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The RCSI have a useful AI prompt training libguide: https://libguides.rcsi.ie/AI/Create

The full AI section is here: https://libguides.rcsi.ie/c.php?g=714015&p=5165626

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Library Guides: Artificial Intelligence: CREATE Prompts

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Library Guides: Artificial Intelligence: CREATE Prompts

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To help us operate cohesively, reduce administrative burden and respect everyone’s time, please ensure your Outlook calendar availability is set to display “Free/Busy, Subject and Location” to HTA colleagues.

We recognise that certain meetings require confidentiality. You retain full control over your privacy settings:

Any event marked as "Private" in Outlook will automatically conceal its title and location, displaying only as "Private/Busy" to others.

External contacts outside HTA will not be able to view meeting titles or locations; this visibility is strictly internal.

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Linkedin Learning MS Copilot the art of prompt writing 

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Linkedin Learning MS Copilot the art of prompt writing 

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Microsoft Copilot: The Art of Prompt Writing Online Class | LinkedIn Learning, formerly Lynda.com

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Unlock the power of Microsoft Copilot by crafting effective prompts across M365 applications.

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AI webinar: The Past, Present, and Futures of Pragmatic and Responsible Adoption of AI in Systematic Reviews

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AI webinar: The Past, Present, and Futures of Pragmatic and Responsible Adoption of AI in Systematic Reviews

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Here is a recording of a talk on “The Past, Present, and Futures of Pragmatic and Responsible Adoption of AI in Systematic Reviews” https://www.ispm.unibe.ch/continuing_education/the_bern_lectures_in_health_science/podcasts/index_eng.html

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Institute of Social and Preventive Medicine (ISPM)

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You are invited to join a webinar: Beyond the “Full HTA Report”: Understanding and Using Different HTA Outputs . After registering, you will receive a confirmation email about joining the webinar.

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 Beyond the “Full HTA Report”: Understanding and Using Different HTA Outputs in Today’s Decision Contexts webinar July 2nd @1pm

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 Beyond the “Full HTA Report”: Understanding and Using Different HTA Outputs in Today’s Decision Contexts webinar July 2nd @1pm

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Welcome! You are invited to join a webinar: Beyond the “Full HTA Report”: Understanding and Using Different HTA Outputs . After registering, you will receive a confirmation email about joining the webinar.

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HTA agencies increasingly deliver a spectrum of outputs (e.g., rapid HTA/rapid reviews, mini HTA, horizon scanning briefs, adaptive/transfer products) to meet urgent and diverse decision needs. While international terminology and mapping work already established baseline definitions (e.g., Merlin et al., 2014), members of the HTA community may have interest in understanding what different outputs are “for,” what they typically contain/omit, and how they should be interpreted and communicated to decision makers, especially as expectations for HTA become more agile across the technology lifecycle and as new evidence sources expand.
Aim of the initiative
To provide an evidence anchored, practice focused update on HTA outputs beyond full HTA reports, building on existing terminology (not redefining it), and to support shared understanding among HTA producers and users.
Learning objectives
- Differentiate major HTA output types (full, rapid, mini, horizon scanning, adaptive/transfer) by decision purpose, typical domains included, and time/resource trade offs.
- Describe what recent empirical analyses show about variation in content and methods across rapid/mini HTA products (McLaughlin et al., 2026).
- Identify good communication practices to prevent misuse (e.g., over interpreting rapid outputs as full HTAs) and to improve transparency about scope and uncertainty.

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SMDM Webinar: Health Technology Assessment in the Era of Artificial Intelligence: Catch Me If You Can

9 July

HEpackageR: making cost-effectiveness models open source with AI Skills

14 July

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🚨 Save the Date! We’re excited to announce the 11th Annual PPI Summer School at the University of Limerick School of Medicine! 📅 11th & 12th June 2026 📍 Kemmy School of Business This flagship event brings together researchers, community partners, policymakers, and public contributors to explore meaningful Public and Patient Involvement (PPI) in health research.
✨ Expect: ✔ Inspiring speakers ✔ Practical workshops ✔ Real-world insights ✔ Opportunities to connect and collaborate
Whether you’re new to PPI or deeply involved, this is a key date for your diary. 📢 More details coming soon – stay tuned! #PPISummerSchool2026 #PPI #HealthResearch #PublicInvolvement #UL #SaveTheDate #ResearchImpact #CommunityEngagement

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All YHEC in-house training courses are free in 2026

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All YHEC in-house training courses are free in 2026

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Welcome to YHEC's training centre, where you can explore our selection of training courses - covering economic modelling, statistics, environmental sustainability, and evidence retrieval and information skills.

To mark our 40th anniversary, we are offering all of our open training courses free of charge as a one-off for this calendar year. This reflects YHEC's long-standing mission to support high-quality research and evidence-informed decision-making in the public interest. The focus, trainers, and academic rigour of these courses are unchanged; they are delivered by the same subject-matter experts who lead our commissioned consultancy work for public- and private-sector clients.

YHEC also provides a range of bespoke, national and international courses that can be tailored to your needs. These are designed flexibly to meet the needs of your organisation, from health systems and HTA bodies to pharmaceutical and medical device companies. Find out more about our bespoke training and support.

Alternatively, for five-day workshops and postgraduate programmes, we recommend exploring the resources at the Centre of Health Economics and the Department of Economics and Related Studies at the University of York.

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Expert-Led Health Economics Training Courses - York Health Economics Consortium

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YHEC offers a range of expert-led health economics training courses. Book online to develop your skills in economic modelling and evidence retrieval.

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R for trial and model-based cost-effectiveness analysis | R for HTA

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We are excited to announce the R for Health Technology Assessment (HTA) workshop that will be held on Monday 22nd June, Tuesday 23rd June & Wednesday 24th June 2026.
Monday 22nd June will be in-person day-long hybrid event hosted by the University of Exeter, while other days will be online only (Zoom link provided on booking). Our program will be announced in April. The overall goal is to present interesting and enlightening presentations on the use of R that will engage an audience of those working in the field of health technology assessment and related analysis.

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Covidence data extraction webinar available

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Covidence data extraction webinar available

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You can watch the recording using the following link: https://scanner.topsec.com/?d=1987&r=show&u=https%3A%2F%2Fus02web.zoom.us%2Fwebinar%2Fregister%2FWN_b9JPmWa9QZOc7rcG2jKJbw&t=96e1ffe839f9c656aca6af52e0219a0ffbcfe0b0

Please open this link in Chrome

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Advancing the Use of Artificial Intelligence in Health Technology Assessment Activities

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Advancing the Use of Artificial Intelligence in Health Technology Assessment Activities

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Advancing the Use of Artificial Intelligence in Health Technology Assessment Activities: Insights and Next Steps from the 2025 HTAi Global Policy Forum | International Journal of Technology Assessment in Health Care | Cambridge Core

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Advancing the Use of Artificial Intelligence in Health Technology Assessment Activities: Insights and Next Steps from the 2025 HTAi Global Policy Forum

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New Book! R for Health Technology Assessment

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New Book! R for Health Technology Assessment

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Gianluca Baio, Howard Thom, Petros Pechlivanoglou

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Artificial Intelligence (AI) Readiness to Support Evidence Synthesis by Workflow: Findings From a Review of Reviews

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Artificial Intelligence (AI) Readiness to Support Evidence Synthesis by Workflow: Findings From a Review of Reviews

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This review found peer-reviewed evidence supporting AI readiness for human-supervised automation of title/abstract screening. However, evidence documenting AI readiness for other evidence synthesis tasks remains limited. DistillerSR and EPPI-Reviewer demonstrated the broadest feature support and strong evidence for AI-powered title/abstract screening. Our study highlights the potential of AI to improve efficiency while maintaining high sensitivity in the screening stage. AI-powered screening may serve as a critical first step toward scaling rapid reviews into living evidence syntheses.

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A comprehensive systematic review dataset is a rich resource for training and evaluation of AI systems for title and abstract screening

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A comprehensive systematic review dataset is a rich resource for training and evaluation of AI systems for title and abstract screening

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A comprehensive systematic review dataset is a rich resource for training and evaluation of AI systems for title and abstract screening | Research Synthesis Methods | Cambridge Core

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A comprehensive systematic review dataset is a rich resource for training and evaluation of AI systems for title and abstract screening - Volume 16 Issue 2

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Protocol for the Creation of the GIN-McMaster Guideline Development Checklist Extension for Integrating Artificial Intelligence in the Guideline Enterprise 

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Protocol for the Creation of the GIN-McMaster Guideline Development Checklist Extension for Integrating Artificial Intelligence in the Guideline Enterprise 

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New article: Protocol for the Creation of the Guidelines International Network–McMaster Guideline Development Checklist Extension for Integrating Artificial Intelligence in the Guideline Enterprise (Guidelines-Artificial Intelligence Extension)

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Click on this link, register and watch the recording of this Overview of Covidence webinar. Open this link in Chrome.

https://scanner.topsec.com/?d=1987&r=show&u=https%3A%2F%2Fus02web.zoom.us%2Fwebinar%2Fregister%2FWN_Scm-czvRTlSBm9jkKVcYyQ&t=8dda3a110f1205147dddc2180bc6bed6c8e88861

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Alternatives to Pubmed and Clinicaltrials.gov

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Alternatives to Pubmed and Clinicaltrials.gov

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Information retrieval: What options are there if access to the main sources of scientific literature is no longer possible?

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How could scientific information retrieval for evidence-based assessments of medical interventions be conducted if the US PubMed and ClinicalTrials.gov databases became inaccessible? In a joint paper, the Institute for Quality and Efficiency in Health Care (IQWiG), the Federal Joint Committee (G-BA), and Cochrane Germany explore this question in order to prepare for the worst-case scenario.

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Webinar on RAISE (responsible AI in evidence synthesis) 19 September 2025

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Webinar on RAISE (responsible AI in evidence synthesis) 19 September 2025

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The EAHIL Evidence-Based Information SIG are pleased to announce that our next webinar will be on 19 September 2025. The topic for the webinar will be the RAISE (Responsible AI in Evidence Synthesis) Guidelines and the webinar will provide an overview of what the guidelines are and how to implement the recommendations in practice.

We are delighted that we will be joined by Ella Flemyng, James Thomas and Anna Noel-Storr who will lead the Zoom session on 19 September at 14:00-15:30 UK time (BST) and 15:00-16:30 CEST – joining instructions below.

Webinar joining instructions:

Friday 19 September 2025. 15:00-16:30 CEST, 14:00-15:30 BST.

Join Zoom Meeting

https://lshtm.zoom.us/j/87020438979?pwd=24qdiFnC6kaEgGAv3dBwrebbki7mAI.1&from=addon

Meeting ID: 870 2043 8979

Password: 825005

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There's a new useful feature in Elicit called Elicit Reports – fully automated, research-backed answers, inspired by systematic reviews.

For Basic (non subscription) users, reports always extract data from up to 10 papers, no matter how many papers are found and screened in earlier steps. Elicit Plus, Pro, and Team users can optionally extract from more papers per report.

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Use AI to search, summarize, extract data from, and chat with over 125 million papers. Used by over 2 million researchers in academia and industry.

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Guidance on conducting T/A and full text screening

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Guidance on conducting T/A and full text screening

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Nice, easy to read guidance on conducting T/A and full text screening, with a little bit about innovations in study classifiers, deduplication and ML for relevant scores. While developed by Covidence it is system agnostic

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Getting started with AI presented by Barrie Tyner

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Getting started with AI presented by Barrie Tyner

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Barrie gave an excellent presentation on the use of AI to support evidence synthesis. Slides are attached

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Generative artificial intelligence use in evidence synthesis: A systematic review

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Generative artificial intelligence use in evidence synthesis: A systematic review

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Generative artificial intelligence use in evidence synthesis: A systematic review | Research Synthesis Methods | Cambridge Core

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Generative artificial intelligence use in evidence synthesis: A systematic review

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Results from a pragmatic review to quantify workload efficiencies and cost savings by applying artificial intelligence in evidence synthesis

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Results from a pragmatic review to quantify workload efficiencies and cost savings by applying artificial intelligence in evidence synthesis

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Conclusion

Automation through AI is key to unlocking the potential of real-time, dynamic evidence generation creating substantial efficiencies in evidence generation efforts. While AI tools have made significant headway in supporting these processes, challenges and opportunities lie ahead. Integration between tools to facilitate data synthesis remains a prominent gap. Different review topics may require tailored synthesis methods, therefore interoperability between tools is crucial to ensure a smooth flow of data between SLR stages while the necessity for human oversight can build trust in the automated process. Recent examples of automated analysis and reporting for comparative analyses and health economic modeling using open codes have also been proposed, suggesting a wider application of AI tools is possible to allow a real-time monitoring of new evidence in decision-making.

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Abstract

Introduction

Researchers are increasingly exploring the use of artificial intelligence (AI) tools in evidence synthesis, a labor-intensive, time-consuming, and costly effort. This review explored and quantified the potential efficiency benefits of using automated tools as part of core evidence synthesis activities compared with human-led methods.

Methods

We searched the MEDLINE and Embase databases for English-language articles published between 2012 and 14 November 2023, and hand-searched the ISPOR presentations database (2020-2023) for articles presenting quantitative results on workload efficiency in systematic literature reviews (SLR) when AI automation tools were utilized. Data on efficiencies (time- and cost-related) were collected.

Results

We identified 25 eligible studies: 13 used machine learning, 10 used natural language processing, and once each used a systematic review automation tool and a non-specified AI tool. In 17 studies, a >50% time reduction was observed, with 5-to 6-fold decreases in abstract review time. When the number of abstracts reviewed was examined, decreases of 55%-64% were noted. Studies examining work saved over sampling at 95% recall reported 6- to 10-fold decreases in workload with automation. No studies quantified the economic impact associated with automation, although one study found that there was an overall labor reduction of >75% over manual methods during dual-screen reviews.

Discussion

AI can reduce both workload and create time efficiencies when applied to evidence gathering efforts in SLRs. These improvements can facilitate the implementation of novel approaches in decision making that consider the real-life value of health technologies. Further research should quantify the economic impact of automation in SLRs.

Keywords: artificial intelligence, systematic review, evidence synthesis, efficiencies, machine learning

Introduction

Automation through artificial intelligence (AI) has been considered the most rapidly evolving field in healthcare and related research. AI has the potential to analyze large pools of diverse data and process heterogeneous information following structured prompts/instructions, and its applications to healthcare, from facilitating early diagnosis and monitoring, to improving overall patient access and quality and efficiency of care, have been increasingly documented ( Alami et al., 2020). To date, wider AI applications in health economics and outcomes research (HEOR) and research to inform policy-making (including health technology assessment [HTA]) have failed to gain significant traction. The recent AI position statement by the HTA body in England (National Institute for Health and Care Excellence [NICE]), which sets up the principles around the use of AI methods in the generation and reporting of evidence for the technology submissions, may change the HEOR status quo and influence other HTA bodies around the world ( NICE, 2024).

The application of machine learning (ML) in pharmacoepidemiology and HEOR has been previously used to advance cohort or feature analytics (confounder adjustment, causal inference) and to predict clinical response or adverse reactions to a drug ( Padula et al., 2022; Wyss et al., 2022). Recently, and most apparently during and after the COVID-19 pandemic, HTA bodies have recognized significant challenges in how to process a higher volume of evidence efficiently and rigorously paralleled with a demand to consider a wider evidence base and deliver decisions under short notice ( Hair et al., 2021; Daniels et al., 2015). This is equally true in systematic literature reviews (SLR), a cornerstone of evidence-based medicine and policy-making in healthcare decision-making, which aims to identify and synthesize data and/or information for a targeted population or disease problem in a reproducible and unbiased manner. SLRs are, however, labor intensive and costly ( Michelson and Reuter, 2019), often taking months to complete and requiring significant effort and training from a team of researchers ( Bashir et al., 2018; Shojania et al., 2007). A 2017 analysis using data from the PROSPERO registry confirmed that the time and staff needed to conduct systematic reviews was considerable ( Borah et al., 2017), with reviews routinely requiring 6 months and, in more complex topics, several years for completion ( Featherstone et al., 2015; Ganann et al., 2010; Khangura et al., 2012). In a 2018 case study, for example, the average time to complete a systematic review was 66 weeks and 881 person-hours ( Pham et al., 2018). However, given the increased demands by policymakers to explore more complex methodologies to increase trust in data and provide sound evidence for their decision-makers (such as bias quantification methods, surrogate analyses, and long-term survival extrapolations), it remains a struggle for all stakeholders involved (decision-makers, pharmaceutical industries, researchers) of how to prioritize personnel training and resource needs to meet higher evidentiary needs and stricter methodological requirements while ensuring the evidence produced is up-to-date and findings are timely, relevant, and accurate for decision-making ( Sarri et al., 2023). Therefore, the concept of living (regularly updating) systematic reviews (LSR) was introduced as a novel approach to evidence identification and synthesis that aims to continually update a review, using rigorous methodology, to incorporate relevant new evidence as it becomes available ( Community, 2024).

In response to these challenges, researchers have begun to embrace AI tools that show how to increase efficiency in SLRs through automation and active learning. In addition to ML, text mining, natural language processing (NLP) and deep learning are all layers that can be grouped under the broad and ever-evolving umbrella of AI and offer a potential solution to the challenges faced by today's evidence synthesis researchers ( Hirschberg and Manning, 2015; Singh et al., 2023). In essence, these tools aim to complete specific review tasks through different applications (active learning, human or researcher in the loop learning) ( van de Schoot et al., 2021) by incorporating probabilistic reasoning to deal with uncertainty in the decisions and whereby the algorithms improve with data experience. ML now underpins most of modern AI and can be unsupervised, seeking a pattern in the data presented to it, or supervised, when it learns from information fed into it by a human who has labeled it (for example, by adding the definitive excluded code to screening) ( Table 1). In evidence synthesis, AI automation technologies are largely suggested as a method to assist in the time-consuming screening of citations, as this represents the rate-limiting step in the timely completion of SLRs and HTAs ( Beller et al., 2013), although previous SLRs have also covered automation processes for all SLR steps (search strategies, text mining, data extraction, synthesis) ( Jonnalagadda et al., 2015; O'Mara-Eves et al., 2015; Marshall and Wallace, 2019). A recent review of economic models submitted to the National Institute for Health and Care Excellence outlined shortcomings, both structural and methodological, of the current approaches to literature reviews and highlighted the opportunities available with the expanded use of AI ( Daly et al., 2022).

TABLE 1.

Types of artificial intelligence

Definition

Focus

Relationship to AI

Previous reports have detailed the types of automated technologies available for each stage of evidence synthesis and their impact on results including proposed frameworks for their implementation and barriers to their adoption ( Padula et al., 2022; Marshall and Wallace, 2019; Hocking et al., 2022). However, much less effort has been placed on quantifying their impact in terms of efficiencies and cost savings through a review of existing literature.

Against this background, we conducted a pragmatic literature review to explore and quantify the potential benefits gained from the introduction of AI-automated tools in core evidence literature review steps (screening, data extraction, reporting). Our aim was to comprehensively examine the literature to determine whether the use of AI-automated tools was associated with time efficiencies and/or cost savings as compared with traditional (manual) human-led methods. Issues around the development and training of AI tools including metrics around their accuracy and reproducibility of results, although crucial in generating trust for their broader applications and adoption in literature reviews, were topics beyond the scope of this review.

Materials and methods

Following a pre-designed review protocol, we conducted a structured search of the Embase and MEDLINE databases for English-language, publicly available literature published between 2012 and 14 November 2023 using key AI-related terms: "artificial intelligence", "machine learning", "automation", "web application", and "living", the latter of which we included to reflect the regular updating of evidence in SLRs and HTAs that is often accomplished by leveraging automation. A supplementary hand search was conducted of The Professional Society for Health Economics and Outcomes Research (ISPOR) presentations database for related abstracts between 2020 and 2023. Reporting was guided by the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guidelines ( Page et al., 2021). We searched for and included published, freely available articles presenting quantitative results on workload efficiency estimates such as reduced time to undertake any tasks related to performing systematic reviews using a type of automation compared with human effort and/or corresponding cost savings. For the purposes of this review, we considered automated tools to be any instrument or system fitting under the general AI umbrella. A good introduction of systematic review automation is provided by Marshall and Wallace (2019), and readers can familiarize themselves with the terminology of AI and ML tools in research synthesis as listed in Table 1.

Studies referring to the role of AI as part of HEOR-related methods such as databases/patient cohort identification, causal inference, predictive modelling or economic analyses were excluded. We also excluded studies presenting methodological guidelines on AI use and commentaries on this topic. Screening of titles/abstracts and full texts was carried out independently by two (human) reviewers with any disagreements resolved by a third, more senior reviewer.

Data extraction was completed by a single reviewer using a pre-specified template and validated by a second reviewer. We extracted key publication and AI characteristics but also included key themes and comments regarding the key outcomes of interest (decreased workload, time and cost savings). We sought articles that evaluated any efficiencies by using metrics including time-to-review (i.e., for abstracts, etc.), number-to-review (i.e., number of abstracts reviewed) or work saved over sampling at 95% recall (WSS@95%). WSS@95% measures efficiency by providing an estimate of the work saved while screening for eligible articles, when compared with traditional manual screening, to find 95% of eligible articles ( Cohen et al., 2006; Chai et al., 2021). Data outlining quantified savings in time, cost and/or workload were extracted as reported in the source literature. Data is presented narratively with no pooling of data. Performance metrics of individual tools including specific considerations in their development and validation were beyond the scope of this paper and were not extracted or presented subsequently.

Results

In total, 2,564 studies were eligible for title/abstract review after de-duplication, of which 32 proceeded to full-text screening. Eleven studies were deemed to be eligible for inclusion based on full-text review. An additional 14 studies were included via a hand search of the ISPOR presentations database, resulting in a total of 25 studies ( Pham et al., 2018; Borowiack et al., 2023; Cichewicz et al., 2023; Egunsola et al., 2023; Liu et al., 2023a; Liu et al., 2023b; Venkata et al., 2023; Bhagat et al., 2022; Hubscher et al., 2022; Liu et al., 2022; Rajadhyax et al., 2022; Stansfield et al., 2022; Clark et al., 2021; Gates et al., 2021; Queiros et al., 2020; Queiros et al., 2022; van Haastrecht et al., 2021; Abogunrin et al., 2020; Popoff et al., 2020; Kebede et al., 2023; Qin et al., 2021; Witzmann et al., 2021; Yamada et al., 2020; Ji and Yen, 2015; Jonnalagadda and Petitti, 2013) that were eligible for inclusion in this review. The literature attrition is shown as a PRISMA diagram in Figure 1.

FIGURE 1.

PRISMA diagram summarizing study eligibility and screening process.

Included publications

All 25 studies ( Pham et al., 2018; Borowiack et al., 2023; Cichewicz et al., 2023; Egunsola et al., 2023; Liu et al., 2023a; Liu et al., 2023b; Venkata et al., 2023; Bhagat et al., 2022; Hubscher et al., 2022; Liu et al., 2022; Rajadhyax et al., 2022; Stansfield et al., 2022; Clark et al., 2021; Gates et al., 2021; Queiros et al., 2020; Queiros et al., 2022; van Haastrecht et al., 2021; Abogunrin et al., 2020; Popoff et al., 2020; Kebede et al., 2023; Qin et al., 2021; Witzmann et al., 2021; Yamada et al., 2020; Ji and Yen, 2015; Jonnalagadda and Petitti, 2013) examined improvements in time efficiency associated with automation in screening, updating and/or analysis during the SLR process, whereas one study ( Cichewicz et al., 2023) examined the impact on labour reduction from the inclusion of automation in this process ( Table 2). No studies were identified to quantify the economic (cost savings) impact linked to automation in evidence synthesis.

Nature of automation

Thirteen ( Pham et al., 2018; Borowiack et al., 2023; Hubscher et al., 2022; Stansfield et al., 2022; Gates et al., 2021; Queiros et al., 2020; Queiros et al., 2022; van Haastrecht et al., 2021; Abogunrin et al., 2020; Popoff et al., 2020; Kebede et al., 2023; Witzmann et al., 2021; Yamada et al., 2020) studies used nine different tools to implement 15 different AI methods. Eleven methods (73%) were used in the screening stage of the review. The remainder were divided as follows: two methods (13%) focused on data extraction and two (13%) focused on risk-of-bias assessment. The ambiguous benefits of the data extractions, combined with the reported advantages, indicate that AI platforms have taken hold with varying success in evidence synthesis. However, the results are qualified by the reliance on the self-reporting of study authors.

Regarding specific methods used, 10 studies ( Cichewicz et al., 2023; Egunsola et al., 2023; Liu et al., 2023a; Liu et al., 2023b; Venkata et al., 2023; Bhagat et al., 2022; Liu et al., 2022; Qin et al., 2021; Ji and Yen, 2015; Jonnalagadda and Petitti, 2013) used NLP, one study ( Clark et al., 2021) used a systematic review automation (SRA) tool and one study ( Rajadhyax et al., 2022) used a general AI tool (i.e., not specifically defined). While the application of ML techniques varied somewhat among studies, all those that used ML utilized some variation on the traditional ML approach where results from human searches are used to "train" the ML algorithm, after which potentially eligible citations were classified and categorized by the automation tool. Studies that utilized NLP were generally used to provide updates to existing databases and/or reviews and relied upon NLP as part of the screening process.

Effect of automation on workload and time saving

Improvements in time efficiency were evaluated in three ways: time-to-review (19 studies) ( Pham et al., 2018; Borowiack et al., 2023; Cichewicz et al., 2023; Egunsola et al., 2023; Liu et al., 2023a; Liu et al., 2023b; Venkata et al., 2023; Bhagat et al., 2022; Hubscher et al., 2022; Liu et al., 2022; Rajadhyax et al., 2022; Stansfield et al., 2022; Clark et al., 2021; Gates et al., 2021; Queiros et al., 2020; Queiros et al., 2022; van Haastrecht et al., 2021; Abogunrin et al., 2020; Popoff et al., 2020), number of abstracts screened (three studies) ( Pham et al., 2018; Kebede et al., 2023; Qin et al., 2021) and WSS@95% (four studies) ( Witzmann et al., 2021; Yamada et al., 2020; Ji and Yen, 2015; Jonnalagadda and Petitti, 2013); one study ( Pham et al., 2018) used both time-to-review and number of abstracts ( Table 1). All 19 studies using time-to-review as their primary outcome observed substantial time savings with the use of AI. Among these, 15 studies reported on the total time required to review abstracts, with the improvement in time-to-review ranging from 36.0% ( Cichewicz et al., 2023) to more than 99.0% ( Liu et al., 2023a). Of the full 25 included studies, 17 found a >50% time reduction. The largest time savings noted among the eligible studies was associated with the use of an AI tool designed to provide live updates to SLRs ( Liu et al., 2023a). Several studies of this "live update" technology have examined its efficiency and found that in two studies ( Liu et al., 2023a; Liu et al., 2023b) time savings ranged from 75.0% to 99.8% while in two other studies ( Egunsola et al., 2023; Liu et al., 2022) estimated the time to replicate network meta-analyses (NMA), time savings ranged from two to 10 min to complete the entire analysis, representing time savings of 99.0% compared with a fully manual process completed by human reviewers. The time required per task was drastically decreased with AI automation, with one study ( Stansfield et al., 2022) reporting that the time to review individual abstracts could be as low as 7 seconds (compared with approximately 60 s per record by humans ( Devane et al., 2014)), which itself contributed to an estimated savings of 25 h in that study. Another study ( Egunsola et al., 2023) reported a total time of two minutes to replicate a full NMA. Clark et al. (2021) compared the time-to-review in two teams (one manual, one automated) reviewing the eligibility criteria for a single published systematic review and noted a 72.0% decrease in time required, with manual reviewers requiring 41 h and 33 min to complete the eligibility screening, as compared with only 11 h and 48 min for reviewers assisted by automation.

Another study ( Cichewicz et al., 2023) indirectly estimated cost savings based on the hours required to complete tasks estimating a labor reduction of more than 75.0% with associated cost savings of 79.6% (range: 73.0%-91.0%) during dual-screen reviews (i.e., where AI acts as a single screener). For single-screen reviews (i.e., where AI was employed as a second reviewer), they estimated a decrease in hours-to-complete of 33.0%, which translates to costs savings averaging 39.8% (range: 36.0%-45.0%) based on the anticipated manpower saved.

Several studies used a defined number of abstracts for review and compared the time required for manual versus automated/assisted methods. Again, significant efficiencies were observed. Two studies, one in which more than 2,700 abstracts were screened ( van Haastrecht et al., 2021) and the other that involved screening of over 33,000 abstracts ( Popoff et al., 2020), noted five-to six-fold decreases in the time required to review the abstracts, findings that were observed to be scalable and consistent. Another study ( Gates et al., 2021) observed that, when reviewing 75 abstracts, automation used to assist reviewers resulted in a 17.1% time savings, but that when automation was used to replace reviewers, up to 44.6% of time was saved. Elsewhere, ML algorithms were able to complete in 31 min what would have taken an individual reviewer up to 85 h to complete ( Popoff et al., 2020), and an estimated total of 25 h of screening time was saved when three sets of citations totaling more than 22,000 citations were screened using AI tools ( Stansfield et al., 2022).

Three studies ( Pham et al., 2018; Kebede et al., 2023; Qin et al., 2021) evaluated the impact of automation on the number of abstracts reviewed, all of which noted similar decreases in the screening burden of between 55.0% and 64.1%. All three studies compared automated review with manual review and concluded that automation (i.e., ML) has the potential to replace at least one reviewer in the screening process. One study ( Kebede et al., 2023) calculated that the minimum decrease in review burden was 61.0% (based on a total of more than 9,800 potential abstracts for review) but that if ML was used to replace one of two reviewers, the workload reduction could be up to 80.3%. All studies reported high sensitivity as well, indicating that quality is not lost when automation is used to replace one of the reviewers. One study ( Pham et al., 2018) that used both time to review and number of abstracts reviewed observed that the use of ML not only decreased the number of abstracts reviewed by up to 63.0% but also calculated that this translated to a savings of between 91 and 95 h per systematic review, suggesting that the impact of automation can significantly improve the efficiency of the SLR screening process.

Finally, four studies ( Witzmann et al., 2021; Yamada et al., 2020; Ji and Yen, 2015; Jonnalagadda and Petitti, 2013) used WSS@95% to quantify the reduction in workload associated with automation, reporting between six- and 10-fold improvements in workload with ML. In one study ( Yamada et al., 2020), ML was compared with manual screening for eight previously published systematic reviews. The authors reviewed published SLRs and extracted both correct (those reviewed as part of the published SLR) and incorrect (those not reviewed as part of the published SLR) articles and used those articles to train an AI algorithm. They noted that a six-fold decrease in workload was observed when all articles were used but that when two correct articles were randomly selected by a researcher and used to initiate ML, the process was accelerated further, with a maximum 10-fold decrease in workload observed. Elsewhere, two studies used methods relating to text word searching and found that efficiencies improved by up to one-third. One of them ( Ji and Yen, 2015) used MEDLINE elements such as TI (title), AB (abstract), MH (MeSH heading), PT (publication type) and AU (author name) in various combinations and noted average improvements of between 36.0% and 37.0%, while the other study ( Jonnalagadda and Petitti, 2013) used distributional semantics to assist with abstract text word screening and noted improvements ranging from 6.0% to 30.0% (median: 13.0%). Finally, in one study ( Witzmann et al., 2021) that used text mining to evaluate five review topics (three clinical, one economic, and one utility review) with datasets varying from 288 to 9,123 articles, there was a >61.0% reduction in the number of articles needing manual review for all topics.

Discussion

AI technologies and methods to speed up the production of SLRs by reducing manual burden and promote cost savings have recently emerged. To date, much of the discussion has focused on the type of each AI tool and its validation properties and computing technicalities, with less emphasis on systematically generating evidence around these tools' efficiency metrics in SLRs. To close this gap, our pragmatic review sought to quantify the impact of using automated tools in evidence synthesis (review, economic modelling) in terms of efficiencies and cost savings and provide an evidence base for the implementation of automation in SLR methodologies.

The innovation of SLR automation tools (e.g., ML algorithms, automated screening, and automated data extraction) and advanced web-based economic models using cloud-based tools can have a significant impact on time and given a presumed decrease in manpower, potential cost savings for both manufacturers and decision makers. van Dijk et al. (2023) have shown that the use of an AI reviewer can reduce the number of articles to be reviewed by human reviewers to as low as 23.0%, which aligns with other work by Yao et al. (2024) that showed in a systematic review that the time savings using an AI reviewer can range from seven to 86 h when reviewing titles and abstracts. Despite these demonstrated benefits, the uptake of these tools has been slow, mainly due to human factor-related barriers ( Tachkov et al., 2022), limited validation of SRA tools ( O'Connor et al., 2018) or data-related barriers and distrust of the tools ( van Altena et al., 2019) due to lacking transparency of ML systems. Results from this review demonstrated that automation in SLR can substantially decrease the time required to complete the review, decreasing the number of articles required to be reviewed at the full-text level by a factor of five to six and resulting in up to a 10-fold decrease in workload. Our review revealed a literature (data) gap on quantifying the potential economic impact (cost savings) associated with the improved efficiencies through automation in evidence synthesis tasks. We only found one study ( Cichewicz et al., 2023) that estimated potential cost savings based on manpower decreases that could indirectly translates to cost savings. Given that human reviewers are known to average 60 s per citation reviewed ( Wang et al., 2020), as compared with AI tools that can replicate an entire review in minutes ( Liu et al., 2023a; Liu et al., 2023b), significant potential lies ahead with the use of such technologies for conducting SLRs. Michelson and Reuter (2019) recently examined the costs associated with SLR work in the pharmaceutical industry and found that, in the United States, a typical SLR costs in excess of $141,000 when costs for time and manpower are considered. Further, among the 10 largest pharmaceutical companies and 10 largest academic centers, between $16 million and $18 million is spent annually on SLRs (figures which may be underestimated, as it is unclear if they include other types of literature reviews such as pragmatic reviews). Based on our findings that automation can at minimum assist and at best replace one of two reviewers during the identification and screening process, there is the potential for significant savings associated with the decreased labor requirements. More work is needed to accurately quantify these potential savings opportunities, though, as automation itself will be associated with development and implementation costs and, as with any technology, some experience and training in its use (by reviewers) are required to maximize its efficiency. Furthermore, while these results provide important evidence regarding the efficiencies gained through automation, other aspects such as performance metrics of individual tools, safe data access, new structures of data classification and technological acceptance criteria should be factored into future research in this area.

Creating time efficiencies in the SLR process allows for the redistribution of efforts, with the potential to shift the more labor-intensive activities to automation, freeing up human researchers to consider a wider breadth of questions regarding a new technology (beyond the fundamental safety and efficacy questions) and redirect time to resolve more complex methodological topics in comparative effectiveness research. Reason et al. (2024) suggest that this shifting of effort could be exploited to advantage in the development of health economic models, where automation can be used to rapidly review, adapt, and expand existing models, thus removing the delays associated with human action and allowing for the further development of concepts. This is especially valuable in the case of new technologies, which are often associated with a high degree of uncertainty, complicated clinical scenarios but also represent the possibility for considerable added value for patients. Automation in the HTA/SLR environment could therefore support stakeholders, for example, by allowing for the exploration of non-traditional methods for data collection and real-world evaluations to aid AI-based decisions.

The incorporation of AI tools into literature reviews, however, is not without challenges. As with any technology designed and programmed by humans, the quality of the output is dictated largely by the quality of the input. Many publications have previously outlined concerns around diversity and inclusion in AI system design, development, and deployment and have highlighted how ignoring these issues may exaggerate existing discrimination, health inequalities and algorithmic oppression, leading to AI systems being perceived as untrustworthy and unfair ( Shams et al., 2023; Chen et al., 2024).

Many researchers acknowledge a trade-off when using AI or automation tools, where AI may allow a task to be completed more quickly but may not be completed to the same standard as when completed by humans ( Control ECfDPa, 2022). Additionally, AI tools such as large language models are not themselves deterministic, meaning that responses will not be identical each time a particular question is asked ( Qureshi et al., 2023). As a result, some researchers may feel that the use of AI forces users down a specific path of decision-making while not understanding how the tool made specific decisions ( Control ECfDPa, 2022). Whether to use AI in a given study is a fundamental decision facing researchers, as is the appropriate choice of stopping criteria ( van Dijk et al., 2023). AI algorithms do not autonomously decide where or how to be applied ( van de Schoot et al., 2021), as their application relies upon a combination of human skill and training using large volumes of data, which can be difficult to collect due to the ethical implications ( Ali et al., 2023). However, in the case of the usage of AI for conducting SLRs, ethical implications are less of a concern as the data being processed are secondary, publicly available data. Regardless of the robustness of input, the evidence we found shows that AI in SLR applications is currently biased heavily toward one predominant task, namely, determination of study eligibility. Although this is a logical first step in applying AI to evidence synthesis tasks, given its labor-intensive nature, the application of AI to other tasks within the SLR, and ultimately HTA, process demands more attention ( de la Torre-López et al., 2023).

The efficiency gains noted in one of the included studies ( Egunsola et al., 2023) may have been overestimated by ignoring the time required for data preparation and training of the AI tool itself. Nevertheless, some included studies ( Queiros et al., 2020; Abogunrin et al., 2020) with more conservative estimates indicate that using an AI tool saves considerable resources even when all preparation and training time is accounted for. A small number of studies in our review considered the utilization of AI for other SLR steps by replicating entire studies, including data analysis. Although these studies demonstrated significant efficiency improvements, these findings require further validation across several disease areas and SLR topics. de la Torre-López et al. (2023), in a comprehensive review of the state of AI technology in SLRs, identified challenges with the AI applications in more advanced tasks beyond simple "selection-based" tasks such as research question formulation, defining of inclusion/exclusion criteria and reporting SLR results and the need for more active human involvement in AI-assisted SLR efforts. Finally, while researchers may embrace the use of AI tools to assist in their efforts, the technology remains a relative "black box", where end users do not fully understand the inner workings of the technology ( Quinn et al., 2021). As such, a potential lack of trust in the automated results may persist and presents a barrier to widespread acceptance of-and expansion of-the use of AI tools in the HTA space ( Tachkov et al., 2022; van Altena et al., 2019). Some work has already been done by Abogunrin et al. (2023) to enable the transparent reporting of data generated by AI tools during the conduct of SLRs. Standardized best practices for AI use in HEOR activities, similar to other areas in healthcare, will provide the framework for methods reporting for both researchers and decision-makers and foster trust and transparency in the results produced by these tools ( Author Anyonomus, 2021). The "Vienna Principles" established by the International Collaboration for the Automation of Systematic Reviews have outlined the basic principles for automation across the spectrum of review tasks, continuous improvement and how their integration can adhere to high-quality standards ( Kraker et al., 2016). The latest PRISMA guidelines already provide some direction on reporting of the use of AI for conducting SLRs, though there is still gap as to the explainability of the underlying algorithms used in tools with AI embedded in them. Likewise, current quality assessment tools do not adequately address quality assessment in automated SLRs. The NICE AI position statement provides the first HTA-developed principles for guiding the integration of AI tools in evidence generation and reporting and can provide the foundation for HTA cross-collaboration efforts in this area. Of note, our review quantitatively supported the NICE statement regarding the less established value demonstration of AI tools, in terms of efficiencies and cost savings, for data extraction steps compared to the tools used for evidence identification. The risk-tier based system suggested by both the EU AI regulation ( Europarl, 2023) and NICE position statement ( NICE, 2024) should guide reliable and trustworthy integration of such tools in evidence synthesis activities to support decision-making in healthcare.

Conclusion

Automation through AI is key to unlocking the potential of real-time, dynamic evidence generation creating substantial efficiencies in evidence generation efforts. While AI tools have made significant headway in supporting these processes, challenges and opportunities lie ahead. Integration between tools to facilitate data synthesis remains a prominent gap. Different review topics may require tailored synthesis methods, therefore interoperability between tools is crucial to ensure a smooth flow of data between SLR stages while the necessity for human oversight can build trust in the automated process. Recent examples of automated analysis and reporting for comparative analyses and health economic modeling using open codes have also been proposed, suggesting a wider application of AI tools is possible to allow a real-time monitoring of new evidence in decision-making.

Funding Statement

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. Funding for this review was provided by F. Hoffmann-La Roche Ltd., Basel, Switzerland. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

SA: Conceptualization, Funding acquisition, Methodology, Writing-review and editing. JM: Conceptualization, Writing-original draft, Writing-review and editing. CZ: Conceptualization, Methodology, Validation, Writing-review and editing. GS: Conceptualization, Methodology, Validation, Writing-review and editing.

Conflict of interest

Authors GS and JM are employees of Cytel, Inc. Authors SA and CZ are employees of F. Hoffman-La Roche Ltd and own F. Hoffman-La Roche Ltd. stocks.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Articles from Frontiers in Pharmacology are provided here courtesy of Frontiers Media SA

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How much can we save by applying artificial intelligence in evidence synthesis? Results from a pragmatic review to quantify workload efficiencies and cost savings

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Researchers are increasingly exploring the use of artificial intelligence (AI) tools in evidence synthesis, a labor-intensive, time-consuming, and costly effort. This review explored and quantified the potential efficiency benefits of using ...

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Generative AI for Health Technology Assessment

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Generative AI for Health Technology Assessment

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Generative Artificial Intelligence for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations: An ISPOR Working Group Report - PubMed

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Although generative AI technology holds promise with respect to HTA applications, it is still undergoing rapid developments and improvements. Continued careful evaluation of their applications to HTA is required. Both developers and users of research incorporating these tools, should familiarize the …

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There has been an update of the tool (November 2024) with a significant number of changes. There is more information here. ROBINS-I-V2

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We are pleased to announce Version 2 of the ROBINS-I tool, launched on 22 November 2024. Access the tool here:
ROBINS-I V2 (editable document) or ROBINS-I V2 (PDF document) The Risk Of Bias In Non-randomized Studies – of Interventions, Version 2 (ROBINS-I V2) aims to assess the risk of bias in a

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Here are two freely available ebooks on R as recommended by HTA staff.

R for Data Science (2e) and Big Book of R

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This is the website for the 2nd edition of "R for Data Science". This book will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it and visualize.

In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you'll learn how to clean data and draw plots-and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You'll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You'll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualizing, and exploring data.

This website is and will always be free, licensed under the CC BY-NC-ND 3.0 License. If you'd like a physical copy of the book, you can order it on Amazon. If you appreciate reading the book for free and would like to give back, please make a donation to Kākāpō Recovery: the kākāpō (which appears on the cover of R4DS) is a critically endangered parrot native to New Zealand; there are only 248 left.

If you speak another language, you might be interested in the freely available translations of the 1st edition:

Please note that R4DS uses a Contributor Code of Conduct. By contributing to this book, you agree to abide by its terms.

Acknowledgements

R4DS is hosted by https://www.netlify.com as part of their support of open source software and communities.

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Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund

# data[].attributes.attachment_link.description
This is the website for the 2nd edition of "R for Data Science". This book will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it and visualize.

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The CICER team shared information about a newly developed quality appraisal tool for assessing systematic reviews: the ReMarQ tool. This tool not only focuses on the quality of reporting but also explicitly considers the use of the GRADE approach – a requirement for us to facilitate guideline groups in formulating recommendations. Additionally, several questions are focused on whether the search strategy is fully available for straight forward updating – again.

Key features of the ReMarQ tool include:

1)      It assesses only the methodology, and is therefore agnostic to study type.

2)      It explicitly asks whether the body of evidence (beyond risk of bias) was assessed—for example, through use of GRADE.

3)      It includes several questions related to the search strategy and its transparency.

4)      Early indications suggest it may be quicker to use than AMSTAR 2.

Below are links to the development paper and a comparative study with AMSTAR 2:

Reported methodological quality of medical systematic reviews: Development of an assessment tool (ReMarQ) and meta-research study | Research Synthesis Methods | Cambridge Core

Comparison between two tools assessing the methodological quality of systematic reviews: ReMarQ and AMSTAR 2 - Marques‐Cruz - 2025 - Clinical and Public Health Guidelines - Wiley Online Library

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Quickstar: a tool to assess the methodological quality (risk of bias) of systematic reviews

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Quickstar: a tool to assess the methodological quality (risk of bias) of systematic reviews

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Inter Library Loan Service now available

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Inter Library Loan Service now available

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An interlibrary loan service is now available to HTA colleagues. If you can't access the full text of a journal article and you've checked our online journal holdings, email Marie with details of the ILL request(s) mcarrigan@hiqa.ie

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Updates to manually entering references to endnote

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Updates to manually entering references to endnote

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Guide to manually entering references to Endnote

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Healthcare leaders met at ISPOR Europe 2025, the leading European conference for HEOR, 9-12 November for discussion and dissemination of the latest trends in healthcare.

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Attaching instructions on how to add the revised HIQA house style to endnote

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The AI/ML working group has compiled a list of AI tools that could be useful in evidence synthesis. Feel free to add tools that you find useful or interesting to this document:

http://edm/HTA/DirectorateDocuments/TEMPORARY%20SSR/Library/AI%20tools/AI%20tools%20in%20evidence%20synthesis.xlsx?Web=1

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Hear about NICE's new AI statement of intent.

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National Institute for Health and Care Excellence (NICE)

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Navigating the future: AI at NICE virtual event

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Evaluation instrument on AI search tools

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Evaluation instrument on AI search tools

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Development of an Evaluation Instrument on Artificial Intelligence Search Tools for Evidence Synthesis | Canadian Journal of Health Technologies

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Quelle était la question?
Les producteurs de synthèses de données probantes sont soumis à des appels contradictoires : certains les enjoignent à adopter et d’autres à éviter les technologies récentes reposant sur l’intelligence artificielle (IA). Notre question était : comment évaluer les outils d’IA, qui évoluent à vitesse grand V, afin d’améliorer la production de synthèses de données probantes tout en maintenant des normes de qualité élevées?
Qu’avons-nous fait?
Afin de faire progresser la science de la recherche documentaire menant à la production de synthèses des données probantes à l’Agence des médicaments du Canada (CDA-AMC), l’équipe du Service d’information sur la recherche a mis sur pied un processus pour évaluer les outils de recherche prometteurs reposant sur l’IA. Nous avons répertorié 51 outils à l’automne 2023, et avons établi des critères de sélection, évalué des caractéristiques particulières et créé un instrument indépendant destiné à appuyer la surveillance et l’évaluation continues des nouveaux outils.
Qu’avons-nous trouvé?
Comme on assiste à un développement rapide des outils de recherche reposant sur l’IA, il faut un instrument d’évaluation flexible pour guider les décisions sur l’adoption et permettre la comparaison des outils. Nous avons dressé une liste de caractéristiques essentielles et souhaitables pour des outils destinés à appuyer les tâches de recherche documentaire menées par CDA-AMC. Nous avons ensuite pu créer un instrument flexible destiné à évaluer les nouveaux outils de recherche reposant sur l’IA dans une optique de synthèse des données probantes.
Qu’est-ce que ça signifie?
CDA-AMC a opérationnalisé un processus reproductible visant à surveiller et à évaluer les outils de recherche reposant sur l’IA. Notre approche à l’égard de l’évaluation des technologies d’IA fera progresser les méthodes de recherche documentaire utilisées par CDA-AMC, et notre instrument d’évaluation sera utile à tout producteur de synthèses de données probantes souhaitant adopter des outils de recherche reposant sur l’IA.

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Advancements in AI, automation, and language processing are increasingly influencing the creation and use of evidence syntheses. AI language models (e.g., ChatGPT, Claude) and automation tools (e.g., ASReview, Laser AI, DistillerSR) offer new possibilities for tasks like search strategy development, screening and data extraction, risk of bias assessment, and writing evidence syntheses. These tools can significantly speed up the process of producing or updating evidence syntheses, benefiting researchers and users alike. However, understanding the strengths and limitations of these technologies is critical to maintaining quality.

In this webinar series, we will explore the role of AI in evidence synthesis, examine how it can complement traditional methods and provide a platform for experts to discuss the opportunities, challenges, and risks involved. This series targets those with foundational knowledge of systematic reviews who want to stay updated on AI developments in evidence synthesis.

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Artificial Intelligence (AI) methods in evidence synthesis: Learning Live webinar series

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Welcome to this series of Cochrane Learning Live webinars, all dedicated to the topic of Artificial Intelligence (AI) methods in evidence synthesis!

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Online Leeds Mixed Methods Systematic Review Courses in March-April 2025.

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Online Leeds Mixed Methods Systematic Review Courses in March-April 2025.

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There are 6 live half day sessions, plus optional introductory sessions, with an excellent line up of experienced tutors.

Participants will have access to 1 to 1.5 hrs pre-recorded lectures to watch prior to each live session. The live sessions are highly interactive with practical activities and discussion, building on the videos.  You can book either or both courses.

See brief course descriptions below, or the full details: https://bit.ly/2QbD1DV

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Course image

Course overview

Mixed methods systematic reviews combine qualitative and quantitative evidence to answer complex applied health and public health questions. These methods are becoming increasingly important as we seek to understand why interventions may have different results for different people in different situations. This course will explore the current thinking on approaches to mixed methods systematic reviews.

You can attend all eight sessions, or just the 'Review' course (five half-days) or the evidence search course (three sessions).

Conducting a Mixed Methods Systematic Review
Online course. Live sessions:
Following this course (delivered in four half-day sessions) participants should have a strong understanding of:

The purpose and value of mixed-methods systematic reviews

Methods for synthesising qualitative and quantitative research including explanatory sequential, exploratory sequential, and convergent synthesis methods.

Searching the Evidence Base Workshop
Online course. Live sessions:
This course, delivered over two half days, will develop:

Online course format

The course materials and highly interactive live sessions will be delivered by software which will enable group discussions, break-out room discussion, quizzes and recording of sessions.

A brief introductory session will allow participants and presenters to introduce themselves and the course.

Participants will be given access to 1 to 1.5 hrs pre-recorded lectures that can be watched in their own time prior to each live session.

During the live interactive sessions, participants will complete hands-on activities and engage in discussion. The online sessions are spaced out in digestible chunks over several days.

Who the course is for?

This course is for you if you are interested in understanding how mixed-method synthesis provides a deeper understanding of complex interventions and their implementation. It is suitable for you if you already have an understanding of systematic review methods - i.e. you have had in-depth training and/or conducted a systematic review (either quantitative or qualitative) before.

The mixed methods course is not suitable for you if you are new to systematic reviews. It does not provide basic systematic review (screening, data extraction, quality assessment) or meta-analysis training. The 'review' course focuses on advanced synthesis methods.

The Searching the Evidence Base course has basic and advanced elements, some sessions are split, allowing those with less experience to practice core skills while the more experienced try advanced and emerging methods.

Presenters

Dr Katy Sutcliffe is the Deputy Director of the Department of Health Reviews Facility at the Evidence for Policy and Practice Information and Coordination Centre ( EPPI-Centre), Institute of Education, University College London. Katy specialises in developing methods for research synthesis, in particular for qualitative and mixed methods reviews.

Dr Meena Khatwa, Dr Gillian Stokes, Dr Preethy D'Souza and Dr Michelle Richardson from from UCL will facilitate group work with Katy.

Judy Wright (University of Leeds) is a senior information specialist with expertise in finding evidence for mixed methods reviews. Judy leads the AUHE Evidence Synthesis team, supporting systematic reviews, economic models and realist syntheses.

Natalie King (University of Leeds) is an information specialist with expertise in finding evidence for systematic reviews, economic models and mixed methods reviews including realist syntheses.

Guest Speakers

Professor Joanne Greenhalgh (University of Leeds) is a social research methodologist applying realist methods to healthcare evaluation. Joanne has expertise in realist syntheses.

Professor Helen Elsey (University of York) is a professor in global public health. Helen and colleagues have recently completed a NIHR-PHR funded mixed methods review with the Campbell Collaboration

Fees

You can attend all eight sessions, or the five 'review' course or the three 'searching' sessions.

8 sessions £700 full price, £500 for staff or students based at the University of Leeds.

5 sessions £520 full price, £350 for staff or students based at the University of Leeds.

3 sessions £180 full price, £150 for staff or students based at the University of Leeds.

Further information
To book your place please contact: Denise Womersley, d.l.womersley@leeds.ac.uk
Booking is on a first-come first-served basis. Bookings close 29th February 2024
2023 online course testimonials

"Very good, very in-depth information outside of the live sessions and all of high quality."

"The course exceeded my expectations. I found it more engaging than I expected. I think the fact that my group for activities all had a similar level of experience helped us to learn together"

"The activities were all very engaging and helped to reinforce the knowledge gained from the recorded lectures."

[Q and A sessions] "Interesting AND useful. The course presenters were so knowledgeable."

"The presenters were very helpful and informative."

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Mixed Methods Systematic Review and Searching the Evidence Online Courses

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Mixed methods systematic reviews combine qualitative and quantitative evidence to answer complex applied health and public health questions.

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Responsible AI in Evidence Synthesis (RAISE): guidance and recommendations

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Responsible AI in Evidence Synthesis (RAISE): guidance and recommendations

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"This document includes recommendations for the main roles in the evidence synthesis ecosystem involved in ensuring responsible AI use. It was led by representatives from the International Collaboration for Automation in Systematic Reviews, Cochrane and Campbell, in collaboration with other organisations that produce and publish evidence synthesis, including JBI (formerly Joanna Briggs Institute), the Collaboration for Environmental Evidence, and Wellcome, as well as members of the International Collaboration for Automation in Systematic Reviews steering group."

Inviting feedback on RAISE: https://futurecochrane.org/newnews/cochrane-and-ai-recent-developments-and-join-us-at-the-global-evidence-summit

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Responsible AI in Evidence Synthesis (RAISE): guidance and recommendations

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Plain Language Summaries to Support Patient Expert Input to HTA

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Plain Language Summaries to Support Patient Expert Input to HTA

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Health Technology Assessment international - HTAi

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New Book: Patient Involvement in Health Technology Assessment

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New Book: Patient Involvement in Health Technology Assessment

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You have full access to this open access Book

Patient Involvement in Health Technology Assessment

Overview

Editors:

1. Karen M. Facey

Usher Institute, University of Edinburgh, Edinburgh, UK

2. Anke-Peggy Holtorf

Health Outcomes Strategies GmbH, Basel, Switzerland

3. Ann N.V. Single

Ashgrove, Australia

This book is open access, which means that you have free and unlimited access

Provides comprehensive instruction on patient involvement in relation to Health Technology Assessment

Provides academic, policy, HTA practice and patient organisation perspectives focussed on HTA

Arranged into sections on policy foundation, case studies and methodology allowing readers to refine their understandin

Buy print copy

Tax calculation will be finalised at checkout

About this book

This extensively revised and updated Open Access book offers a comprehensive guide to involving patients in health technology assessment (HTA). Defining patient involvement as r esearch into patient aspects (patients' experiences, preferences, perspectives, needs) and patient participation in HTA (individual HTAs, methods, processes, governance and policy) it includes detailed explanations of methodologies and approaches, as well as case studies.

The book begins by introducing HTA and considering the ethics of patient involvement. It then defines the complementary strands of research and participation before providing chapters on key methods including: qualitative evidence synthesis, patient preference studies, ethnographic fieldwork, social media analysis using AI, natural language processing and deep learning, patient-led collaborative research to inform economic modelling, and evaluation of patient involvement. It concludes with carefully selected case studies highlighting specific approaches and considerations among HTA bodies on five continents, health technology developers and patient organisations.

With cohesive contributions from more than 120 authors from a variety of disciplines around the globe and most chapters including patient authors, Patient Involvement in Health Technology Assessment concludes with urgent calls to action for this field which has rapidly gained momentum in the past decade. As both a practical manual and academic text, it serves as a source of inspiration for policymakers, practitioners, researchers, patient advocates and postgraduate students around the world who seek to improve HTA by taking account of patients' knowledge, experiences, needs and preferences.

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Article Open access

Discover the latest articles, books and news in related subjects.

Table of contents (33 chapters)

Jurisdictional Approaches and Stakeholder Views

Editors and Affiliations

Usher Institute, University of Edinburgh, Edinburgh, UK

Karen M. Facey

Health Outcomes Strategies GmbH, Basel, Switzerland

Anke-Peggy Holtorf

Ashgrove, Australia

Ann N.V. Single

About the editors

Karen Facey, PhD, is a visiting senior research fellow at the University of Edinburgh, chartered statistician and honorary member of the Faculty of Public Health. She has worked as a statistician for pharmaceutical companies and the UK Medicines Regulatory Agency. In 2000, she set up the first national health technology assessment (HTA) agency in Scotland and since 2003 has been an independent consultant. She is a member of the Scottish HTA appraisal committee, the SUSTAIN-HTA consortium and the Myeloma Patients' Europe Patient Evidence Research Advisory Committee. Karen has been active in HTA international (HTAi) for the past 20 years and was founding chairperson of the HTAi Interest Group for Patient and Citizen Involvement in HTA. She is on the editorial board of several journals and has research interests in HTA policy, patient involvement, rare diseases and real world data. In 2014, she was named as one of the top 100 practising scientists in the UK for her work on HTA and patient well-being.

Anke-Peggy Holtorf, PhD, MBA, is Director of Health Outcomes Strategies GmbH with focus on value of health technologies and related policy decisions, health technology assessment, outcomes research and health economics. She contributed to global projects including work in Low- and Middle-Income Countries or initiatives for involving patients along the technology lifecycle. Anke-Peggy acts as Secretary of the Board (2024-27) at Health Technology Assessment international (HTAi). She has published broadly on subjects related to evidence-based decision making and policies in healthcare or the methods for including the patient perspective in such decisions. As Steering Committee member and project coordinator for the Patient and Citizen Involvement in HTA Interest Group, she has co-led several projects (e.g., 'Patient and Citizen Involvement in HTA in LMICs', '360° Stakeholder Experiences with PI in HTA in Europe', and 'Patient Insight Research for HTA'). Anke-Peggy teaches as Adjunct Faculty at the University of Utah and as guest lecturer in other university programs.

is the CEO of Patient Voice Initiative, a patient-led not for profit working to ensure patients are seen, heard and valued in healthcare decision-making, especially HTA. She's worked in patient involvement in HTA for 25 years, first directing it within Scotland's HTA agency and then sharing and developing good practice in HTAi's Patient and Citizen Involvement Interest Group. From 2019-2023, she chaired this Interest Group and is now its Outgoing Chair and HTAi's first President (2025-27) from the patient community sector. Ann is a faculty member of the International Patient Advocacy Management Masters Course at Ann Single, MA, Università Cattolica del Sacro Cuore and a member of Rare Voices Australia's Scientific and Medical Advisory Committee. She served as a Reference Committee member on the Australian Government's HTA Policy and Methods Review (2022-2024). With a background in government and patient organisations, her focus is improving health outcomes by integrating patients and their knowledge in healthcare decisions.

Accessibility Information

PDF accessibility summary

This PDF has been created in accordance with the PDF/UA-1 standard to enhance accessibility, including screen reader support, described non-text content (images, graphs), bookmarks for easy navigation, keyboard-friendly links and forms and searchable, selectable text. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. Please note that a more accessible version of this eBook is available as ePub.

EPUB accessibility summary

This ebook is designed with accessibility in mind, aiming to meet the ePub Accessibility 1.0 AA and WCAG 2.2 Level AA standards. It features a navigable table of contents, structured headings, and alternative text for images, ensuring smooth, intuitive navigation and comprehension. The text is reflowable and resizable, with sufficient contrast. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com.

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# data[].attributes.attachment_link.title
Patient Involvement in Health Technology Assessment

# data[].attributes.attachment_link.description
This extensively revised and updated Open Access book offers a comprehensive guide to engaging patients in health technology assessment

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Use of AI in evidence generation: NICE position statement

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Use of AI in evidence generation: NICE position statement

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This position statement sets out NICE's view on the use of AI methods in the generation and reporting of evidence considered by its evaluation programmes.

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Use of AI in evidence generation: NICE position statement

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NICE helps the NHS and wider health and care system deliver the best care to people, fast, while ensuring value for the taxpayer. We do this by developing guidance, advice and information through a diverse range of programmes, that share the same core process of identification, assessment and interpretation of evidence.

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How to write a killer conference abstract: The first step towards an engaging presentation. 

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Some not so obvious tips for submitting conference abstracts

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Helen Kara responds to our previously published guide to writing abstracts and elaborates specifically on the differences for conference abstracts. She offers tips for writing an enticing abstract for conference organisers and an engaging conference presentation. Written grammar is different from spoken grammar. Remember that conference organisers are trying to create as interesting and stimulating an event as they can, and variety is crucial.
The Impact blog has an 'essential 'how-to' guide to writing good abstracts'. While this post makes some excellent points, its title and first sentence don't differentiate between article and conference abstracts. The standfirst talks about article abstracts, but then the first sentence is, 'Abstracts tend to be rather casually written, perhaps at the beginning of writing when authors don't yet really know what they want to say, or perhaps as a rushed afterthought just before submission to a journal or a conference.' This, coming so soon after the title, gives the impression that the post is about both article and conference abstracts.

I think there are some fundamental differences between the two. For example:

Article abstracts are presented to journal editors along with the article concerned. Conference abstracts are presented alone to conference organisers. This means that journal editors or peer reviewers can say e.g. 'great article but the abstract needs work', while a poor abstract submitted to a conference organiser is very unlikely to be accepted.

Articles are typically 4,000-8,000 words long. Conference presentation slots usually allow 20 minutes so, given that - for good listening comprehension - presenters should speak at around 125 words per minute, a conference presentation should be around 2,500 words long.

Articles are written to be read from the page, while conference presentations are presented in person. Written grammar is different from spoken grammar, and there is nothing so tedious for a conference audience than the old-skool approach of reading your written presentation from the page. Fewer people do this now - but still, too many. It's unethical to bore people! You need to engage your audience, and conference organisers will like to know how you intend to hold their interest.

The competition for getting a conference abstract accepted is rarely as fierce as the competition for getting an article accepted. Some conferences don't even receive as many abstracts as they have presentation slots. But even then, they're more likely to re-arrange their programme than to accept a poor quality abstract. And you can't take it for granted that your abstract won't face much competition. I've recently read over 90 abstracts submitted for the Creative Research Methods conference in May - for 24 presentation slots. As a result, I have four useful tips to share with you about how to write a killer conference abstract.

First, your conference abstract is a sales tool: you are selling your ideas, first to the conference organisers, and then to the conference delegates. You need to make your abstract as fascinating and enticing as possible. And that means making it different. So take a little time to think through some key questions:

What kinds of presentations is this conference most likely to attract? How can you make yours different?

What are the fashionable areas in your field right now? Are you working in one of these areas? If so, how can you make your presentation different from others doing the same? If not, how can you make your presentation appealing?

There may be clues in the call for papers, so study this carefully. For example, we knew that the Creative Research Methods conference, like all general methods conferences, was likely to receive a majority of abstracts covering data collection methods. So we stated up front, in the call for papers, that we knew this was likely, and encouraged potential presenters to offer creative methods of planning research, reviewing literature, analysing data, writing research, and so on. Even so, around three-quarters of the abstracts we received focused on data collection. This meant that each of those abstracts was less likely to be accepted than an abstract focusing on a different aspect of the research process, because we wanted to offer delegates a good balance of presentations.

Currently fashionable areas in the field of research methods include research using social media and autoethnography/ embodiment. We received quite a few abstracts addressing these, but again, in the interests of balance, were only likely to accept one (at most) in each area. Remember that conference organisers are trying to create as interesting and stimulating an event as they can, and variety is crucial.

Second, write your abstract well. Unless your abstract is for a highly academic and theoretical conference, wear your learning lightly. Engaging concepts in plain English, with a sprinkling of references for context, is much more appealing to conference organisers wading through sheaves of abstracts than complicated sentences with lots of long words, definitions of terms, and several dozen references. Conference organisers are not looking for evidence that you can do really clever writing (save that for your article abstracts), they are looking for evidence that you can give an entertaining presentation.

Third, conference abstracts written in the future tense are off-putting for conference organisers, because they don't make it clear that the potential presenter knows what they'll be talking about. I was surprised by how many potential presenters did this. If your presentation will include information about work you'll be doing in between the call for papers and the conference itself (which is entirely reasonable as this can be a period of six months or more), then make that clear. So, for example, don't say, 'This presentation will cover the problems I encounter when I analyse data with homeless young people, and how I solve those problems', say, 'I will be analysing data with homeless young people over the next three months, and in the following three months I will prepare a presentation about the problems we encountered while doing this and how we tackled those problems'.

Fourth, of course you need to tell conference organisers about your research: its context, method, and findings. It will also help enormously if you can take a sentence or three to explain what you intend to include in the presentation itself. So, perhaps something like, 'I will briefly outline the process of participatory data analysis we developed, supported by slides. I will then show a two-minute video which will illustrate both the process in action and some of the problems encountered. After that, again using slides, I will outline each of the problems and how we tackled them in practice.' This will give conference organisers some confidence that you can actually put together and deliver an engaging presentation.

So, to summarise, to maximise your chances of success when submitting conference abstracts:

1. Make your abstract fascinating, enticing, and different.

2. Write your abstract well, using plain English wherever possible.

3. Don't write in the future tense if you can help it - and, if you must, specify clearly what you will do and when.

4. Explain your research, and also give an explanation of what you intend to include in the presentation.

This post originally appeared on the author's personal blog and is reposted with permission. Note: This article gives the views of the author, and not the position of the Impact of Social Science blog, nor of the London School of Economics. Please review our Comments Policy if you have any concerns on posting a comment below. About the Author
While that won't guarantee success, it will massively increase your chances. Best of luck!

Dr Helen Kara has been an independent social researcher in social care and health since 1999, and is an Associate Research Fellow at the Third Sector Research Centre, University of Birmingham. She is on the Board of the UK's Social Research Association, with lead responsibility for research ethics. She also teaches research methods to practitioners and students, and writes on research methods. Helen is the author of Research and Evaluation for Busy Practitioners (2012) and Creative Research Methods in the Social Sciences (April 2015), both published by Policy Press. She did her first degree in Social Psychology at the LSE.

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How to write a killer conference abstract: The first step towards an engaging presentation.

# data[].attributes.attachment_link.description
Helen Kara responds to our previously published guide to writing abstracts and elaborates specifically on the differences for conference abstracts. She offers tips for writing an enticing abstract for conference organisers and an engaging conference presentation. Written grammar is different from spoken grammar.

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LIVING GUIDELINES ON THE RESPONSIBLE USE OF GENERATIVE AI IN RESEARCH

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LIVING GUIDELINES ON THE RESPONSIBLE USE OF GENERATIVE AI IN RESEARCH

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Generative AI tools, such as ChatGPT, are disrupting many different activities, including research, and will continue to do so. While these tools are powerful and useful for some tasks, they present challenges and limitations that users need to be aware of.

The European Commission, along with the countries and stakeholders represented in the ERA Forum, decided to jointly develop a set of recommendations that could provide simple and actionable guidance to the European research community to foster the adoption of the technology in a responsible manner.

The guidelines launched today are the result of this joint effort. They aim to consolidate different ideas and establish directions that can reassure those who use generative AI in the scientific domain.

These guidelines will be updated regularly to keep pace with the very fast technological development in this area. The research community is encouraged to contribute their views and ideas on how to enhance future versions through the feedback form.

Living Guidelines on the responsible use of generative AI in research ( Factsheet)

More on AI in Science

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LIVING GUIDELINES ON THE RESPONSIBLE USE OF GENERATIVE AI IN RESEARCH PUBLISHED

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Generative AI tools, such as ChatGPT, are disrupting many different activities, including research, and will continue to do so. While these tools are powerful and useful for some tasks, they present challenges and limitations that users need to be aware of.

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Generative AI for Health Technology Assessment

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Generative AI for Health Technology Assessment

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A nice paper describing Generative AI for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations

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View a PDF of the paper titled Generative AI for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations, by Rachael Fleurence and 7 other authors

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Abstract:This review introduces the transformative potential of generative Artificial Intelligence (AI) and foundation models, including large language models (LLMs), for health technology assessment (HTA). We explore their applications in four critical areas, evidence synthesis, evidence generation, clinical trials and economic modeling: (1) Evidence synthesis: Generative AI has the potential to assist in automating literature reviews and meta-analyses by proposing search terms, screening abstracts, and extracting data with notable accuracy; (2) Evidence generation: These models can potentially facilitate automating the process and analyze the increasingly available large collections of real-world data (RWD), including unstructured clinical notes and imaging, enhancing the speed and quality of real-world evidence (RWE) generation; (3) Clinical trials: Generative AI can be used to optimize trial design, improve patient matching, and manage trial data more efficiently; and (4) Economic modeling: Generative AI can also aid in the development of health economic models, from conceptualization to validation, thus streamlining the overall HTA process. Despite their promise, these technologies, while rapidly improving, are still nascent and continued careful evaluation in their applications to HTA is required. To ensure their responsible use and implementation, both developers and users of research incorporating these tools, should familiarize themselves with their current limitations, including the issues related to scientific validity, risk of bias, and consider equity and ethical implications. We also surveyed the current policy landscape and provide suggestions for HTA agencies on responsibly integrating generative AI into their workflows, emphasizing the importance of human oversight and the fast-evolving nature of these tools.

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From: Rachael Fleurence [ view email]
[v1] Tue, 9 Jul 2024 09:25:27 UTC (618 KB)

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# data[].attributes.attachment_link.title
Generative AI for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations

# data[].attributes.attachment_link.description
This review introduces the transformative potential of generative Artificial Intelligence (AI) and foundation models, including large language models (LLMs), for health technology assessment (HTA).

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ROBVALU: a tool for assessing risk of bias in studies about people’s values, utilities, or importance of health outcomes

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ROBVALU: a tool for assessing risk of bias in studies about people’s values, utilities, or importance of health outcomes

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ROBVALU: a tool for assessing risk of bias in studies about people’s values, utilities, or importance of health outcomes

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People’s values are an important driver in healthcare decision making. The certainty of an intervention’s effect on benefits and harms relies on two factors: the certainty in the measured effect on an outcome in terms of risk difference and the certainty in its value, also known as utility or importance. The GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) working group has proposed a set of questions to assess the risk of bias in a body of evidence from studies investigating how people value outcomes. However, these questions do not address risk of bias in individual studies that, similar to risk-of-bias tools for other research studies, is required to evaluate such evidence. Thus, the Risk of Bias in studies of Values and Utilities (ROBVALU) tool was developed. ROBVALU has good psychometric properties and will be useful when assessing individual studies in measuring values, utilities, or the importance of outcomes. As such, ROBVALU can be used to assess risk of bias in studies included in systematic reviews and health guidelines. It also can support health research assessments, where the risk of bias of input variables determines the certainty in model outputs. These assessments include, for example, decision analysis and cost utility or cost effectiveness analysis for health technology assessment, health policy, and reimbursement decision making. Healthcare decision making relies on evidence on the relative effectiveness, safety, and cost effectiveness of an intervention evaluated in appropriate studies.12 Choosing between different interventions (such as preventive, diagnostic, or treatment strategies) depends on the importance or value that people place on specific health states or health outcomes.2 Values have a major role at different levels of decision making, from the individual level to the healthcare system level. In this context, people’s values reflect the importance they place on outcomes of interest that …

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Guidance on terminology, application, and reporting of citation searching: the TARCiS statement

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Guidance on terminology, application, and reporting of citation searching: the TARCiS statement

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The Terminology, Application, and Reporting of Citation Searching (TARCiS) statement has been published in the BMJ: https://doi.org/10.1136/bmj-2023-078384

In addition, there is a website with all the TARCiS items and the "Terminology and Reporting Item Checklist" for download: tarcis.unibas.ch

Briefly, TARCiS provides critical guidance on (i) when and (ii) how to conduct citation searching as a supplementary search technique in systematic evidence syntheses, and (iii) on how to report it. It is hoped that TARCiS will lead to the standardization of these processes and that systematic review teams will incorporate TARCiS into their workflows.

As to (i): It is important to emphasize that TARCiS does not universally recommend the use of citation searching in systematic evidence syntheses. Thus, the TARCiS statement can also be used as a rationale for the informed omission of citation searching when appropriate.

As to (iii): The TARCiS reporting item checklist is in line with PRISMA-S but providing enhanced reporting standards (in fact, there has been a significant overlap in the PRISMA-S and the TARCiS study groups).

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information storage and retrieval / standards

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national center for biotechnology information

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Guidance on terminology, application, and reporting of citation searching: the TARCiS statement - PubMed

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Guidance on terminology, application, and reporting of citation searching: the TARCiS statement

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AIML working group May 2024 AI tools for grammar and rephrasing

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AIML working group May 2024 AI tools for grammar and rephrasing

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Our first AIML working group meeting was presented by Kieran, Barrie and Marie. You can view slides here: http://edm/HTA/DirectorateDocuments/TEMPORARY%20SSR/Library/AI%20tools/Report%20writing%20slides.pptx

Following on from the presentation, Kirsty shared an interesting Cochrane talk on Artificial intelligence (AI) technologies in Cochrane. The webinar is available here: https://training.cochrane.org/resource/artificial-intelligence-technologies-in-cochrane

Melissa shared a webinar from the Netherlands Research Integrity Network (NRIN) on the Ethics of Using AI in Research: https://www.youtube.com/watch?v=hFwNmfmLDaw

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Equity, Diversity And Inclusion In Evidence Synthesis

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Equity, Diversity And Inclusion In Evidence Synthesis

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Few single randomised trials change practice, and this is how it should be. Instead, bodies of evidence from several trials are collated through evidence synthesis, generally systematic reviews. It is the results of these reviews that lead to practice and guideline changes. Because evidence synthesis can have important consequences for which care is provided, and how, it is essential that equity, diversity and inclusion is explicitly considered within synthesis.

PRO EDI is an initiative led by Trial Forge, Evidence Synthesis Ireland and the Campbell Collaboration to try and make it easier for those doing systematic reviews, especially ones including randomised trials, to explicitly consider equity, diversity and inclusion in their reviews. The initiative benefits from an international advisory group to help us make PRO EDI relevant to all reviews and reviews, wherever the reviewers may be based in the world, and whatever the topic of the review. Our starting points were PROGRESS-PLUS, which maps out equity, diversity and inclusion topics that reviewers should consider; and the INCLUDE Ethnicity Framework, which has operationalised consideration of ethnicity for trial design.

How you can help

PRO EDI has developed a table for extracting information about participant characteristics from randomised trials included in a systematic review. Having these data is the starting point of being able to make judgements about whether the review findings apply equally to all those who could benefit from the treatment or initiative being reviewed.

We would be delighted if you used PRO EDI in your review. We would also be extremely grateful for examples you could send us (e.g., a link to a review) of PRO EDI being used. We would be interested in your general feedback too, especially on the time taken to use PRO EDI (e.g., does PRO EDI use up a lot of time, or more or less what you would have used before? Does it use less time?).

Note that currently, PRO EDI is only specifically designed for randomised controlled trials; however, we think it can be useful for other study designs too.

The short version of the PRO EDI participant characteristics table (same items but less guidance) can be downloaded free here. An accessible version can be downloaded free here.

The full version of the PRO EDI participant characteristics table can be downloaded free here.

We've designed a feedback form for you to use and are looking for one completed form per review, which can be downloaded here.

There is guidance in the PRO EDI table itself, but we also have guidance on how to use the information extracted using the PRO EDI table. This includes some examples although we aim to add more examples during 2024.

Any comments or questions about PRO EDI, please email info@trialforge.org

How to cite
PRO EDI participant characteristics table
PRO EDI participant characteristics table 22/3/2024; https://www.trialforge.org/trial-diversity/pro-edi-improving-how-equity-diversity-and-inclusion-is-handled-in-evidence-synthesis/ [insert access date].
Guidance
PRO EDI interpretation guidance 25/4/2024; https://www.trialforge.org/trial-diversity/pro-edi-improving-how-equity-diversity-and-inclusion-is-handled-in-evidence-synthesis/ [insert access date].

Licencing and permission to use

Once we have completed development work, the plan is to make the PRO EDI materials available for free here. You will not need a licence or to ask us for permission to use the PRO EDI table or associated documents. We only ask that you acknowledge the tool, if you have used it, by citing it (see citation on this page).

Funder

The PRO EDI development work has been publicly funded by Ireland's Health Research Board through Evidence Synthesis Ireland, grant ESI-2021-001.

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PRO EDI: improving how equity, diversity and inclusion is handled in evidence synthesis * Trial Forge

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Equity, diversity and inclusion in evidence synthesis Few single randomised trials change practice, and this is how it should be. Instead, bodies of evidence from several trials are collated through evidence synthesis, generally systematic reviews. It is the results of these reviews that lead to practice and guideline changes.

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Guidance on review type selection for health technology assessments

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Guidance on review type selection for health technology assessments

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Guidance on review type selection for health technology assessments: key factors and considerations for deciding when to conduct a de novo systematic review, an update of a systematic review, or an overview of systematic reviews - Systematic Reviews

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Background A systematic review (SR) helps us make sense of a body of research while minimizing bias and is routinely conducted to evaluate intervention effects in a health technology assessment (HTA). In addition to the traditional de novo SR, which combines the results of multiple primary studies, there are alternative review types that use systematic methods and leverage existing SRs, namely updates of SRs and overviews of SRs. This paper shares guidance that can be used to select the most appropriate review type to conduct when evaluating intervention effects in an HTA, with a goal to leverage existing SRs and reduce research waste where possible. Process We identified key factors and considerations that can inform the process of deciding to conduct one review type over the others to answer a research question and organized them into guidance comprising a summary and a corresponding flowchart. This work consisted of three steps. First, a guidance document was drafted by methodologists from two Canadian HTA agencies based on their experience. Next, the draft guidance was supplemented with a literature review. Lastly, broader feedback from HTA researchers across Canada was sought and incorporated into the final guidance. Insights Nine key factors and six considerations were identified to help reviewers select the most appropriate review type to conduct. These fell into one of two categories: the evidentiary needs of the planned review (i.e., to understand the scope, objective, and analytic approach required for the review) and the state of the existing literature (i.e., to know the available literature in terms of its relevance, quality, comprehensiveness, currency, and findings). The accompanying flowchart, which can be used as a decision tool, demonstrates the interdependency between many of the key factors and considerations and aims to balance the potential benefits and challenges of leveraging existing SRs instead of primary study reports. Conclusions Selecting the most appropriate review type to conduct when evaluating intervention effects in an HTA requires a myriad of factors to be considered. We hope this guidance adds clarity to the many competing considerations when deciding which review type to conduct and facilitates that decision-making process.

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Lunch & Learn: Overview of Community Drug Schemes 

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Lunch & Learn: Overview of Community Drug Schemes 

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Kieran provided the directorate with a presentation on an Overview of Community Drug Schemes.

The presentation recording and presentation slides are now available on SharePoint.

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Avoiding Predatory Journals: Some useful links

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Avoiding Predatory Journals: Some useful links

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Louise Larkin has compiled a useful list of resources in the Project management training folder in sharepoint. Some key items in the folder:

Video recording and presentation slides on project management

Project gantt chart repository – compiled following a suggestion at T&I. Project leads please feel free to add gantt charts for completed projects you have led on.

Project gantt chart templates

Links to some project management apps

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Lunch & Learn: using Covidence for data extraction and quality assessment

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Lunch & Learn: using Covidence for data extraction and quality assessment

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Kirsty O'Brien presented on using Covidence for data extraction and quality assessment. The recording is available here

A reminder that all Lunch and Learn content is available here.

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Shibu has compiled icons to use in powerpoint presentations. They can be accessed here

Karen Joran has created a slide deck of with base slides that could be used across the directorate for internal and external presentations. The aim is to reduce duplication of effort associated with creating PowerPoint slides from scratch. They can be accessed here

There is also a downloadable pack of high-quality icons compiled by Michelle Norris and Valerie, with help from Comms, available here.

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Updated recommendations for the Cochrane rapid review methods guidance for rapid reviews of effectiveness

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Updated recommendations for the Cochrane rapid review methods guidance for rapid reviews of effectiveness

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Updated recommendations for the Cochrane rapid review methods guidance for rapid reviews of effectiveness

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This article provides updated guidance on methods for conducting rapid reviews of effectiveness, targeted at Cochrane and other stakeholders interested in the methodology of rapid reviews. The guidance, developed by the Cochrane Rapid Reviews Methods Group, builds upon previous interim guidance, and incorporates changes based on an evaluation of its application, a scope of the literature on rapid review methodology, and input from a diverse group of experts in rapid review methods. The guidance consists of 24 specific recommendations supporting the conduct of rapid reviews, applicable both within and outside Cochrane. It underscores the importance of considering the appropriateness of undertaking rapid reviews and advocates for a tailored, iterative approach to each review. Key defining features of rapid reviews, such as restricted methods, how the dimension of timelines factors into rapid reviews, and the involvement of knowledge users (eg, patient and public partners, healthcare providers, policy makers), are outlined. The paper presents a definition of a Cochrane rapid review and additional considerations for rapid reviews of effectiveness to enhance the efficiency of the review process. In conclusion, the Cochrane Rapid Review Methods Group’s updated guidance, complemented by examples, seeks to guide methodological decisions in the design and conduct of rapid reviews, facilitating timely decision making in healthcare. ### Summary points

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Resources for making visually appealing presentations

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Resources for making visually appealing presentations

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Here's some links to useful resources for making visually appealing presentations as discussed at the T&I meeting in December 2023.

https://www.canva.com/  - Used by Comms

https://www.flaticon.com/

http://edm/HTA/COVID/RQ%2058%20Public%20Health%20Strategies/Outputs/DoH%20Presentation%20061123/Review%20of%20national%20public%20health%20strategies.pptx

https://www.mapchart.net/world.html

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Development of the Reporting Infographics and Visual Abstracts of Comparative studies (RIVA-C) checklist and guide

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Development of the Reporting Infographics and Visual Abstracts of Comparative studies (RIVA-C) checklist and guide

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Abstract

People often use infographics (also called visual or graphical abstracts) as a substitute for reading the full text of an article. This is a concern because most infographics do not present sufficient information to interpret the research appropriately and guide wise health decisions. The Reporting Infographics and Visual Abstracts of Comparative studies (RIVA-C) checklist and guide aims to improve the completeness with which research findings of comparative studies are communicated and avoid research findings being misinterpreted if readers do not refer to the full text. The primary audience for the RIVA-C checklist and guide is developers of infographics that summarise comparative studies of health and medical interventions. The need for the RIVA-C checklist and guide was identified by a survey of how people use infographics. Possible checklist items were informed by a systematic review of how infographics report research. We then conducted a two-round, modified Delphi survey of 92 infographic developers/designers, researchers, health professionals and other key stakeholders. The final checklist includes 10 items. Accompanying explanation and both text and graphical examples linked to the items were developed and pilot tested over a 6-month period. The RIVA-C checklist and guide was designed to facilitate the creation of clear, transparent and sufficiently detailed infographics which summarise comparative studies of health and medical interventions. Accurate infographics can ensure research findings are communicated appropriately and not misinterpreted. By capturing the perspectives of a wide range of end users (eg, authors, informatics editors, journal editors, consumers), we are hopeful of rapid endorsement and implementation of RIVA-C.

Data availability statement

Data are available on reasonable request. Data are available on reasonable request from the corresponding author.

https://creativecommons.org/licenses/by/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

WHAT IS ALREADY KNOWN ON THIS TOPIC

Many health professionals, researchers and patients use infographics as a substitute for reading full-text articles, believe infographics should be detailed enough so they do not have to read the full text, and view infographics as tools to help them save time by not having to read the full text. This is a problem as many infographics do not present enough of the information that is needed to interpret research appropriately and guide wise healthcare decisions.

Introduction

There is limited rigorously developed guidance on how to appropriately report research and its findings using infographics. Some guidelines only provide recommendations for design and formatting 8 and others were not developed using a rigorous process. 9 10

Aim and scope

The aim of the Reporting Infographics and Visual Abstracts of Comparative studies (RIVA-C) checklist and guide is to facilitate the creation of clear and sufficiently detailed infographics which accurately summarise comparative studies of health and medical interventions. RIVA-C could improve the completeness with which research findings of comparative studies are communicated and avoid research findings being misinterpreted if readers (eg, health professionals and researchers) do not refer to the full-text article. This article describes the development of the 10-item RIVA-C checklist and guide. It reports the methods used to reach consensus on the checklist and outlines each checklist item, including an elaboration and examples of appropriate reporting.

The primary audience for the RIVA-C checklist and guide is developers of infographics which summarise comparative studies of health and medical interventions. This includes but is not limited to infographic designers, journal editors, researchers, health professionals and members of the public. RIVA-C is not a guide for creating graphics within a research article. To best understand how to implement the checklist recommendations, we encourage people to read the explanation and examples document ( online supplemental file 1).

Methods

The need for a checklist and guide was identified by our review of 129 infographics that summarised comparative studies of health and medical interventions, finding potential checklist items that were infrequently reported (eg, potential harms of an intervention, measures of precision, risk of bias). 1 The steering group used these findings to develop a draft checklist (20 items that could feasibly be incorporated) for the first round of a two-round, modified Delphi survey ( online supplemental file 2).

An online consensus meeting was held with members of the steering group in February 2023 to discuss the findings from the round 2 survey and refine the RIVA-C checklist and guide). The RIVA-C checklist and guide was then piloted and further refined by infographics editors or authors of infographics at The BMJ, Physiotherapy Evidence Database (PEDro-a research database of over 60 000 trials, systematic reviews and guidelines relevant to physiotherapy) 16 and the Journal of Physiotherapy (#1 ranked journal in Rehabilitation and Orthopaedics) over a 6-month period. A more complete description of the methods and findings of the Delphi process can be found in online supplemental file 5.

Results

The final 10-item RIVA-C checklist is sub-divided into 3 categories: (1) study characteristics; (2) results and (3) conclusion/take away message ( online supplemental file 6). A more detailed explanation and examples document can be found in online supplemental file 1. This includes text and graphical examples for each item, with examples from a randomised controlled trial, systematic review and cohort study to showcase how to adhere to each item when summarising different types of studies. We have also included exemplar infographics from the Journal of Physiotherapy and PEDro. Neither of these entities have a dedicated infographics developer so the example infographics were produced with substantially less resources than the BMJ examples.

Discussion

The RIVA-C checklist and guide should be used by anyone creating an infographic that summarises the findings of a comparative study of a health and medical intervention. This includes, but is not limited to, journal infographic designers, journal editors, researchers, health professionals, patients and policy-makers. Infographics will likely be clearer and more complete if journals, and their editors, endorse the checklist to their in-house infographic developers and recommend authors adhere to it when creating their own infographics. We recommend journals endorse RIVA-C similar to other checklists listed on the EQUATOR Network (eg, Consolidated Standards of Reporting Trials [CONSORT], Preferred Reporting Items for Systematic Reviews and Meta-Analyses [PRISMA], TIDieR). One way they can do this is by including a link to the checklist on the 'instruction for authors' page and including some information about the importance of appropriate reporting (or harm of inappropriate reporting) in infographics. Another option is for journals to email the RIVA-C checklist and guide to authors at the time their article is accepted for publication.

Some people may argue a checklist for reporting research findings in infographics is inappropriate because infographics are designed to draw in readers using visuals and encourage them to read the full text. In reality, the existence of an infographic often decreases full-text views, 2 5 7 possibly because readers are using them as a substitute for reading the full text. 6 Our extensive pilot testing of RIVA-C suggests it does not compromise visual appeal (as demonstrated by our example infographics) and is highly acceptable to infographic designers as it offers design flexibility.

We believe it is crucial to evaluate the implementation of the RIVA-C checklist and guide and have planned a series of evaluation studies. This includes a quantitative study investigating whether infographics developed according to RIVA-C improve the transparent interpretation of research findings, knowledge retention and intention to change practice, and a qualitative study exploring whether infographics developed according to RIVA-C are more useful to readers than infographics developed without this guidance. Findings from this evaluation will inform the need to update or modify the checklist to increase impact. The steering group also hopes this checklist will encourage the development of checklists to improve the reporting of infographics summarising other types of research (eg, prognostic studies and diagnostic studies).

Data availability statement

Data are available on reasonable request. Data are available on reasonable request from the corresponding author.

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Development of the Reporting Infographics and Visual Abstracts of Comparative studies (RIVA-C) checklist and guide

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People often use infographics (also called visual or graphical abstracts) as a substitute for reading the full text of an article. This is a concern because most infographics do not present sufficient information to interpret the research appropriately and guide wise health decisions.

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This might be a good resource for generating ideas for poster submissions or for infographics more generally.

Visual abstract checkilist (RIVA-V): Development of the Reporting Infographics and Visual Abstracts of Comparative studies (RIVA-C) checklist and guide | BMJ Evidence-Based Medicine

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HTA colleagues who attended ISPOR and EPH in 2023 spoke about their takeaways from these conferences at the HTA away day in December. Resources mentioned in that session are available here for your information.

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 How IVDs come to market presented by Andrew Dullea, IR team

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 How IVDs come to market presented by Andrew Dullea, IR team

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Andrew presented this lunch and learn session on 21 November. In case you missed it or want to watch the content the recording is available here.

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Trends in Guideline Development presented by Tom and Barrie, HRB CICER team.  

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Trends in Guideline Development presented by Tom and Barrie, HRB CICER team.  

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Tom presents highlights from his attendance at GIN 2023 conference and Barrie shares useful tips on how to maximise your organisation's visibility on social media. The recording and slides are now available on SharePoint.

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Lunch and Learn Session on EAG Admin, September 2023

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Lunch and Learn Session on EAG Admin, September 2023

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Video Conferencing, Web Conferencing, Webinars, Screen Sharing

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Zoom is the leader in modern enterprise video communications, with an easy, reliable cloud platform for video and audio conferencing, chat, and webinars across mobile, desktop, and room systems. Zoom Rooms is the original software-based conference room solution used around the world in board, conference, huddle, and training rooms, as well as executive offices and classrooms. Founded in 2011, Zoom helps businesses and organizations bring their teams together in a frictionless environment to get more done.

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Click on the link below to see a webinar on "Data Extraction in Covidence" presented by Laura Mellor on Tues Oct 3, 2023.
View Recording (gotowebinar.com)

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A step-by-step guide on how to use cross-references and captions in HIQA reports produced by the HTA directorate

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Here is the link and password to the recording for Covidence training on 27 June 2023

https://us02web.zoom.us/rec/share/s8MXH6tGxNGdQGYylWbbt_73lHrXjjrFsoRZu6oN8ZTBjfChhQaK6TEz0NapmAKg.GR1d7AvxSK_Y8XGE
Passcode: wi*!s3TT

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Systematic reviews of economic evaluations April 2022

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Systematic reviews of economic evaluations April 2022

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Paul Carty presented on the topic of “Systematic reviews of economic evaluations”, Paul covered why and how to carry out a review of cost effectiveness and presented an example of a cost effectiveness review from his own work. See the slides attached. A video recording is available from the shared drive: Y:\HIQA\HTA\Methodology meetings\Methodology meeting April 2022

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Marie presented on searching systematically in June 2022. The presentation covered the following topics:

How to construct a search strategy

Database searching

Using search filters

Other search methods

PRISMA 2020

Attached is the powerpoint presentation. The recorded presentation is available from our shared drive:

http://edm/HTA/DirectorateDocuments/Evidence%20synthesis%20methodology/HTA%20internal%20methodology%20meetings/Methodology%20meeting%20June%202022/Methodology%20meeting%20June%202022%20zoom%20recording.mp4

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Previous HTA Methodology session recordings are available in the Evidence Synthesis Methodology section of Sharepoint http://edm/HTA/DirectorateDocuments/Evidence%20synthesis%20methodology/HTA%20internal%20methodology%20meetings

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Our endnote toubleshooting guide is available on sharepoint. Feel free to add issues and possible solutions/workarounds to this guide.

Endnote 20 troubleshooting.docx

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2022 Repository of Quality Assessment and Risk of Bias Tools - A comprehensive resource for finding and selecting a risk of bias or quality assessment tool for evidence synthesis projects. Continually updated.

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Online Mixed Methods Systematic Review Courses March 2024 

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Online Mixed Methods Systematic Review Courses March 2024 

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The course is for you if you are interested in understanding how mixed-method synthesis provides a deeper understanding of complex interventions and their implementation. It is suitable for you if you already have an understanding of systematic review methods – i.e. you have had in-depth training and/or conducted a systematic review (either quantitative or qualitative) before. The course looks at purpose and value of mixed-methods systematic reviews and covers methods for synthesising qualitative and quantitative research including explanatory sequential, exploratory sequential, and convergent synthesis methods including an overview of realist synthesis methods. The mixed methods course is NOT suitable for you if you are new to systematic reviews as it does not provide basic systematic review (screening, data extraction, quality assessment) or meta-analysis training.

Tutors and facilitators:

Dr Katy Sutcliffe, Dr Meena Khatwa, Dr Gillian Stokes, Dr Preethy D'Souza and Dr Michelle Richardson  (UCL), Prof Joanne Greenhalgh and Judy Wright (University of Leeds), Prof Helen Elsey (University of York)

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Course image

Course overview

Mixed methods systematic reviews combine qualitative and quantitative evidence to answer complex applied health and public health questions. These methods are becoming increasingly important as we seek to understand why interventions may have different results for different people in different situations. This course will explore the current thinking on approaches to mixed methods systematic reviews.

You can attend all eight sessions, or just the 'Review' course (five half-days) or the evidence search course (three sessions).

Conducting a Mixed Methods Systematic Review
Online course. Live sessions:
Following this course (delivered in four half-day sessions) participants should have a strong understanding of:

The purpose and value of mixed-methods systematic reviews

Methods for synthesising qualitative and quantitative research including explanatory sequential, exploratory sequential, and convergent synthesis methods.

Searching the Evidence Base Workshop
Online course. Live sessions:
This course, delivered over two half days, will develop:

Online course format

The course materials and highly interactive live sessions will be delivered by software which will enable group discussions, break-out room discussion, quizzes and recording of sessions.

A brief introductory session will allow participants and presenters to introduce themselves and the course.

Participants will be given access to 1 to 1.5 hrs pre-recorded lectures that can be watched in their own time prior to each live session.

During the live interactive sessions, participants will complete hands-on activities and engage in discussion. The online sessions are spaced out in digestible chunks over several days.

Who the course is for?

This course is for you if you are interested in understanding how mixed-method synthesis provides a deeper understanding of complex interventions and their implementation. It is suitable for you if you already have an understanding of systematic review methods - i.e. you have had in-depth training and/or conducted a systematic review (either quantitative or qualitative) before.

The mixed methods course is not suitable for you if you are new to systematic reviews. It does not provide basic systematic review (screening, data extraction, quality assessment) or meta-analysis training. The 'review' course focuses on advanced synthesis methods.

The Searching the Evidence Base course has basic and advanced elements, some sessions are split, allowing those with less experience to practice core skills while the more experienced try advanced and emerging methods.

Presenters

Dr Katy Sutcliffe is the Deputy Director of the Department of Health Reviews Facility at the Evidence for Policy and Practice Information and Coordination Centre ( EPPI-Centre), Institute of Education, University College London. Katy specialises in developing methods for research synthesis, in particular for qualitative and mixed methods reviews.

Dr Meena Khatwa, Dr Gillian Stokes, Dr Preethy D'Souza and Dr Michelle Richardson from from UCL will facilitate group work with Katy.

Judy Wright (University of Leeds) is a senior information specialist with expertise in finding evidence for mixed methods reviews. Judy leads the AUHE Evidence Synthesis team, supporting systematic reviews, economic models and realist syntheses.

Natalie King (University of Leeds) is an information specialist with expertise in finding evidence for systematic reviews, economic models and mixed methods reviews including realist syntheses.

Guest Speakers

Professor Joanne Greenhalgh (University of Leeds) is a social research methodologist applying realist methods to healthcare evaluation. Joanne has expertise in realist syntheses.

Professor Helen Elsey (University of York) is a professor in global public health. Helen and colleagues have recently completed a NIHR-PHR funded mixed methods review with the Campbell Collaboration

Fees

You can attend all eight sessions, or the five 'review' course or the three 'searching' sessions.

8 sessions £700 full price, £500 for staff or students based at the University of Leeds.

5 sessions £520 full price, £350 for staff or students based at the University of Leeds.

3 sessions £180 full price, £150 for staff or students based at the University of Leeds.

Further information
To book your place please contact: Denise Womersley, d.l.womersley@leeds.ac.uk
Booking is on a first-come first-served basis. Bookings close 29th February 2024
2023 online course testimonials

"Very good, very in-depth information outside of the live sessions and all of high quality."

"The course exceeded my expectations. I found it more engaging than I expected. I think the fact that my group for activities all had a similar level of experience helped us to learn together"

"The activities were all very engaging and helped to reinforce the knowledge gained from the recorded lectures."

[Q and A sessions] "Interesting AND useful. The course presenters were so knowledgeable."

"The presenters were very helpful and informative."

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Mixed Methods Systematic Review and Searching the Evidence Online Courses

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Mixed methods systematic reviews combine qualitative and quantitative evidence to answer complex applied health and public health questions.

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Step-by-step guide for setting up a Zoom meeting using the HTA Zoom accounts.

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How to BCC external email addresses for Zoom and Teams meeting invites

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How to BCC external email addresses for Zoom and Teams meeting invites

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Step-by-step guide for bcc'ing external email addresses for Zoom and Teams meeting invites:

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This webinar, part of the Cochrane Rapid Reviews & Learning Live webinar series, aims to summarize considerations and recommendations on planning and conducting systematic searches for rapid reviews, including useful software/web-tools.

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Welcome to this series of Cochrane Learning Live webinars, all dedicated to the topic of Rapid Reviews. These webinars are designed to provide you with a clear roadmap, helping you navigate the intricacies of rapid review methodologies. Whether you're a researcher, a healthcare professional, or anyone who relies on evidence to make informed decisions, these webinars can be helpful for you.

We are pleased to be joined throughout the series by leading experts in this field, and you can find out more about each session and access the webinar recordings below. All sessions are freely open to everyone.

Click on the video to the right to watch a brief introduction to the series.

Upcoming sessions

Wednesday 28 February 2024, 14:00 UTC
How to do a rapid qualitative review
Professor Andrew Booth, Professor in Evidence Synthesis, Sheffield Centre for Health and Related Research (SCHARR), University of Sheffield.
SIGN UP

Tuesday 12 March 2024, 09:00 UTC
How to do a rapid scoping review
Fiona Campbell, Senior Lecturer in Evidence Synthesis, Institute of Population Health Sciences and Innovation Observatory, Newcastle University.
SIGN UP

Watch the recordings of previous sessions

Evidence synthesis and certainty of evidence ratings in rapid reviews [January 2024]
Gerald Gartlehner, Professor of Clinical Epidemiology & head of the Department of Evidence-based Medicine and Evaluation, Danube University Krems, Austria. Co-convenor or the Cochrane Rapid Reviews Methods Group
[ click here for recording & accompanying slides]

Team considerations, study selection, risk of bias and data extraction in rapid reviews [December 2023]
Barbara Nussbaumer-Streit, Co-director, Cochrane Austria and Researcher, Department for Evidence-based Medicine and Evaluation, Danube University Krems. Co-convenor of the Cochrane Rapid Reviews Methods Group
[ click here for recording & accompanying slides]

Literature search in rapid reviews [November 2023]
Irma Klerings, Information specialist, Department for Evidence-based Medicine and Evaluation, University for Continuing Education Krems.
[ click here for recording & accompanying slides]

Bridging the gap: Knowledge user involvement in rapid reviews [October 2023]
Chantelle Garritty, Manager, Global Health and Guidelines Division, Public Health Agency of Canada (PHAC). Professor, School of Epidemiology and Public Health, University of Ottawa. Co-convenor of the Cochrane Rapid Reviews Methods Group.
Candyce Hamel, Senior Epidemiologist, Canadian Association of Radiologists. Co-convenor or the Cochrane Rapid Reviews Methods Group
[ Click here for recording & accompanying slides]

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Cochrane Rapid Reviews: Learning Live webinar series

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Welcome to this series of Cochrane Learning Live webinars, all dedicated to the topic of Rapid Reviews. These webinars are designed to provide you with a clear roadmap, helping you navigate the intricacies of rapid review methodologies. Whether you're a researcher, a healthcare professional, or anyone who relies on evidence to make informed decisions, these webinars can be helpful for you.

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National PPI Ignite Month - Webinars for HTA

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National PPI Ignite Month - Webinars for HTA

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October is National PPI Ignite Month - plenty of webinars and sessions to choose from!

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Guidance to best tools and practices for systematic reviews

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Guidance to best tools and practices for systematic reviews

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A useful and comprehensive overview of tools and practices for undertaking different types of evidence synthesis

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Guidance to best tools and practices for systematic reviews : JBI Evidence Synthesis

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or consistently apply these updated methods. In addition, guideline developers, peer reviewers, and journal editors often disregard current methodological standards. Although extensively acknowledged and explored in the methodological literature, most clinicians seem unaware of these issues and may…

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CADTH Lecture Series – Reimagining Health Technology Assessment

Wednesday, September 27, 2023

1:00 p.m. to 2:00 p.m. ET

Free Online Event

https://www.cadth.ca/events/cadth-lecture-series-reimagining-health-technology-assessment

CADTH Webinar – Equity in Evidence

Thursday, October 19, 2023

1:00 p.m. to 2:00 p.m. ET

Free Online Event
https://www.cadth.ca/events/cadth-webinar-equity-evidence

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Event Date: Wednesday, September 27, 2023, from 1:00 p.m. to 2:00 p.m.
Register today for our virtual CADTH Lecture Series featuring Brian O’Rourke, President of the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and former President and CEO of CADTH. In 2019, the…

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Course notes on Identifying Economic Evidence for HTA. Attended  by Marie Carrigan

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Course notes on Identifying Economic Evidence for HTA. Attended  by Marie Carrigan

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I attended an online course organised by YHEC on identifying Economic Evidence for HTA. I've written down a few notes which I've attached to this post, if you would like access to the slides I'm happy to share via email.

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Upcoming Training Opportunities for HTA 

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Upcoming Training Opportunities for HTA 

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HTA Upcoming training opportunities - 15th Sept

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How can environmental impacts be incorporated in health technology assessment, and how impactful would this be?

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How can environmental impacts be incorporated in health technology assessment, and how impactful would this be?

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How can environmental impacts be incorporated in health technology assessment, and how impactful would this be?

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Published in Expert Review of Pharmacoeconomics & Outcomes Research (Ahead of Print, 2023)

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Lunch and Learn Session on Project Management - Louise Larkin, July 2023

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Lunch and Learn Session on Project Management - Louise Larkin, July 2023

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http://edm/HTA/DirectorateDocuments/Training%20materials/Project%20Management%20Training/Lunch%20and%20learn%20Project%20Management%20July%202023.pptx

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Upcoming Conferences for HTA directorate

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Upcoming Conferences for HTA directorate

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Plain language summaries: enhancing patient-centred care and improving accessibility of health research

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Plain language summaries: enhancing patient-centred care and improving accessibility of health research

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If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center's RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

Introduction

As part of the Open Science movement, plain language summaries (PLSs) could make scientific research more understandable and accessible to all audiences. They aim to clearly communicate research results without using technical language.1 Regulatory bodies increasingly require these summaries to disseminate clinical studies' results as part of the efforts to make them more transparent to the public.2 For example, the European Union Clinical Trials Regulation and the Dutch Ethics Committee require clinical trials to provide public summaries adapted for non-medical audiences within 6-12 months after completion. In the USA, the National Institutes of Health promotes broad and transparent dissemination of clinical trial information, and both the Plain Writing Act and Final Rule support plain language in clinical studies.

However, PLS application into practice is still inconsistent, heterogeneous and encounters resistance.3-5 For example, there is no consensus on a common terminology for PLS or where to locate it within the article, limiting its accessibility. Moreover, the summaries do not have a standardised target audience (eg, healthcare professionals and non-experts), and they are written for various reading levels. There are also concerns about their effectiveness, production cost and scalability (eg, if editors, researchers or lay authors should write them).

In this piece, we argue that patients should be the target audience of clinical research PLS, discuss implementation barriers and propose solutions.

Patients as the primary audience

The target audience of PLSs is diverse. It ranges from healthcare professionals, researchers or policy-makers to patients, caregivers or members of the public who are non-experts. We agree with others who have proposed that patients and caregivers should be the primary audience of clinical research PLS because we believe this strategy aligns with the objectives of evidence-based medicine and patient-centred care.6 PLSs have been shown to make research more accessible, understandable and increase knowledge compared with scientific ...

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Plain language summaries: enhancing patient-centred care and improving accessibility of health research

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As part of the Open Science movement, plain language summaries (PLSs) could make scientific research more understandable and accessible to all audiences.

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What tool do undergraduate pharmacy students prefer when grading systematic review evidence: AMSTAR-2 or ROBIS?

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Free webinar 21 Sept 2023 at 12pm: The Future of Cancer in Ireland: cancer screening early diagnosis

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Free webinar 21 Sept 2023 at 12pm: The Future of Cancer in Ireland: cancer screening early diagnosis

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Our 2023 webinar series 'The Future of Cancer in Ireland: Health Economic Perspectives' aims to facilitate discussion and introduce new perspectives on value‐based healthcare, what this looks like in the context of cancer care and what value‐based health initiatives, such as a greater focus on disease prevention, outcomes and patient centricity, mean for cancer services in Ireland.

Hosted and managed by the Healthcare Outcomes Research Centre in the RCSI School of Population Health, the 'Future of Cancer in Ireland: Health Economic Perspectives' series comprises of four webinars featuring national and international experts, policymakers, healthcare professionals and patients.

In this third webinar, speakers will discuss cancer screening and early diagnosis, reflecting on this theme from a population health perspective alongside current examples of what works well in the Irish healthcare system.

Our speakers are:

Dr Alan Smith, Specialist in Public Health Medicine at HSE National Screening Service

Professor Patrick Redmond, GP and Associate Professor in General Practice, RCSI, and

Dr James O'Mahony, Assistant Professor, School of Economics, University College Dublin.

The webinar will be moderated by Dr Ronan Glynn, Health Sector Lead, EY Ireland and former Deputy CMO.

This webinar series is in association with Janssen Sciences Ireland, Novartis Pharmaceuticals Ireland and EY Ireland.

Find more information on previous webinars in this series here.

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The Future of Cancer in Ireland - Cancer screening and early diagnosis

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In this webinar, speakers will discuss cancer screening and early diagnosis; reflecting on this theme from a population health perspective. More ...

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Free "Introduction to Health Utilities" lunch time lecture 21 Sept 2023 at 12pm

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Free "Introduction to Health Utilities" lunch time lecture 21 Sept 2023 at 12pm

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Utility measures of health-related quality of life are preference values that patients attach to their overall health status. In clinical trials, utility measures summarize both positive and negative effects of an intervention into one value between 0 (equal to death) and 1 (equal to perfect health). These measures allow for comparison of patient outcomes of different diseases, and for comparison between various health care interventions.

This webinar is an introductory session to health utilities.

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Home / Training / NHS and Outcomes Training / Introduction to Health Utilities (Lunchtime Lecture)

Thursday 21 September at 12pm

£0.00

Description

Utility measures of health-related quality of life are preference values that patients attach to their overall health status. In clinical trials, utility measures summarize both positive and negative effects of an intervention into one value between 0 (equal to death) and 1 (equal to perfect health). These measures allow for comparison of patient outcomes of different diseases, and for comparison between various health care interventions.

This webinar is an introductory session to health utilities.

Course Trainers

The training day presenter will be Andria Joseph (Project Director)

Location

This Webinar will take place online

Related Training Courses

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Introduction to Health Utilities (Lunchtime Lecture) - YHEC - York Health Economics Consortium

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Utility measures of health-related quality of life are preference values that patients attach to their overall health status. In clinical...

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Recording of Endnote 20 presentation available

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Recording of Endnote 20 presentation available

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The "Introduction to Endnote 20" training presented by Marie Carrigan and Kirsty O'Brien is available from here

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This guide is also available on sharepoint: Endnote How to Guide.docx

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Introducing the new Cochrane Handbook for Diagnostic Test Accuracy

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Introducing the new Cochrane Handbook for Diagnostic Test Accuracy

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The 2nd version of the Cochrane Handbook for SRs of DTAs has been published and is available for free online

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Access the Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy

About the Handbook

The Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy is the official guide that describes in detail the process of preparing and maintaining systematic reviews of test accuracy for Cochrane. The Handbook has been produced by the Cochrane Screening and Diagnostic Test Methods Group. It is a guide for those conducting systematic reviews of test accuracy and a reference for more experienced authors. The Handbook will be updated regularly to reflect advances in systematic review methodology and in response to feedback from users.

Contact the Editors

For further information and for any Handbook enquiries, please contact support@cochrane.org.

The Handbook editorial team includes Jon Deeks, Patrick M Bossuyt (Senior Scientific Editors), Yemisi Takwoingi and Mariska M Leeflang (Associate Scientific Editors).

How to cite the Handbook

To cite the full Handbook available in PDFs on our website, please use the following:

Deeks JJ, Bossuyt PM, Leeflang MM, Takwoingi Y (editors). Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. Version 2.0 (updated July 2023). Cochrane, 2023. Available from https://training.cochrane.org/handbook-diagnostic-test-accuracy/current.

To cite the print edition of the Handbook, please use:

Deeks JJ, Bossuyt PM, Leeflang MM, Takwoingi Y (editors). Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy. 1st edition. Chichester (UK): John Wiley & Sons, 2023

Details of how to cite individual chapters in either of these versions are available in each chapter

Permission to re-use material from the Handbook

Academic or other non-commercial re-use of Handbook material

To request permission to re-use material from the Handbook for academic and non-commercial purposes, please fill in this short webform. This will be sent to the Cochrane Support team who will notify Jon Deeks and Patrick Bossuyt, the Handbook Senior Editors, as appropriate. If approved, these requests will be granted free of charge on condition that the source is acknowledged.

Commercial re-use of Handbook material

Commercial re-use includes any use of the Handbook that will be used in a product for which there is a monetary fee, and/or where it is associated in any way with any product or service. For all enquiries related to the commercial re-use of Handbook material please contact Wiley Global Permissions, John Wiley & Sons, Ltd.

Request permission to re-use material from the Cochrane Handbook

Please only fill in this form if you intend to reproduce material from the Handbook using screenshots or lengthy direct quotations (more than 5 lines of text). You do not need to request permission to use short quotations (though these must be appropriately cited), or to cite the Handbook as a source. See How to cite the Handbook.

Previous versions

Archived copies of the following previous versions of the Handbook are available:

You may also be interested in:

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Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy

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The Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy is the official guide that describes in detail the process of preparing and maintaining systematic reviews of test accuracy for Cochrane. The Handbook has been produced by the Cochrane Screening and Diagnostic Test Methods Group.

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Upcoming HTA Training courses.

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HTA Upcoming training opportunities - 11th July 2023

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Download the Endnote Click plugin to help you access journal articles that are open access or available from HIQA’s subscriptions. It also allows you to easily export references to your EndNote Library.
·       Add the extension to your browser Download Endnote Click

·       Create your own Endnote Click account (this is separate from your Endnote Library account)

·       Type HIQA and select Health Information and Quality Authority to allow access to our library subscriptions

·       When you are viewing an article record on an academic journal website or database, EndNote Click will display a pop up icon on the left hand side of the screen if there is a freely available PDF version of the article.

·       If there isn't a PDF available, the pop up icon won't appear, so in this case, you will need to find an alternative method of adding the reference to EndNote.

·       Click on the pop up and this will add it to your locker, the storage area in EndNote Click. The saving option defaults to saving to EndNote.com but on the left hand side menu of your locker there is an option to 'Export to EndNote Desktop'. If you click on this, it will create a RIS file and you can save and add it to your library as for other online export methods.

·       This process will also attach the PDF to the reference in your EndNote library. The free version of EndNote Click only provides you with 100 MB of storage in your EndNote Click locker, so once you have added the reference to your EndNote library you may wish to delete the PDF from your EndNote Click locker.

For further information on Endnote Click, here is a useful guide: https://clarivate.libguides.com/endnote_training/endnote_click

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Access millions of research papers in one click.

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Save time accessing full-text PDFs with the free EndNote Click browser plugin.

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Conferences relevant to HTA - Updated for August 2023.

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Previous Directorate Presentations are stored on SharePoint here: Directorate Presentations - All Documents