Clinical trial NCT05221697: Effect of an ML Electronic Alert Management System to Reduce the Use of ED Visits and Hospitalizations
Official title: Effect of an Electronic Alert Management System Using Caregivers' Observations and Machine Learning Algorithm to Reduce the Use of Emergency Department Visits and Unplanned Hospitalizations Among Older People
Study type: INTERVENTIONAL
Overall status: ACTIVE_NOT_RECRUITING
Phase: NA
Conditions: Emergencies
Keywords: Emergency Department visits,
machine learning
smartphone
Lead sponsor: Presage
Sponsor class: INDUSTRY
Collaborators: name: Assistance Publique Hopitaux De Marseille
name: Assistance Publique - Hôpitaux de Paris
name: University Hospital, Lille
First posted: 2022-02-03
Enrollment: 800
Sex: ALL
Minimum age (years): 75
Standard ages: OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: PREVENTION

Brief 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).

Detailed description: On a weekly basis, after home visit, HCAs reported on participants' functional status using a smartphone application that recorded 23 functional items about each participant (e.g., ability to stand, move, eat, mood, loneliness). Predictive system using Machine learning techniques (i.e., leveraging random forest predictors) was developed and generated 7 to 14-day predictive alerts for the risk of ED visit to nurses.

This questionnaire focused on functional and clinical autonomy (ie, activities of daily life), possible medical symptoms (eg, fatigue, falls, and pain), changes in behavior (eg, recognition and aggressiveness), and communication with the HA or their surroundings. This questionnaire is composed of very simple and easy-to-understand questions, giving a global view of the person's condition. For each of the 23 questions, a yes/no answer was requested. Data recorded by HAs were sent in real time to a secure server to be analyzed by our machine learning algorithm, which predicted the risk level and displayed it on a web-based secure medical device called PRESAGE CARE, which is CE marked. Particularly, when the algorithm predicted a high-risk level, an alert was displayed in the form of a notification on the screen to the coordinating nurse of the health care network center of the district. This risk notification was accompanied by information about recent changes in the patients' functional status, identified from the HAs' records, to assist the coordinating nurse in interacting with family caregiver and other health professionals.

In the event of an alert, the coordinating nurse called the family caregiver to inquire about recent changes in the patient's health condition and for doubt removal and could then decide to ask for a health intervention according to a health intervention model developed before the start of the study. In brief, this alert-triggered health intervention (ATHI) consisted of calling the patient's nurse (if the patient had regular home visits of a nurse) or the patient's general practitioner and informing them of a worsening of the patient's functional status and a potential risk of an ED visit or unplanned hospitalization in the next few days according to the eHealth system algorithm. This model of ATHI had been presented and approved by the Agences Régionales de Santé of the regions involved in our study

Eligibility criteria: 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)

Primary outcomes:
measure: Unplanned Hospitalization rate
timeFrame: through study completion, an average of 1 year
measure: Event-free survival (EFS)
timeFrame: through study completion, an average of 1 year
measure: 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
measure: Cost-effectiveness
timeFrame: through study completion, an average of 1 year
Secondary outcomes:
measure: Impact on users : time needed to complete questionnaire
timeFrame: through study completion, an average of 1 year
measure: Intervention rate
timeFrame: through study completion, an average of 1 year
measure: Intervention time
timeFrame: through study completion, an average of 1 year
measure: Time needed to analysis patient statut
timeFrame: through study completion, an average of 1 year
measure: Impact on quality of care
timeFrame: through study completion, an average of 1 year
measure: Impact on Professional' Relationship and coordination
timeFrame: through study completion, an average of 1 year
Other outcomes:
Locations:
city: Le Chesnay
country: France
facility: Grand Versailles
city: Marseille
country: France
facility: Marseille-1
Study officials:
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