Application of Lean Principles to the Comprehensive Geriatric Assessment to Reduce Cycle Time
Bibliographic record
Abstract
Background: Prolonged cycle times for new geriatric medicine assessments at the Centre for Healthy Aging have reduced the capacity to see patients. Using a time series design, the aim of the project was to decrease the average cycle time for new patients during one geriatrician's clinic from 114 to 90 minutes by May 1, 2024. Methods: Lean methodology was used for diagnostics by creating a value stream map of the workflow. This informed change ideas to improve efficiency by implementing a shared note within the electronic health record for information sharing and an assessment guide for targeted cognitive testing. The primary outcome measure was total cycle time. Balancing measures were patient clinic experience scores and counseling time. Process measures included caregiver interview time, pre-clinic intake completion rate, assessment guide use rate, and nursing assessment time. Results: Total cycle time decreased 19% from 114 minutes (19 patients) to 93 minutes (33 patients). Pre-clinic intake assessment completion rate increased from 60 to 80% and caregiver interview time decreased from 45 to 33 minutes. There was 100% uptake of the assessment guide, and nursing assessment time decreased from 43 to 31 minutes. Counseling time remained stable, and the average clinic experience scores did not decline from the baseline. Conclusions: This is the first study examining potential methods to improve efficiency of the comprehensive geriatric assessment by using value stream mapping. Spread of change ideas across the centre will be examined next with the goal of increasing capacity using available resources.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".