Implementation of the GSA KAER Toolkit in a Large Clinic System: Workflow Modifications and EMR Tools
Bibliographic record
Abstract
Abstract We implemented the KAER toolkit in the University of Washington primary care clinics. In this session we share the workflows implemented to promote the KAER model and share the tools we developed within EPIC, the system's electronic medical record (EMR). We collaborated with clinic staff to develop interdisciplinary workflows including: training patient service representatives, social workers, nurses, and medical assistants (MAs) about 'red flags;' training medical assistants to complete the Patient Health Questionnaire (PHQ-9) and Montreal Cognitive Assessment (MoCA); and assuring they are appropriately entered into flowsheets in EPIC. We created a checklist (EPIC 'SmartPhrase') and educated the clinics' interdisciplinary teams to utilize it within their scope of practice. Additionally, we created an order set (EPIC 'SmartSet') of commonly ordered tests and referrals to expedite evaluation of patients with suspected cognitive impairment. Lastly, we created a direct link from our EMR to our website containing community resources.
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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 | 0.023 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".