Addressing SNOMED CT Implementation Challenges Through Multi-disciplinary Collaboration
Bibliographic record
Abstract
This article describes the challenges of implementing SNOMED CT into electronic clinical documentation systems for discharge summaries, synoptic operative notes and ambulatory documentation. Four significant implementation challenges were identified throughout these projects, which required collaboration between specialists across several disciplines to resolve. The challenges included: designing the graphical user interface for selecting SNOMED CT values, gathering and validating template specifications that use SNOMED CT subsets, handling SNOMED CT subsets and extensions, and, creating algorithms and the technological infrastructure to generate fast, meaningful, non-redundant search results. Our experiences suggest that, while the usage of SNOMED CT in tertiary care settings is promising, collaboration between specialists from multiple disciplines is needed to utilize their unique project management, data modeling, technical, and clinical skills in overcoming implementation challenges.
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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 | 0.192 | 0.202 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".