Operations Research and Artificial Intelligence in Healthcare Management
Bibliographic record
Abstract
This work is a collection of papers presented at the two editions of the Francophone summer school on health service management.The second edition held in 2022 at Laval University in Quebec City (Canada) focused on innovation in healthcare organizations in a post-pandemic context.The third edition, for its part, was held in 2023 at Jean-Moulin University in Lyon 3 (France).The two editions had the same main theme, the redefinition of healthcare organizations through today's complexity.The COVID-19 pandemic has highlighted many issues in adapting healthcare and social services establishments (health and medico-social establishments).More than ever, European and Canadian facilities have to undergo major transformations and face major challenges if they are to respond to these concerns.Innovative solutions can only be found through a detailed understanding of the many particularities of the environment and context.Sharing knowledge and experience to question the many facets of these challenges better is therefore all the more relevant and important.To address this issue, the second edition of the summer school, focused on the theme: "Innovation in a post-pandemic health and social services system", analyzing the following subjects: (1) The patient at the heart of the reorganization of healthcare and services, ( 2) Healthcare organization, (3) Innovation in care trajectories, (4) Digital transformation and artificial intelligence serving health and social care institutions, (5) Learning health systems-operationalization and evaluation.The third edition focused on the rethinking of healthcare organizations.Indeed, in the last decades, healthcare organizations have been going through a period of turmoil.Staff shortages, overcrowding in hospitals and primary care, supply disruptions, computer hacking, funding problems, and abuse scandals are all having a profound effect on health establishments and services, giving rise not only to unease among staff but also in society as a whole, and to questions about the missions of institutions and the resources needed to carry them out effectively and efficiently.While most of these crises are not new, their combination and the scale of their repercussions highlight the need to think differently about organizations to heal the damage and make them more resilient in the face of future challenges.To answer these challenges, several perspectives were approached by the participants of the Summer schools.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".