Enhancing Palliative Care Delivery in Grey Bruce (Practice, Case Study Paper IFIC North America 2024)
Bibliographic record
Abstract
Background/Introduction: The Grey Bruce Ontario Health Team (GBOHT), population of 80,000, faces unique challenges in providing palliative care services due to its older, rural population. Coupled with the large, geographic dispersion of primary care and specialists, the region has identified the need to enhance its palliative care services to meet the growing demands of the community. This paper aims to explore the current state of palliative care in the GBOHT, the visioning session outcomes, identified priorities, and the implementation of the Primary Palliative Care Navigation Program within the OHT.Baseline Indicators and Population Characteristics: In 2022-2023, key baseline indicators revealed that a significant percentage of deaths in GBOHT occurred in hospitals, with a high proportion of individuals visiting the emergency room in the last 30 days of their lives2. Moreover, a substantial percentage received home care services and physician home visits in the last 90 days of life. These statistics underscore the importance of enhancing palliative care services to provide better end-of-life care to individuals within the GBOHT3. Approach Visioning Session and Identified Priorities: A visioning session involving interdisciplinary palliative care providers working in within the OHT catchment highlighted the need to define services and care models, address human resource challenges, enable information sharing, strengthen education and training, implement supportive technology systems, secure funding and investments, and improve communication and collaboration. These priorities serve as the foundation for the development of the Primary Palliative Care Navigation Program (PPCNP).Primary Palliative Care Navigation Program:The PPCNP aims to build capacity for primary care physicians to deliver primary-level palliative care effectively. The program aligns with the OPCN Health Services Delivery Framework and features a dedicated nurse navigator role to facilitate patient care coordination, education, and capacity building. Through the use of digital technology, such as Hypercare and CloudDX, the program enhances communication, symptom monitoring, and care delivery between partners. Results Program Overview and Impact: The Palliative Care Nurse Navigator embedded within primary care settings supports the provision of primary palliative care through assessment and symptom monitoring (via virtual enabling technology), care escalation pathways, and end-of-life planning. Pre/post-launch surveys of participating family physicians indicated increased comfort, knowledge, and skill in managing palliative care issues, as well as improved collaboration with community partners. Patient care outcomes have shown positive results, with high satisfaction rates among patients and caregivers. Implications: The successful implementation of the Primary Palliative Care Navigation Program has led to increased buy-in from primary care physicians, reduced administrative burden, and improved patient care outcomes. Evaluation undertaken on patient satisfaction (in conjunction with Ontario Health) has shown positive satisfaction amongst patients and carers. The initial success of the program has paved the way for its expansion across additional primary care facilities in GBOHT, with plans for a regional primary palliative care network in the future. Conclusion: Enhancing and integrating palliative care delivery within the GBOHT is a crucial step towards improving end-of-life care for individuals in the region. Through the implementation of the Primary Palliative Care Navigation Program and the collaboration of interdisciplinary palliative and primary care providers, GBOHT is on track to build a comprehensive palliative care network that prioritizes patient-centered care, education, and technology integration for the benefit of all. Grey Bruce Health Stats [Internet]. www.publichealthgreybruce.on.ca. [cited 2024 Jun 5]. Available from: https://www.publichealthgreybruce.on.ca/healthstatsMondor L, Hall R, Wodchis W. HSPN Population Segmentation Technical Report [Internet]. 2022 [cited 2024 Jun 5]. Available from: https://hspn.ca/wp-content/uploads/2022/03/HSPN_SEGMENTATION_TECH_APPENDIX_March_2022.pdfQureshi M, Robinson MC, Sinnarajah A, Chary S, de Groot JM, Feldstain A. Reflecting on Palliative Care Integration in Canada: A Qualitative Report. Current Oncology [Internet]. 202 Jul 9;28(4):2753-62. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8293234
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".