Impact of real-world remote symptom monitoring program on hospitalizations and ICU admissions.
Bibliographic record
Abstract
377 Background: Previous randomized controlled trials have demonstrated benefits to patients from remote symptom monitoring (RSM) with electronic patient-reported outcomes (ePROs) including healthcare utilization. However, less is known about the impact of RSM in diverse, real-world populations. Methods: This cross-sectional analysis from a hybrid, type 2 implementation-effectiveness trial evaluated the impact of RSM on healthcare utilization amongst patients with cancer receiving chemotherapy, immunotherapy, monoclonal antibody, or targeted therapy at two academiccancer centers in the Southeastern United States. Modified Poisson regression models with robust standard error and 95% confidence interval (CI) was used to calculate the relative risk (RR) of any hospital or ICU utilization between patients receiving RSM and controls for 3 and 6 months after index date. Models were controlled for age at index, race, sex, cancer type, cancer stage, insurance, prior treatment, comorbidities, RUCA, and follow-up during COVID-19 pandemic. Additional logistic regression models were used to estimate odds ratios (OR) for subset analysis stratified by race (Black or African American, Other, or White), rurality using Rural-Urban Commuting Area Codes, and neighborhood disadvantage using Area Deprivation Index (ADI). Results: From 5/2021-2/2024, 1215 patients were enrolled in RSM; 27% were Black, 16% lived in a rural area, and 25% lived in an area with high neighborhood disadvantage. The populations receiving RSM were similar to the control population (n = 4559); 26% were Black, 22% lived in a rural area, and 28% lived in area with high neighborhood disadvantage. The unadjusted relative risk of hospitalization for patients receiving RSM and control patients were 0.70 (95% CI, 0.63-0.70) and 0.77 (95% CI, 0.71-0.85), respectively. In adjusted analyses, hospitalizations were lower amongst patients receiving RSM compared to control patients with a RR of 0.82 (95% CI 0.73-0.92). Similar patterns were observed for ICU admissions (RR 0.59; 95% CI,0.40-0.88). Analysis by patient subgroup was similar to the overall analysis. A lower odd of hospitalizations and ICU admissions at 6 months was observed across all subset analyses: Black vs. White patients (OR 0.80; OR 0.48); rural vs. urban patients (OR 0.78; OR 0.68); and patients living in areas of high vs. lower neighborhood disadvantage (OR 0.59; OR 0.33). Conclusions: The use of RSM amongst patients receiving treatment for cancer is associated with reductions in hospitalizations and ICU admissions in real-world, diverse settings. Further work to expand this intervention nationally is needed. Clinical trial information: NCT04809740 .
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".