Improving timely access and patient-centered outcomes for oncology patients in rural Colorado.
Bibliographic record
Abstract
326 Background: Rural populations face substantial barriers in accessing home health, palliative, and end-of-life care, especially among medically complex patients. Geographic isolation, limited transportation, provider shortages, and insufficient in-home care infrastructure contribute to delayed or absent services, poor health outcomes, and caregiver burden. A retrospective chart review of home care and hospice referrals at a rural Colorado hospital (May 2019–May 2020) found that 24% of referred patients experienced care delays or received no care at all. In response to these findings, Vail Health developed Shaw at Home, a three-year pilot program aimed at improving access to in-home palliative care, increasing completion of advance care planning (ACP) documents, and enhancing patient and caregiver experience. Methods: To inform program design, a 2021 chart review of oncology patients at Vail Health Shaw Cancer Center (VHSCC) found that 62% (n=427) had stage III or IV cancer, for which national guidelines recommend palliative care referral. Based on this identified need, Shaw at Home was launched in June 2023. The interdisciplinary program provides symptom management, ACP, psychosocial support, and care coordination in home and clinic settings. Services are delivered by an advanced practice provider, nurse, chaplain, and clinical social worker. The program initially targeted 100 patients in its first year with 5% projected annual growth. Metrics tracked include ACP completion, visit volume, and patient experience. Results: Since June 2023, Shaw at Home has served 143 unique patients with over 1500 encounters. Advance Care Planning (ACP) document completion increased from 12% pre-implementation to 53%, indicating stronger engagement in care planning. Patient experience, assessed using the RAND Serious Illness Survey for Community-Based Care, showed high levels of satisfaction, communication, and care coordination and exceeded national benchmarks. These outcomes demonstrate the program’s effectiveness in delivering accessible and patient-centered palliative care in the community. Conclusions: Initial findings indicate that Shaw at Home addresses key gaps in rural palliative care, improving timely access and patient-centered outcomes for oncology patients. The model demonstrates feasibility and impact in underserved mountain communities and offers a scalable framework for extending palliative care in similar rural settings.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".