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Record W2099757990 · doi:10.1177/1355819614566832

Integrating funds for health and social care: an evidence review

2015· review· en· W2099757990 on OpenAlexaboutno aff
Anne Mason, Maria Goddard, Helen Weatherly, Martin Chalkley

Bibliographic record

VenueJournal of Health Services Research & Policy · 2015
Typereview
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
FundersNational Institute for Health and Care Research
KeywordsIntegrated careHealth careAgency (philosophy)Unintended consequencesEmpirical evidenceSocial careCost–benefit analysisBusinessPublic economicsMedicineActuarial scienceNursingEconomicsPolitical scienceEconomic growthSociology

Abstract

fetched live from OpenAlex

OBJECTIVES: Integrated funds for health and social care are one possible way of improving care for people with complex care requirements. If integrated funds facilitate coordinated care, this could support improvements in patient experience, and health and social care outcomes, reduce avoidable hospital admissions and delayed discharges, and so reduce costs. In this article, we examine whether this potential has been realized in practice. METHODS: We propose a framework based on agency theory for understanding the role that integrated funding can play in promoting coordinated care, and review the evidence to see whether the expected effects are realized in practice. We searched eight electronic databases and relevant websites, and checked reference lists of reviews and empirical studies. We extracted data on the types of funding integration used by schemes, their benefits and costs (including unintended effects), and the barriers to implementation. We interpreted our findings with reference to our framework. RESULTS: The review included 38 schemes from eight countries. Most of the randomized evidence came from Australia, with nonrandomized comparative evidence available from Australia, Canada, England, Sweden and the US. None of the comparative evidence isolated the effect of integrated funding; instead, studies assessed the effects of 'integrated financing plus integrated care' (i.e. 'integration') relative to usual care. Most schemes (24/38) assessed health outcomes, of which over half found no significant impact on health. The impact of integration on secondary care costs or use was assessed in 34 schemes. In 11 schemes, integration had no significant effect on secondary care costs or utilisation. Only three schemes reported significantly lower secondary care use compared with usual care. In the remaining 19 schemes, the evidence was mixed or unclear. Some schemes achieved short-term reductions in delayed discharges, but there was anecdotal evidence of unintended consequences such as premature hospital discharge and heightened risk of readmission. No scheme achieved a sustained reduction in hospital use. The primary barrier was the difficulty of implementing financial integration, despite the existence of statutory and regulatory support. Even where funds were successfully pooled, budget holders' control over access to services remained limited. Barriers in the form of differences in performance frameworks, priorities and governance were prominent amongst the UK schemes, whereas difficulties in linking different information systems were more widespread. Despite these barriers, many schemes - including those that failed to improve health or reduce costs - reported that access to care had improved. Some of these schemes revealed substantial levels of unmet need and so total costs increased. CONCLUSIONS: It is often assumed in policy that integrating funding will promote integrated care, and lead to better health outcomes and lower costs. Both our agency theory-based framework and the evidence indicate that the link is likely to be weak. Integrated care may uncover unmet need. Resolving this can benefit both individuals and society, but total care costs are likely to rise. Provided that integration delivers improvements in quality of life, even with additional costs, it may, nonetheless, offer value for money.

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)0.025
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.656
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.002
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.003
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 head0.442
GPT teacher head0.723
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 teacher head, not a consensus.

Study designNot applicable
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

Citations134
Published2015
Admission routes1
Has abstractyes

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