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Health Information Technology and Healthcare Information System

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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Related Experiment Video

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Digital Tools to Support the Systematic Review Process: An Introduction.

Lena Schmidt1, Ian Cree2, Fiona Campbell1

  • 1National Institute for Health and Care Research Innovation Observatory, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK.

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Digital tools streamline systematic reviews, enhancing evidence synthesis for better decision-making. AI integration offers efficiency but requires careful evaluation for adoption in research and clinical practice.

Keywords:
artificial intelligenceautomation toolsmachine‐learningpathologysystematic review automation

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Area of Science:

  • Medical Informatics
  • Evidence-Based Medicine
  • Health Research Methodology

Background:

  • Systematic reviews are crucial for evidence-based decision-making but are time-consuming.
  • Current methods can delay the integration of new evidence into practice.

Purpose of the Study:

  • To introduce digital tools that enhance systematic review processes.
  • To highlight the functionality, availability, and validation of these tools.

Main Methods:

  • A review of digital evidence synthesis tools for systematic reviews was conducted.
  • Tools were identified for various stages: search, reference management, study selection, data extraction, and appraisal.
  • Emphasis was placed on validated tools with independent evaluations.

Main Results:

  • Comprehensive tools (EPPI-Reviewer, Covidence, DistillerSR, JBI-SUMARI) support full review cycles.
  • Specialized tools exist for evidence search, reference management, study selection prioritization, and risk bias assessment.
  • Machine learning and AI enhance efficiency but necessitate informed end-user evaluation.

Conclusions:

  • Digital tools, especially AI-integrated ones, significantly advance systematic review methodology.
  • These tools can improve the timeliness and quality of evidence for decision-making.
  • Findings are relevant for clinicians, researchers, and systematic review producers.