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Record W4378194782 · doi:10.1111/jebm.12534

Introducing value‐based healthcare perspectives into hospital performance assessment: A scoping review

2023· review· en· W4378194782 on OpenAlexaff
Wenbo He, Meixuan Li, Liujiao Cao, Rui Liu, Jiuhong You, Fangyuan Jing, Jiawen Zhang, Wei Zhang, Mengling Feng

Bibliographic record

VenueJournal of Evidence-Based Medicine · 2023
Typereview
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsHealth careProtocol (science)Reliability (semiconductor)Process managementProcess (computing)Computer scienceKnowledge managementMedicineManagement scienceEngineeringAlternative medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: Value-based healthcare (VBHC) puts patient outcomes at the center of the healthcare process while optimizing the use of hospital resources across multiple stakeholders. This scoping review was conducted to summarize how VBHC had been represented in theory and in practice, how it had been applied to assess hospital performance, and how well it had been ultimately implemented. METHODS: For this review, we followed the PRISMA-ScR protocol and searched five major online databases for articles published between January 2006 and July 2022. We included original articles that used the concept of VBHC to conduct performance assessments of healthcare organizations. We extracted and analyzed key concepts and information on the dimensions of VBHC, specific strategies and methods for using VBHC in performance assessment, and the effectiveness of the assessment. RESULTS: We identified 48 eligible studies from 7866 articles. Nineteen nonempirical studies focused on the development of a VBHC performance assessment indicator system, and 29 empirical studies reported on the ways and points of introducing VBHC into performance assessment and its effectiveness. Ultimately, we summarized the key dimensions, processes, and effects of performance assessment after introducing VBHC. CONCLUSION: Current healthcare performance assessment has begun to focus on implementing VBHC as an integrated strategy, and future work should further clarify the reliability of metrics and their association with evaluation outcomes and consider the effective integration of clinical outcomes and patient-reported outcomes.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.047
metaresearch head score (Gemma)0.137
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.047
Threshold uncertainty score0.246

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0470.137
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0260.027
Science and technology studies0.0010.003
Scholarly communication0.0070.008
Open science0.0030.004
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.358
GPT teacher head0.592
Teacher spread0.234 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations16
Published2023
Admission routes1
Has abstractyes

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