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.

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

16. 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

17. 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

18. 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

19. 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

20. Retweets See the Most Engagement
AverageClicks
T-Test with Bonferroni Correction for Multiple Comparisons

21. Example Feedback from Patients

22. 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.

23. 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.

24. 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.

25. Contact Us
Katja Reuter, PhD
katja.reuter@usc.edu
Anirvan Chatterjee
anirvan.chatterjee@ucsf.edu
linkedin.com/in/katjareuter
@dmsci
twitter.com/anirvan