How AI and real-world data generate predictive insights for drug development

Executive summary

Predictive AI in pharma depends on clinically rich, longitudinal real-world data that can be accessed, harmonized, governed, and trusted for drug development decisions, enabling predictive insights across the drug lifecycle.

Predicting what may happen next in a patient journey is one of the most valuable promises of AI in pharma. The challenge is rarely the algorithm alone. More often, it’s whether teams can access, connect, and govern the clinically meaningful data needed to support predictions they can trust.

Organizations can use AI and real-world data to support patient stratification, understand disease progression, identify biomarkers associated with future outcomes, optimize evidence-generation strategies, and prioritize investments across research, development, and commercialization.

However, generating predictive insights requires longitudinal, patient-level data with sufficient clinical depth and context. Many organizations still struggle to combine clinical, imaging, laboratory, biomarker, genomic, and real-world data into a foundation that supports predictive analytics at scale.

Key takeaway

Predictive AI only becomes useful when the data behind it is clinically rich, connected, governed, and trusted enough to inform drug development decisions.

What data foundations are needed for predictive AI in pharma?

For pharmaceutical companies investing in AI, the goal is not more algorithms. It’s better decisions.

Questions often include:

• Which patients are most likely to benefit from treatment?
• Which populations are at greatest risk of disease progression?
• Which biomarkers are associated with future outcomes?
• Where can earlier intervention improve patient outcomes?
• Which development programs should be prioritized?
• Which patient populations warrant further study or investment?

Answering these questions requires data that can:

• Capture longitudinal patient journeys over time
• Combine clinical, imaging, laboratory, biomarker, and genomic information
• Support patient-level predictive modeling
• Provide sufficient clinical context to explain model outputs
• Remain consistent across countries, sites, and therapeutic areas
• Support reuse across multiple AI initiatives

When these capabilities are in place, predictive AI becomes more than a modeling exercise. It becomes a way to reduce uncertainty and improve decisions across discovery, development, and commercialization.

What happens when predictive AI lacks reliable real-world data?

Predictive insights can help organizations make more informed decisions across research, development, medical affairs, and commercial functions.

Without robust AI-ready data foundations, organizations risk:

• Prioritizing the wrong patient populations
• Overlooking signals of disease progression or treatment response
• Limiting the value of AI investments
• Duplicating effort across indications and geographies
• Delaying research and development activities
• Making strategic decisions without a complete understanding of real-world patient behavior

When the data foundation is weak, teams may make decisions from an incomplete view of the patient journey. That can slow development, increase costs, duplicate effort, and cause companies to miss opportunities to intervene earlier or prioritize more promising programs.

Why is predictive AI difficult to scale in pharma? 

That’s where many AI initiatives run into difficulty. Generating predictive insights from real-world data requires more than large datasets; it requires data that is clinically meaningful, connected across sources, and ready to support repeatable analysis. 

Organizations must identify relevant patient populations, access clinically meaningful information, integrate multiple data types, and create environments that support scalable AI development. 

In practice, that means solving several connected challenges: gaining access to clinically rich hospital data, defining fit-for-purpose cohorts, integrating multiple data types, managing governance, and creating an approach that can scale beyond one project.

Limited access to clinically rich hospital data

Many predictive AI use cases depend on detailed clinical information that traditional secondary data sources often do not provide.

This may include:

• Longitudinal clinical records
• Radiology data
• Radiotherapy data
• Laboratory results
• Biomarker information
• Treatment history

Much of this information remains within hospital systems, where access can be difficult and data structures vary significantly between institutions.

Defining cohorts for predictive AI use cases

Predictive models are only as useful as the cohorts behind them. To answer specific clinical questions, teams need patient groups that are defined consistently and aligned to the outcome they want to understand.

Examples include:

• Progression risk
• Treatment response
• Patient stratification
• Early intervention opportunities
• Biomarker-defined populations

Clinical information is often recorded differently across sites and stored in multiple formats, making it difficult to identify comparable patient populations consistently.

Integrating multi-modal data for predictive analytics

The strongest predictive insights often emerge when multiple forms of data are analyzed together.

These may include:

• Clinical data
• Imaging
• Radiotherapy
• Laboratory results
• Biomarker data
• Genomic information

Without effective integration, important clinical context may be lost, reducing confidence in predictive insights and limiting their value for decision-making.

Managing governance and compliance

Predictive AI initiatives often require organizations to combine data from multiple healthcare institutions, countries, and data sources while coordinating across clinical, data science, regulatory, and governance teams.

Organizations must navigate:

• Ethics approvals
• Privacy requirements
• Governance frameworks
• Data protection regulations
• Institution-specific processes

Managing these requirements can slow projects and limit the ability to scale successful approaches.patient experience in a way that can support decision-making.

Scaling AI beyond individual projects

Many pharmaceutical organizations successfully demonstrate value in a single AI initiative but struggle to apply the same approach across additional indications, programs, or regions.

Each new project often requires teams to:

• Identify and access new data sources
• Establish new governance and approval processes
• Recreate patient cohorts
• Harmonize data again
• Build new analytical workflows

As a result, AI initiatives can become slow, resource-intensive, and difficult to scale across the organization. Without a repeatable approach to data access, governance, and analytics, AI success can remain trapped in isolated projects instead of becoming a scalable source of value.

These challenges point to the same underlying requirement: predictive AI needs a repeatable data foundation, not a one-off data assembly effort for each new question.

What is needed to generate predictive insights from real-world data?

To generate predictive insights that support drug development decisions, organizations need more than large datasets. They need a foundation that enables access to clinically rich data, consistent cohort definition, integrated multi-modal information, appropriate governance, and reuse across multiple AI initiatives.

Key requirements include:

Clinically rich longitudinal data

Access to detailed patient-level information, including clinical records, imaging, laboratory results, biomarkers, genomics, and treatment history.

Fit-for-purpose patient populations

Consistently defined cohorts aligned to specific research questions, outcomes, and predictive modeling objectives.

Integrated multi-modal data

Connected clinical, imaging, laboratory, biomarker, and genomic information that preserves the context needed for predictive analytics.

Scalable AI-ready foundations

Approaches that support privacy, compliance, and repeatable analysis across studies, indications, institutions, and geographies.

When these capabilities are in place, organizations are better positioned to move from isolated AI projects to scalable predictive analytics programs that support decisions across discovery, development, and commercialization

Building the data foundations for predictive AI 

AI can help identify patients at risk of disease progression, anticipate treatment response, and uncover patterns that support drug development decisions. To do that reliably, companies need access to clinically rich, longitudinal real-world data that reflects how patients are treated in practice.

These same data foundations also support broader real-world evidence generation, from cohort discovery and feasibility assessment to evidence strategy and regulatory-grade analysis.

BC Platforms brings these capabilities together across data access, harmonization, cohort discovery, imaging, secure analytics, and consulting support, so teams can move from isolated AI experiments to repeatable predictive analytics programs.

Through our global data partner network, researchers can tap into longitudinal, patient-level datasets spanning more than 150 partners across 35+ countries and more than 187 million patient lives. Depending on the research question, those datasets may include clinical records, laboratory data, imaging, biomarkers, genomics, and disease-specific outcomes.

BC Unify transforms data from multiple healthcare systems and sources into a consistent, research-ready structure, making it easier to compare cohorts and generate predictive insights across institutions, countries, and therapeutic areas.

Meanwhile, with BC Catalyst researchers can identify patient populations, explore biomarker-defined cohorts, analyze treatment pathways, and uncover patterns that support patient stratification and predictive modeling.

Where imaging is important to understanding outcomes or treatment response, BC Image enables extraction, de-identification, harmonization, and large-scale processing of imaging data while preserving clinical context.

In addition, BC Mosaic provides a secure environment for governed collaboration, advanced analytics, and AI development, enabling teams to work with sensitive patient-level data within a compliant framework.

Finally, where required, our consulting team support AI readiness, study design, data strategy, implementation, and analytical workflows.

Why BC Platforms

BC Platforms helps life sciences organizations move from AI ambition to AI-ready execution. By combining access to clinically rich real-world data, harmonized data foundations, advanced analytics capabilities, and scientific expertise, we help organizations generate predictive insights that support decisions across discovery, development, and commercialization.

This enables:

• More reliable patient stratification and risk prediction
• Faster generation of AI-ready datasets
• Scalable AI initiatives across indications and geographies
• Consistent data foundations that can be reused across multiple programs
• Predictive insights that support drug development and strategy decisions

Conclusion

AI can improve decisions across discovery, development, and commercialization when built on data that is fit for prediction. From patient stratification and disease progression risk to treatment response prediction and portfolio prioritization, predictive insights can help organizations anticipate outcomes and reduce uncertainty. However, AI is only as effective as the data behind it. Generating reliable predictive insights requires clinically rich, longitudinal real-world data that can be accessed, integrated, governed, and reused at scale.

Organizations that establish these foundations are better positioned to convert AI investments into actionable insights that support smarter decisions across the drug lifecycle.