Published on in Vol 24, No 9 (2022): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/40387, first published .
Real-world Implementation of an eHealth System Based on Artificial Intelligence Designed to Predict and Reduce Emergency Department Visits by Older Adults: Pragmatic Trial

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

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

Journals

  1. Saragosa M, Zagrodney K, Rabeenthira P, King E, McKay S. How Might We Have Known? Using Administrative Data to Predict 30-Day Hospital Readmission in Clients Receiving Home Care Services from 2018 to 2021. Health Services Insights 2023;16 View
  2. Arnaud E, Petitprez E, Ammirati C, Nemitz B, Dequen G, Gignon M, Ghazali D. L’intelligence artificielle dans les structures d’urgences : place de la formation et de la garantie humaine. Annales françaises de médecine d’urgence 2023;13(3):169 View
  3. Olender R, Roy S, Nishtala P. Application of machine learning approaches in predicting clinical outcomes in older adults – a systematic review and meta-analysis. BMC Geriatrics 2023;23(1) View
  4. de Siqueira Silva Í, de Araújo A, Lopes R, Silva C, Xavier P, de Figueirêdo R, Brito E, Lapão L, Martiniano C, de Araújo Nunes V, da Costa Uchôa S. Digital home care interventions and quality of primary care for older adults: a scoping review. BMC Geriatrics 2024;24(1) View