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The Prediction Factor

Medical Decision in the Age of Big Data

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Published on Friday, February 12, 2021 by João Fernandes

Summary

This panel seeks to create a dialogue among researchers interested in the use of big data to manage health, in the construction of data processing tools for AI purposes, as well as in the regulation of data access. Whether the papers are based on quantitative or qualitative research, they will be invited to shed light on the impacts of big data on health practices, individual futures, especially for minority groups, and collective imaginary.

 

Announcement

Argument

Healthcare professionals are making increasing use of decision-making support systems. Those tools thrive on the accumulation of socio-demographic and medical data. Ever more detailed databases allow the actors to refine and accelerate diagnoses. Computer-assisted analysis of a large number of clinical studies offer the possibility of optimizing the use of health resources (drugs, organs, vaccines, tests, hospitalizations) by anticipating reactions to treatments and personalizing care. The creation of large national databases dedicated to Covid-19 demonstrate that, more than ever, big data are thought to be the future of healthcare, especially when uncertainty dominates debates.

The use of big data and predictive tools in health raises many questions at the articulation of socio-technical, legal and ethical levels. Among them: What are the impacts of predictive devices on patients’ care trajectories? What do they imply in terms of equal access to treatments, informed choice and protection of personal data? To what extent should predictive tools (using algorithms, genomic sequencing etc.) and those who design them be allowed to control/impact medical decisions?

This panel seeks to create a dialogue among researchers interested in the use of big data to manage health, in the construction of data processing tools for AI purposes, as well as in the regulation of data access. Whether the papers are based on quantitative or qualitative research, they will be invited to shed light on the impacts of big data on health practices, individual futures, especially for minority groups, and collective imaginary.

Present a paper and evaluation

Single paper submissions should be in the form of abstracts of up to 250 words. They should include the paper’s main arguments, methods, and contributions to STS. We encourage submissions to clearly situate their work in relation to STS scholarship.

Deadline: 8 March 2021

Contact: leclainchepiel@gmail.com

Evaluation of extract submissions will take place during the month of March and notifications of acceptance will be sent on April 19, 2021.

This event will be online.

Panel scientific coordinators

  • Marie Le Clainche Piel, CNRS - Centre d'Etude des Mouvements Sociaux;
  • Adeline Perrot, Ethox Centre - University of Oxford;
  • Margo Bernelin, CNRS - Université de Nantes

Places

  • Toronto, Canada

Date(s)

  • Monday, March 08, 2021

Keywords

  • Big Data, Healthcare, Prediction, Personalized medicine, Inequality

Contact(s)

  • Perrot Adeline
    courriel : adeline [dot] perrot [at] ndph [dot] ox [dot] ac [dot] uk

Reference Urls

Information source

  • Perrot Adeline
    courriel : adeline [dot] perrot [at] ndph [dot] ox [dot] ac [dot] uk

To cite this announcement

« The Prediction Factor », Call for papers, Calenda, Published on Friday, February 12, 2021, https://calenda.org/842207

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