All posts by Richard Bridgen

HEE Midlands and East Technology Enhanced Learning: Working Together in a Digital Future.

On the 27th February 2019 we attended the HEE Midlands and East Technology Enhanced Learning: Working Together in a Digital Future workshop.

Presentations can be found at https://padlet.com/lksandtel_me/lmyfqhxmvv62

The first from Dr Neil Ralph @DrNRalph, HEE National TEL Programme Manager, was titled “Embracing the digital revolution to educate and train in the NHS”. He argued that technology use is prevalent in society and among NHS staff. 80% of staff are currently using TEL, and 96% would do so if offered. He highlighted the success of e-LfH (e-Learning for Healthcare): 980,000 users; 24,000 sessions, and gave a tantalising glimpse of the forthcoming HEE Learning Solution. Slides are here.

Sue Lacey Bryant @SueLaceybryant, Topol Review Programme Manager, and Sangeetha Sornalingam @sangeetha104, GP and Clinical Fellow HEE, introduced the Topol Review: Preparing the healthcare workforce to deliver the digital future: https://topol.hee.nhs.uk/.  Patients will be at the centre of new technologies and be empowered to take greater charge of their care using digital tools.  Technology offer the gift of time, whereby clinicians will be able to spend more time with their patients. 

The four themes are genomics; artificial intelligence and robotics; digital medicine; and organisational development. TEL will play a vital role in preparing NHS staff for future developments in these areas. There  TOPOL says that ‘the adoption of digital healthcare should be grounded in compelling real-world evidence of clinical efficacy and cost effectiveness’ and that ‘healthcare professionals will need to access training resources and educational programme…to build their digital readiness;’ these are some of the fantastic opportunities for Library and Knowledge Services staff.  What will TEL and LKS look like in 2029 and how will we prepare? Slides are here

At the subsequent Midlands and East LKS Network Event on 16th May, Sue highlighted some recommendations.  Librarians and knowledge specialists need to keep a watching brief and consider how we can enable or support the wider workforce. 

As a minimum LKS need to be aware of developments to be able to signpost trusted sources to staff and the wider public, offer training in critical appraisal of sources to staff and the wider public and to inform the research taking place in this area.   There could also be a role for LKS in supporting digital skill development of the current workforce.

LKS has a key role in meeting the needs of current workforce providing space, signposting and support for CPD and lifelong learning.  OD5 and OD6 are key recommendation for LKS teams; they cover knowledge management and the dissemination of the evidence.  LKS can lead on enabling staff to learn from experience and develop and use systems to disseminate and learn from early adoption and share examples.

Richard Price @RichardPriceUK, Learning Technologies Advisor HEE, gave a thought provoking presentation on “Artificial Intelligence: hype vs. reality”. What is a myth and what isn’t was actually quite surprising.  He spoke about different types  artificial intelligence, some of which contain a human element and others which didn’t, and explained that human adaptability is somewhat behind the pace of technological development  Slides are here.

Andi Blackmore, HEE E-LfH Project Manager, gave a deeper update on e-LfH (e-Learning for Healthcare): including its design and development process. Slides are here.

There was also a Randomised Coffee Trial, where we were paired to share knowledge with someone we hadn’t met before, and a Knowledge Café on shaping the Midlands and East TEL network. It was interesting to see how TEL staff in the NHS are seeking to develop networks along the lines of those that already exist for LKS staff.

TEL staff are possibly an even smaller professional group within the NHS than LKS staff. However we all come under the same HEE umbrella and we share the same aim to improve the skills and knowledge of all NHS staff. We are natural allies.

Stephen Ayre & Richard Bridgen

#MidEastTEL

Introduction to text mining and machine learning in systematic reviews

By Tom Roper, Clinical Librarian, Royal Sussex County Hospital

A group of librarians from NICE, Public Health England, universities and NHS Library and Knowledge services were privileged to attend a workshop on Text Mining and Machine Learning in Systematic Reviews, led by [James Thomas] (http://iris.ucl.ac.uk/iris/browse/profile?upi=JTHOA32), Professor of Social Research and Policy at the EPPI-Centre.  James designed [EPPI-Reviewer[ (https://eppi.ioe.ac.uk/CMS/Default.aspx?alias=eppi.ioe.ac.uk/cms/er4), software to manage all types of literature review, including systematic reviews, meta-analyses, ‘narrative’ reviews and meta-ethnographies, and leads Cochrane’s [Project Transform](https://community.cochrane.org/help/tools-and-software/project-transform).

James outlined the problem: we systematically lose research, and then spend a great deal of effort and money on trying to find it again. We need to use correct methods, and, moreover, need to be seen to be correct. There are quantitative issues as well: Cochrane reviewers screen more than 2 million citations a year.  Can this considerable human effort be made more manageable by the judicious use of text mining and machine learning? While tools are being developed to help this task, their development is uneven, as is their adoption.

James distinguished between three types of machine learning, rules-based (unfashionable in computer science circles, he warned), unsupervised, and supervised, and gave us opportunities to try out tools based on these approaches using our own devices.

Rules-based approaches are accurate, but fragile – they either work, or fail completely. Unsupervised approaches work by leaving a machine to identify patterns in the data, for example by clustering documents, for example [LDAVis ]( http://eppi.ioe.ac.uk/ldavis/index.html#topic=6&lambda=0.63&term=) based, you don’t need me to tell you, on Latent Dirichlet Allocation.

Supervised approaches require a human or humans to give the machine training data; after a while, from a 280,000 row spreadsheet in an example James quoted, a statistical model can be constructed which can then be used with new material to determine whether or not a study is a randomised controlled trail or not. Training data comes from people, including data generated for other purposes, data created for the project itself  and crowd-sourced data, as in the case of [Cochrane Crowd ]( http://crowd.cochrane.org/index.html), which mobilises Cochrane Citizen Scientists to decide whether or not the subject of a database record is an RCT.

In systematic reviews, these approaches may be used to identify studies by citation screening or classification, to map research activity, and to automate data extraction, including performing Risk of Bias assessment and extraction of statistical data. Readers may be familiar with tools that take a known set of citations, and use word frequency counts, or analysis of phrases and adjacent terms to create word or phrases lists or visualisations.  Similarly, term extraction and automatic clustering can be used to do statistical and linguistic analysis on text, for human review, and, if deemed useful, modification of an initial search strategy. [Voyant Tools]( https://voyant-tools.org/) is one example, as are [Bibexcel]( https://homepage.univie.ac.at/juan.gorraiz/bibexcel/), [Termine]( http://www.nactem.ac.uk/software/termine/) and even the use of Endnote’s subject bibliography feature to generate lists of keywords.

Citation networks can be used for supplementary searching – will this change, James asked, if or when all bibliographic data becomes open? Useful tools here, apart from traditional ones such as Web of Science, include [VosViewer]( http://www.vosviewer.com/). We also spent some time playing with [EPPI-Reviewer]( https://eppi.ioe.ac.uk/eppireviewer-web/home), the EPPI-Centre’s own tool for systematic reviewers and with [Carrot2 Search](http://search.carrot2.org/stable/search)

In the future, James suggested that there is a great deal of interest in a “surveillance” approach to finding evidence, which can automatically identify if a review or some guidance needs updating. Cochrane are developing the [Cochrane Evidence Pipeline](https://community.cochrane.org/help/tools-and-software/evidence-pipeline) which aims to triage citations found by machine or crowd-sourced methods can either be triaged by the relevant Cochrane Review Group, or assessed using machine-learning.

While the workshop focussed on systematic reviews, for a jobbing librarian like me in a clinical setting, searches to support systematic review will make up only a small part of the workload. Nevertheless, searches still need to be conducted soundly and rigorously. Can artificial intelligence and machine learning help? Certainly some of the tools James showed are useful when formulating search strategies. A group within London and Kent Surrey and Sussex NHS Libraries is developing a search protocol for the region. We may well find ourselves referencing some of these tools. It is always stimulating to hear a world leader in a field talk, and I’m sure all the workshop participants would join me in thanking both Professor Thomas for giving up his time, and Health Education England for organising the workshop.

The tools James described, and more, may be found on the [EPPI-Centre website] (http://eppi.ioe.ac.uk/cms/Default.aspx?tabid=3677). See also the National Centre for Text Mining’s page of [software tools] (http://www.nactem.ac.uk/software.php)

For a systematic review on the subject see:

O’Mara-Eves A, Thomas J, McNaught J, Miwa M, Ananiadou S. Using text mining for study identification in systematic reviews: a systematic review of current approaches. Syst Rev. 2015 Jan 14;4:5. doi: 10.1186/2046-4053-4-5.

For a more recent overview, I would recommend Julie Glanville’s chapter on Text Mining for Information Specialists in Paul Levay and Jenny Craven’s new book on systematic searching:

Glanville J. Text mining for information specialists. In: Craven J, Levay P, editors. Systematic searching:  practical ideas for improving results.  London : Facet Publishing 2018. p.147-169.

Evidence Standards for Digital Health published

NHS England has been working with NICE, MedCity, Public Health England, NHS Digital and DigitalHealth.London on a project aimed at helping digital health innovators, commissioners, investors and grant funders to understand what a ‘good’ level of evidence for digital health technologies looks like.

NICE has published the ‘Evidence Standards Framework for Digital Health Technologies’, which details evidence of effectiveness for intended use and evidence of economic impact – and which will be key to supporting the speed and uptake of digital health tools.

You can view the new standards at www.nice.org.uk/digital-evidence-standards. There’s also:

  • an article published today in the Lancet
  • a short YouTube film which explains the new standards
  • a blog from Indra Joshi

If you have any questions at all or would like any further information, please let me know.

Nicola Fulton | Communications and Engagement Manager
nicola.fulton1@nhs.net
Empower the Person, Digital Transformation Portfolio, NHS England