The Prevalence of No-Shows and Cancellations Rate in Outpatient Physical Therapy Practice and Its Relationship to Age and Gender
Bibliographic record
Abstract
Background: No-Show and late canceled appointments significantly impact out-patient Physical therapist productivity, patient clinical outcomes, and the clinic's revenue-generating capacity. No-show and appointment cancelation cost the out-patient Physical Therapy practice in this case study $114,505.58CAD in 2017. This study seeks to understand, identify, and provide solutions unique to our local setting for the problem of no-shows and appointment cancellation.Methods: This study uses the 2017 de-identified patient’s attendance records of an out-patient Physical Therapy clinic in Calgary, Canada. Patient data, including sex, age, scheduled appointment, no-show, and cancellation history, were examined. The data were analyzed using chi-square to determine any significant differences in attendance patterns among these groups.Results: A total of 6,162 scheduled appointments were aggregated from the EHR. The overall no-show and cancelation was 20.6%. Male had a slightly higher rate of no-show/cancelation (20.8%) versus females (20.6%), which was not statistically significant (p = 0.734). In the adult age groups, no-show and cancelation rates were highest for 12-20y/o (31.4%), 21-30y/o (31.3%), and 41-50y/o (22.3%). These groups accounted for 50.6% of total revenue loss. There was a significant overall difference among the age groups (p < 0.0001) in no-show/cancelation. The top four reasons for no-show and cancellation include forgetting the appointment, family and personal emergency, lack of transportation, and a scheduling conflict with another equally important appointment.Conclusion: Evidence indicates that no-show and appointment cancelation rates are high in Canadian health institutions leading to poor productivity, inefficiency, and revenue loss. This study seeks to provide an evidence-based intervention.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".