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

August 28, 2023 updated by: Presage

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

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).

Study Overview

Status

Active, not recruiting

Conditions

Intervention / Treatment

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

Study Type

Interventional

Enrollment (Estimated)

800

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • France
      • Le Chesnay, France, 78150
        • Grand Versailles
      • Marseille, France, 13011
        • Marseille-1

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

75 years and older (Older Adult)

Accepts Healthy Volunteers

No

Description

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)

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Prevention
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
No Intervention: Control group
usual care
Experimental: Intervention
PRESAGE Care ATIH + Nurse or GP consultation
Device: PRESAGE CARE
Participants in this arm will be followed by HCA and might benefit from Nurse health interventions

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Unplanned Hospitalization rate
Time Frame: through study completion, an average of 1 year

Comparison between unplanned hospitalization ratio from 2 randomized groups (intervention and control arms).

P values <.05 will be considered statistically significant.
through study completion, an average of 1 year
Event-free survival (EFS)
Time Frame: through study completion, an average of 1 year

Comparison average Time for first adverse event between intervention and control groups.

P values <.05 will be considered statistically significant.
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)
Time Frame: through study completion, an average of 1 year

Comparison of the average score of EQ5D-3L quality of life scale (European Quality of Life 5 Dimensions and 3 Lines) between intervention and control groups.

P values <.05 will be considered statistically significant.
through study completion, an average of 1 year
Cost-effectiveness
Time Frame: through study completion, an average of 1 year
Incremental cost-effectiveness ratio (ICER), QALY. Willingness-to-pay thresholds of €30,000 per quality-adjusted life year (QALY) and €90,000 per QALY were used to define a very cost-effective and cost-effective strategy, respectively
through study completion, an average of 1 year

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Impact on users : time needed to complete questionnaire
Time Frame: through study completion, an average of 1 year
Time needed to complete questionnaire (minutes) : a time of less than 2 minutes will be considered acceptable
through study completion, an average of 1 year
Intervention rate
Time Frame: through study completion, an average of 1 year
Part of alert which leads to interventions and intervention time (%). Rate of over 70% is considered acceptable.
through study completion, an average of 1 year
Intervention time
Time Frame: through study completion, an average of 1 year
Mean of the duration between day of alert and day of intervention (in days). A delay of less than 4 days is considered acceptable.
through study completion, an average of 1 year
Time needed to analysis patient statut
Time Frame: through study completion, an average of 1 year
Time needed to analysis patient statut (hours and minutes) : a time of less than 15 minutes by patient will be considered acceptable
through study completion, an average of 1 year
Impact on quality of care
Time Frame: through study completion, an average of 1 year
Positive or very positive impact on quality of care : rate of over 80% is considered acceptable.
through study completion, an average of 1 year
Impact on Professional' Relationship and coordination
Time Frame: through study completion, an average of 1 year
Positive or very positive impact on professionnal relationship and coordination :rate of over 80% is considered acceptable.
through study completion, an average of 1 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Presage

Collaborators

Assistance Publique - Hôpitaux de Paris
Assistance Publique Hopitaux De Marseille
University Hospital, Lille

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

September 1, 2020

Primary Completion (Actual)

December 31, 2021

Study Completion (Estimated)

June 30, 2024

Study Registration Dates

First Submitted

January 7, 2022

First Submitted That Met QC Criteria

January 21, 2022

First Posted (Actual)

February 3, 2022

Study Record Updates

Last Update Posted (Actual)

August 30, 2023

Last Update Submitted That Met QC Criteria

August 28, 2023

Last Verified

August 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • PRESAGE_2021-01

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

IPD Plan Description

anonymized Statistical data will be available

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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