Conducting two evidence syntheses in six weeks - experiences with and evaluation of a pilot project.

Background: Evidence synthesis organisations are trying to meet commissioners' needs for rapid responses to their evidence synthesis commissions. In this project we piloted an intensive process, working to complete evidence syntheses within six-weeks, rather than the standard lead time of 4-6 months. Our objective was to explore how researchers experience working intensively, identify barriers and facilitators, and determine how a more intensive approach to evidence synthesis could be more systematically introduced in the future.

The effect of machine learning tools for evidence synthesis on resource use and time-to-completion: protocol for a retrospective pilot study.

Background: Machine learning (ML) tools exist that can reduce or replace human activities in repetitive or complex tasks. Yet, ML is underutilized within evidence synthesis, despite the steadily growing rate of primary study publication and the need to periodically update reviews to reflect new evidence. Underutilization may be partially explained by a paucity of evidence on how ML tools can reduce resource use and time-to-completion of reviews.

Automating risk of bias assessment in systematic reviews: a real-time mixed methods comparison of human researchers to a machine learning system.

Background: Machine learning and automation are increasingly used to make the evidence synthesis process faster and more responsive to policymakers' needs. In systematic reviews of randomized controlled trials (RCTs), risk of bias assessment is a resource-intensive task that typically requires two trained reviewers. One function of RobotReviewer, an off-the-shelf machine learning system, is an automated risk of bias assessment.

Machine learning in systematic reviews: Comparing automated text clustering with Lingo3G and human researcher categorization in a rapid review.

Systematic reviews are resource-intensive. The machine learning tools being developed mostly focus on the study identification process, but tools to assist in analysis and categorization are also needed. One possibility is to use unsupervised automatic text clustering, in which each study is automatically assigned to one or more meaningful clusters.

Can Remote Patient Monitoring Be the New Standard in Primary Care of Chronic Diseases, Post-COVID-19?

One lesson from the current COVID-19 pandemic is the need to optimize health care provision outside of traditional settings, and potentially over longer periods of time. An important strategy is remote patient monitoring (RPM), allowing patients to remain at home, while they transmit health data and receive follow-up services. We conducted an overview of the latest systematic reviews that had included randomized controlled trials with adult patients with chronic diseases.