Unburdening Primary Healthcare with an Open Source AI
Bibliographic record
Abstract
Background: Primary care practitioners have been experiencing adverse effects, such as burnout, due to the task load they have daily. In response to the growing administrative and cognitive burdens on primary care practitioners, this project introduces an open-source artificial intelligence (AI) platform designed to act as a real-time partner during patient encounters. The system innovates beyond traditional digital scribing by providing proactive, customizable assistance to reduce cognitive load, streamline documentation, and optimize interactions with electronic health records (EHRs). A collaboration between Conestoga College's SMART Centre and Ontario physician leaders, the platform aims to address physician burnout and workflow inefficiencies exacerbated by electronic health records (EHRs). Key objectives include ensuring security, privacy, scalability, and adaptability through open-source licensing to foster transparency and collaboration. Proposed Methods: This pilot project will employ a mixed-methods approach, collecting data on practitioners' time management and job satisfaction to evaluate the platform's impact. Descriptive statistics and thematic analysis will guide the assessment of efficiency gains and satisfaction improvements. This AI platform seeks to mitigate the adverse effects of EHR usability issues, which contribute significantly to clinician burnout and diminished patient care quality. Implications: By providing ethical, non-intrusive, and equitable solutions, the initiative prioritizes clinician well-being and enhanced patient outcomes. It represents a scalable, innovative approach to transforming primary healthcare delivery while maintaining a commitment to transparency and security.
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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 | 0.052 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".