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Engaging Patients in Research: Does algorithmically created content have a role to play in patient engagement?
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Engaging Patients in Research: Does algorithmically created content have a role to play in patient engagement?

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This presentation is a follow-up on the previous version: Spreading Research and Engaging Disease Communities – One Automated Tweet at a Time. Here we share new data and argue that Algorithmic content …

This presentation is a follow-up on the previous version: Spreading Research and Engaging Disease Communities – One Automated Tweet at a Time. Here we share new data and argue that Algorithmic content creation can serve as a potent model for ongoing value generation to foster patient loyalty and research participant recruitment.

Published in Social Media , Technology , Business
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  • The green bubbles have grown in numbers and significance quite dramatically in past months.
  • We started off by looking at 186 different biomedical hashtags.For each one, we looked at three factors:1. How frequently people post using that hashtag — are people talking about this?2. How many publications on that topic were published at UCSF the previous year — do we have anything to say about this?3. The number of Twitter accounts that include that hashtag in the description — do people strongly self-identify as being involved in this community?From that list, we selected 8 hashtags that met our criteria all 3 ways — people cared about it, people were taking about it, and we had something to say about it
  • So we created eight new Twitter accounts, one for each of the topics. They were accounts like @UCSFDiabetes, @UCSFWeightLoss, and @UCSFDepressionAnd on each account, we’d automatically post tweets of several different types
  • So we created eight new Twitter accounts, one for each of the topics. They were accounts like @UCSFDiabetes, @UCSFWeightLoss, and @UCSFDepressionAnd on each account, we’d automatically post tweets of several different types
  • We started off doing six weeks of active outreach using social media best practices, trying to get users to follow and interact with us by doing 4 things:Follow other Twitter usersMentioning other influential Twitter users in our tweetsPosting #FollowFriday tweets mentioning other influential Twitter usersActively soliciting feedback from readers
  • And then we stopped. And let the system run on auto-pilot. After 17 months, our 8 accounts had a combined total of over 3,000 followers, and over 2300 clicks on the tweets we posted
  • We started off looking at which types of content got retweeted and clicked on the most. Unsuprisingly, having a tweet composed of the full name of a publication was pretty much the least popular option — probably because that text’s not written for lay people to read
  • T-tests with bonferroni correction for multiple comparisonsWhile there wasn’t enough data for us to validate all the tweet types, it’s clear that retweets saw the most engagementOf these, both types of RTs performed significantly better than either profiles or pubmed links (p < 0.01 corrected)
  • …and we got really positive feedback from patients

Transcript

  • 1. Engaging Patients in Research Katja Reuter1, PhD, and Anirvan Chatterjee2 Bradley Voytek3, PhD, John Daigre1 1 Southern California Clinical and Translational Science Institute (SC CTSI), University of Southern California (USC) 2 Clinical and Translational Science Institute (CTSI at UCSF), University of California, San Francisco (UCSF) 3 University of California, San Francisco (UCSF), Department of Neurology Does algorithmically created content have a role to play in patient engagement? Presented at AMIA, CRI 14, Apr 10, San Francisco
  • 2. Disclosure All authors disclose that they (as well as their life partners) have no relationships with commercial interests.
  • 3. A Shifting Landscape of Opportunity Source: Pew Research Center surveys, 1995-2014. 60% of U.S. adults search for health information online. (PEW Research, 2009)
  • 4. It’s Time to Rethink Scientific Outreach “Scientists are failing at communicating science to the public.” (The Welcome Trust, 2001; Wilcox, 2012)
  • 5. Learning from the Publishing Industry “ How Algorithmically Created Content will Transform Publishing: http://www.forbes.com/sites/danwoods/2012/08/13/how- algorithmically-created-content-will-transform-publishing/ Algorithms can provide acceleration for steps in content creation that are better performed by machines. Fred Zimmerman, CEO of Nimble Books
  • 6. Our Key Question Does algorithmically created content have a role to play in patient engagement?
  • 7. We Developed an Information System that … Content Discovery Conversion Notifications Editing Automated Publishing Automatically scans data sources for disease-specific content, e.g., PubMed, Clinicaltrials.gov, U niversity News, UCSF researchers/groups on Twitter. Automatically creates tweets using disease-specific #hashtags and shortened URLs. Automatically schedules the tweet for posting using social media content management system.
  • 8. Example Tweets New PubMed articles by UCSF researchers Content from selected UCSF Twitter accounts
  • 9. Editing Scheduled Tweet Social Media Content Management System: Buffer
  • 10. Why Twitter? Symplur: The Rise of Patient Communities on Twitter by Auden Utengen. Sep 2010 June 2012 Growth of Patient Communities on Twitter (green bubbles)
  • 11. Measuring Popularity of Hashtags For example, within a 24-hour period there were… 1,500 tweets posted using #diabetes; reaching 1.6 million Twitter users Source: Hashtracking.com, Oct 8th, 2012
  • 12. Selecting Disease Topic Areas Hashtag # Hashtag Uses per Day (August 15, 2012) # Twitter Accounts w/keyword in description # UCSF Publications (2011) #HIV 1006 1910 127 #Diabetes 1733 3279 76 #AIDS 585 8227 64 #Depression 1016 4085 55 #BreastCancer 499 2167 55 #Dementia 605 505 42 #Stroke 131 3503 37 #Obesity 541 1059 36
  • 13. New Twitter Accounts Targeting Each Hashtag #diabetes @UCSFDiabetes
  • 14. List of accounts twitter.com/UCSFRemix/ucsf-disease- research/members Tweet stream: twitter.com/UCSFRemix/lists/ucsf-disease-research
  • 15. Automatically Post Relevant Tweets  New scientific publications: PubMed  New clinical trials: ClinicalTrials.goc  Links to a researcher profile  University news articles  Retweets of relevant content from University groups  Retweets of relevant content, copyedited by a communicator @UCSFDiabetes
  • 16. Results after 6 Weeks Key Metrics Total number of… After 6 weeks Followers 867 Tweets generated and sent 1,042 Clicks by Twitter users 1,149 Active Outreach
  • 17. Results after 1 year and 4 Months Key Metrics Total number of… After 6 weeks After 1 year and 5 Months Followers 867 3,094 Tweets generated and sent 1,042 3,442 Clicks by Twitter users 1,149 2,365 Active Outreach No Active Outreach
  • 18. What Content is Most Popular? Clinical Trials Researcher Profile Publication University News Story Retweet Copyedited Retweet - 0.05 0.10 0.15 0.20 0.25 0.30 - 0.50 1.00 1.50 2.00 2.50 3.00 Retweetspertweet Clicks per tweet Interactions per tweet, by tweet type
  • 19. Retweets See the Most Engagement AverageClicks T-Test with Bonferroni Correction for Multiple Comparisons
  • 20. Example Feedback from Patients
  • 21. We Thank … This project was funded through an IT Innovation Contest Award from the University of California, San Francisco (UCSF) and supported by the Clinical and Translational Science Institute (CTSI) at UCSF.
  • 22. Conclusions  Algorithmic content creation can accelerate and enhance the traditional process of content creation at little cost.  Retweets of research-related content see the highest engagement.  Disease communities value such an effort.  University groups in charge of communications save time while increasing their information output.
  • 23. Recommendations  Algorithmic content creation can support new types of hybrid content that is collaboratively created by humans and machines.  Potent model for ongoing value generation to foster patient loyalty and research participant recruitment.  More research is necessary to assess the effectiveness of different types of content.  Consider involvement of influencers.
  • 24. Contact Us Katja Reuter, PhD katja.reuter@usc.edu Anirvan Chatterjee anirvan.chatterjee@ucsf.edu linkedin.com/in/katjareuter @dmsci twitter.com/anirvan