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Record W2548091519 · doi:10.1177/0951484816670192

Medical innovation and the sustainability of health systems: A historical perspective on technological change in health

2016· article· en· W2548091519 on OpenAlexaff
Pascale Lehoux, Federico Roncarolo, Robson Rocha de Oliveira, Hudson Silva

Bibliographic record

VenueHealth Services Management Research · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsSustainabilityHealth carePoliticsPerspective (graphical)Healthcare systemHealth technologyBusinessTechnological changePath dependencyEngineering ethicsKnowledge managementPolitical scienceEconomic growthEconomicsEconomic systemEngineeringComputer science

Abstract

fetched live from OpenAlex

New medical technology challenges the sustainability of healthcare systems in several countries. Drawing on secondary sources of data, the aim of this article is to generate a better understanding of the historical Research & Development dynamics that have contributed to shape today’s medical innovation ecosystem. We describe key technological achievements along three historical periods – the 1950s, the 1980s and the 2000s – and situate them within their broader political, social, cultural and economic contexts. Our analyses bring forward self-reinforcing dynamics between technology, medical specialization, individualization of disease and the concentration of resources in academic teaching centres. We argue that the way medical innovation has been financed, designed and commercialized since the 1950s has engendered path dependency, which exacerbates the sustainability challenges healthcare systems are now facing. We conclude on the need for innovation design principles that could protect the sustainability of healthcare systems.

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)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0030.016
Scholarly communication0.0060.008
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.390
GPT teacher head0.525
Teacher spread0.136 · 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 designTheoretical or conceptual
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

Citations32
Published2016
Admission routes1
Has abstractyes

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