Accepted for/Published in: JMIR Formative Research
Date Submitted: Jun 27, 2024
Open Peer Review Period: Jul 3, 2024 - Aug 28, 2024
Date Accepted: Mar 5, 2025
(closed for review but you can still tweet)
Cost-effectiveness analysis of an ML-based eHealth system to predict and reduce emergency department visits and unscheduled hospitalizations of older people living at home: a retrospective study
ABSTRACT
Background:
Dependent older people or those losing their autonomy are 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 is a comparative, retrospective and controlled trial on data collected between May 31, 2021 and May 31, 2022 in one health care and home nursing located in Brittany, France. Participants had to be older than 75 years, living at home, and receiving assistance from the home care service for at least 1 month. We implemented on intervention group an eHealth system that produces an alert for a high risk of emergency department visits or hospitalization. After each home visit, the Home 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 to14 days. In case of 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 in the intervention group that were not followed by an alert, only 4 (0.56%) resulted in hospitalization (P<.001), confirming the relevance of the system's alerts. Over the course of the study, 37 hospitalizations were recorded for 25 patients (25/120, 20.8%). Additionally, 9 patients were admitted to a nursing home (9/120, 7.50%), and 7 patients passed away (7/120, 5.8%). Patients in the intervention group remained at home significantly more often (56/60, 93.33%) than those in the control group (48/60, 80%) (P=0.03). The total cost of primary care and hospitalization during the study was €167K, with €108K (108/167, 64.81%) attributed to the intervention group (P=0.2).
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. Clinical Trial: ClinicalTrial Registration : NCT05221697
Citation
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