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The COVID-19 pandemic sparked a global health crisis, but more recently, it has shared light on another critical issue... an information crisis. While COVID-19 is not the cause of this problem, it has significantly contributed to its reputation. The World Health Organization (WHO) coined “infodemic” to describe the rapid and widespread dissemination of accurate as well as inaccurate information.
Infodemia, poses several serious challenges for researchers, analysts, professors, governmental bodies, and all those who need reliable and trustworthy evidence to inform their decisions and actions. One of these challenges is misinformation. A prime example of misinformation during COVID-19 is the belief that 5G wireless technology can transmit viruses.
Another challenge is the phenomenon known as cherry-picking. Cherry-picking occurs when individuals selectively choose information or data that aligns with their preconceived beliefs or biases while ignoring contradictory evidence. Misinformation aggravates the health industry’s challenges and can erode the public’s trust in authoritative sources. It is vital now, more than ever, to combat it and there are several ways to do this.
One way is to promote evidence-based practice through conducting and using systematic reviews and meta-analyses. These are rigorous methods of synthesising the available research on a specific topic or question, using predefined and transparent criteria to identify, select, appraise, and synthesise studies. Today, with the advancements in technology, tools like Laser AI exist that make conducting systematic reviews more efficient and reliable. Done correctly, systematic reviews and meta-analyses can provide comprehensive and unbiased summaries of the current state of knowledge, identify gaps and uncertainties, and evaluate the quality and certainty of the evidence.
Systematic reviews and meta-analyses are often used interchangeably, but their objectives and outputs are different. A type of literature review called a systematic review follows a systematic process to collect, analyse, and report data from multiple studies that address a specific question or objective. Meta-analysis is a statistical method that quantitatively combines the results of multiple studies, this provides more of an accurate estimate of the effect of a treatment or intervention.
A systematic review can include a meta-analysis, but not all of them include one. Additionally, a meta-analysis can be completed on the basis of a systematic review but it’s not required. An issue that can arise is when authors conducting a meta-analysis exhibit a tendency to select specific studies; this can lead to selection bias and, as a result, can worsen the quality of results. Sometimes, a meta-analysis is not possible or appropriate because the studies are too heterogeneous , for example, have different outcomes or designs. In such cases, a systematic review can use other methods to synthesise the findings, such as narrative synthesis.
In addition to systematic reviews and meta-analyses, Artificial intelligence (AI) tools can help to automate and streamline the systematic review process, making it more efficient and accurate. For example, AI tools can be used to search for and retrieve relevant studies, extract data from studies, and assess the quality of studies.
Authors should follow established reporting guidelines when preparing their publications to ensure the quality and transparency of systematic reviews and meta-analyses. Reporting guidelines provide a set of items or criteria that authors should follow to enable readers to assess the methods and validity of the review. Reporting guidelines also help peer reviewers and editors appraise the quality of the review and provide constructive feedback.
One of the most widely used reporting guidelines for systematic reviews and meta-analyses is the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. It provides a minimum set of evidence-based items that should be reported in systematic reviews and meta-analyses that primarily focus on evaluating the effects of interventions. PRISMA includes a 27-item checklist and a four-phase flow diagram illustrating the study selection process.
The PRISMA statement was originally published in 2009 but was recently updated in 2020 to reflect advances in the systematic review methodology and terminology. The PRISMA 2020 statement includes new reporting guidance on protocol registration, data availability, risk of bias assessment, network meta-analysis, subgroup analysis, sensitivity analysis, and certainty of evidence. The PRISMA 2020 statement also provides an expanded checklist that details reporting recommendations for each item, an abstract checklist, and revised flow diagrams for original and updated reviews.
PRISMA is endorsed by many journals and organisations and is associated with better reporting quality. However, depending on the type and scope of the review, authors may need to use other guidelines or extensions that complement or modify basic PRISMA. For example:
PRISMA is an integral part of a systematic review and meta-analysis development process. Without it, it may raise doubts if the whole process is correct. Also, any inconsistency in the number of reported references can be a serious limitation which lowers the quality of the review. For that reason, it’s crucial to put some effort into PRISMA preparation. You can develop your PRISMA based on the publicly available framework.
AI tools, like Laser AI, that has the PRISMA module built-in can be beneficial in saving time. The potential advantage of Laser AI over traditional PRISMA development is that you don’t have to worry about filling all the gaps and calculating if numbers in the PRISMA are correct - Laser AI will do it for you.
Systematic reviews and meta-analyses are powerful tools to combat misinformation in the infodemic era. They can provide reliable and comprehensive summaries of the best available evidence on a given topic or question and highlight areas of uncertainty or controversy. However, to ensure the credibility and usefulness of systematic reviews and meta-analyses, authors should follow appropriate reporting guidelines that facilitate transparency and reproducibility. AI tools, like Laser AI, can help improve the reporting process by eliminating mistakes and making it more efficient and accurate. By doing so, authors can contribute to advancing knowledge and informing decision-making in health and medicine.
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As a passionate writer with a strong drive for strategic growth, Shelby leverages storytelling techniques to provide value for Evidence Prime's audience.
Evidence Synthesis Specialist at Evidence Prime. She is responsible for testing new solutions in Laser AI and conducting evidence synthesis research.
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