The Association of Medical Preoperative Evaluation Using Clinical Video Telehealth With Hospital Length of Stay: Descriptive Analysis
Bibliographic record
Abstract
BACKGROUND: Preoperative medical evaluation serves to identify risk factors and optimize patients before surgery. Providing a telehealth option in the perioperative setting has played a significant role in reducing barriers to quality perioperative health care. OBJECTIVE: We aimed to evaluate how telemedicine preoperative evaluations using Clinical Video Telehealth (CVT) impact hospital length of stay. METHODS: We performed a retrospective chart review between 2016 and 2017 of adult patients who underwent evaluations in our hospitalist-run preoperative medicine clinic. Patients seen in our preoperative CVT program were compared to patients seen in person to evaluate the association of visit type (preoperative CVT versus in-person evaluation) with hospital length of stay, defined as hospital stay from postoperative day 0 to discharge. There were 62 patients included in this retrospective study. RESULTS: The adjusted incidence rate ratio (IRR) for hospital length of stay was significantly shorter in patients who underwent preoperative CVT compared to an in-person visit (IRR 0.52, 95% CI 0.29-0.92, P=.02). CONCLUSIONS: After adjusting for age and comorbidities, we show that preoperative telemedicine in the perioperative setting is associated with a shorter hospital length of stay compared to in-person visits. This suggests that telemedicine can play a viable role in this clinical setting.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".