Crowdsourcing health discoveries : from anecdotes to aggregated self-experiments
Thesis: Ph. D., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2013.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2014
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| Online Access: | http://hdl.handle.net/1721.1/91433 |
| _version_ | 1811081912375050240 |
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| author | Eslick, Ian S. (Ian Scott) |
| author2 | Frank Moss. |
| author_facet | Frank Moss. Eslick, Ian S. (Ian Scott) |
| author_sort | Eslick, Ian S. (Ian Scott) |
| collection | MIT |
| description | Thesis: Ph. D., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2013. |
| first_indexed | 2024-09-23T11:54:23Z |
| format | Thesis |
| id | mit-1721.1/91433 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:54:23Z |
| publishDate | 2014 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/914332022-01-18T17:12:32Z Crowdsourcing health discoveries : from anecdotes to aggregated self-experiments Anecdotes to aggregated self-experiments Eslick, Ian S. (Ian Scott) Frank Moss. Massachusetts Institute of Technology. Department of Architecture. Program in Media Arts and Sciences. Program in Media Arts and Sciences (Massachusetts Institute of Technology) Architecture. Program in Media Arts and Sciences. Thesis: Ph. D., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2013. Cataloged from PDF version of thesis. Includes bibliographical references (pages 305-315). Nearly one quarter of US adults read patient-generated health information found on blogs, forums and social media; many say they use this information to influence everyday health decisions. Topics of discussion in online forums are often poorly-addressed by existing, clinical research, so a patient's reported experiences are the only evidence. No rigorous methods exist to help patients leverage anecdotal evidence to make better decisions. This dissertation reports on multiple prototype systems that help patients augment anecdote with data to improve individual decision making, optimize healthcare delivery, and accelerate research. The web-based systems were developed through a multi-year collaboration with individuals, advocacy organizations, healthcare providers, and biomedical researchers. The result of this work is a new scientific model for crowdsourcing health insights: Aggregated Self-Experiments. The self-experiment, a type of single-subject (n-of-1) trial, formally validates the effectiveness of an intervention on a single person. Aggregated Personal Experiments enables user communities to translate anecdotal correlations into repeatable trials that can validate efficacy in the context of their daily lives. Aggregating the outcomes of multiple trials improves the efficiency of future trials and enables users to prioritize trials for a given condition. Successful outcomes from many patients provide evidence to motivate future clinical research. The model, and the design principles that support it were evaluated through a set of focused user studies, secondary data analyses, and experience with real-world deployments. by Ian Scott Eslick. Ph. D. 2014-11-04T21:36:21Z 2014-11-04T21:36:21Z 2013 2013 Thesis http://hdl.handle.net/1721.1/91433 893620978 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 315 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Architecture. Program in Media Arts and Sciences. Eslick, Ian S. (Ian Scott) Crowdsourcing health discoveries : from anecdotes to aggregated self-experiments |
| title | Crowdsourcing health discoveries : from anecdotes to aggregated self-experiments |
| title_full | Crowdsourcing health discoveries : from anecdotes to aggregated self-experiments |
| title_fullStr | Crowdsourcing health discoveries : from anecdotes to aggregated self-experiments |
| title_full_unstemmed | Crowdsourcing health discoveries : from anecdotes to aggregated self-experiments |
| title_short | Crowdsourcing health discoveries : from anecdotes to aggregated self-experiments |
| title_sort | crowdsourcing health discoveries from anecdotes to aggregated self experiments |
| topic | Architecture. Program in Media Arts and Sciences. |
| url | http://hdl.handle.net/1721.1/91433 |
| work_keys_str_mv | AT eslickiansianscott crowdsourcinghealthdiscoveriesfromanecdotestoaggregatedselfexperiments AT eslickiansianscott anecdotestoaggregatedselfexperiments |