by MarksMan Healthcare | 0 Comments Healthcare Decision Making , Living Systematic Literature Reviews , Machine Learning , Reproducibility
Living systematic literature reviews (SLRs) are a type of SLRs that are continually updated by periodically including relevant new evidence as and when it becomes available. SLRs are often considered to occupy the top of the evidence pyramid because they synthesize evidence from different sources and present a summary of the evidence, thus enabling clinical and policy-level decision-making.
Thus, it becomes essential that SLRs are of high quality, and are updated to include the latest available information.(1) Traditional SLRs that are published in high-quality journals can be expected to be of high quality, but lag when it comes to the ‘updated’ aspect because such SLRs represent static depictions of snapshots of the evidence at the time the research was published.(2) With the emergence of new evidence in the field, some of the recommendations given in an SLR that was published previously might become outdated, thereby challenging the validity of the guidelines that were developed using the SLR.(3)
Thus, while it is difficult to update an SLR, failure to do so results in lower accuracy and recency of the SLRs.(4)
Living SLRs is an approach that tries to resolve this problem. Living SLRs are high-quality, up-to-date, sometimes online, evidence summaries that help identify new trends and developments in the field. A living SLR involves regular literature screening (e.g., monthly), through which newly detected studies are added to the review. Accordingly, metrics such as meta-analysis or other summary measures are also updated with new study results, thereby leading to an updated review of findings and conclusions.(5)
Living SLRs are prepared following a review process similar to that of regular SLRs; however, after the initial publication, the literature is monitored and new results are incorporated as they become available. Continuous monitoring makes it possible to offer the most recent data at all times and further supports the validation of previous conclusions based on the most recent findings in the given field. This guarantees that clinical recommendations, which are largely based on SLRs, take benefit of the most recent clinical data.(5)
Since living SLRs necessitate a continuous workflow, the effort required is moderate, coordinated over long periods, and involves a gradual evolution in the review team, as opposed to the intensive, sporadic effort of standard SLRs and traditionally updated SLRs. Approaches such as machine learning (RCT classifier) and citizen science (Cochrane Crowd) are often utilized to expedite the evidence-screening process.(6)
Recently, especially with living SLRs that are available online, there have been efforts to improve data visualization and relevance, thereby enhancing the user experience, through the usage of AI. Recent innovations have made it possible for the user to select the outcome of interest, with the usage of features such as interactive portals, user-friendly platforms, customizable inclusion criteria, and automatically scheduled updates.(7)
Living SLRs do have certain challenges as well; probably the most important ones are related to the workload as it requires a larger investment than traditional SLRs. An equally challenging concern is the need to engage a large and dedicated team to constantly work on the updates, including tracking ongoing studies, locating full-text articles, chasing trial authors for data, screening the articles, data management, updating PRISMA chart, and results tables. With offline (published) living SLRs, editors need to set up peer reviews in advance to prevent delays, which can also be challenging.(8) The process of republishing reviews and triggering a new DOI may also negatively affect citation counts and impact factors. The process requires a continuous workflow with frequent statistical analysis which can lead to an inflated false-positive rate.(8)
With more research published in the scientific literature over the past few years, the potential pool of qualified studies for any particular SLR is expected to grow with time. With the advent of new technology to improvise the healthcare process, living SLR is proving as a realistic approach for updating SLRs.(9) Though automation in the form of AI is increasingly being used to speed up research screening, human intervention is inevitable for ensuring high-quality screening and data extraction from relevant studies.
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