Lean six sigma process improvement in specimen receiving to improve stat chemistry turnaround times
Bibliographic record
Abstract
As a consequence of stat turnaround times (TATs) chronically exceeding 60 minutes, our laboratory was facing pressure to divert limited resources toward the implementation of an emergency department satellite laboratory.Peer-reviewed literature in clinical laboratory quality assurance and improvement indicates that between 60-70% of errors occur at the pre-analytical level.Thus, we sought to improve overall TATs by focusing on reducing pre-analytical lag times.Lean six sigma process improvement owes its origins to industry, and may be universally applied in healthcare settings to improve outcomes.We report the application of Lean six sigma process improvement tools in the clinical laboratory specimen accession and processing area of a busy tertiary care center to improve chemistry stat TATs.The prospective before-and-after redesign encompassed a detailed evaluation of existing system, assessment of established monitors and historical data, formulation and implementation of a plan, and post-move data collection and analysis.Allocation of laboratory space was based on Lean six sigma quality improvement methods.Test TAT and volumes were obtained from the LIS.Spaghetti diagrams were utilized to assess workflow in the existing space and in layout planning for the new space.An assessment of the pre-analytical steps in the receiving and processing area, in tandem with pre and post move Pareto chart data enabled the calculation of the reduction of defects per million opportunities that could be ascribed to this effort.12 months mean ED CMP TATs before the move was 44.4 minutes with 90% of results reported in 60 minutes or less; after the move this improved to a mean of 37.1 minutes with 90% of results reported in 49 minutes or less.12-month ED troponin mean TAT was 49.5 minutes with 83% of results reported in 60 minutes or less; after the move this improved to mean TAT of 43.4 minutes with 90% of results reported in 55 minutes or less.Given seven touch points per result, this project enabled a 75% reduction in defects per million opportunities.Lean-six sigma tools facilitated the identification and elimination of inefficiencies in specimen receiving to enable sustained improvements in TATs.Thus, defining and measuring problems, planning, taking necessary steps and implementing them are effective techniques to improve throughput in pre-analytical specimen handling.The one-time expenses associated with the moves were minimal, and the costavoidance of satellite laboratory oversight and operation is substantial.Lean six sigma techniques can be applied in a cost-effective manner to minimize preanalytical wastes and improve patient care.
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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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".