Improving Autopsy Report Turnaround Times by Implementing Lean Management Principles
Bibliographic record
Abstract
The autopsy is an integral part of the service of a large academic pathology department. Timely reporting is central to providing good service and is beneficial for many stakeholders, including the families, the clinical team, the hospital, and the wider community. The current study aimed to improve hospital-consented autopsy reporting times (turnaround time, TAT) by using lean principles modified for a healthcare setting, with an aim of signing out 90% of autopsies in 90 days. An audit of current and historical TATs was performed, and a working group incorporating administrative, technical, and professional staff constructed a value stream map documenting the steps involved in constructing an autopsy report. Two areas of delay were noted: examination of the microscopy and time taken to sign-out the report after the weekly autopsy conference. Several measures were implemented to address these delays, including visual tracking using a whiteboard and individualized tracking sheets, weekly whiteboard huddles, and timelier scheduling of clinicopathologic conference rounds. All measures resulted in an improvement of TATs. In the 30 months prior to the institution of lean, 37% of autopsies (53/144) were signed out in 90 days, with a wide variation in reporting times. In the 30 months following the institution of lean, this improved to 74% (136/185) ( P < .0001, Fisher exact test), with a marked reduction in variability. Further, the time from autopsy to presentation at weekly clinicopathological rounds was also reduced (median: 73 days prior to lean; 63 days post-lean). The application of lean principles to autopsy sign-out workflow can significantly improve TATs and reduce variability, without changing staffing levels or significantly altering scheduling structure.
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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 | 0.049 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".