Why Pharma Needs a Domain-Led Approach to Real-World Evidence

Data in pharmaceuticals has always been complicated, but now, with recent changes in regulations around real-world evidence, that complexity is compounding. Real-world evidence (RWE) must reflect how medicines perform outside of clinical trials, but the data to do so is becoming increasingly fragmented.

Now, pharmaceutical organizations are contending with more than the steep demands of clinical trials. They are in a precarious balancing act between the promise of AI-accelerated insights and the reality of their messy and fragmented data. To balance these two forces, without sacrificing speed and quality, pharmaceuticals need a domain-led approach.

Technology Has Its Limits

According to recent research from Deloitte, complex requirements for drug trials and tougher regulatory requirements are drawing out development cycles longer. That’s driving up costs for pharmaceutical companies. Importantly, the same research shows that quicker and more efficient R&D processes are slowing down cost increases. Beyond immediate cost savings, the strategic imperative is clear: refined data pipelines are essential for accelerating time-to-insight and ensuring regulatory confidence.

Of course, organizations have turned to AI systems and automation to tighten R&D processes, including the management of RWE. The issue, however, is that algorithms don’t possess the clinical nuance needed to distinguish between statistical correlation and therapeutic reality, a gap that can only be bridged with deep subject matter expertise. Pharmaceutical firms cannot afford to entertain risks like miscontextualization and inaccuracy; most RWE use cases involve high stakes. 

Without disciplined data engineering and MLOps foundations, even the most sophisticated AI models will amplify bad inputs, increasing the risk of biased or non‑reproducible RWE. The consequences stretch across legal, operational, and reputational dimensions. Consequently, many leaders are finding that collaborating with technology partners who already possess this hybrid DNA (deep tech combined with therapeutic context) bridges this gap faster than internal upskilling alone.

When Abundance Becomes a Barrier

Volume doesn’t always mean value. Friction builds, too, with organizations particularly struggling at the last mile to make collected data actually usable in production. They need to build robust data pipelines and integrate disparate sources while operationalizing AI systems in a way that’s consistently governable and explainable. 

Ultimately, upstream weaknesses in data engineering decisions dictate downstream chaos. The way data is collected, then mapped, standardized, and monitored determines the derivable value. But most pharmaceuticals deal with an enormous amount of data streams. Healthcare is notorious for being one of the most diverse sectors when it comes to data sources. EHRs, wearable, patient-reported outcomes, clinical notes, and more often come in multiple formats.

Aggregation isn’t the only challenge. The early-stage management and translation of raw and heterogeneous data into clinically meaningful evidence is a barrier that organizations face. That’s where using platforms with built-in medical ontologies and connectors can really pay off, turning what used to be months of data cleanup into just weeks for teams to start seeing results.

The Case for the Domain-Led Framework

To navigate these complexities, forward-thinking leaders are embedding domain-led frameworks into their RWE architecture as a safeguard against the ‘black box’ trap that’s linked with an over-dependency on AI. In successful RWE strategies, AI scales the workload but domain expertise governs the logic. 

And future-proofing RWE so it evolves alongside regulations doesn’t mean a total rewrite of data infrastructure. Here, the ‘how,’ not the ‘what’ defines the direction: How standards are aligned and scaled, how models are continuously monitored, how training grows with regulatory updates.

Context-based expertise woven in with the data curation lifecycle means that organizations also avoid the pitfalls of applying generic algorithms to highly specific therapeutic datasets. It’s both targeted and scalable. Data engineering, data science, and medical teams are aligned around shared standards for data models, ontologies, and validation criteria. This sets the basis for constant defensibility and traceability, two factors under increasing regulatory scrutiny. 

Instead of generically applying algorithms across a huge array of datasets and workflows, this domain-led approach takes a much more targeted approach. Advantageous outcomes include: 

• Accurate data standardization and interoperability. 
• Expert-validated insights. 
• Stronger alignment with compliance. 
• Reduced bias and hallucinations. 

Together, these elements of the framework enable pharmaceutical firms to implement operationalization schemes that support continuous model monitoring, drift detection, and re‑training under regulatory constraints.

As a result, we are seeing a shift toward modular data engineering, where pipelines are designed to be adaptable, transparent, and rigorously validated by scientific standards. This means that, as AI evolves around transparency or real-time data use, organizations can more adeptly adjust pipelines, validation rules, and documentation without having to rebuild from the ground up.

RWE will only ever be as strong as the framework behind it. A domain-led approach provides the necessary scaffolding to manage complexity, ensuring that as data volume grows, the depth and reliability of clinical insights scale in tandem.