UnknownNot Applicable

Effect of an ML Electronic Alert Management System to Reduce the Use of ED Visits and Hospitalizations

France800 participantsStarted 2020-09-01

Plain-language summary

Development, validation and impact of an alert management system using social workers' observations and machine learning algorithms to predict 7-to-14-day alerts for the risk of Emergency Department (ED) Visit and unplanned hospitalization. Multi-center trial implementation of electronic Home Care Aides-reported outcomes measure system among patients, frail adults \>= 65 years living at home and receiving assistance from home care aides (HCA).

Who can participate

Age range75 Years

SexALL

See this in plain English?

AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.

Inclusion Criteria: * age of 75 yo mini * receiving the help of a social worker * patient should give their consent * patient should had seen their primary care professional within the past 12 months Exclusion Criteria: * People with severe dependence (French national instrument, which stratifies dependency level from group iso-resources (GIR) : 1 (very severe dependency) and 2 (severe dependency)

What they're measuring

Unplanned Hospitalization rate

Timeframe: through study completion, an average of 1 year

Event-free survival (EFS)

Timeframe: through study completion, an average of 1 year

Impact on older adults and relatives' quality of life (European Quality of Life 5 Dimensions and 3 Lines scale)

Timeframe: through study completion, an average of 1 year

Cost-effectiveness

Timeframe: through study completion, an average of 1 year

Trial details

NCT IDNCT05221697

SponsorPresage

Sponsor typeINDUSTRY

Study typeINTERVENTIONAL

Primary completion2021-12-31

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