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
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
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
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
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
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
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.
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.
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
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.
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:
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.