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Record W4401809861 · doi:10.55016/ojs/sppp.v14i1.74017

Healthcare Funding Policies for Reducing Fragmentation and Improving Health Outcomes

2021· article· en· W4401809861 on OpenAlexaboutno aff
Jason M. Sutherland

Bibliographic record

VenueThe School of Public Policy Publications · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsFragmentation (computing)Health careBusinessPublic economicsEconomic growthComputer scienceEconomics

Abstract

fetched live from OpenAlex

This policy paper examines potential roles of ‘funding policy’ to address yawning gaps in continuity of healthcare delivery and inequality in health outcomes across Canada. Funding policy is but one tool available to healthcare decision makers to affect the volume, type, timeliness and cost of healthcare services, products and devices provided to a country’s residents. Healthcare funding policies involve trade-offs and conflicts that may drive the ‘price’ of change to be too high. As Canada exits the COVID-related healthcare crises, the price of healthcare reform may be changing which may cause provinces and territories to consider new healthcare funding policies. The paper begins with a brief background outlining the utility of funding policies to affect the cost-efficiency, effectiveness and equity of healthcare delivered in provinces and territories. Two complementary policy options are proposed that show considerable promise to improve value from healthcare funding. The paper is written from the perspective of the role of the federal government and highlights possible strategies for the federal government to remove barriers to support funding policy reforms in provinces and territories. The federal government is not responsible, nor does it play a meaningful role, in developing, implementing or monitoring funding policy for healthcare organizations or individual providers. This role rests with the respective provinces and territories with the exceptions of federally-insured populations. Nonetheless, the federal government could show leadership in the domain of funding policy by removing provinces’ and territories’ barriers to policy reforms. To bridge the federal government’s strengths with provinces’ and territories’ barriers, a number of specific recommendations are offered to the federal government for its consideration. First, this policy paper recommends that substantive and meaningful funding policy reform should not consist of solely of paying organizations or people differently. There should be commensurate advancements in: national standards for new streams of data and reporting, organization-building and skills development, and robust risk adjustment. These barriers are not show-stoppers, and activities to remove many barriers are fairly straightforward. Second, the federal government could take a leadership role in immediately supporting episode-based payments. These activities could include national efforts to define episodes of care, link data, establish non-binding payment amounts, and publish health outcome performance measures. Third, deliberate federal leadership is needed to establish and grow new streams of data critical to measuring value – including patient-reported outcomes and non-insured health services, such as physiotherapies and mental health services. These activities will be ground-breaking in Canada from the perspective of measuring health and population outcomes. Fourth, the federal government could lead efforts to link social care data with healthcare data. The measurement and analyses of this data will create a roadmap for future interventions to improve population health. The policies proposed in this paper are based on the federal government ‘nudging’ provinces and territories to make changes in funding policy by removing barriers. The policies are incremental from an international perspective, though have important consequences for healthcare organizations and individual providers if they were to be implemented and transformational to healthcare performance were the policies proven to be successful in the long-term.

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 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)0.031
metaresearch head score (Gemma)0.076
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.280
Threshold uncertainty score0.558

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.076
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0040.007
Scholarly communication0.0110.008
Open science0.0030.008
Research integrity0.0080.005
Insufficient payload (model declined to judge)0.0150.001

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 head0.145
GPT teacher head0.369
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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