Cost-effectiveness analysis of an artificial intelligence-based eHealth system to predict and reduce emergency department visits and unscheduled hospitalizations of older people living at home: a retrospective study. (Preprint)

Abstract

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.

The objective of this study was to analyze the clinical and economic impacts of an eHealth device in real life compared to the usual monitoring of older people living at home.

This study is a single-center, 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 older than 75 years, living at home, and receiving assistance from the home care service for at least 1 month. We implemented 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.

The use of the Presage Care device reduced the number emergency hospitalization by nearly 32 % compared to the control arm. In total, the use of the Presage Care system reduced hospital costs by 45.72%. This result is a minority result in view of all the additional costs: in home care, in post-hospitalizations consultations and in the major risk of re-hospitalization. In the intervention arm, among the 726 visits not followed by an alert, only 4(0.56%) hospitalizations followed the visit (P<.001), which confirm the relevance of the alerts issued by the system

This study shows encouraging results on the impact of a remote medical monitoring system for the older adults, both in terms of reducing the number of emergency department visits and the cost of hospitalization.

NCT05221697