Rural palliative care in cancer: Using telemedicine to bridge the gap.
Bibliographic record
Abstract
e13807 Background: Despite the rapid growth in palliative care (PC) services, many rural regions remain without access to specialty palliative care. Community-based telemedicine may offer solutions to underserved populations from rural areas within the United States. Methods: This is a retrospective review of the creation of 2 separate rural clinics attached to the VCU Massey Comprehensive Cancer Center at CMH in South Hill, VA in 2018 (with interruption related to the Covid-19 pandemic) and in Tappahannock, VA in 2021. In addition to operational descriptions a chart review was conducted of all encounters for these clinics. Care provided was coordinated by the University PC service and the oncology clinics, including nurses and oncologists. Regulatory, legal, Information Technology (IT), and systems logistics were developed in partnership for 6-9 months prior to each pilot. There were over 100 encounters from the two periods of care for CMH (2018 until paused for pandemic towards the end of 2020; and March of 2021 to current) and greater than 50 at Tappahannock. All visits were conducted in the rural oncology clinics with assistance of oncology nursing during the encounter. Results: The average patient age was 57, and over 90% had solid tumors. On average, patients had 1-2 telemedicine visits. The most common reason for referral was symptom management, predominantly pain. Edmonton Symptom Assessment scales were collected at all visits by oncology nursing. Physical exams were completed with electronic stethoscopes and supported by oncology nursing. Medications were prescribed to rural pharmacies electronically directly by the PC physician; for the periods of clinic over the course of the Covid-19 public health emergency this included prescriptions for controlled substances (almost exclusively long and short acting opioids). For patients requiring advance care planning documents were completed electronically or by trained interdisciplinary team members in clinic in partnership with the PC physician. Technological issues occurred rarely (less than 1% of visits) and resolved without IT involvement. Conclusions: Our pilot program integrated specialist palliative care into two rural oncology clinics providing supportive care, including symptom management and goals of care discussions. Further research should define optimal integration of PC telemedicine into rural oncology.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".