Qualitative Insights Into Non-attendance for Scheduled Radiology Appointments at a Specialist Hospital in Saudi Arabia
Bibliographic record
Abstract
BACKGROUND: Patient non-attendance for radiology appointments is an international problem with significant implications for healthcare recipients and healthcare efficiency. Non-attendance impacts patient health, waiting lists, and other hospital departments while increasing staff stress, anxiety, and fatigue. Understanding the reasons behind patient non-attendance is crucial for developing effective strategies to help improve attendance rates. OBJECTIVE: This study explored the reasons for patient non-attendance for scheduled radiology appointments and identified potential strategies to enhance attendance at a specialist hospital in Saudi Arabia. METHODS: Using semi-structured interviews, nine men and eight women who were purposively sampled and had missed scheduled radiology appointments at the research site were interviewed. Thematic analysis was employed to identify the key themes represented by the data. FINDINGS: This qualitative study revealed the multifaceted nature of patient non-attendance for scheduled radiology appointments at the specialist hospital in Saudi Arabia. Five themes underlying non-attendance were identified. First, scheduling conflicts were a significant barrier. Second, a lack of adequate knowledge about health conditions was evident. Third, physician-patient miscommunication was a critical issue. Fourth, transportation difficulties, especially for those living far from the hospital or without personal transportation, were a key factor in non-attendance. Finally, personal reasons, such as fear of medical procedures and the patient's health status, also contributed. The study identified two main areas for improvement: implementing an effective appointment reminder system and enhancing the radiology department by extending hours and addressing non-attendance more effectively. These strategies underscore the need for a patient-centered approach to reduce barriers to attendance. CONCLUSION: The findings suggest that patient non-attendance is multifactorial, involving personal and hospital-specific reasons. Strategies to improve attendance should thus be multifaceted, including better scheduling systems, enhanced patient education and communication, and reminder systems. These insights can inform targeted interventions to reduce non-attendance rates, ultimately improving healthcare delivery and resource utilization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".