TY  - JOUR
AU  - Havreng-Théry, Charlotte
AU  - Fouchard, Arnaud
AU  - Denis, Fabrice
AU  - Veyron, Jacques-Henri
AU  - Belmin, Joël
PY  - 2025
DA  - 2025/4/11
TI  - Cost-Effectiveness Analysis of a Machine Learning–Based eHealth System to Predict and Reduce Emergency Department Visits and Unscheduled Hospitalizations of Older People Living at Home: Retrospective Study
JO  - JMIR Form Res
SP  - e63700
VL  - 9
KW  - monitoring
KW  - older adult
KW  - predictive tool
KW  - home care aide
KW  - emergency department visit
KW  - cost-effectiveness
KW  - artificial intelligence
KW  - electronic health
KW  - eHealth
KW  - emergency department
KW  - unscheduled hospitalization
KW  - aging
KW  - retrospective study
KW  - medico-economic
KW  - living at home
KW  - nursing home
KW  - emergency visit
KW  - Brittany
KW  - France
KW  - machine learning
KW  - remote monitoring
KW  - digital health
KW  - health informatics
AB  - Background: Dependent older people or those losing their autonomy are at risk of emergency hospitalization. Digital systems that monitor health remotely could be useful in reducing these visits by detecting worsening health conditions earlier. However, few studies have assessed the medico-economic impact of these systems, particularly for older people. Objective: The objective of this study was to compare the clinical and economic impacts of an eHealth device in real life compared with the usual monitoring of older people living at home. Methods: This study was a comparative, retrospective, and controlled trial on data collected between May 31, 2021, and May 31, 2022, in one health care and home nursing center located in Brittany, France. Participants had to be aged >75 years, living at home, and receiving assistance from the home care service for at least 1 month. We implemented among the intervention group an eHealth system that produces an alert for a high risk of emergency department visits or hospitalizations. After each home visit, the home care aides completed a questionnaire on participants’ functional status using a smartphone app, and the information was processed in real time by a previously developed machine learning algorithm that identifies patients at risk of an emergency visit within 7 to 14 days. In the case of predicted risk, the eHealth system alerted a coordinating nurse who could then inform the family carer and the patient’s nurses or general practitioner. Results: A total of 120 patients were included in the study, with 60 in the control group and 60 in the intervention group. Among the 726 visits from the intervention group that were not followed by an alert, only 4 (0.6%) resulted in hospitalizations (P<.001), confirming the relevance of the system’s alerts. Over the course of the study, 37 hospitalizations were recorded for 25 (20.8%) of the 120 patients. Additionally, of the 120 patients, 9 (7.5%) were admitted to a nursing home, and 7 (5.8%) died. Patients in the intervention group (56/60, 93%) remained at home significantly more often than those in the control group (48/60, 80%; P=.03). The total cost of primary care and hospitalization during the study was €167,000 (€1=US $1.09), with €108,000 (64.81%) attributed to the intervention group (P=.20). Conclusions: This study presents encouraging results on the impact of a remote medical monitoring system for older adults, demonstrating a reduction in both emergency department visits and hospitalization costs. Trial Registration: ClinicalTrials.gov NCT05221697; https://clinicaltrials.gov/study/NCT05221697 
SN  - 2561-326X
UR  - https://formative.jmir.org/2025/1/e63700
UR  - https://doi.org/10.2196/63700
UR  - http://www.ncbi.nlm.nih.gov/pubmed/40215100
DO  - 10.2196/63700
ID  - info:doi/10.2196/63700
ER  -