084 PLUGGED-IN (Providing Likeable and Understandable Guidelines using GRADE in the EMR with Direct links to Individual patient data) Phase 2
Bibliographic record
Abstract
Background Traditional clinical decision support systems in Electronic Medical Records (EMR) use algorithms with inclusion/exclusion criteria to provide direction to clinicians. Improved systems for developing trustworthy guidelines (e.g. GRADE,) typically include many weak recommendations unsuited for clear inclusion/exclusion criteria, and in which the right decision varies from patient to patient. Through PLUGGED-IN phase 1 we developed a conceptual framework and a guideline authoring/publication platform to allow use of trustworthy guidelines directly as decision support in EMRs, not dependent on traditional algorithms or reproduction of the content. Our framework is based on a multilayered guideline presentation format developed in DECIDE. Objectives To implement and test our novel approach to decision support where relevant patient specific information is shown alongside evidence based recommendations in EMRs. Methods We used a web guideline published through the MAGIC (Making Grade the Irresistible Choice) application, which allowed our EMR partner to make use of its structured content, ontology-coded clinical questions and recommendation-specific EMR elements. Results The EMR system was able to interact with the guideline, suggest relevant recommendations displayed along with relevant patient specific information (lab tests, measurements, medications), and offer these to facilitate direct ordering. We will show real examples and live products. Discussion Results suggest we can offer a complementary approach to traditional algorithm-based systems that is compatible with a large number of EMRs. Implications for Guideline Developers/Users PLUGGED-IN provides a model for direct use of guidelines as decision support in EMRs.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.013 |
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".