Shiv M. Gaglani, BA; M. Ryan Haynes, PhD
Acknowledgment: The authors thank Johns Hopkins School of Medicine professors Catherine DeAngelis, MD, for her help with editorial review, and Harry Goldberg, PhD, and Patricia Thomas, MD, for their insights into curricular design. These contributors received no compensation for their assistance.
Financial Support: In part by the DreamIt Health Technology Accelerator (Philadelphia, Pennsylvania), and the PhD Innovation Initiative Grant from the Johns Hopkins Office of the Provost (Baltimore, Maryland).
Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M13-2286.
Requests for Single Reprints: Shiv M. Gaglani, BA, 733 North Broadway, Suite 137, Baltimore, MD 21205-2196; e-mail, email@example.com.
Current Author Addresses: Mr. Gaglani and Dr. Haynes: Johns Hopkins University School of Medicine, 733 North Broadway, Suite 137, Baltimore, MD 21205-2196.
Author Contributions: Conception and design: S.M. Gaglani, M.R. Haynes.
Analysis and interpretation of the data: S.M. Gaglani.
Drafting of the article: S.M. Gaglani, M.R. Haynes.
Critical revision of the article for important intellectual content: S.M. Gaglani, M.R. Haynes.
Final approval of the article: S.M. Gaglani, M.R. Haynes.
Administrative, technical, or logistic support: S.M. Gaglani.
Gaglani S., Haynes M.; What Can Medical Education Learn From Facebook and Netflix?. Ann Intern Med. 2014;160:640-641. doi: 10.7326/M13-2286
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Published: Ann Intern Med. 2014;160(9):640-641.
We had a sobering realization during our first semester in medical school: The Web sites that society's future physicians use to socialize (Facebook) and watch television (Netflix) are managed by more sophisticated algorithms than the tools we use to learn medicine. These data-driven companies have developed interfaces and algorithms to capitalize on the modern “attention economy,” and they measure success through such metrics as “daily active users” and “time on site.” They analyze millions of data points on individual and group use (“Big Data”) to develop personalized recommendations, among other techniques, that keep users engaged (1). Given our backgrounds in neuroscience and computer science, we decided to ask whether similar methods could be applied to medical education and discuss potential opportunities and barriers to these applications.
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