MétaCan
Menu
Back to cohort

Innovative Financing Models and Future Directions in Healthcare

2024· book-chapter· en· W4405279070 on OpenAlex
Bhupinder Pal Singh Chahal, Umang Sharma, Bhumika Bansal

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in human services and public health (AHSPH) book series · 2024
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsYorkville University
Fundersnot available
KeywordsHealth careBusinessFinanceEconomicsEconomic growth

Abstract

fetched live from OpenAlex

This study explores innovative financing models and future directions in healthcare, with a focus on assessing financial impact on digital health outcomes and advancing sustainable healthcare innovation. As digital health technologies like AI, IoT, and telemedicine transform patient care, the need for adaptable and value-based financing mechanisms becomes critical. Traditional models, often inadequate for the rapid pace of digital health, are compared to novel approaches such as public-private partnerships, outcome-based financing, and tiered pricing. The research examines how these models can drive equitable access, incentivize innovation, and improve patient outcomes globally. Through evaluating financial strategies, this study provides insights into scalable frameworks that prioritize both fiscal sustainability and impactful health outcomes, paving the way for a resilient, technologically integrated healthcare ecosystem.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.858
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.007
Open science0.0000.000
Research integrity0.0000.001
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 head0.024
GPT teacher head0.274
Teacher spread0.250 · 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