Clinical Trials Logo

Clinical Trial 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).


Clinical Trial 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 Design


Related Conditions & MeSH terms


NCT number NCT05221697
Study type Interventional
Source Presage
Contact
Status Active, not recruiting
Phase N/A
Start date September 1, 2020
Completion date June 30, 2024

See also
  Status Clinical Trial Phase
Completed NCT05552989 - Towards Better Preparedness for Future Catastrophes - Local Lessons-learned From COVID-19
Not yet recruiting NCT04915690 - Investigation on the Practice Status of Emergency Stuff
Not yet recruiting NCT03424096 - Primary Palliative Care Education, Training, and Technical Support for Emergency Medicine N/A
Completed NCT02534324 - The Effect of Pre-discharge Blood Pressure of Patients With Asymptomatic Severe Hypertension in Emergency Department N/A
Completed NCT00991471 - The Effect of an Physician-Nurse Supplementary Triage Assistance Team on Emergency Department Patient Wait Times N/A
Recruiting NCT03257319 - Inhaled vs IV Opioid Dosing for the Initial Treatment of Severe Acute Pain in the Emergency Department Phase 3
Recruiting NCT05005117 - Laparoscopic Approach for Emergency Colon Resection N/A
Recruiting NCT03917368 - Ultrasound Evaluation of the Jugular Venous Pulse (US-JVP) N/A
Completed NCT04601922 - Qualitative Study of Long Term Cardiovascular Risk Prediction in the Emergency Department
Recruiting NCT05497830 - Machine Learning for Risk Stratification in the Emergency Department (MARS-ED) N/A
Active, not recruiting NCT06220916 - The Greek Acute Dance Injuries Registry
Recruiting NCT05543772 - Evaluation of Blood Sampling From a Pre-existed Peripheral Intravenous Catheter Line Phase 4
Recruiting NCT05496114 - Medical Checklists in the Emergency Department N/A
Recruiting NCT06072534 - Evaluation of Effectiveness of Two Different Doses of Mivacurium in Rapid Sequence Intubation N/A
Not yet recruiting NCT05528211 - Safety and Efficacy of Emergent TAVI in Patients With Severe AS
Completed NCT05818215 - Impact of the Qatar 2022 FIFA World Cup on PED Use and Misuse Patterns
Recruiting NCT04615065 - Acutelines: a Large Data-/Biobank of Acute and Emergency Medicine
Active, not recruiting NCT04648449 - Artificial Intelligence (AI) Support in Medical Emergency Calls
Not yet recruiting NCT04431986 - ER2 Frailty Levels and Incident Adverse Health Events in Older Community Dwellers
Completed NCT05597059 - The Diagnostic Value of the First Clinical Impression of Patients Presenting to the Emergency Department (PREKEYDIA)