Optimizing acute stroke treatment process: insights from sub-tasks durations in a prospective observational time and motion study
Bibliographic record
Abstract
Background Rapid treatment is critical in managing acute ischemic stroke (AIS) to improve patient outcomes. Various strategies have been used to optimize this treatment process, including the Acute Stroke Protocol (ASP) activation, and minimizing the duration of key performance metrices, such as door-to-needle time (DNT), CT-to-needle time (CTNT), CT-to-groin puncture time (CTGP), and door-to-groin puncture time (DGPT). However, identifying the delay-causing sub-tasks within the ASP could yield novel insights, facilitating optimization strategies for the AIS treatment process. Methods This two-phase prospective observational time and motion study aimed to identify sub-tasks and compare their respective durations involved in the treatment process for AIS patients within ASPs. The study compared sub-task durations between “routine working hours” and “evenings and weekends” (after-hours), as well as between stroke neurologists and non-stroke neurologists. Additionally, the established performance metrices of AIS were compared among the aforementioned groups. Results Phase 1 identified and categorized 34 sub-tasks into five broad categories, while Phase 2 analyzed the ASP for 389 patients. Among the 185 patients included in the study, 57 received revascularization treatment, with 30 receiving intravenous (IV) thrombolysis only, 20 receiving endovascular thrombectomy (EVT) only, and 7 receiving both IV thrombolysis and EVT. Significant delays were observed in sub-tasks including triage, registration, patient history sharing, treatment decisions, preparation of patients, preparation of thrombolytic agents, and angiosuite preparation. The majority of these significant delays (P < 0.05) were observed when were performed by a non-stroke neurologist and during after-hours operations. Furthermore, certain sub-tasks were exclusively performed during after-hours or when the treatment was provided by a non-stroke neurologist. Consequently, DNT, CTNT, and CTGP were significantly prolonged for both non-stroke neurologists and off-hours treatment. DGPT was significantly longer only when the ASP was conducted by non-stroke neurologists. Conclusions The study identified several sub-tasks that lead to significant delays during the execution of the ASP. These findings provide a premise to design targeted quality improvement interventions to optimize the ASP for these specific delay-causing sub-tasks, particularly for non-stroke neurologists and after-hours. This approach has the potential to significantly enhance the efficiency of the AIS treatment process.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".