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Record W2295047338 · doi:10.5430/jha.v5n3p40

Quality and patient experience: A six-dimensional approach for the future of healthcare

2016· article· en· W2295047338 on OpenAlexvenueno aff
Wen-Ta Chiu, Rachele Hwong, Jason Chiu, Bill X. Huang, JJ Stewart, Tina T. Tsai, Su-Yen Wu, Spencer Liu, Nicole Chorvat, Sasha Yu, Jon Aquino, Matthew Lin, Q. M. Jonathan Wu

Bibliographic record

VenueJournal of Hospital Administration · 2016
Typearticle
Languageen
FieldHealth Professions
TopicPatient Satisfaction in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsMedicaidPurchasingCommunity hospitalQuality (philosophy)Health careBusinessPaymentValue-Based PurchasingPatient satisfactionMedical emergencyNursingEmergency departmentPurchasing processMedicineOperations managementMarketingPolitical scienceEngineering

Abstract

fetched live from OpenAlex

The Affordable Care Act (ACA) has significantly altered the American healthcare system. Through the establishment of the ACA, Centers for Medicare and Medicaid Services (CMS) introduced Value-Based Purchasing (VBP), a pay-for-performance program, to the hospital payment system. From a community hospital’s standpoint, a multifaceted approach to quality and patient satisfaction on better managing the health of the community was recognized: what begins in the community ends in the hospital as a valuable indicator for each individual’s well-being. This article depicts the process of utilizing a six-dimensional approach on engaging stakeholders to improve quality of care and patient satisfaction: (1) inpatient, (2) emergency department, (3) employee, (4) physician relationships, (5) outpatient, and (6) community. As the effect of the ACA becomes more prominent, hospitals should maintain their organizational flow and care coordination through the six-dimensional approach to bring patients and their families back to the center of their 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 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)0.001
metaresearch head score (Gemma)0.000
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 score0.031
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.075
GPT teacher head0.435
Teacher spread0.360 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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