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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 creation can serve as a potent model for ongoing value generation to foster patient loyalty and research participant recruitment.
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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
Disclosure
All authors disclose that they (as well as their life partners) have no relationships with commercial interests.
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)
It’s Time to Rethink Scientific Outreach
“Scientists are failing at communicating science to the public.” (The Welcome Trust, 2001; Wilcox, 2012)
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
Our Key Question
Does algorithmically created content have a role to play in
patient engagement??
We Developed an Information System that …
ContentDiscovery
ConversionNotifications
EditingAutomated Publishing
Automatically scans data sources for disease-specific content, e.g., PubMed, Clinicaltrials.gov, University 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.
Example Tweets
New PubMed articles by UCSF researchers
Content from selected UCSF Twitter accounts
Editing Scheduled Tweet
Social Media Content Management System: Buffer
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)
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
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
New Twitter Accounts Targeting Each Hashtag
#diabetes
@UCSFDiabetes
List of accounts twitter.com/UCSFRemix/ucsf-disease-research/membersTweet stream:twitter.com/UCSFRemix/lists/ucsf-disease-research
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
Results after 6 Weeks
Key MetricsTotal number of…
After 6 weeks
Followers 867
Tweets generated and sent 1,042
Clicks by Twitter users 1,149
Active Outreach
Results after 1 year and 4 Months
Key MetricsTotal 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
What Content is Most Popular?
0.50 1.00 1.50 2.00 2.50 3.00 -
0.05
0.10
0.15
0.20
0.25
0.30
0.35
Series1; 0.29
0.13
0.01 0.03
0.20 0.24
Interactions per tweet, by tweet type
Clicks per tweet
Retw
eets
per
twee
t
Retweets See the Most EngagementAv
erag
e Cl
icks
T-Test with Bonferroni Correction for Multiple Comparisons
Example Feedback from Patients
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.
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.
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.
Contact Us
Katja Reuter, [email protected]
Anirvan [email protected]
linkedin.com/in/katjareuter@dmsci
twitter.com/anirvan
