The power of digital health: what can we learn from one million posts?

Digital health is sexy. Last year our take on the eight technologies that will change health and care was the most popular piece on our website and we continue to support the NHS to engage through our Digital Health and Care Congress and other means.

But there is far more to digital health than how it is used in the NHS. For example, early last year Public Health England’s ‘Sugar Smart’ app was leading app download charts and had been downloaded more than one million times.

For me, one of the most exciting areas is whether and how we can gain insight from unstructured conversations in open chatrooms and forums. This rich ‘third space’ is where millions of people are already discussing their health and reflecting on their interaction with services. There is therefore enormous potential to understand the depth and detail of experience, a new form of mass insight unconstrained by the hierarchy and time constraints of doctor–patient communication or the structure of formal research processes such as questionnaires or focus groups.

The Fund has been involved in a small pilot project in this space, which links two of the eight technologies we highlighted last year: peer-to-peer networks and machine learning. This project has been funded by the Wellcome Trust and led by Demos – the cross-party think-tank ­– and the University of Sussex through their Centre for the Analysis of Social Media (CASM). The final report is out today.

The project sought to test whether machine learning and natural language-processing software can be applied to health issues. To do this we adapted existing CASM software to look at experience of one health area, mental health. We analysed more than one million posts from more than 47,000 users who posted on six open forums between June 2004 and May 2016. All these posts were visible to the public, and the forums did not require a username or password to access.

We tested the software’s ability to distinguish and categorise three types of information from this data:

  • ‘cries for help’ in times of crisis
  • experience with specific treatments, eg, cognitive behavioural therapy
  • the relationship between mental and physical health across three areas: respiratory conditions, diabetes and musculo-skeletal conditions.

While the ability of the technology to do this varied, we could identify, count and correlate instances across the sample, and further identify very rich and meaningful accounts of experience in all of the areas above.

This is one of the first times to our knowledge that unstructured health data in a highly complex and nuanced health area has been collected and classified in this way. In the longer term, this method could be used to:

  • allow owners of forums to better understand the topics and issues discussed and to tailor possible service offers such as self-management
  • help NHS and other service providers develop a better, deeper and more truthful understanding of users’ experience of services and more thoughtful design in response
  • give health regulators access to additional insight about organisational performance and safety.

The information in these posts has not been designed to answer specific questions. This lends it enormous strengths, especially in that it is a source of unbiased, unguarded, full and complex accounts. But this strength can also be a weakness. The data’s lack of built-in focus means that it required careful and detailed interpretation, and this process is context-specific and value-laden. The sensitivity and specificity with which information is categorised also needs to be improved.

NHS decision-makers we talked to saw big potential in this sort of analysis, though were also well aware of the pitfalls. For example, forum users are self-selected and some demographic groups are less likely to be included in online analysis. They were also keenly interested in the ethics of undertaking work like this. This study received ethical clearance from the University of Sussex, and we were careful to follow guidelines in retrieving, storing and handling this data. But as this technique develops we need to ensure that existing research ethics and codes developed for traditional health research are fit for purpose for this new form of knowledge.

In conclusion, we are only in the foothills of applying machine learning to complex health issues and there are many technical and ethical hurdles to overcome. This study has demonstrated that we can identify, understand and construct wider meaning from millions of complex and unstructured online conversations about important issues that affect our health.

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#549338 Sarah
Darzi Fellow with Health Education England, for Kent Surrey and Sussex

Very exciting developments!! I'd be interested to have a conversation about this medium, particularly its use in gaining insight across professional boundaries on a programme for improving intellectual disabilities access to healthcare, without focus groups or questionnaires!!

#549339 Mark Sadler

Interesting study we would be happy to meet and share with you.
We are using software originally developed out of Falmouth Uni but now analysing 70 million online games reviews from forums, chat rooms, App Store and more.
Our Natural Language Processing software (Pulse) is now being used to analyse open text comments in the Friends and Family feedback. And it's remarkably accurate, the software reads and understand every comment and sorts it from minus 100 to plus 100 on a sentiment scale, it understands the theme or topic and sorts the comments accordingly.
We started by asking the software to sort into the CQC's key lines (safe, caring, effective etc), We then added Carman's Healthcare Dimensions as suggested to us by NHS England. But the software tells us what are the emerging categories that we should be looking at, it spots trending topics and every category gets scored. CQC will be looking at it shortly to help with regulation.
We also have a digital FFT & review platform (Hootvox) and the two plug together providing both qualitative and quantitative measurement 'live'.
We can track, analyse and compare patient journeys. We know that Southampton Uni have played with a simple version of this technology and NHS England's Insight Team commissioned another university to investigate sentiment analysis and NLP to confirm if it could become a viable solution for Healthcare.
As I said we are happy to meet and talk with anyone who is interested, and we would be happy to apply our software to other applications.

#549340 Bobbie

Interesting development. Hope you look at the inequalities dimension too....

Have you tried the sugar smart app?? There are so many products it doesn't recognise.....

#549347 Emilio santelic...
MD And Prof School Public health U Chilec
Clinica Las Condes @ School Public health

In UK healthunlock are working with this kind of technologies
Could be of your interest take in touch
with them Their CEO Jorge Armanet
Please let me know your answers I am looking for to apply its in Chile

#549350 Mat Rawsthorne
Service User Researcher
University of Nottingham

Dr Neil Coulson and I are looking at how this can be applied in my ESRC funded PhD "Big White Wall: Big Black Box?" to understand the patterns of interaction and their relation to clinical outcomes in an RCT of online peer support as part of the study
please get in touch as I am just starting on this journey!

#549435 Mark Sadler

Facebook are using the same technology to look for signs of mental health in an attempt to prevent suicides.

Another company is using the same tech to identify online grooming.

#549534 Mark Sadler

I found this old article we wrote about PokemonGo from analysing sentiment in over 100,000 Appstore reviews - imagine what we could do for hospitals.

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