JMIR Publications

ABSTRACT

Background:

Frail older people use emergency services extensively and digital systems that monitor health remotely could be useful in reducing this use by detecting worsening health conditions earlier.

Objective:

We aimed to implement a system that produces alerts when the machine learning algorithm identifies a short-term risk for EDV and to examine health interventions realized after these alerts and users’ experience.

Methods:

Uncontrolled multi-center trial conducted in community-dwelling older adults receiving assistance from home aides (HAs). After each home visit, HAs completed a questionnaire on the participants' functional status using a smartphone application and the information was processed in real time by a previously developed machine learning algorithm that identifies patients at risk of EDV within 14 days. In case of risk, the eHS alerted a coordinating nurse who could inform the family carer and the patient's nurses or general practitioner. The primary outcomes were the rate of emergency department visit and number of death after alert-triggered health interventions (ATHI) and users' experience of eHS. Secondary outcome was the accuracy of eHS in predicting EDV.

Results:

We included 206 patients (average 85 years old (SD 8); 161/206, 78% female) which received aid from 109 HAs, and mean follow-up was 10 months. HAs monitored 2 656 visits which resulted in 405 alerts. Two EDVs were recorded following the 131 alerts with ATHI (2/131, 1.5%), whereas 36 EDVs were recorded following the 274 alerts that did not result in ATHI (36/274, 13.4%, P<.001). Five patients died during the study. All had alerts, 4 did not have ATHI and were hospitalized and 1 had an ATHI (P=.0.038). Most users reported a positive experience about the eHS. The sensitivity and specificity of alerts for predicting EDVs within 14 days were 0.83 (95%CI 0.72-0.94) and 0.86 (95%CI 0.85-0.87), respectively.

Conclusions:

The eHS has been successfully implemented, was appreciated by users, and produced relevant alerts. ATHI were associated with a lower rate of EDV, that suggests that the eHS might be useful to lessen EDV in this population. Clinical Trial: clinicaltrials.gov Identifier: NCT05221697. The research protocol was approved by ANSM (The French Agency for the Safety of Health Products): ID RCB: 2021-A02131-40–CPP 1-21-072.