Name
Title | ||
|---|---|---|
Participatory surveillance of diabetes device safety: a social media-based complement to traditional FDA reporting. |
Abstract | ||
|---|---|---|
Background and objective Malfunctions or poor usability Of devices measuring glucose or delivering insulin are reportable to the FDA. Manufacturers submit 99.9% of these reports. We test online social networks as a complementary source to traditional FDA reporting of device-related adverse events. Methods Participatory surveillance of members of a non-profit online social network, TuDiabetes.org, from October 2011 to September 2012. Subjects were volunteers from a group within TuDiabetes, actively engaged online in participatory surveillance. They used the free TuAnalyze app, a privacy-preserving method to report detailed clinical information, available through the network. Network members were polled about finger-stick blood glucose monitors, continuous glucose monitors, and insulin delivery devices, including insulin pumps and insulin pens. Results Of 549 participants, 75 reported device-related adverse events, nearly half (48.0%) requiring intervention from another person to manage the event. Only three (4.0%) of these were reported by participants to the FDA. All TuAnalyze reports contained outcome information compared with 22% of reports to the FDA. Hypoglycemia and hyperglycemia were experienced by 48.0% and 49.3% of participants, respectively. Discussion Members of an online community readily engaged in participatory surveillance. While polling distributed online populations does not yield generalizable, denominator-based rates, this approach can characterize risk within online communities using a bidirectional communication channel that enables reach-back and intervention. Conclusions Engagement of distributed communities in social networks is a viable complementary approach to traditional public health surveillance for adverse events related to medical devices. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1136/amiajnl-2013-002127 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION |
Field | DocType | Volume |
Data mining,Public health surveillance,Social media,Social network,Online community,Usability,Polling,Adverse effect,Medicine,The Internet | Journal | 21 |
Issue | ISSN | Citations |
SP4 | 1067-5027 | 1 |
PageRank | References | Authors |
1.09 | 2 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kenneth D. Mandl | 1 | 275 | 67.17 |
| Marion McNabb | 2 | 1 | 1.09 |
| Norman Marks | 3 | 1 | 1.09 |
| Elissa R. Weitzman | 4 | 28 | 3.84 |
| Skyler Kelemen | 5 | 12 | 2.24 |
| Emma M. Eggleston | 6 | 1 | 1.09 |
| Maryanne Quinn | 7 | 1 | 1.09 |