Shifting the Paradigm: The Dress-COV Telegram Bot as a Tool for Participatory Medicine

Michela Franchini, Stefania Pieroni, Nicola Martini, Andrea Ripoli, Dante Chiappino, Francesca Denoth, Michael Norman Liebman, Sabrina Molinaro and Daniele Della Latta
Additional contact information
Michela Franchini: Data Learn Lab, Institute of Clinical Physiology of the National Research Council, 56124 Pisa, Italy
Stefania Pieroni: Data Learn Lab, Institute of Clinical Physiology of the National Research Council, 56124 Pisa, Italy
Nicola Martini: Data Learn Lab, Gabriele Monasterio Foundation, 1, 56124 Pisa, Italy
Andrea Ripoli: Data Learn Lab, Gabriele Monasterio Foundation, 1, 56124 Pisa, Italy
Dante Chiappino: Data Learn Lab, Gabriele Monasterio Foundation, 1, 56124 Pisa, Italy
Francesca Denoth: Data Learn Lab, Institute of Clinical Physiology of the National Research Council, 56124 Pisa, Italy
Michael Norman Liebman: IPQ Analytics, Kennett Square, PA 19348, USA
Sabrina Molinaro: Data Learn Lab, Institute of Clinical Physiology of the National Research Council, 56124 Pisa, Italy
Daniele Della Latta: Data Learn Lab, Gabriele Monasterio Foundation, 1, 56124 Pisa, Italy

IJERPH, 2020, vol. 17, issue 23, 1-19

Abstract: 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.

Keywords: COVID-19; SARS-CoV-2; participatory medicine; co-morbidity profile; telegram bot (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1660-4601/17/23/8786/pdf (application/pdf)
https://www.mdpi.com/1660-4601/17/23/8786/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:17:y:2020:i:23:p:8786-:d:451738

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().