Bibliographic record
Abstract
What Is the Issue? Emergency departments (EDs) across Canada are currently under strain, resulting in patients experiencing long wait times and delays in receiving care. Across the country, long wait times have had devasting effects on patients and staff alike. Finding solutions to improve patient flow and efficiency is paramount to improving the quality of care for patients and well-being for those that work in the ED. This report aims to provide an overview of emerging technologies, such as artificial intelligence (AI) and vital sign monitors, which may reduce wait times in the ED or manage patient safety. What Are the Technologies? AI triage and clinical decision support models use machine-learning algorithms to analyze patient data and recommend acuity levels for patients as well as suggestions for next steps. Portable or wearable vital sign monitors continuously monitor vital signs such as blood pressure or oxygen levels for patients in the waiting area. Alerts are sent when any irregularities are detected. Digital information tools provide real-time updates to patients or clinicians working in the ED about average wait times, patient volumes, and available resources. Telemedicine connects patients via synchronous video calls to remote providers who assess patients and initiate care by ordering tests or imaging. What Is the Potential Impact? AI triage systems and digital information tools could improve patient flow by streamlining triage or redirecting patients with low-acuity care needs to choose alternative ED sites. These systems may also allow for better resource utilization. Vital sign monitors and staff-facing digital information tools could improve patient safety for patients waiting for care or at risk of extended waiting periods by allowing prompt treatment escalation. Telemedicine may allow for faster consultations for patients with low-acuity care needs. What Else Do We Need to Know? Before widespread adoption, more robust studies with larger sample sizes need to be conducted in Canada to examine how well these technologies can work compared to standard care and in real-life situations. Further evaluations could assist clinicians in gaining a better understanding of these technologies and how they may alleviate ED overcrowding, as well as facilitate their implementation, if appropriate. AI-powered solutions show potential in enhancing patient flow, although regulatory frameworks, guidelines, and policies are needed to address accountability, errors, algorithm biases, and data privacy concerns. Technological advancements, no matter how promising, cannot replace human care and intuition. Proposed models may have the greatest impact when used in tandem with human experience and empathy.
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 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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".