Understanding Health Care Conversations on Social Media
•
6 j'aime•2,959 vues
Using big data systems to understand health care professional conversations in public social media. The document discusses various sources of big data that can be used to analyze conversations between health care professionals, including search behavior, closed professional networks, engagement with medical information, and public social media. It provides examples of analyzing topics of discussion and identifying influential professionals. The goal is to gain insights from online conversations to inform areas like customer needs, messaging, developing advocates, and measuring results.
Recommandé
Recommandé
Contenu connexe
Tendances
Tendances (20)
En vedette
En vedette (9)
Similaire à Understanding Health Care Conversations on Social Media
Similaire à Understanding Health Care Conversations on Social Media (20)
Plus de CREATION
Plus de CREATION (15)
Dernier
Dernier (20)
Understanding Health Care Conversations on Social Media
- 1. 1 Using Big Data Systems to Understand Health Care Professional Conversations in Public Social Media Paul Grant Chief Innovation Officer Creation Healthcare @paulgrant
- 2. 2 The views and opinions expressed in the following PowerPoint slides are those of the individual presenter and should not be attributed to Drug Information Association, Inc. (“DIA”), its directors, officers, employees, volunteers, members, chapters, councils, Communities or affiliates, or any organization with which the presenter is employed or affiliated. These PowerPoint slides are the intellectual property of the individual presenter and are protected under the copyright laws of the United States of America and other countries. Used by permission. All rights reserved. Drug Information Association, Drug Information Association Inc., DIA and DIA logo are registered trademarks. All other trademarks are the property of their respective owners. Disclaimer
- 4. 4 Photo credit: adactio / Foter / Creative Commons Attribution 2.0 Generic (CC BY 2.0)
- 5. 5 A sizeable proportion of consumers are happy for companies to use their personal data, providing they benefit through more targeted marketing Photo credit: http://www.ey.com/Media/vwLUExtFile/BigData/$FILE/ey-bigdata_v3.png
- 6. 6 Mainframe data Illustration credit: Modification from an image by HP/Syncsort So-called ‘Dark Data’ Traditional enterprise data Big Data CRM ERP Data warehouse Web Social Log files Machine data Semi- structured Unstructured Risk? Opportunity?
- 7. 7 we know we know Practical tip: Big data insights audit we know we don’t knowwe don’t know we don’t know we don’t know we know we know we know we don’t know we don’t know Source:Adapted from http://www.doceo.co.uk/tools/knowing.htm
- 8. 8 Search behavior Closed professional networks Engagement with medical information User generated content – Professional collaboration sites – Public social media Semi-structured & unstructured sources
- 9. 9 Source 1: Search behavior
- 10. 10 Source 2: ‘Closed’ physician networks
- 11. 11 Product complaint Public response Passive (Listening) Active (Stimulating) Environment Evaluation Escalation Engagement Monitoring and triage Sed risus nulla, sollicitudin eget ornare vitae, blandit vitae lectus. Ut quis arcu ut nulla facilisis tempus blandit a nibh. jibbajabba said Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed risus nulla, sollicitudin eget ornare vitae, blandit vitae lectus. Ut quis arcu ut nulla facilisis tempus blandit a nibh. Sed aliquam consectetur libero vitae pellentesque. Pellentesque posuere imperdiet sem, vitae aliquet eros elementum placerat. dwight said Conversation of interest Compliment Complaint Question Challenge Information Off-topic Company Person Product Company Person Product Company Person Product Company Person Product Company Person Product ALLOW: Go to step 3, escalate, engage ALERT: Go to step 2, escalate ACTION: Go to step 2, remove, explain Other Step 2 Company Person Product Positive Negative Ignore Thank the person for commenting Correct misinformation Non-compliant Explain why the comment was removed; remind of terms or code constraints Factual Incorrect Adverse Event Drug Safety Compliant Legal / Medical Opinion Information check Content check Regulatory check Private response Situation room Communications, PR, Legal, Medical, etc. 2 3 4 Response 5 Report Acknowledge issue; next steps Answer with approved content or redirect Request 1 6 Product team Inappropriate Channel Conversation platforms Practical tip: Listening and engaging
- 12. 12 Source 3: Engagement with MedInfo
- 13. 13 13
- 14. 14 What happens if we focus on a word like ‘fridge’
- 15. 15 Clear issue already detectable week one, escalation within business to avoid week two peak Normal Issue
- 16. 16 Practical tip: ‘Mash-up’ data sources Rank City Population MI requests Ratio 1&2 London/City of London(England) 7556900 818 0.011% 3 Birmingham(England) 984333 216 0.022% 10 Manchester(England) 395515 177 0.045% 4 Glasgow(Scotland) 610268 159 0.026% 6 Leeds(England) 455123 143 0.031% 22 Nottingham(England) 246654 141 0.057% 5 Liverpool(England) 468945 139 0.030% 18 Belfast(Northern Ireland) 274770 135 0.049% 9 Bristol(England) 430713 115 0.027% 31 Newcastle upon Tyne(England) 192382 108 0.056% 8 Edinburgh(Scotland) 435791 105 0.024% 44 Dundee(Scotland) 151592 77 0.051% 7 Sheffield(England) 447047 66 0.015% 62 Newport(Wales) 117326 65 0.055% 12 Leicester(England) 339239 63 0.019% 16 Cardiff(Wales) 302139 62 0.021% 23 Southampton(England) 246201 57 0.023% 38 Walsall(England) 172141 57 0.033% 26 London Borough of Harrow(England) 216200 52 0.024% 90 Lincoln(England) 89228 52 0.058% 43 Oxford(England) 154566 47 0.030% 17 Bradford(England) 299310 45 0.015% 24 Reading(England) 244070 45 0.018% UK population data source: http://www.populationlabs.com/UK_Population.asp
- 17. 17 Health Care Professionals are increasingly collaborating online through social networks Source 4:
- 18. 18 “The public physician might author blogs, create videos, publish e-books, share news, or curate links on Twitter. The public physician is involved not just in social dialogue but in the creation of retrievable content allowed by the democratization of media and facilitated by easy‐to‐use technology…”
- 19. 19
- 20. 20 >400 million Tweets to date and averaging 300,000 Tweets per day Illustration credit: Creation Pinpoint® data presented at Stanford Medicine X 2014
- 21. 21 Discussion between rheumatologists about how to manage RA when DMARDs have failed, and the application of biologics in this situation, and where there are particular considerations such as joint sepsis Twitter, for clinical conversations! Source: https://twitter.com/rheumi_/statuses/370599136531591168
- 22. 22 Practical tip: Aggregate! 106 comment Twitter conversation, April 2013 Word cloud of EM debate on heart failure treatment Source: https://twitter.com/TBayEDguy/status/322087728760094720
- 23. 23 Health reform – issues and concerns Source: http://hcpdols.com/healthreform/
- 24. 24 Varies by role and location Source: http://hcpdols.com/healthreform/
- 25. 25 Photo credit: Todd Buchanan http://vimeo.com/98482319
- 26. 26 The digital world changes the model of influence 26 Hierarchy based on seniority, experience and publications Collaborative ‘flattened’ relationships, not ordinarily common in real-world Traditional KOL model: Emerging DOL model:
- 27. 27 Source: Creation Pinpoint® study into inflammation in the United Kingdom 1284 nodes, 1633 edges
- 28. 28 Practical tip: Identify ‘DOLs’ Source: http://www.creationpinpoint.com/unveiling-the-creation-pinpoint-oncology-project/
- 29. 29 Influence on other DOLs… Source: http://www.creationpinpoint.com/unveiling-the-creation-pinpoint-oncology-project/
- 30. 30 Conversations and topics Source: http://www.creationpinpoint.com/unveiling-the-creation-pinpoint-oncology-project/
- 31. 31 Network of conversational influence Source: http://www.creationpinpoint.com/unveiling-the-creation-pinpoint-oncology-project/
- 32. 32 Practical tip: Online ‘big data’ research Therapy area What topics around your therapy area and/or indication would you focus on? 2 Brand & comparative products Would you want to study ‘mentions’ of your brand and/or comparative products? 3 Region What region or countries would you focus on? 4 Include patients? In addition to HCP perspectives, would you also look at the conversations of patients & consumers? 5 Market factors Are there market factors to be aware of or do you have existing insights to compare outputs with? Such as new data; product launch; competitor activity; changes in policy? 6 Your goals What would you hope to gain from online ‘big data’ research? E.g. discover customer needs; plan messaging; develop advocates; measure results 1
- 33. 33 Paul Grant Chief Innovation Officer Creation Healthcare @paulgrant Join the conversation #DIA2015 Discussion
Notes de l'éditeur
- Photo credit: <a href="https://www.flickr.com/photos/adactio/9276962702/">adactio</a> / <a href="http://foter.com">Foter</a> / <a href="http://creativecommons.org/licenses/by/2.0/">CC BY</a>
- Consumers knowingly permit personal data to be stored and processed by companies, if that means TARGETED marketing and less ‘noise’. Online survey of just over 2,000 consumers and 748 senior business decision makers.
- http://1.bp.blogspot.com/-Rq96-BUy_eI/U4atZXzutOI/AAAAAAAANUY/djvqPZaTT1k/s1600/c3109ba0-e5d9-11e3-bad7-22000aa7091d-large.jpeg
- Starting with the structured data, and extrapolation of that data into geo-coded records, it is possible to create a summary dashboard that instantly gives more insights than the typical service oriented KPIs like turn-around-time. Here, our dashboard is interactive.
- So, if I click on the category ‘pharmacist’ I can quickly identify that there are four products which have repeated queries. If not already, I can have my marketing team produce a campaign directed at resolving these queries proactively, potentially alleviating unnecessary concerns and helping to build the knowledge needed at the point of dispensing.
- Or in another dashboard, I can investigate unstructured data in more depth by focusing in on human identified anomalies. What happens when I click ‘fridge’ – well I see which products are having repeated questions relating to this topic and I can see through context sensitive charts the other tokens and specific verbatim concerns. This may lead to a new SRD (standard response document) or in a more data-driven model, my HCP portal may dynamically update to ensure that this topic is served as a higher priority in search.
- Mapping verbatim tokens over time, I can determine anomalies relative to ‘normal’. Automated triggers and ‘intelligence events’ can notify my cross functional team to prevent unnecessary resource wastage or reputational damage. In this case, a simple packaging change resulted in numerous queries because it was no longer clear whether the product could be stored out of the fridge.
- Identifying anomalies can lead to better customer service through medical representative and the field force. One way of achieving this is to ‘mash-up’ data from different and open data sets – such as the geographical population distribution in the United Kingdom. Here, the data prompts an investigation of Newport and Lincoln – why is there a disproportionate leaning on medical information? Is this good? Does it expose an unmet need or information gap?
- Overall connectivity, weighted by percentage of HCPs most influenced (top 20) by this person.