Efficiency of Text Message Contact on Medical Safety in Outpatient Surgery: Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Establishing pre- and postoperative contact with patients is part of successful medical management in outpatient surgery. In France, this is mostly done via telephone. Automated information with short message service (SMS) reminders might be an interesting alternative to increase the rate of compliance with preoperative instructions, but no study has shown the safety of this approach. OBJECTIVE: The objective of this study was to evaluate the impact of pre- and postoperative automated information with SMS reminders on medical safety in outpatient surgery. METHODS: We conducted a retrospective, single-center, nonrandomized, controlled study with a before-after design. All adult patients who had outpatient surgery between September 2016 and December 2017 in our university hospital center were included. Before April 2017, patients were contacted by telephone by an outpatient surgery nurse. After April 2017, patients were contacted by SMS reminder. All patients were contacted the day before and the day after surgery. Patients contacted by SMS reminder were also contacted on day 7 after surgery. The primary end point was the conversion rate to full-time hospitalization. Secondary end points were hospitalization causes (anesthetic, surgical, organizational) and hospitalization costs. RESULTS: A total of 4388 patients were included, 2160 before and 2228 after the introduction of SMS reminders. The conversion rate to full-time hospitalization was 34/4388 (0.77%) with a difference between SMS group (8/2228, 0.36%) and telephone group (26/2160, 1.20%). The cost of SMS reminders was estimated as half that of telephone calls. CONCLUSIONS: In this work, we report a decrease in the rate of conversion to full-time hospitalization with the use of pre- and postoperative SMS reminders. This new approach could represent a safe and cost-effective method in an outpatient surgery 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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".