Effectiveness of Personalised Phone Calls and Short Message Service Reminders in Improving Patient Attendance at a Radiology Department
Bibliographic record
Abstract
BACKGROUND: Patients missing scheduled hospital appointments pose significant challenges, including resource waste and delayed patient care. This study evaluated the effectiveness of personalised reminder systems (phone calls and short message service (SMS)) in improving patient attendance rates at a radiology department. METHODS: The study was conducted at a hospital facility in Saudi Arabia. The intervention involved reminding 493 patients of their radiology appointments using their preferred method (phone call or SMS). Demographic, clinical, and other factors were considered in analysing the impact of these reminders on appointment attendance. Attendance rates before the intervention were further compared with attendance rates after the intervention to assess the effectiveness of the studied strategies. RESULTS: Patient reminders affected overall patient attendance, with a 5% improvement compared to the attendance rates before the intervention. Phone call reminders were found to be more effective than SMS, particularly among older patients (41-60 years). The attendance rate for patients receiving phone call reminders ranged from 35% to 85%, whereas those receiving SMS reminders had a 15-65% attendance range. The study indicated marital status and distance as key factors associated with attendance. Chi-square analysis also highlighted significant differences in attendance rates before and after the intervention, particularly among female patients, single and divorced individuals, and those with at least secondary education. Patients living more than 35 km from the hospital and those referred from other hospitals were more likely to miss appointments, irrespective of the intervention. CONCLUSION: Personalised phone call reminders seem to be more effective than SMS in reducing missed appointments, especially among older patients. This study highlighted the importance of considering patient demographics and preferences in designing reminder systems to enhance healthcare appointment adherence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".