Bibliographic record
Abstract
Background: Failure to attend scheduled appointments in the outpatient clinic represents a challenge to health care. Almost quarter billion dollar was lost in the United Kingdom in 2001; Saudi Arabia has 29.5% “no-show” rates, and these findings are encouraging to explore the reasons and to implement strategies to improve attendance. Moreover, we found 0.12% reduction in no-show, 1 month after using phone call reminder associated with the application of strategies to increase attendance rate used in similar work. Context: To identify reasons of no-show and to use strategy to decrease the substantial no-show rates. Aims: This study aimed to minimize significant loss of time and money and to decrease the dissatisfaction and worsening of patient's clinical outcome. Settings and Design: The study was conducted in pain management clinic in King Abdulaziz Medical City National Guard Hospital, pain clinic, staffed by one consultant, one staff physician, one fellow and two nurses, and one patient service coordinator, responsible for answering phone calls and booking appointments; patient receives phone call 2 days prior to their scheduled appointment by obtaining their contact number from Bestcare® health information system. Data of all patients booked from the begging of August to the end of October 2017 were collected and analyzed, and no show rate was calculated by dividing the number of no show to the number of all booked appointments, showing that a mean average in 3 months of 100 visit per month, with a mean average of no show within 3 months of 0.34% no show rate. Subjects and Methods: Contact information of all patients who had been booked in the month of November 2017 were collected; we applied strategy for all phone calls. First, we identified if the patient had answered or not and if he/she has answered a welcoming and orientation statement was used (i.e., who we are and why we are calling and where is exactly our clinic and the time of the appointment). Second we have to identify that the answering is either the patient or the caregiver to avoid breaching the confidentiality. Third we identified the appointment status if the patient is attending or not, and finally. Fourth we used verbal contract to inform the patient or the caregiver that in case of failure to show-up; patient might be discharged from the clinic to decrease the waiting list which can lead to further health deterioration of other patients. Results: Total no-show has decreased from 0.34% to 0.22% within 1 month of implementation. Conclusions: Significant strategies can be applied to enhance attendance rate implemented through telephone call which led to 0.12% reduction of no-show in 1 month compared to 3 months average no-show rate.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".