effect of an ml electronic alert management system to reduce the use of ed visits and hospitalizations

Conditions: Emergencies

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).

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)

Keywords: emergency department visits, machine learning smartphone