Improving quality through process change: a scoping review of process improvement tools in cancer surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Surgery is a cornerstone of treatment for malignancy. However, significant variation has been reported in patterns and quality of cancer care for important health outcomes, including perioperative mortality. Surgical process improvement tools (SPITs) have been developed that focus on enhancing the processes of care at the point of care, as a means of quality improvement. This study describes SPITs and develops a conceptual framework by synthesizing the available literature on these novel quality improvement tools. METHODS: A scoping review was conducted based on instruments developed for quality improvement in surgery. The search was executed on electronically indexed sources (MEDLINE, EMBASE, and the Cochrane library) from January 1990 to March 2011. Data were extracted, tabulated and reported thematically using a narrative synthesis approach. These results were used to develop a conceptual framework that describes and classifies SPITs. RESULTS: 232 articles were reviewed for data extraction and analysis. SPITs identified were classified into 3 groups: clinical mapping tools, structure communication tools and error reduction instruments. The dominant instrument reported were clinical mapping tools, including: clinical pathways (113, 48%), fast track (46, 20%) and enhanced recovery after surgery protocols (36, 15%). Outcomes reported included: length of stay (174, 75%), readmission rates (116, 50%), morbidity (116, 50%), mortality (104, 45%), and economic (60, 26%). Many gaps in the literature were recognized. CONCLUSION: We have developed a conceptual framework of SPITs and identified gaps in current knowledge. These results will guide the design and development of new quality instruments in surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it