Reducing Missed Outpatient Appointments in Pediatric Urology: A Pre-Post Analysis of an Automated Text Reminder Intervention
Bibliographic record
Abstract
INTRODUCTION: Missed outpatient appointments, or no-shows, decrease clinic efficiency and are a major challenge in delivering high-quality, cost-effective care. Given the ubiquity of smartphones, automated text reminders are an attractive tool to promote appointment adherence. Thus, our study aims to describe the prevalence and predictors of pediatric urology clinic appointment no-shows and the effectiveness of an automated text reminder system. METHODS: We surveyed all in-person and telehealth pediatric urology clinic appointments within our institution's health system from January to December 2023. Ethnicity, native language, need for interpreter, clinic site, visit type, appointment time, and insurance type were measured. The primary outcome was appointment no-show rate. Secondary outcomes were appointment cancellation and rescheduling rates. Odds for no-show, cancellation, and rescheduling were compared between the preintervention and postintervention groups. A univariate and multinomial logistic regression model was used with attended visits as the reference for the outcome. RESULTS: A total of 15,315 outpatient urology clinic appointments were scheduled; 60.0% attended, 24.1% rescheduled, 9.4% cancelled, and 6.5% missed/no-show. Implementation of text reminders was associated with more visit cancellations (odds ratio [OR] 1.20 [1.01-1.42]), but fewer rescheduled visits (OR 0.78 [0.70-0.86]) and no-shows (OR 0.72 [0.60-0.85]). Spanish language, follow-up visits, and nurse visits were less likely to no-show (OR 0.66 [0.54-0.79], 0.80 [0.68-0.93], and 0.15 [0.06-0.37], respectively). Patients with public-payer insurance or appointments before 10:00 am had a higher odds of no-show (OR 3.38 [1.95-5.86] and 1.25 [1.04-1.51], respectively). CONCLUSIONS: An automated text reminder system is effective in reducing no-show rates for pediatric urology clinic appointments in an academic tertiary care 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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".