Enroll in our advanced training sessions, where we guide companies on integrating AI into research, development, and clinical workflows, improving decision-making and accelerating innovation.

Leverage hands-on learning experiences that demonstrate practical applications of AI tailored to the uses in life sciences, from drug discovery to regulatory compliance.

Offered virtually or on-site for your team.

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Topics Covered:

Solution component functionality overview

Similarities and differences of leading LLMs

Choosing LLMs

Natural language prompt creation

Regulatory and compliance challenges

Operational Best Practices

Enroll in an intuitive and engaging course that will provide you with a comprehensive understanding of the fundamental principles of accessing, integrating, connecting, and analyzing Real World Data.

Our curriculum will cover the essential aspects of selecting appropriate solutions, acquiring suitable data, and initiating natural language analysis.

Offered virtually or on-site for your team.

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Topics Covered:

Common RWD data types, values, and uses

New and upcoming RWD data types to consider leveraging

Leveraging unstructured data

Connecting and linking of RWD sources

Evaluating and selecting data sources

Regulatory and compliance challenges

Operational Best Practices

Receive an industry-targeted assessment of the market value of your real-world data (RWD). With increasing demand for high-quality, actionable data to drive healthcare innovations and maximize your return.

Receive a comprehensive analysis of your datasets, leveraging advanced methodologies and market insights to determine their commercial value.

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Common Metrics:

Data Quality and Completeness: Ensuring datasets meet industry standards for usability and reliability.

Accuracy and Freshness: Deliver the right data with the right frequency to maximize the accuracy and timeliness of insights.

Relevance and Uniqueness: Evaluating the alignment of your data with market needs, including its uniqueness in competitive landscapes.

Potential Applications: Identifying the diverse use cases for your data, from drug development and clinical research to regulatory submissions and health economics.

Improvement Metrics: Identify ways to leverage new data collection methods to increase the value and applicability of your data.

Link complex healthcare data sources to reveal a complete, connected view of each patient’s journey.

Connect fragmented claims, diagnostics, EHR, lab data, and more to create clear patient journey timelines. Life sciences companies gain deep insights into treatment pathways, adherence patterns, and points of intervention—empowering more informed engagement strategies with providers and patients.

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Common Metrics:

Data Linkage Completeness: Proportion of patients successfully connected across claims, EHR, lab, and diagnostics data.

Journey Insight Depth: Number of measurable touchpoints or variables contributing to treatment understanding.

Time-to-Insight: Reduction in time required to generate actionable journey analytics for targeted interventions.

Identify emerging safety risks sooner through intelligent, AI-powered surveillance of real-world evidence and clinical reports.

Continuously analyze real-world data, EHRs, and patient reports to identify and categorize adverse events with speed and precision. This enables pharmacovigilance teams to detect safety signals earlier, streamline regulatory reporting, and improve patient safety outcomes.

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Common Metrics:

Signal Detection Time: Reduction in time from event occurrence to identification of potential safety signals.

Event Classification Accuracy: Precision and recall in detecting and categorizing adverse events.

Regulatory Reporting Compliance: Percentage of events accurately reported within required timeframes.

Optimize clinical trial operations by forecasting patient eligibility, refining site selection, and anticipating recruitment performance.

Improve trial design and execution by predicting eligible patient populations, identifying optimal trial sites, and modeling recruitment outcomes. This reduces delays, improves diversity in enrollment, and accelerates trial timelines, ensuring therapies reach patients faster.

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Common Metrics:

Recruitment Efficiency: Time reduction in identifying and enrolling qualified participants.

Trial Cost Reduction: Percentage decrease in operational or site management costs due to AI optimization.

Diversity and Representativeness: Improvement in demographic or geographic diversity of enrolled participants.

Identify promising therapeutic opportunities with AI-powered data analysis and modeling.

Analyze clinical, genomic, diagnostic, and market data to uncover patterns that inform the development of innovative therapies. By modeling potential outcomes and unmet needs, life sciences companies can prioritize the most promising therapeutic opportunities with confidence.

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Common Metrics:

Opportunity Identification Rate: Number of new therapeutic areas or mechanisms of action identified per cycle.

Predictive Accuracy: Precision of AI models in forecasting clinical or market success probabilities.

R&D Investment ROI: Return on investment based on AI-informed portfolio prioritization.

Link diverse data sources to create robust, regulatory-ready real world evidence.

Streamline the creation of high-quality real world evidence by linking diverse data sources and standardizing outputs for regulatory and scientific use. This provides life sciences companies with robust, scalable evidence to support label expansions, payer negotiations, and post-market studies.

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Common Metrics:

Evidence Volume and Quality: Number of high-quality, regulatory-acceptable RWE outputs produced per year.

Study Cycle Time: Reduction in time from data acquisition to publication or submission-ready evidence.

Regulatory or Payer Acceptance Rate: Percentage of RWE studies adopted in reimbursement or label-expansion decisions.

Instantly surface insights from vast volumes of publications to guide discovery and strategy.

Accelerate scientific discovery by scanning thousands of publications, extracting relevant findings, and linking them to your therapeutic area. Life sciences teams gain faster insights into emerging research, competitive activity, and potential biomarkers—reducing weeks of manual review to actionable knowledge in hours.

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Common Metrics:

Insight Extraction Speed: Time saved in identifying relevant publications, biomarkers, or clinical findings.

Research Coverage: Percentage of literature and data sources analyzed across targeted therapeutic areas.

Evidence Relevance Score: Proportion of extracted insights directly applicable to current R&D or clinical initiatives.