http://www.cell.com/article/S2666379122004244/pdf

Evidence synthesis, digital scribes, and translational challenges for artificial intelligence in healthcare

Background: There is an unmet need for review methods to support priority-setting, policy-making and strategic planning when a wide variety of interventions from differing disciplines may have the potential to impact a health outcome of interest. This article describes a Modular Literature Review, a novel systematic search and review method that employs systematic search strategies together with a hierarchy-based appraisal and synthesis of the resulting evidence. // Methods: We designed the Modular Review to examine the effects of 43 interventions on a health problem of global significance. Using the PICOS (Population, Intervention, Comparison, Outcome, Study design) framework, we developed a single four-module search template in which population, comparison and outcome modules were the same for each search and the intervention module was different for each of the 43 interventions. A series of literature searches were performed in five databases, followed by screening, extraction and analysis of data. “ES documents”, source documents for effect size (ES) estimates, were systematically identified based on a hierarchy of evidence. The evidence was categorised according to the likely effect on the outcome and presented in a standardised format with quantitative effect estimates, meta-analyses and narrative reporting. We compared the Modular Review to other review methods in health research for its strengths and limitations. // Results: The Modular Review method was used to review the impact of 46 antenatal interventions on four specified birth outcomes within 12 months. A total of 61,279 records were found; 35,244 were screened by title-abstract. Six thousand two hundred seventy-two full articles were reviewed against the inclusion criteria resulting in 365 eligible articles. // Conclusions: The Modular Review preserves principles that have traditionally been important to systematic reviews but can address multiple research questions simultaneously. The result is an accessible, reliable answer to the question of “what works?”. Thus, it is a well-suited literature review method to support prioritisation, decisions and planning to implement an agenda for health improvement.

Modular literature review: a novel systematic search and review method to support priority setting in health policy and practice

Introduction Solutions like crowd screening and machine learning can assist systematic reviewers with heavy screening burdens but require training sets containing a mix of eligible and ineligible studies. This study explores using PubMed's Best Match algorithm to create small training sets containing at least five relevant studies. Methods
 Six systematic reviews were examined retrospectively. MEDLINE searches were converted and run in PubMed. The ranking of included studies was studied under both Best Match and Most Recent sort conditions. Results
 Retrieval sizes for the systematic reviews ranged from 151 to 5,406 records and the numbers of relevant records ranged from 8 to 763. The median ranking of relevant records was higher in Best Match for all six reviews, when compared with Most Recent sort. Best Match placed a total of thirty relevant records in the first fifty, at least one for each systematic review. Most Recent sorting placed only ten relevant records in the first fifty. Best Match sorting outperformed Most Recent in all cases and placed five or more relevant records in the first fifty in three of six cases. Discussion
 Using a predetermined set size such as fifty may not provide enough true positives for an effective systematic review training set. However, screening PubMed records ranked by Best Match and continuing until the desired number of true positives are identified is efficient and effective. Conclusions
 The Best Match sort in PubMed improves the ranking and increases the proportion of relevant records in the first fifty records relative to sorting by recency.

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/FECF66AF5B0A358A06B4D09865649C88/S0266462320002159a.pdf/div-class-title-creating-enriched-training-sets-of-eligible-studies-for-large-systematic-reviews-the-utility-of-pubmed-s-best-match-algorithm-div.pdf

Creating enriched training sets of eligible studies for large systematic reviews: the utility of PubMed's Best Match algorithm

Introduction The COVID-19 pandemic is exacerbating a wide range of symptoms of poor mental health among emergency medical service (EMS) ambulance populations. Evidence suggests that using organisational support can improve employee outcomes and in turn, patient outcomes. Understanding why EMS staff do and do not use support services is therefore critical to improving uptake, ensuring equitable access, and potentially influencing workforce well-being, organisational sustainability and patient care delivery. This systematic review aims to identify what support is available and any perceived barriers and facilitators to accessing and utilising organisational support. Methods and analysis Searches performed between 18 February 2022 and 23 February 2022 will be used to identify studies that report barriers and facilitators to EMS employee support among all government/state commissioned EMS ambulance systems. Electronic databases, AMED, CINAHL, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, EMBASE, EMCARE, HMIC, Medline and PsycINFO will be searched. All relevant English-language studies of adult employees of government/state commissioned EMS ambulance organisations published since December 2004 will be screened and relevant data extracted by two independent reviewers. A third reviewer will resolve any disagreements. The primary outcome is the identification of perceived barriers or facilitators to EMS staff using organisational support for mental health. The secondary outcome is the identification of supportive interventions offered through or by ambulance trusts. Study selection will follow Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, and the methodological quality of included studies will be appraised by administering rating checklists. A narrative synthesis will be conducted to report qualitative and quantitative data and will include population characteristics, methodological approach and information about barriers and facilitators. Ethics and dissemination Ethical approval is not required because only available published data will be analysed. Findings will be disseminated through peer-reviewed publication and conference presentation. PROSPERO registration number CRD42022299650.

https://bmjopen.bmj.com/content/bmjopen/12/10/e062775.full.pdf

Why do ambulance employees (not) seek organisational help for mental health support? A mixed-methods systematic review protocol of organisational support available and barriers/facilitators to uptake

Developing a search strategy for a systematic review is a time-consuming process in which small errors around the formatting and compilation of terms can have large consequences. Microsoft Excel was identified as a potentially useful software to streamline the process and reduce manual errors. Ultimately a spreadsheet was created that largely automates the process of creating a single-line search string with correctly formatted terms, Boolean operators and parentheses.

A Microsoft Excel Approach to Reduce Errors and Increase Efficiency in Systematic Searching.

Background: To improve the speed and quality of literature searching for systematic reviews (SRs) we developed an online tool, the Polyglot Search Translator (PST). This tool helps to automatically translate searches across multiple databases by modifying the database-specific syntax, removing the need to do this manually.Objectives: To determine whether the PST improves the speed and quality of database searching for SRs.Methods: From a pool of 20 randomly selected database search strategies from reviews of drug and non-drug interventions, diagnostic tests, prevalence, prognosis and health technology assessments, each of 20 participants were randomly assigned 10 searches and randomly allocated to translate the search manually or with the PST. The participants were asked to translate each search into three databases from the following: PubMed or Ovid MEDLINE, Embase.com or Ovid Embase, the Cochrane Library, CINAHL, Web of Science or Scopus. We measured: 1) the time taken to translate the search strategies using manual methods or the PST (these were compared against each other); 2) the number of search results found by the participants, compared to the number found by a reference standard developed through consensus by two Cochrane information specialists; 3) errors found in the participants' searches, identified through consensus by the two Cochrane information specialists (manual errors will be compared to PST errors).Results: Preliminary results show that when compared with a manual method, using the PST to translate a search saved an average of 10 minutes per database; this increased to over 100 minutes per database on the most complex search. As more data become available, a more accurate representation of the time saving is expected. The number of search results found and errors in the searches are still being analysed.Conclusions: Initial data show that the PST saves time when searching for studies for inclusion in a SR. Analysis has not yet begun of the quality of search translations; these data will be presented.Patient or healthcare consumer involvement: Although not directly involving patients or health care consumers, the PST could open pathways for less experienced individuals to be involved in a SR and speed up SR production, with the benefit to consumers of having access to more up-to-date SRs.

The Polyglot Search Translator (PST): Evaluation of a tool for improving searching in systematic reviews: A randomised cross-over trial

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The Polyglot Search Translator (PST): Evaluation of a tool for improving searching in systematic reviews: A randomised cross-over trial