Shifting the Paradigm

The Dress-COV Telegram Bot as a Tool for Participatory Medicine

Bibliographic Data

ID15513323
AuthorsMichela Franchini (0000-0001-5272-341X, Istituto di Fisiologia Clinica), Stefania Pieroni (0000-0002-8130-4967, Istituto di Fisiologia Clinica, corresponding author), Nicola Martini (0000-0003-3008-0163, Fondazione Toscana Gabriele Monasterio), Andrea Ripoli (Fondazione Toscana Gabriele Monasterio), Dante Chiappino (0009-0005-1314-7073, Fondazione Toscana Gabriele Monasterio), Francesca Denoth (0000-0002-7825-1753, Istituto di Fisiologia Clinica), Michael Liebman (0000-0002-8626-8432, IPQ Analytics, Kennett Square, PA 19348, USA), Sabrina Molinaro (0000-0001-7221-0873, Istituto di Fisiologia Clinica), Daniele Della Latta (0000-0003-0628-2312, Fondazione Toscana Gabriele Monasterio)
Year2020
Volume17
Issue23
Pages8786-8786
Publication date2020-11-26
Open AccessYes
TypeARTICLE
VenueInternational Journal of Environmental Research and Public Health (JOURNAL)
Journal identifiersISSN: 1661-7827 • E-ISSN: 1660-4601
PublisherMultidisciplinary Digital Publishing Institute (PUBLISHER • CH)
DOI10.3390/ijerph17238786
PMID33256160
OpenAlexW3110000107
LanguageEN
Citations received3
References cited25

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic management is limited by great uncertainty, for both health systems and citizens. Facing this information gap requires a paradigm shift from traditional approaches to healthcare to the participatory model of improving health. This work describes the design and function of the Doing Risk sElf-assessment and Social health Support for COVID (Dress-COV) system. It aims to establish a lasting link between the user and the tool; thus, enabling modeling of the data to assess individual risk of infection, or developing complications, to improve the individual's self-empowerment. The system uses bot technology of the Telegram application. The risk assessment includes the collection of user responses and the modeling of data by machine learning models, with increasing appropriateness based on the number of users who join the system. The main results reflect: (a) the individual's compliance with the tool; (b) the security and versatility of the architecture; (c) support and promotion of self-management of behavior to accommodate surveillance system delays; (d) the potential to support territorial health providers, e.g., the daily efforts of general practitioners (during this pandemic, as well as in their routine practices). These results are unique to Dress-COV and distinguish our system from classical surveillance applications

Business · Citizen journalism · Computer security · Coronavirus disease 2019 (COVID-19 · Empowerment · Function (biology · Health care · Knowledge management · Operations management · Pandemic · Participatory design · Political science · Promotion (chess · Risk analysis (engineering · Work (physics · World Wide Web · Computer Science · COVID-19 Digital Contact Tracing · Data-Driven Disease Surveillance · Digital Mental Health Interventions · Engineering · Medicine

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