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.
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Using Big Data Systems to Understand Health Care Professional Conversations in Public 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
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Disclaimer
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
11. 11
Product complaint
Public response
Passive
(Listening)
Active
(Stimulating)
Environment Evaluation Escalation Engagement
Monitoring and triage
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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
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…”
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/
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:
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
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.
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.