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Record W4388543079 · doi:10.1177/11786329231211096

Optimization of Care Pathways Through Technological, Clinical, Organizational and Social Innovations: A Qualitative Study

2023· article· en· W4388543079 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueHealth Services Insights · 2023
Typearticle
Languageen
FieldMedicine
TopicClinical practice guidelines implementation
Canadian institutionsUniversité LavalCentre intégré de santé et de services sociaux de Chaudière-AppalachesInstitut universitaire de cardiologie et de pneumologie de Québec
FundersMinistère de la SantéMitacsMinistère de la Santé et des Services sociaux
KeywordsThematic analysisHealth careGrounded theoryContext (archaeology)Participant observationPsychological interventionKnowledge managementQualitative researchProcess (computing)Public relationsPsychologyProcess managementBusinessNursingMedicineSociologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Numerous calls at national and international level are leading some countries to seek to redesign the provision of healthcare and services. Care pathways have the potential to improve outcomes by providing a mechanism to coordinate care and reduce fragmentation and ultimately costs. However, their implementation still shows variable results, resulting in them being considered as complex interventions in complex systems. By mobilizing an emerging approach combining action research and grounded theory methodology, we conducted a pilot project on care pathways. We used a strongly inductive process, to mobilize comparison and continuous theoretical sampling to produce theories. Forty-two interviews were conducted, and participant observations were made throughout the project, including 60 participant observations at meetings, workshops and field observations. The investigators kept logbooks and recorded field notes. Thematic analysis was used with an inductive approach. The present model explains the factors that positively or negatively influence the implementation of innovations in care pathways. The model represents interactions between facilitating factors, favourable conditions for the emergence of innovation adoption, implementation process enablers and challenges or barriers including those related specifically to the local context. What seems to be totally new is the embodiment of the mobilizing shared objective of active patient-partner participation in decision-making, data collection and analysis and solution building. This allows, in our opinion, to transcend professional perspectives for the benefit of patient-oriented results. Finally, the pilot project has created expectations in terms of spread and scaling. Future research on care pathway implementation should go further in the evaluation of the multifactorial impacts and develop a methodological framework of care pathway implementation, as the only existing proposition seems limited. Furthermore, from a social science perspective, it would be interesting to analyse the modes of social valuation of the different actors to understand what allows the transformation of collective action.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.312
GPT teacher head0.555
Teacher spread0.244 · 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