Ironing out the problem of outpatient infusion wait times: look for process improvements first
Bibliographic record
Abstract
Rationale, aims and objectives: Iron sucrose remains a top expenditure in Fraser Health Authority. Audit data from an iron sucrose standardized order set (SSO) pilot coupled with the addition of iron isomaltoside to formulary resulted in the implementation of a regional SSO. Survey objectives were to clarify how iron infusion referrals are triaged, determine iron infusion wait times and identify what other services can impact wait times prior to regional SSO implementation. Method: Information was collected from a web-based survey sent to outpatient unit staff at all 11 sites within the health authority Results: Survey response rate was 73%. Urgent and non-urgent referral definitions varied and included laboratory parameters, prescriber specification, consideration of procedure dates and evidence of symptoms. Urgent referrals wait times are usually within the same week and non urgent wait times varied from same week booking to up to 3 months. Outpatient units provide a multitude of services that require urgent appointment times that may require scheduling ahead of non-urgent iron infusion referrals. Outpatient clinics deal with multiple other clinical reasons other than iron infusions which contribute complicate the triage and booking process and can lead to long wait times. Wait time reduction could be the result of utilizing a SSO that displayed all the information required by clinic staff and streamlined the booking process rather than the addition of iron isomaltoside to formulary. Conclusions: With the implementation of a regional SSO the iron infusion referral process may be simplified, thereby shortening appointment wait times. It is recommended that comparable information regarding iron infusion wait times be collected after these changes in practice.
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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 | 0.060 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".