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Record W2605932242 · doi:10.1136/bmjopen-2016-014089

Retrospective economic analysis of the transfer of services from hospitals to the community: an application to an enhanced eye care service

2017· article· en· W2605932242 on OpenAlexaff
Thomas Mason, Cheryl L. Jones, Matt Sutton, Evgenia Konstantakopoulou, David F. Edgar, Robert A. Harper, Stephen Birch, John G Lawrenson

Bibliographic record

VenueBMJ Open · 2017
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsMcMaster University
FundersNational Institute for Health and Care ResearchCollege of Optometrists
KeywordsMedicineService (business)Eye careOptometryFamily medicineMedical emergencyMarketing

Abstract

fetched live from OpenAlex

OBJECTIVE: This research aims to evaluate the wider health system effects of the introduction of an intermediate-tier service for eye care. SETTING: This research employs the Minor Eye Conditions Scheme (MECS), an intermediate-tier eye care service introduced in two London boroughs, Lewisham and Lambeth, in April 2013. DESIGN: Retrospective difference-in-differences analysis comparing changes over time in service use and costs between April 2011 and October 2014 in two commissioning areas that introduced an intermediate-tier service programme with changes in a neighbouring area that did not introduce the programme. DATA SOURCES: MECS audit data; unit costs for MECS visits; volumes of first and follow-up outpatient attendances to hospital ophthalmology; the national schedule of reference costs. MAIN OUTCOME MEASURES: Volumes and costs of patients treated. RESULTS: In one intervention area (Lewisham), general practitioner (GP) referrals to hospital ophthalmology decreased differentially by 75.2% (95% CI -0.918% to -0.587%) for first attendances, and by 40.3% for follow-ups (95% CI -0.489% to -0.316%). GP referrals to hospital ophthalmology decreased differentially by 30.2% (95% CI -0.468% to -0.137%) for first attendances in the other intervention area (Lambeth). Costs increased by 3.1% in the comparison area between 2011/2012 and 2013/2014. Over the same period, costs increased by less (2.5%) in one intervention area and fell by 13.8% in the other intervention area. CONCLUSIONS: Intermediate-tier services based in the community could potentially reduce volumes of patients referred to hospitals by GPs and provide replacement services at lower unit costs.

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)0.000
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 score0.044
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.053
GPT teacher head0.447
Teacher spread0.394 · 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

Citations18
Published2017
Admission routes1
Has abstractyes

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