47 Increase Clinic Attendance Among Adolescents and Young Adults: A novel cost-effective method
Bibliographic record
Abstract
Abstract Background Transition clinics have been introduced to address the unique needs of adolescent and young adult (AYA) populations, however clinic attendance continues to be an issue. Although poor clinic attendance among the AYA population has been well known, solutions to address this has been limited. Some factors that have been associated with missed appointments include forgetfulness, negative previous clinic experiences and clinic schedules. With widespread use of digital technologies among AYA, the application of digital solutions to increase attendance at healthcare appointments has been explored, but little is known on its effectiveness. Objectives To determine the effect of text messaging appointment reminders on uninformed no show rates in an AYA transition clinic. Uninformed no show rate was defined as an absence from clinic (not related to a medical emergency) without communication with the clinic. Design/Methods A pilot prospective cohort study with a retrospective control group was conducted in an AYA general hematology transition clinic. In order to establish the current no show rate at the clinic, a retrospective review of AYA patients who attended the clinic between April 2013-August 2015 was conducted. Thereafter, all patients who had an appointment scheduled between February 2016 and December 2017 were included in the study and received a text message reminder of their appointment 48 hours prior to their appointment. Monthly uninformed no show rates were collected, and a student’s t-test was conducted to determine if there was a significant difference in uninformed no show rates before and after the introduction of text message reminders. Results Eighty-six participants consented to participate in the study and received a text message reminder of their appointment. From April 2013- August 2015 a total of 51 clinic days with 236 appointments occurred. During this time the mean uninformed no show rate was 39.9%. SMS appointment reminders were sent from February 2016 to December 2017 for 48 clinic days for a total of 206 clinic appointments. The mean uninformed no show rate after the introduction of text message appointment reminders was 22.6%. The introduction of text messaging appointment reminders significantly (p<0.01) decreased uninformed no show rates by 17.3%. Conclusion Text message reminders are an effective low cost method in reminding AYA patients about their appointments. By using innovative, cost effective and practical strategies like text messaging reminders to increase clinic attendance, we not only improve the care of our patients but also reduce the financial and clerical burden to the system resulting from missed appointments.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.015 | 0.002 |
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".