+ Tweets about hospital quality: A mixed methods study Felix Greaves Harkness Fellow, Harvard School...

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Tweets about hospital quality: A mixed methods study

Felix Greaves

Harkness Fellow, Harvard School of Public Health / Imperial College London

fgreaves@hsph.harvard.edu

@felixgreaves

+Co-authors / conflict of interest

Anthony Laverty

Daniel Cano-Ramirez

Stephen Pulman

Karo Moilanen

Ara Darzi

Christopher Millett

Dr Pulman and Dr Moilanen are founders of Theysay Ltd, a sentiment analysis spin off company from Oxford University

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+Social media use is normal

81% of UK population use the Internet

61% use a social networking site

Source: Oxford Internet Survey 2013

+We love describing things on the internet

+We compared ratings with traditional measures of quality

Vs.

Associations between Internet-based patient ratings and conventional surveys of patient experience in the English NHS. Greaves F et al. BMJ Qual Saf. 2012;21(7):600-5.

+Cloud of patient experience

Free Text

Greaves F et al. Harnessing the cloud of patient experience: using social media to detect poor quality healthcare. BMJ Qual Saf.2013 Mar;22(3):251-5.

+ A new trend: ‘Big Data’ and social media analytics

+Sentiment analysis

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+Sentiment analysis of patientexperience is possible

Overall CleanlinessDignity• rude • dirty • rude• excellent • floor • told• hours • filthy • thank• pain • bed • friendly• communicatio

n • blood • attitude

Greaves F et al. Use of sentiment analysis for capturing patient experience from free-text comments posted online. J Med Internet Res. 2013 Nov 1;15(11):e239.

+NHS Insight dashboard

+Aims

To describe the frequency with which people are talking about hospitals on Twitter

To understand what proportion of tweets to hospitals are related to care quality

To examine whether there are associations between Twitter sentiment and other measures of care quality

+Methods

Collected the Twitter names of all English NHS acute hospitals in April 2012

Collected all tweets ‘mentioning’ hospitals for 1 year

Qualitative analysis of 1000 random tweets

Automated sentiment analysis of all tweets Compared with hospital quality metrics

+Results76 of 166 hospitals were on Twitter198,499 tweetsMean tweets per trust: 2647 Median: 796, range: 0 to 88169

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-109

9

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-129

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-149

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-169

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-189

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-209

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-229

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-249

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-269

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-289

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Number of tweets

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+Time of tweets

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00 05 11 16 22Hour of day

+Mean: 508 tweets per dayMedian: 405Range: 62 to 3601

+Qualitative analysis –Most tweets aren't about quality

All tweets: 47% were positive, 47% were neutral and 5.6% were negative.

Tweets about quality: 77% were positive, 2% were neutral and 21% were negative .

Main theme % Minor theme % Sub theme %

Care Quality 11.4Patient centred 9.8

Staff 5.5Environment 1.1

Access 1.4Effective 2.9    

Safe 0.4  

Fundraising 38.7  

About a patient in hospital 10.5Promotion 8.8

Organisational Information 7.1Health Information 4.4

Non-specific content 20.7

+Interaction with staff

Home from [@named hospital] after a weeks stay...we feel blessed to have been cared for by such an amazing team. Thank you [named ward] x

At the [@named hospital] just had an operation on me foot. Outstanding care as usual, & the nurse has just made me a cracking cup of tea :-)

 [@named hospital] [named ward] - Disgusted with your treatment of my mother. Will be making huge complaints.

+Environment

Be nice if this room had been cleaned before we got it. Blood filled cap from an iv on the bedside cabinet, unflushed toilet [@named hospital] 

Spent a night in [named hospital] with my son. Excellent care - spotlessly clean. Thank you [@named hospital]

Don't suppose there is any chance of full english [@namedhospital] Been here since 3 yesterday no hot food or drink #poor 

+Access/Timeliness

[@named hospital] Thanks for squeezing me in with orthoptist [named staff member] today. Great service just so sad that waiting list for [named surgeon] so long :-( 

[@named hospital] where the waiting time is ridiculous waited 3hr yesterday, 3lots of bloods took, 2hrs so far today for a blood test again!

Waiting at [@named hospital]- appointment was over 2 hours ago. can we get takeout delivered??

+Effectiveness / Safety

[@named hospital]Yes pls. Main concern now is the doctor overprescribing. We worked out the error but vulnerable patient might not 

[@named hospital] Also looking at a scan from 2010 when u didn't get scanned until 2011 not good, wrong person, terrible, disgusting

+Multimedia

[@named hospital] Cannot believe I have been served this for my 18 month old. Tastes disgusting and hardly nutritional

[permission obtained to use]

+People can be rude

[@named hospital] [Named chief executive] should come down onto the wards n see what's really going on under her nose. I wish my nan was in [another hospital]

[@named hospital] Shit on floor wet sheets, visitors having to change bedding, shit in toilet, ignorant staff- [name ward]!! Stay away !

+Twitter sentiment compared to traditional quality measures

Twitter Metric Conventional Measure Spearman Rho p value

Twitter sentiment Survey measure of “Overall, how would you rate the quality of care you received”

0.15 0.30

Twitter sentiment Risk adjusted mortality rate 0.15 0.24

Agreement 71%, Kappa 0.39

+Limitations

• Only one way messages, not conversations

• Ability of sentiment analysis approach• Sarcasm / Irony• Culturally specific

• Vulnerable to external effects

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Like an angel

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Stank of urine

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Cup of tea

+Tweets about the NHS are affected by external factors

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+Conclusions

Twitter is being used by patients to discuss care quality

But…it’s only a minority of the tweets

Signal at risk of being lost in the noise

Twitter may be more useful to drive quality improvement than understand comparative performance

Less about Big Data – more about small human stories

+Wider lessons from social media

A powerful tool for spreading ideas

Allows crowdsourcing information

Flattens hierarchies

Allows new approaches to patient engagement

+Caution…

Self selecting

Fickle

New ethical questions

Reality vs. hype

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@felixgreaves

fgreaves@hsph.harvard.edu

+What affects the public’s decision about where to go to hospital

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If better than average

If worse than average

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