Health Tech: Crowdsourcing Advances in Health and Medicine | Shiv Gaglani
CardioSource WorldNews | A famous Japanese proverb states, “None of us is as smart as all of us”–an apt summary of why crowdsourcing has become so popular. The anglicized, Merriam-Webster definition of crowdsourcing is “the practice of obtaining needed services, ideas, or content by soliciting contributions from a large group of people and especially from the online community rather than from traditional employees or suppliers.” Whether or not you know it, you have almost certainly contributed to crowdsourcing efforts.
Take online credit card forms, for example. Have you ever wondered where the difficult-to-read words at the bottom of the form come from? They are words from books that need to be digitized. Called Recaptcha, this crowdsourcing application kills two birds with one stone: first it helps prevent credit card fraud by automated software bots, and second it helps digitize books that computers are not able to read. One of the words tends to be easy to read, and is meant to calibrate how reliable you are as a user. The other word is the difficult one, and your response is combined with dozens of others to ultimately decide on what the word is.
This example captures three key components of successful crowdsourcing applications: (a) finding the right crowd of contributors, (b) incentivizing them to make useful contributions, and (c) filtering out poor contributions. There now exist a number of successful crowdsourcing applications in medicine. One of the most impactful is Wikipedia’s WikiProject Medicine, which has created more than 180,000 health & medicine articles containing more than 1,000,000 references across 275 languages. These bring in nearly 10 billion page views a year, accounting for almost 5% of traffic to Wikipedia, a top 10 visited site in the world. According to a 2013 study in Medical Teacher, more than 90% of medical students and clinicians consult Wikipedia and that, based on learning outcomes, it is an appropriate resource alongside UpToDate and AccessMedicine. In a 2013 survey, WikiProject Medicine identified approximately 21,000 editors who had made 5 or more edits to the site. Of these, around 300 core editors had submitted more than 250 edits that year, more than half of whom were clinicians or trainees. Key drivers of these editors were to give back to society by ensuring accurate health information on Wikipedia as well as improving Wikipedia as a way to learn. The University of California, San Francisco School of Medicine has even developed an elective for fourth year medical students who earn credit by editing Wikipedia. Furthermore, Wikipedia has added automated bots that help filter out bad information and problem editors. It’s incredible to reflect on how a relatively small group of editors have been able to create and update the most accessed repository of health information on the Internet.
Moving on to another crowdsourcing application, FoldIt is an online puzzle game played by tens of thousands of people who compete and collaborate to fold proteins, a notoriously difficult task for computers. Developed by the University of Washington, FoldIt is part of the Citizen Science movement through which people contribute to scientific advancements. Already, the game has led to the crowdsourced solution to a 15-year old protein structure problem, namely the solution of the crystal structure of retroviral protease that is providing new insights into the design of antiretroviral drugs. More recently, the game also led to the discovery of a new family of histidine triad proteins potentially involved in preventing amyloid fiber formation. One of the co-authors of this study, James Bardwell, noted: “It shows that anybody with a 3-D mentality, including gamers, can do something that previously only scientists did, and in doing so they can help scientific progress.” In this case, the key incentive for making useful contributions is the fun gameplay, like solving a crossword or Sudoku puzzle.
Crowdsourcing diagnoses for patients is another application. CrowdMed is an online platform that allows patients to submit their medical cases, for a monthly fee, to “medical detectives” who will perform an in-depth analysis that gives patients a launching pad to speak to their physicians. A 2016 paper in the Journal of Medical Internet Research analyzed 397 CrowdMed cases and found that patients had visited a median five physicians and incurred $10,000 in medical expenses prior to submitting. Approximately 60% of these patients reported that the CrowdMed process gave them insights that led them closer to the correct diagnosis and 57% reported estimated decreases in medical expenses, though only 50% would recommend the process to a friend. Another example: a 2014 study in the same journal by Johns Hopkins ophthalmologists used Amazon’s Mechanical Turk service to test a crowdsourcing method to screen for diabetic retinopathy. They found that “with minimal training, an anonymous, untrained workforce recruited through a public crowdsourcing platform can rapidly and correctly categorize fundus photos of diabetic patients as normal or abnormal.” The images were interpreted for a total cost of US $1.10/eye.
There are many additional examples of crowdsourcing in health and medicine. It’s important to note the potential limitations of crowdsourcing, such as disseminated liability and accuracy issues, whether real or imagined. For example, in spite of much evidence supporting the accuracy of Wikipedia, there remains a general perception in the healthcare system that it is an unreliable source of information. Given the success of crowdsourcing applications thus far and development of support tools, such as the Wikipedia-editing bots, I believe that we’ll be seeing more and not less crowdsourcing in healthcare.
Shiv Gaglani is an MD/MBA candidate at the Johns Hopkins School of Medicine and Harvard Business School. He writes about trends in medicine and technology and has had his work published in Medgadget, The Atlantic, and Emergency Physicians Monthly.
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