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Social Media ResearchBrief Presentation for FDAMay 2013Slides available for download at:http://bit.ly/CHIDSSMforFDAWG
Presenters Ritu Agarwal, PhD Professor and Dean’s Chair of Information Systems Co-Director of CHIDS Kenyon Crowley, MB...
Topic agenda Introduction of research team and CHIDS Gao Online ratings of healthcare services Information diffusion i...
StrategyTechnologyPolicyCHIDSMission Designed to research, analyze, andrecommend solutions to challengessurrounding the i...
Research focus areasImpact andComparativeEffectiveness ofHealthInformationSystemsNew Models ofCare (ACO, HIE,PCMH, CareTra...
Lack of information in the information age Jessie Gruman Three-time Cancer Survivor Hodgkin’s disease, cervicalcancer, ...
Web 2.0 comes to doctors 53 + and growing!01000002000003000004000002005 2006 2007 2008 2009 2010ratings physiciansSources...
Do online ratings reflect doctor quality?Sources: Gao, Greenwood, McCullough, and Agarwal 20128 May 2013
Knowledge flow in online patient communitySources: Goh, Gao and Agarwal 20139 May 2013
Stopping the biggest man-made epidemicin the United States Prescription drug overdose Kills 40,000 people last year Mor...
Trust and Influence in Social Media Who are the most influential in socialmedia? Not just the people who have themost fa...
Observed data and model predictions for Hurricane Sandy data(first tweets by hour)0510152025303540455055Thur23Fri7Fri15Fri...
Preference Mapping from Social Media Rather than doing focusgroups and surveys, wecan monitor socialmedia to understandco...
Social Media Recruiting Social Media Monitoringcan be used to identifyindividuals with an interestor concern Piloting wi...
SEPARATING SOCIAL INFLUENCEFROM OPINION REPORTING INSOCIAL MEDIAResearch Question15 May 2013
16 May 2013
There Is Social Influence in Online RatingsMay 20, 2013 What Implications does this have? What is the value of online ra...
Bias in Online RatingsMay 20, 2013 Hu, Pavlou and Zhang (2009) Acquisition bias Under-reporting bias18 May 2013
Bias in Online RatingsMay 20, 2013 Li and Hitt (2008)19 May 2013
Bias in Online RatingsMay 20, 2013 Wu and Huberman(2008) Perceived uniqueness in identity20 May 2013
The Social Bias in Online RatingsMay 20, 2013Social Context Influences RatingsSocial Bias in Social Networks21 May 2013
Demo: Negative Rating ConditionMay 20, 2013Friend:1.00AFTER CaseMy rating: 2.30BEFORE caseMy rating: 2.80Average Rating: 3...
Insights from the ModelMay 20, 2013 Social Bias Focal rating is unbiased ONLY when there is no socialfactor Path Depend...
Discussion How can these findings, techniques and futureextensions of this research be applied to FDApatient preferences ...
Follow up Thank You! Kenyon Crowley kcrowley@rhsmith.umd.edu Mobile: (919) 649-2279 Slides available for download at:...
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Social media research at CHIDS for FDA patient prefs wg 05 20 2013

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Brief presentation of selection of social media research conducted at Robert H. Smith School of Business; used for seeding discussion of how to use innovative social network analytical techniques applied to FDA priorities.

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Transcript of "Social media research at CHIDS for FDA patient prefs wg 05 20 2013"

  1. 1. Social Media ResearchBrief Presentation for FDAMay 2013Slides available for download at:http://bit.ly/CHIDSSMforFDAWG
  2. 2. Presenters Ritu Agarwal, PhD Professor and Dean’s Chair of Information Systems Co-Director of CHIDS Kenyon Crowley, MBA, MSIS, CPHIMS Deputy Director of CHIDS Director of Health Innovation, COEHITR Gordon Gao, PhD Associate Professor of Information Systems Co-Director of CHIDS Bill Rand, PhD Director, Center for Complexity in Business Assistant Professor of Marketing Il-Horn Hann, PhD Associate Professor if Information Systems Co-Director, Center for Digital Innovation, Thought and Strategy (DIGITS)2 May 2013
  3. 3. Topic agenda Introduction of research team and CHIDS Gao Online ratings of healthcare services Information diffusion in online communities Pain management preferences concept Rand Trust and Influence in social media Urgent diffusion Preference mapping Social media recruiting Hann Social influence and biases in online rating communities Discussion, concluding remarks and questions3 May 2013
  4. 4. StrategyTechnologyPolicyCHIDSMission Designed to research, analyze, andrecommend solutions to challengessurrounding the introduction andintegration of information and decisiontechnologies into the health care system Improve the practice and delivery of healthcare by offering researched solutions thatimpact safety, quality, access, efficiency, andreturn on investment Works with many partners acrossacademia, government, clinical orgs andindustry4 May 2013
  5. 5. Research focus areasImpact andComparativeEffectiveness ofHealthInformationSystemsNew Models ofCare (ACO, HIE,PCMH, CareTransitions)HealthcareAnalytics (Data-driven HealthServices Insights,Modeling,Operations)Consumers,Quality &Transparency,and Social MediaMay 20135
  6. 6. Lack of information in the information age Jessie Gruman Three-time Cancer Survivor Hodgkin’s disease, cervicalcancer, colon cancer Then a fourth one hit her “I searched online but found thatcomparative quality informationon surgeons specializing instomach cancer was virtuallynonexistent.” (Health Affairs,2013)6 May 2013
  7. 7. Web 2.0 comes to doctors 53 + and growing!01000002000003000004000002005 2006 2007 2008 2009 2010ratings physiciansSources: Gao, McCullough, Agarwal, and Jha 2012024681012142005 2006 2007 2008 2009 20107 May 2013
  8. 8. Do online ratings reflect doctor quality?Sources: Gao, Greenwood, McCullough, and Agarwal 20128 May 2013
  9. 9. Knowledge flow in online patient communitySources: Goh, Gao and Agarwal 20139 May 2013
  10. 10. Stopping the biggest man-made epidemicin the United States Prescription drug overdose Kills 40,000 people last year More than deaths from traffic accidents Distribution of morphine increased 600% from 1997-2007 Use social media as a surveillance tool Text mining analysis on conversations in pain mgmt forums Optimize strategies to use social media to curb the growth of drugoverdose? Identify factors that affect a patients risk attitude toward drugoverdose; Leverage the social network structure to reduce drug overdose(information hub, influentials, followers, etc.)10 May 2013
  11. 11. Trust and Influence in Social Media Who are the most influential in socialmedia? Not just the people who have themost fans, but the boundary spanners Who are the most trusted? Can be measured by observingbehavior over time Trust is the best predictor of “virality”and information diffusion11
  12. 12. Observed data and model predictions for Hurricane Sandy data(first tweets by hour)0510152025303540455055Thur23Fri7Fri15Fri23Sat7Sat15Sat23Sun7Sun15Sun23Mon7Mon15Mon23Tue7Tue15Tue23Wed7Wed15Wed23Thur7Thur15Thur23Fri7Fri15Fri23Sat7Sat15Sat23Sun7HourNumberofTweetsperHourObserved dataGeometric decay NHPP modelConstant intensity NHPP modelSum of geometric decay NHPP modelUrgent Diffusion Constructing a model of thediffusion of information when thetime scale of external events is asfast or faster than the diffusionprocess Social media can be used toidentify key individuals who getinformation out soon Sometimes the social network isirrelevant The process really depends on thepresence of new information12 May 2013
  13. 13. Preference Mapping from Social Media Rather than doing focusgroups and surveys, wecan monitor socialmedia to understandconsumer preferences Enables a quicker andfaster way to estimateconsumer preferences13 May 2013
  14. 14. Social Media Recruiting Social Media Monitoringcan be used to identifyindividuals with an interestor concern Piloting with fluidentification Can target based onlocation14 May 2013
  15. 15. SEPARATING SOCIAL INFLUENCEFROM OPINION REPORTING INSOCIAL MEDIAResearch Question15 May 2013
  16. 16. 16 May 2013
  17. 17. There Is Social Influence in Online RatingsMay 20, 2013 What Implications does this have? What is the value of online rating systems? How credible is this information for consumers? Risk reduction still possible? In general: Can we separate ‘true’ evaluations fromsocial influence in social networks?17 May 2013
  18. 18. Bias in Online RatingsMay 20, 2013 Hu, Pavlou and Zhang (2009) Acquisition bias Under-reporting bias18 May 2013
  19. 19. Bias in Online RatingsMay 20, 2013 Li and Hitt (2008)19 May 2013
  20. 20. Bias in Online RatingsMay 20, 2013 Wu and Huberman(2008) Perceived uniqueness in identity20 May 2013
  21. 21. The Social Bias in Online RatingsMay 20, 2013Social Context Influences RatingsSocial Bias in Social Networks21 May 2013
  22. 22. Demo: Negative Rating ConditionMay 20, 2013Friend:1.00AFTER CaseMy rating: 2.30BEFORE caseMy rating: 2.80Average Rating: 3.30HomophilySocial BiasSocial Bias in Social Networks22 May 2013
  23. 23. Insights from the ModelMay 20, 2013 Social Bias Focal rating is unbiased ONLY when there is no socialfactor Path Dependence Early ratings affect later ratings, therefore changing overallrating trajectory Un-Correctability Without knowing each users social preference, it isimpossible to correct this bias23 May 2013
  24. 24. Discussion How can these findings, techniques and futureextensions of this research be applied to FDApatient preferences goals and objectives? Questions24 May 2013
  25. 25. Follow up Thank You! Kenyon Crowley kcrowley@rhsmith.umd.edu Mobile: (919) 649-2279 Slides available for download at: http://bit.ly/CHIDSSMforFDAWG25 May 2013

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