An Integrated Methodology for Process Improvement and Delivery System Visualization at a Multidisciplinary Cancer Center
Bibliographic record
Abstract
Multidisciplinary cancer centers require an integrated, collaborative, and stream-lined workflow in order to provide high quality of patient care. Due to the complex nature of cancer care and continuing changes to treatment techniques and technologies, it is a constant struggle for centers to obtain a systemic and holistic view of treatment workflow for improving the delivery systems. Project management techniques, Responsibility matrix and a swim-lane activity diagram representing sequence of activities can be combined for data collection, presentation, and evaluation of the patient care. This paper presents this integrated methodology using multidisciplinary meetings and walking the route approach for data collection, integrated responsibility matrix and swim-lane activity diagram with activity time for data representation and 5-why and gap analysis approach for data analysis. This enables collection of right detail of information in a shorter time frame by identifying process flaws and deficiencies while being independent of the nature of the patient's disease or treatment techniques. A case study of a multidisciplinary regional cancer centre is used to illustrate effectiveness of the proposed methodology and demonstrates that the methodology is simple to understand, allowing for minimal training of staff and rapid implementation.
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.
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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".