliographic record

VenueHealth Policy · 2016
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsInstitute of Health EconomicsUniversity of Toronto
FundersEuropean Observatory on Health Systems and Policies
KeywordsQuality (philosophy)MedicineRanking (information retrieval)Family medicinePatient safetyUnintended consequencesBusinessHealth careEconomic growthPolitical scienceEconomics

Abstract

fetched live from OpenAlex

This article maps current approaches to public reporting on waiting times, patient experience and aggregate measures of quality and safety in 11 high-income countries (Australia, Canada, England, France, Germany, Netherlands, New Zealand, Norway, Sweden, Switzerland and the United States). Using a questionnaire-based survey of key national informants, we found that the data most commonly made available to the public are on waiting times for hospital treatment, being reported for major hospitals in seven countries. Information on patient experience at hospital level is also made available in many countries, but it is not generally available in respect of primary care services. Only one of the 11 countries (England) publishes composite measures of overall quality and safety of care that allow the ranking of providers of hospital care. Similarly, the publication of information on outcomes of individual physicians remains rare. We conclude that public reporting of aggregate measures of quality and safety, as well as of outcomes of individual physicians, remain relatively uncommon. This is likely to be due to both unresolved methodological and ethical problems and concerns that public reporting may lead to unintended consequences.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation 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 score0.130
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.292
GPT teacher head0.540
Teacher spread0.248 · 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 teacher head, 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

Citations77
Published2016
Admission routes2
Has abstractyes

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