Improving Care Continuity in Oncology Settings: A Lean Management Approach to Minimize Discharges Without Follow-Up Appointments
Bibliographic record
Abstract
OBJECTIVE: This study aimed to reduce the number of patients discharged without scheduled follow-up appointments by implementing lean management principles. METHODS: Conducted at the Sultan Qaboos Comprehensive Cancer Center in Muscat, Oman, the research utilized a one-group pretest-posttest quasi-experimental design to evaluate the impact of lean management interventions on the rate of patient discharges without follow-up appointments. Strategies such as the Kaizen principle, Gemba Walks, cross-functional collaboration, standard work procedures, and waste reduction were employed to enhance operational efficiency. RESULTS: Spanning from Quarter 3 of 2022 to Quarter 2 of 2023, the study demonstrated a significant decrease in the percentage of patients discharged without planned follow-up appointments. The rate dropped from 9% in September 2022 to 0% in March 2023, with statistically significant differences observed (X2= 65.05, p value=<.0001). CONCLUSION: By effectively implementing lean management principles, this research successfully enhanced care continuity for oncology patients after being discharged.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".