Tag Archives: Emerging Technologies

AI For Healthcare: Equipping the Workforce for Digital Transformation

AI For Healthcare was a course created by Health Education England and the University of Manchester, to provide a general overview of AI and how it can and is being used in the health sector. Anyone could access this course for a limited amount of time, although it was designed for healthcare workers in mind.

As someone who’s really interested in AI and machine learning (and a big fan of the Topol Review), I took the plunge and had a go. The course was incredibly useful, providing a great introduction to AI. It showed working examples of how it could be utilised, and the pros and cons of implementing new technologies.

Discussion was actively encouraged, and I chatted with wide variety of people working within the healthcare sector. There was the occasional quiz, but mostly people benefited from the rich conversations taking place in the comments sections.

The course was split into five weeks:

  • Week 1: Motivating AI in Healthcare
  • Week 2: What is Artificial Intelligence
  • Week 3: Data in Healthcare
  • Week 4: Making it Work
  • Week 5: Supporting and Skilling the Workforce

The first week was a brief introduction to the course, and looked at the opportunities and challenges of working within the health sector; using technologies to assist with healthcare in an increasingly demanding setting.  It was also an opportunity to introduce ourselves within the discussion, and how we believe our roles could utilise AI in the future. I mentioned monitoring library usage (seeing what resources/topics are popular) and targeted promotion, making resources more accessible and findable for users, more relevant current awareness updates and taking the edge out of literature searching.

We focused on ethical and social aspects of AI and machine learning, generating interesting discussion around if we would be comfortable with being provided personal information and news regarding our health by AI, and whether AI should be used by healthcare professionals to inform decision making. There was also debate on whether AI could essentially ‘replace’ certain services, such as GPs. The general consensus was that as the technology is designed to support, rather than replace services, that it is not capable or desirable for technology to replace human roles.

Further down the line, we looked at cases of AI in action with regards to identifying cancer in breast images. This was particularly topical as it had been recently reported in the news.

There was also an introduction to ‘team science’ theory, creating interdisciplinary teams to work together on projects. Experts from all kinds of different fields and backgrounds will be required for the development of AI in healthcare. Having a diverse range of professionals with different backgrounds, expertise and insights would be highly beneficial, both to reduce bias in software and to create something which can be used by a wide variety of people. I was keen to point out that LKS workers have great skills around Knowledge Management, accessibility and user-centred design, and that having LKS staff embedded into multidisciplinary teams would be an excellent use of our expertise.

We also looked at the challenges of AI; its implementation, management, and the need to educate and train staff on how to use it effectively. I believe this in particular is a golden opportunity for LKS staff; to educate, train, and advocate for the user, enabling them access to quality technology and providing them a safe space to learn and develop new skills.

All in all, the course was an excellent introduction. Being able to network with healthcare professionals was also very useful, as I was able to gage their thoughts and feelings about AI. The course tutors and mentors were fantastic, contributing to discussion and encouraging people to think outside the box. It was heartening also to see the support and interest from others in the roles of LKS staff, and how AI can be a useful tool in our libraries.

Below is a list of some resources which were recommended by the course:

 

Hannah Wood
Librarian
Weston Area Health Library
hannah.wood8@nhs.net

Virtual Reality: its role in healthcare and healthcare libraries

A webinar held on 28th May 2019.

The Emerging Technologies Group recently held a webinar on Virtual Reality (VR).

The main speaker, Nick Peres, was very engaging about the opportunities that are open to us in using VR.

Several case studies of VR’s use by health libraries were also presented.

  • The Vision of HoloLens 2 (Susan Smith)
  • Loanable virtual reality headsets (Catherine Micklethwaite)
  • Oculus Go and Quest – practical uses of portable VR (John Barbrook)
  • VR – a two pronged approach (Mary Hill and Tim Jacobs)
  • Virtual Reality as a library resources (Ben Vella)

The video recording and slides can be found at: Emerging Technologies Group page

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.