Understanding and Overcoming Barriers to Admissions and Timely Discharges in a Cancer Hospital: A Case Study of National Centre for Cancer Care and Research, Doha, Qatar
Bibliographic record
Abstract
Background Improving access to healthcare is crucial for patient experience, clinical safety, timeliness of care, and reducing staff pressure. The National Centre for Cancer Care and Research (NCCCR), the primary cancer center in Qatar, confronted challenges in delivering quality cancer care and services. Aim This project aimed to identify factors limiting patient admissions and discharges at NCCCR to improve the average patient admission and discharge rates by 50%. Methods The study was conducted at the National Center for Cancer Care and Research (NCCCR) in Qatar from June 2020 to December 2021. Descriptive statistics were used to analyze the average number of inpatient admissions, discharges, and patient length of stay. The Plan-Do-Study-Act (PDSA) Model for Improvement tool was utilized to test changes at the facility level. Results A comparison of baseline data in Quarter 2 (Q2) 2020 with Quarter 4 (Q4) 2021 showed a 37% increase in the average number of inpatient admissions and a 62% increase in inpatient discharges. The number of patients staying 0-10 days increased by 39% from Q2 2020 to Q4 2021. Conclusion This project identified several factors affecting patient admission and discharge services. Implementation of strategies such as establishing a physician-led discharge multidisciplinary committee, conducting frequent bed status evaluations by case managers and physicians, and expanding bed capacity led to significant improvements in the admission and discharge process.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".