Considering non-hospital data in clinical informatics use cases, a review of the National Emergency Medical Services Information System (NEMSIS)
Bibliographic record
Abstract
Background: The National Emergency Medical Services (EMS) Information System (NEMSIS) Technical Assistance Center (TAC) collects and curates EMS activation level records for the United States. Originated as an outcomes assessment and service comparison tool, NEMSIS may have other high value clinical and public health uses. Methods: This study acquired a 100% activation level public dataset for 2019 from NEMSIS TAC and assessed item response quantities. Subsumption of NEMSIS terms within other controlled clinical vocabularies was also considered. Results: None of the assessed terminologies (LOINC, ICD10-CM, SNOMED-CT) could describe meaningful volumes of NEMSIS item response codes. The 2019 activation year dataset included 36,525 non-date/time or calculated distinct item responses for 43 activation descriptive items. Said item responses yielded 2,101,844,053 activation distinct non-blank responses. Several NEMSIS item responses had high clinical and public health value. Conclusions: NEMSIS can support multiple public health use cases in addition to EMS outcomes assessment. A comprehensive custom value set is appropriate to integrate NEMSIS item response codes into controlled terminologies, FHIR or hospital Electronic Health Record applications.
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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 | 0.032 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".