Discoveries and insights from implementing telehealth in a tele-acute unit: A retrospective study
Bibliographic record
Abstract
Objective: This study examines the impact of telehealth nursing interventions on length of stay (LOS) and ratio of LOS to risk-adjusted length of stay comparing tele-acute and traditional units.Methods: Retrospective data from 6,999 patient visits at tele-acute and traditional hospital units between Q2 2020 and Q4 2022 were collected. Bivariate analysis and the Mann-Whitney U Test were used to determine statistical significance. Multivariate regression was conducted to analyze the factors affecting both LOS and the ratio.Results: Regardless of the model, the findings suggest that LOS was greater in the traditional unit. In the LOS model, the stay was 7 hours and 39 minutes longer per admission in the traditional unit. In the risk-adjusted ratio model, the LOS was 5 hours and 14 minutes longer per admission than in the tele-acute unit.Conclusions: This study contributes to a body of literature that is lacking in the use of telehealth nursing in the acute care setting. Our research offers new perspectives on how telehealth can affect operational measures like LOS and discharge times. This contribution is important as it broadens the scope of telehealth’s benefits beyond traditional remote care, highlighting its potential in fast-paced, acute care settings.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".