Discharge Instruction Reminders Via Text Messages After Benign Gynecologic Surgery: Quasi-Experimental Feasibility Study
Bibliographic record
Abstract
BACKGROUND: With the implementation of enhanced recovery after surgery protocols and same-day hospital discharge, patients are required to take on increasing responsibility for their postoperative care. Various approaches to patient information delivery have been investigated and have demonstrated improvement in patient retention of instructions and patient satisfaction. OBJECTIVE: This study aimed to evaluate the feasibility of implementing a postoperative text messaging service in the benign gynecologic population. METHODS: We used a quasi-experimental study design to evaluate patients undergoing outpatient laparoscopic surgery for benign disease with a minimally invasive gynecologist at an academic medical center between October 2017 and March 2018. In addition to routine postoperative instructions, 19 text messages were designed to provide education and support to postoperative gynecologic patients. Patients were contacted by telephone 3 weeks postoperatively and surveyed about their satisfaction and feelings of connectedness during their recovery experience. Demographic and operative information was gathered through chart review. The cost to implement text messages was US $2.85 per patient. RESULTS: A total of 185 patients were eligible to be included in this study. Of the 100 intended intervention participants, 20 failed to receive text messages, leaving an 80% success in text delivery. No patients opted out of messaging. A total of 28 patients did not participate in the postrecovery survey, leaving 137 patients with outcome data (control, n=75; texting, n=62). Satisfaction, determined by a score ≥9 on a 10-point scale, was 74% (46/62) in the texting group and 63% (47/75) in the control group (P=.15). Connectedness (score ≥9) was reported by 64% (40/62) in the texting group compared with 44% (33/75) in the control group (P=.02). Overall, 65% (40/62) of those in the texting group found the texts valuable (score ≥9). CONCLUSIONS: Postoperative text messages increased patients' perceptions of connection with their health care team and may also increase their satisfaction with their recovery process. Errors in message delivery were identified. Given the increasing emphasis on patient experience and cost effectiveness in health care, an adequately powered future study to determine statistically significant differences in patient experience and resource use would be appropriate.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".