A Lay Health Worker–Led Symptom Intervention and Acute Care Use in Older Adults With Cancer
Bibliographic record
Abstract
Importance: Undertreated cancer symptoms are common among older adults, yet effective early identification and intervention remain limited. Objective: To determine whether lay health worker-led symptom assessment reduces acute care use among older adults with cancer. Design, Setting, and Participants: Multisite randomized clinical trial across 43 oncology clinics in California and Arizona conducted from November 2020 through October 2023 with 12 months of follow-up among Medicare Advantage beneficiaries aged 75 years or older with newly diagnosed, recurrent, or progressive cancer. Interventions: Participants were randomized 1:1 into a symptom assessment group (n = 200; usual care with lay health worker-led, telephone-based symptom assessments for 12 months using the Edmonton Symptom Assessment System) or a control group (n = 216; usual care alone), with planned enrollment of 200 in both groups. The lay health workers reviewed assessments with advanced practice practitioners who conducted interventions for symptoms rated 4 or greater or that increased by 2 points or more. Main Outcomes and Measures: Outcomes were determined a priori. The primary outcome was emergency department (ED) use and hospitalizations. Secondary outcomes were total costs, hospice, and, among decedents, acute care within 30 days of death and facility deaths. Results: Among 416 patients, the median age was 82 (range, 75-99) years; 219 (52.6%) were male; 171 (41.1%) had stage 4 disease; and 27 (6.4%) had recurrent disease. The mean risk adjustment factor score was 2.70 (SD, 1.77). Symptom assessment participants had 53% lower odds of ED use (61 [30.5%] vs 103 [47.7%] had ≥1 ED visit; adjusted odds ratio [OR], 0.47; 95% CI, 0.32-0.71), 68% lower odds of hospitalization (37 [18.5%] vs 86 [39.8%] had ≥1 hospitalization; OR, 0.32; 95% CI, 0.20-0.51), and lower mean total costs by $12 000 per participant (P = .01) than control group participants. Among 142 deceased participants (71 in each group), symptom assessment participants had 68% lower odds of ED use within 30 days of death (OR, 0.32; 95% CI, 0.12-0.88) and 75% lower odds of acute care facility death (OR, 0.25; 95% CI, 0.08-0.77). Conclusions and Relevance: A lay health worker-led symptom assessment intervention may be a scalable approach to reduce acute care use. Trial Registration: ClinicalTrials.gov Identifier: NCT04463992.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".