Improving the primary care clinical testing process in southwest Scotland: a systems-based approach
Bibliographic record
Abstract
INTRODUCTION: Across all healthcare environments, inadequately specified patient test requests are commonly encountered and can lead to wasted clinician time and healthcare resources, in addition to either missed or unnecessary testing taking place.Before this work, in a general practice in Southwest Scotland, a mean value of 42% of test requests were already uploaded to ordercomms (a widely used system in general practice for designating clinical testing instructions) at patient presentation, leaving an opportunity for error and wasted clinician time/resources. METHODS: Patient appointment records were retrospectively reviewed in a general practice in Southwest Scotland to monitor the proportion of test requests already uploaded to ordercomms at the time of patient presentation.The use of quality improvement tools and plan-do-study-act cycling allowed the testing of four change ideas attributable to different 'pathways' of origin for test requests.Change ideas included increasing clinician and secondary care/docman origin test requests already on ordercomms prior to patient presentation, reducing patient origin test requests and improving the test requesting system. RESULTS: The percentage of test requests already on ordercomms at patient presentation increased from a mean of 42% to 89% over a 30 week test period. The use of test pre-set templates was a welcome intervention that was agreed to be made accessible to 30+ regional general practices. CONCLUSION: The use of pre-set templates for clinical testing encouraged a 47% rise in test requests already uploaded to ordercomms prior to patient presentation. This saved up to 90 min of clinician time weekly and ensured patients received the correct tests at the appropriate time.Our findings supported the use of pre-set testing templates, in combination with effective information communication, and were recommended for use in any clinical environment requiring patient testing.
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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 | 0.019 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".