Impact of real-world remote symptom monitoring program on hospitalizations and ICU admissions.

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 .

Authors

Institutions

Publication Details

Journal
JCO Oncology Practice
Published
2024-09-30
DOI
https://doi.org/10.1200/op.2024.20.10_suppl.377
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Impact of real-world remote symptom monitoring program on hospitalizations and ICU admissions.

Katherine Parks, Bryan J. Weiner, Angela M. Stover, Carrie C. McNair et al.
JCO Oncology Practice
Machine Learning in Healthcare
article

Impact of real-world remote symptom monitoring program on hospitalizations and ICU admissions.

Katherine Parks, Bryan J. Weiner, Angela M. Stover, Carrie C. McNair, Courtney Williams, Chelsea L. McGowen, Noon Eltoum, Sheila McElhany, Doris Howell, Andrés Azuero, Nicole E. Caston, Jeffrey Franks, Bryanna Diaz, Bradford E. Jackson, Joud El Dick, Jennifer Young Pierce, Ethan Basch, Gabrielle Betty Rocque, Luqin Deng, D’Ambra Dent
article en

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 .

JCO Oncology PracticeVol. 20(10_suppl)
University of North Carolina at Chapel Hill (US), University Health Network (CA), University of Washington (US), Princess Margaret Cancer Centre (CA), University of Alabama at Birmingham (US), USA Mitchell Cancer Institute (US), University of South Alabama (US)
National Institute of Nursing Research
Openalex Percentile: Top 85%
Machine Learning in Healthcare
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.