How AI, ehealth, virtual care and other innovative digital solutions can revolutionize healthcare
Bibliographic record
Abstract
During the Covid-9 pandemic, virtual care, virtual wards and ehealth demonstrated their impact and value in delivering high quality person centered care. Globally, healthcare systems embraced ehealth as an alternative to face to face contact, to ensure their citizens received high quality healthcare whilst managing the Covid-9 pandemic. Subsequently, AI, ChatGPT, and virtual and augmented reality have started to become mainstream digital solutions for healthcare professionals and patients. This workshop looks at the lessons learned from the pandemic, and how healthcare systems can embrace innovative digital solutions to deliver future fit for purpose care. BackgroundPrior to the covid-9 pandemic, virtual and ehealth delivery was variable across Canada. This was in part due to lack of reimbursement for Family Physicians, a perception ehealth is inferior to that face to face, and a lack of prioritization from systems leaders. At the advent of the pandemic nearly all healthcare systems pivoted to ehealth as an essential solution to continuing to provide essential non-acute services to citizens.Subsequently, citizens now accept virtual and ehealth solutions as business as usual, in the way that other industries have embraced the digital revolution.As healthcare systems deliver ehealth as standard of care, the next generation of innovation, AI and virtual and augmented reality, are offering new and innovation solutions to the challenges healthcare systems are facing.Healthcare systems are employing these solutions, but it is not mainstream and many remain uncertain of the benefits and risks.This workshop will explore how we embrace the next generation of digital solutions, the 'metaverse', as part of the natural evolution and advancement of medical/clinical science. Results: Exploring the barriers, enablers and success factors for scaling next generation digital solutions, the benefits and risks, and who needs to do what.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.022 | 0.019 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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 source (direct Gemma or distilled Codex), 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".