Using electronic medical record notes to measure ICU telemedicine utilization
Bibliographic record
Abstract
Given the complexity of high-acuity health care, designing an effective clinical note template can be beneficial to both document patient care and clarify how telemedicine is used. We characterized documented interactions via a standardized note template between bedside intensive care unit (ICU) providers and teleintensivists in 2 Veterans Health Administration ICU telemedicine support centers. All ICUs linked to support centers and providing care from October 2012 through September 2014 were considered. Interactions were assessed based on initiation site, bedside initiator, contact type, and patient care change. Of 14 511 ICU admissions with teleintensivist access, teleintensivist interaction was documented in 21.6% (N = 3136). In particular, contacts were primarily initiated by bedside staff (74.4%), use increased over time, and of contacts resulting in changes in patient care, most were initiated by a bedside nurse (84.3%). Given this variation, future research necessitates inclusion of utilization in evaluation of Tele-ICU and patient outcomes.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".