Impact of a Longitudinal Intervention to Improve Care Coordination between a Hospital and a Hospice: A Quality Improvement Project
Bibliographic record
Abstract
OBJECTIVES: When patients with advanced cancer transition from systemic cancer treatments at MNJ Institute of Oncology to palliative and end-of-life care at a separate stand-alone non-governmental organisation-run hospice facility, there is insufficient transfer of health information, including details of cancer diagnosis and staging, past treatments, imaging reports and goals for future care. Without this information, the hospice care team is not adequately prepared to receive and deliver high-quality palliative care for these patients. This project aims to improve the care coordination between the hospital and hospice. MATERIALS AND METHODS: The measures used are the self-reported confidence score on a scale of 0 to 10 related to knowledge about plan of care among staff who receives patients at hospice at baseline and during and after interventions. Interventions included recognizing the workplace culture and promoting ownership of the tasks, enhancing communication by creating user-friendly transfer forms and on-going assessment of the process. RESULTS: Improvement in the care coordination in terms of communication of patient goals of care, from hospital to hospice. CONCLUSION: QI project and the steps involved helped the team to work towards solutions objectively. Seemingly excellent ideas may not be the most impactful and data collection demonstrates this and helps identify the most successful interventions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".