Crowd-Powered Medical Diagnosis : The Potential of Crowdsourcing for Patients with Rare Diseases
- With the recent rise in medical crowdsourcing platforms, patients with chronic illnesses increasingly broadcast their medical records to obtain an explanation for their complex health conditions. By providing access to a vast pool of diverse medical knowledge, crowdsourcing platforms have the potential to change the way patients receive a medical diagnosis. We developed a conceptual model that details a set of variables. To further the understanding of crowdsourcing as an emerging phenomenon in health care, we provide a contextualization of the various factors that drive participants to exert effort. For this purpose, we used CrowdMed.com as a platform from which we gathered and examined a unique dataset that involves tasks of diagnosing rare medical conditions. By promoting crowdsourcing as a robust and non-discriminatory alternative to seeking help from traditional physicians, we contribute to the acceptance and adoption of crowdsourcing services in health economics.
Author: | Josephine Fischer, Stefan Arnold, Dilara Yesilbas |
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URN: | urn:nbn:de:bsz:mit1-opus4-137209 |
DOI: | https://doi.org/10.48446/opus-13720 |
ISSN: | 2940-0929 |
Parent Title (German): | NextGen Scientific Review |
Subtitle (German): | Annual Perspectives on Next Generation Science |
Publisher: | Hochschulverlag Mittweida |
Place of publication: | Mittweida |
Document Type: | Final Report |
Language: | English |
Year of Completion: | 2023 |
Publishing Institution: | Hochschule Mittweida |
Release Date: | 2023/01/27 |
Tag: | optimization |
GND Keyword: | Maschinelles Lernen; Graphentheorie |
Issue: | 1 |
Page Number: | 9 |
First Page: | 10 |
Last Page: | 18 |
Open Access: | Frei zugänglich |
Licence (German): | Urheberrechtlich geschützt |