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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".