The Challenge: When Evidence Generation Can't Keep Pace with Business Needs
Pharmaceutical and healthcare companies rely on systematic literature reviews (SLR) to inform virtually every critical business decision. Understanding disease epidemiology, treatment paradigms, competitive landscapes, safety profiles, and market access requirements demands comprehensive analysis of published studies.
The challenge is volume and velocity. Medical literature expands exponentially. A single therapeutic area might generate thousands of relevant publications annually across journals, conference proceedings, and regulatory databases. Companies need to review this evidence systematically to derive insights for business strategy, but the manual process is painfully slow.
For one global pharmaceutical company, a typical SLR project required approximately 630 hours from initiation to completion:
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Phase Name
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Duration
|
|---|---|
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Define research objectives
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1 hour
|
|
Develop search protocol
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1 hour
|
|
Establish eligibility criteria
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1 hour
|
|
Conduct literature searches
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3 hours
|
|
Screen and select studies
|
140 hours
|
|
Extract epidemiological data
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221 hours
|
|
Assess study quality
|
13 hours
|
|
Synthesize the data
|
126 hours
|
|
Apply forecasting models
|
63 hours
|
|
Interpret and report findings
|
63 hours
|
The most time-intensive phases were study screening, data extraction, and synthesis. Teams manually reviewed abstracts, extracted data points from tables and text, structured information into databases, and synthesized findings across dozens or hundreds of studies.
This timeline created cascading problems:
Strategic Delays
Market entry decisions, portfolio prioritization, and partnership evaluations waited weeks or months for evidence synthesis. By the time analysis was complete, competitive dynamics had often shifted.
Resource Constraints
The volume of manual effort meant companies could only conduct a limited number of SLRs annually. Important therapeutic areas or emerging research questions went unaddressed because bandwidth didn't exist.
Bias and Error Risk
Manual screening and extraction introduced potential selection bias and data entry errors. A single missed study or transcription mistake could skew conclusions.
Knowledge Decay
By the time an SLR was complete, new publications had emerged. Evidence was already aging before it reached decision-makers.
One Medical Affairs leader described the frustration: "We know the evidence exists to answer critical business questions, but extracting and structuring it takes so long that we're often making decisions based on incomplete or outdated information. We needed to move from periodic, static reviews to continuous, dynamic evidence generation."
The Solution: AI-Powered Living Literature Review Platform
Evalueserve developed a comprehensive AI-enabled systematic literature review solution that provided instant insights and workflow automation in user-friendly platform interface.
The approach combined domain expertise with intelligent automation to accelerate the most time-intensive elements while maintaining the rigor and accuracy required for regulatory and strategic decisions.
Continuously Updated Intelligence
Rather than static reviews that aged immediately upon completion, Evalueserve created a living platform that integrated the latest research continuously. AI-powered automation streamlined data extraction, screening, and synthesis to reduce manual workload while maintaining quality standards.
The platform adapted in real-time as new studies were published, ensuring evidence remained current without requiring complete review restarts.
Internal Workflow Acceleration
AI agents targeted the most time-intensive workflow elements:
Literature Search (67% efficiency gain)
Automated search execution across multiple databases reduced manual effort from 3 hours to approximately 1 hour, ensuring comprehensive coverage without repetitive database querying.
Study Screening (21% efficiency gain)
Data Extraction (43% efficiency gain)
AI extracted structured data from tables and text within PDFs, including epidemiological metrics, safety data, efficacy outcomes, and study characteristics. This reduced extraction effort from 221 hours to approximately 125 hours, though the system could not yet extract data from charts and graphs (requiring human review).
The combination of workflow automation maintained accuracy while dramatically accelerating timelines. Domain experts remained essential throughout the data lifecycle for curation, quality assurance, and insight generation, ensuring AI handled repetitive tasks while humans focused on strategic interpretation.
Instant Insights Interface
Once data was structured in the platform, the AI-powered interface allowed users to ask questions and receive concise, actionable insights in real-time. Rather than waiting for analysts to compile custom reports, stakeholders could query the evidence base directly.
The structured data was visualized with key data points (drug, disease, side effects, patient populations) clearly organized into filterable table views. An automated presentation showing executive summary, methodology, and findings was generated for reference, and data could be exported to Excel for further analysis.
The Impact: 20-25% Time Savings, Continuous Evidence, Strategic Agility
The AI-enabled SLR approach delivered 20-25% efficiency gains, reducing a typical project from approximately 630 hours to 500-516 hours.
Specific time savings by activity:
- Literature searches: 3 hours → 1 hour (67% reduction)
- Study screening: 140 hours → 110 hours (21% reduction)
- Data extraction: 221 hours → 125 hours (43% reduction)
- Overall project: 630 hours → ~500 hours (20-25% reduction)
But the transformation extended well beyond time savings:
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From Static to Living Evidence
The continuously updated platform meant evidence never went stale. As new publications emerged, they were automatically integrated into the knowledge base. Strategic decisions could be based on the most current evidence available rather than reviews that were months out of date.
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Increased Review Capacity
The same team could now conduct significantly more SLRs annually, enabling evidence generation for therapeutic areas and research questions previously deprioritized due to resource constraints. This expanded the evidence foundation supporting portfolio strategy, market access, and medical affairs.
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Reduced Bias and Error
Automated extraction reduced transcription errors and manual data entry mistakes. Systematic screening criteria applied consistently across all studies reduced potential selection bias. Data quality improved while timelines compressed.
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Self-Service Intelligence
Evalueserve’s platform democratized access to evidence. Rather than medical affairs teams serving as bottlenecks for every evidence query, stakeholders across the organization could directly access structured insights. A regional market access team could pull competitive safety profiles. A business development team could query treatment paradigm evolution. All without waiting for custom analyst reports.
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Scalable Platform Architecture
The modular design meant improvements compounded over time. As AI extraction capabilities improved or new data sources were integrated, every existing and future review benefited. The platform became more valuable with each use.
"We moved from being the team that delivers evidence reports on a quarterly basis to being the team that maintains a living evidence platform accessible to the entire organization. We went from reactive to proactive."
Perhaps most importantly, the solution addressed the fundamental tension in evidence-based decision-making: the need for both rigor and speed. Traditional approaches forced companies to choose between comprehensive analysis and timely decisions. The AI-enabled platform delivered both, maintaining systematic methodology while compressing timelines and keeping evidence continuously current.
The same architecture applied beyond pharmaceutical SLRs. Any organization synthesizing large volumes of published research (medical devices, diagnostics, healthcare policy, clinical development) could leverage the platform to transform evidence generation from a bottleneck into a competitive advantage.
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Overview & Impact
A global pharmaceutical company implemented an AI-driven SLR platform, turning manual reviews into living evidence. This enabled continuous insights, increased review capacity, and reduced bias across reviews.
