Optimizing Outpatient Oral Contrast Use in Abdominal CT–A Radiology Pandemic Response Initiative to Reduce Patient Time in the Waiting Room and Reduce Costs, While Improving Patient Experience
Bibliographic record
Abstract
Purpose: The aim was to reduce outpatient wait time and improve patient experience by optimising oral contrast use . Methods: Our multidisciplinary stakeholder collaboration implemented two simultaneous interventions: (1) Creation of ‘oral contrast policy’, limiting recommended indications. (2) Creation of a new shorter oral contrast regime (30 vs 60 min). We conducted a retrospective service evaluation of oral contrast use in outpatient (OP) abdominal CT at baseline and post-intervention. Patient wait times were measured and per-patient cost-savings were reported. An image quality review was performed by 2 blinded abdominal radiologists. Patient experience was evaluated with a standard voluntary survey. Statistical analysis was performed comparing baseline and evaluation outcomes using Chi-square or Fisher Exact test for categorical variables and Student’s t-test or ANOVA for continuous data. Results: Over 1-month periods, OP CT scans were assessed in baseline (pre-pandemic) n = 575, baseline (pandemic) n = 495 and post-intervention n = 545 groups. Oral contrast use reduced from 420/575, 73.0% at baseline to 178/545, 32.7% post intervention. The turn-around time reduced by 15.8 minutes per patient from 70.3 to 54.5 minutes, P < .001 (Interventions 1 and 2). The diagnostic quality did not differ between the oral contrast regimes (Intervention 2, P = 1.0, P = .08). No repeat CTs were needed due to lack of oral contrast (Intervention 1) or poor opacification (Intervention 2). There was oral contrast cost reductions of 69.1–78.4% ( P < .001). Patients reported their overall experience was improved post-intervention (Interventions 1 and 2). Conclusions: Optimising the CT oral contrast service through judicious use and a shorter regime, reduced patient wait times, improved patient experience and preserved diagnostic quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".