Generating Physician Standing Orders for Unplanned Care Scenarios using the HL7 FHIR Patient Summaries
Bibliographic record
Abstract
Evidence-based and standardized clinical order sets proved to be effective for improving efficiency and accuracy of treating urgent and unplanned emergent patient cases as well as to decrease adverse events. However order sets are only incorporated at modern healthcare systems that are used for planned care like the CPOE (Computerized Physician Order Entry) system. The use of these order sets are misaligned with the clinician workflow in the emergency departments receiving frequent unplanned care case. The current care systems used to describe unplanned care cases like the HL7 FHIP IPS (International Patient Summary) do not incorporate order sets. Moreover, the legacy IPS systems do not incorporate the physicians training in charting clinical cases based on SOAP (Subjective, Objective, Assessment, and Plan). In this article, we are describing our efforts in extending our QL4POMR care design system not only to generate the IPS document based on SOAP but also to generate relevant standing order sets. This paper report our effort on collecting standardize order sets from pre-printed formats (PDF) and build an indexer and a search engine to enable the QL4POMR CRUD interface to recommend standing order sets for urgent cases at the time of IPS generation on the fly.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".