Systematic Review Protocol Development

📌Key Takeaways

1Systematic Review Protocol Development addresses: Systematic reviews are the gold standard for synthesizing medical evidence, but conducting them acco...
2Implementation involves 4 key steps.
3Expected outcomes include Expected Outcome: Systematic review teams report identifying 20-30% more relevant studies compared to database searching alone. The visual validation of search comprehensiveness strengthens reviewer confidence and manuscript acceptance rates. Time to complete the search phase decreases by 40%, allowing more resources for quality assessment and synthesis. Living reviews can be updated in days rather than months when new evidence emerges..
4Recommended tools: litmaps.

The Problem

Systematic reviews are the gold standard for synthesizing medical evidence, but conducting them according to PRISMA guidelines requires exhaustive literature searching that can take research teams months to complete. Teams must search multiple databases using carefully constructed queries, screen thousands of abstracts, and document their process meticulously for reproducibility. The risk of missing relevant studies undermines the validity of conclusions, while the manual nature of the process makes it difficult to update reviews as new evidence emerges. Research teams often lack the resources to conduct truly comprehensive searches, leading to reviews that may miss important studies published in less prominent journals or using non-standard terminology. The pressure to publish quickly conflicts with the thoroughness required for high-quality systematic reviews.

The Solution

Litmaps accelerates systematic review development by providing a visual complement to traditional database searching. Research teams use the platform to validate the comprehensiveness of their search strategies by comparing database results against Litmaps' semantic and citation-based discovery. The visualization reveals clusters of related research that may require additional search terms or database queries. Teams use Litmaps to identify all papers citing or cited by their included studies, ensuring no relevant work is missed through citation chaining. The collaborative workspace enables distributed screening, with team members annotating papers as included, excluded, or requiring full-text review. The platform's export capabilities generate documentation suitable for PRISMA flow diagrams and supplementary materials. When reviews require updating, teams can quickly identify new publications that have entered the literature since the original search, dramatically reducing the effort required for living systematic reviews.

Implementation Steps

Understand the Challenge

Pro Tips:

•Document current pain points
•Identify key stakeholders
•Set success metrics

Configure the Solution

Pro Tips:

•Start with recommended settings
•Customize for your workflow
•Test with sample data

Deploy and Monitor

1. Conduct initial database searches per protocol 2. Import results into Litmaps as seed papers 3. Generate map to identify potential gaps in search strategy 4. Refine search terms based on discovered clusters 5. Use citation analysis for forward/backward chaining 6. Distribute screening across team in collaborative workspace 7. Document inclusion/exclusion decisions with annotations 8. Export PRISMA-compliant documentation 9. Set up monitoring for living review updates

Pro Tips:

•Start with a pilot group
•Track key metrics
•Gather user feedback

Optimize and Scale

Refine the implementation based on results and expand usage.

Pro Tips:

•Review performance weekly
•Iterate on configuration
•Document best practices

Expected Results

Expected Outcome

3-6 months

Systematic review teams report identifying 20-30% more relevant studies compared to database searching alone. The visual validation of search comprehensiveness strengthens reviewer confidence and manuscript acceptance rates. Time to complete the search phase decreases by 40%, allowing more resources for quality assessment and synthesis. Living reviews can be updated in days rather than months when new evidence emerges.

ROI & Benchmarks

Typical ROI

250-400%

within 6-12 months

Time Savings

50-70%

reduction in manual work

Payback Period

2-4 months

average time to ROI

Cost Savings

$40-80K annually

Output Increase

2-4x productivity increase

Implementation Complexity

Technical Requirements

Medium2-4 weeks typical timeline

Prerequisites:

•Requirements documentation
•Integration setup
•Team training

Change Management

Medium

Moderate adjustment required. Plan for team training and process updates.

Recommended Tools

litmaps

Frequently Asked Questions

Implementation typically takes 2-4 weeks. Initial setup can be completed quickly, but full optimization and team adoption requires moderate adjustment. Most organizations see initial results within the first week.

Companies typically see 250-400% ROI within 6-12 months. Expected benefits include: 50-70% time reduction, $40-80K annually in cost savings, and 2-4x productivity increase output increase. Payback period averages 2-4 months.

Technical complexity is medium. Basic technical understanding helps, but most platforms offer guided setup and support. Key prerequisites include: Requirements documentation, Integration setup, Team training.

AI Research augments rather than replaces humans. It handles 50-70% of repetitive tasks, allowing your team to focus on strategic work, relationship building, and complex problem-solving. The combination of AI automation + human expertise delivers the best results.

Track key metrics before and after implementation: (1) Time saved per task/workflow, (2) Output volume (systematic review protocol development completed), (3) Quality scores (accuracy, engagement rates), (4) Cost per outcome, (5) Team satisfaction. Establish baseline metrics during week 1, then measure monthly progress.