Assessment of Frailty by the French Version of the Vulnerable Elders Survey-13 on Digital Tablet: Validation Study.

BACKGROUND: Frailty assessment is a major issue in geriatric medicine. The Vulnerable Elders Survey-13 (VES-13) is a simple and practical tool that identifies frailty through a 13-item questionnaire completed by older adults or their family caregivers by self-administration (pencil and paper) or by telephone interview. The VES-13 provides a 10-point score that is also a recognized mortality predictor. OBJECTIVE: This study aims to design an electronic version of the Echelle de Vulnérabilité des Ainés-13, the French version of the VES-13 (eEVA-13) for use on a digital tablet and validate it. METHODS: The scale was implemented as a web App in 3 different screens and used on an Android tablet (14.0× 25.6 cm). Participants were patients attending the outpatient clinic of a French geriatric hospital or hospitalized in a rehabilitation ward and family caregivers of geriatric patients. They completed the scale twice, once by a reference method (self-administered questionnaire or telephone interview) and once by eEVA-13 using the digital tablet. Agreement for diagnosis of frailty was assessed with the κ coefficient, and scores were compared by Bland and Altman plots and interclass correlation coefficients. User experience was assessed by a self-administered questionnaire. RESULTS: In total, 86 participants, including 40 patients and 46 family caregivers, participated in the study. All family caregivers had previously used digital devices, while 13 (32.5%) and 10 (25%) patients had no or infrequent use of them previously. We observed no failure to complete the eEVA-13, and 70% of patients (28/40) and no family caregivers needed support to complete the eEVA-13. The agreement between the eEVA-13 and the reference method for the diagnosis of frailty was excellent (κ=0.92) with agreement in 83 cases and disagreement in 3 cases. The mean difference between the scores provided by the 2 scales was 0.081 (95% CI-1.263 to 1.426). Bland and Altman plots showed a high level of agreement between the eEVA-13 and the reference methods and interclass correlation coefficient value was 0.997 (95% CI 0.994-0.998) for the paper and tablet group and 0.977 (95% CI 0.957-0.988) for the phone and tablet groups. The tablet assessment was found to be easy to use by 77.5% (31/40) of patients and by 96% (44/46) of caregivers. Finally, 85% (39/46) of family caregivers and 50% (20/40) of patients preferred the eEVA-13 to the original version. CONCLUSIONS: The eEVA-13 is an appropriate digital tool for diagnosing frailty and can be used by older adults and their family caregivers. The scores obtained with eEVA-13 are highly correlated with those obtained with the original version. The use of health questionnaires on digital tablets is feasible in frail and very old patients, although some patients may need help to use them.

Real-world Implementation of an eHealth System Based on Artificial Intelligence Designed to Predict and Reduce Emergency Department Visits by Older Adults: Pragmatic Trial.

BACKGROUND: Frail older people use emergency services extensively, and digital systems that monitor health remotely could be useful in reducing these visits by earlier detection of worsening health conditions. OBJECTIVE: We aimed to implement a system that produces alerts when the machine learning algorithm identifies a short-term risk for an emergency department (ED) visit and examine health interventions delivered after these alerts and users' experience. This study highlights the feasibility of the general system and its performance in reducing ED visits. It also evaluates the accuracy of alerts' prediction. METHODS: An uncontrolled multicenter trial was conducted in community-dwelling older adults receiving assistance from home aides (HAs). We implemented an eHealth system that produces an alert for a high risk of ED visits. After each home visit, the HAs completed a questionnaire on participants' functional status, using a smartphone app, and the information was processed in real time by a previously developed machine learning algorithm that identifies patients at risk of an ED visit within 14 days. In case of risk, the eHealth system alerted a coordinating nurse who could then inform the family carer and the patient's nurses or general practitioner. The primary outcomes were the rate of ED visits and the number of deaths after alert-triggered health interventions (ATHIs) and users' experience with the eHealth system; the secondary outcome was the accuracy of the eHealth system in predicting ED visits. RESULTS: We included 206 patients (mean age 85, SD 8 years; 161/206, 78% women) who received aid from 109 HAs, and the mean follow-up period was 10 months. The HAs monitored 2656 visits, which resulted in 405 alerts. Two ED visits were recorded following 131 alerts with an ATHI (2/131, 1.5%), whereas 36 ED visits were recorded following 274 alerts that did not result in an ATHI (36/274, 13.4%), corresponding to an odds ratio of 0.10 (95% IC 0.02-0.43; P<.001). Five patients died during the study. All had alerts, 4 did not have an ATHI and were hospitalized, and 1 had an ATHI (P=.04). In terms of overall usability, the digital system was easy to use for 90% (98/109) of HAs, and response time was acceptable for 89% (98/109) of them. CONCLUSIONS: The eHealth system has been successfully implemented, was appreciated by users, and produced relevant alerts. ATHIs were associated with a lower rate of ED visits, suggesting that the eHealth system might be effective in lowering the number of ED visits in this population. TRIAL REGISTRATION: clinicaltrials.gov NCT05221697; https://clinicaltrials.gov/ct2/show/NCT05221697.

Home care aides' observations and machine learning algorithms for the prediction of visits to emergency departments by older community-dwelling individuals receiving home care assistance: A proof of concept study.

BACKGROUND: Older individuals receiving home assistance are at high risk for emergency visits and unplanned hospitalization. Anticipating their health difficulties could prevent these events. This study investigated the effectiveness of an at-home monitoring method using social workers' observations to predict risk for 7- and 14-day emergency department (ED) visits. METHODS: This was a prospective cohort study of persons ≥75 years, living at home and receiving assistance from home care aides (HCA) at 6 French facilities. After each home visit, HCAs reported on participants' functional status using a smartphone application that recorded 27 functional items about each participant (e.g., ability to stand, move, eat, mood, loneliness). We recorded ED visits. Finally, we used machine learning techniques (i.e., leveraging random forest predictors) to develop a 7- and 14-day predictive algorithm for the risk of ED visit. RESULTS: The study included 301 participants, and the HCA made 9,987 observations. Over the mean 10-month follow-up, 97 participants (32%) had at least one ED visit. Modeling techniques identified 9 contributory factors from the longitudinal records of the HCA and developed a predictive algorithm for the risk of ED visit. The predictive performance (i.e., the area under the ROC curve) was 0.70 at 7 days and 0.67 at 14 days. INTERPRETATION: For frail elders receiving in-home care, information on functional status collected by HCA helps predict the risk of ED visits 7 to 14 days in advance. A survey system for real-time identification of risks could be developed using this exploratory work.