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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Michela Franchini & Stefania Pieroni & Nicola Martini & Andrea Ripoli & Dante Chiappino & Francesca Denoth & Michael Norman Liebman & Sabrina Molinaro & Daniele Della Latta, 2020. "Shifting the Paradigm: The Dress-COV Telegram Bot as a Tool for Participatory Medicine," IJERPH, MDPI, vol. 17(23), pages 1-19, November.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:23:p:8786-:d:451738
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    References listed on IDEAS

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    1. repec:dau:papers:123456789/6950 is not listed on IDEAS
    2. Stephen J. Galli & Mindy Tsai & Adrian M. Piliponsky, 2008. "The development of allergic inflammation," Nature, Nature, vol. 454(7203), pages 445-454, July.
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    Cited by:

    1. Michela Franchini & Sabrina Molinaro & Michelangelo Caiolfa & Massimiliano Salvatori & Stefania Pieroni, 2021. "Facing the National Recovery and Resilience Plan: Sources of Data, Indicators, and Participatory Strategies in Healthcare and Social Fields," IJERPH, MDPI, vol. 18(19), pages 1-15, October.
    2. Michela Franchini & Massimiliano Salvatori & Francesca Denoth & Sabrina Molinaro & Stefania Pieroni, 2022. "Participation in Low Back Pain Management: It Is Time for the To-Be Scenarios in Digital Public Health," IJERPH, MDPI, vol. 19(13), pages 1-18, June.

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