Abstract TMP39: Impact Of Best Modeled Transport On Hospital Patient Volumes, Quantified Outcomes And Healthcare Cost Savings
Bibliographic record
Abstract
Introduction: A previously published conditional probability field-based triage model predicts best transport options for ischemic stroke patients to access Endovascular Therapy. This model predicts whether Drip and Ship or Mothership transport results in a higher probability of excellent outcomes. To inform health system policy, it is critical to understand the change in annual hospital volume, the annual increase in the number of patients expected to have excellent outcomes, and the annual reduction in cost to the healthcare system resulting from changing prehospital transport strategies. Methods: We performed a case study using the stroke system in Alberta, Canada. The population of each 7 km by 7 km grid section in Alberta was obtained through the ArcGIS GeoEnrichment service. This was combined with the annual stroke incidence and large vessel occlusion (LVO) screening tool (ex. LAMS) sensitivity and positive predictive value to model the expected number of suspected LVO patients transported by ambulance. Using transport to the closest hospital as a baseline, the resultant annual change in hospital volume was determined. This was used to calculate the annual increase in the number of patients that will have excellent outcomes and resultant cost savings to the healthcare system from average post-stroke cost data. Results: The change in annual hospital volumes for Alberta is shown in the Figure. There was an increase of 6.65 patients predicted to have an excellent outcome (90-day mRS 0-1) annually, and an increase of 11.93 patients to have good outcomes (90-day mRS 0-2). This resulted in an annual cost savings of $283,463.86 to the healthcare system. Conclusions: Translating the benefit of an existing conditional probability model into the change in annual hospital volume, increase in the annual number of patients with excellent outcomes, and health system cost avoidance, allows one to fully realize the impact of using a modeled transport protocol to the health system.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".