Real‑world evidence to support regulatory and HTA submissions

Generating dossiers with supporting patient cohorts, outcomes, and cost evidence that can withstand stringent review 

Executive summary

Regulatory, HTA, and payer submissions require real‑world evidence that demonstrates how treatments perform in clinical practice, including patient outcomes, treatment patterns, and healthcare resource utilization and cost. Traditional data sources often make it difficult to define reviewer‑relevant patient cohorts, follow patients longitudinally across care settings, and link clinical outcomes with cost in a traceable and defensible manner.

Clinically rich, multi‑modal real‑world data sourced directly from healthcare settings enables the generation of longitudinal patient‑level evidence that supports robust cohort definition, outcome assessment, and integrated cost analyses suitable for external review and decision‑making.

What’s needed for submissions?

For pharmaceutical companies preparing regulatory submissions, HTA filings, and payer discussions, the challenge is not data access but demonstrating how a treatment performs in real‑world clinical practice, including outcomes, treatment patterns, and cost. This requires capturing healthcare resource utilization (HCRU) — such as hospitalizations, procedures, and follow‑up care — to quantify disease burden and therapeutic value.

Reviewers assess how patient cohorts are defined, whether they reflect the target population, and whether analyses can be traced back to the original clinical data. This includes scrutiny of inclusion and exclusion criteria, coverage of the patient journey across care settings, and potential sources of bias.

Traditional data sources make this level of evidence difficult to generate. Claims data offers scale but lacks the clinical detail required for precise cohort definition, while medical records and structured EHR datasets are often limited to single care settings, constraining longitudinal follow‑up. Linking outcomes with cost remains challenging when healthcare resource utilization data is fragmented across systems.

What’s at stake? 

When preparing dossiers for submission, market access teams must demonstrate how a disease is managed in clinical practice and the associated cost of care, based on real-world evidence that will be critically reviewed by external stakeholders. These submissions directly inform reimbursement decisions, pricing negotiations, and the conditions under which a therapy will be made available to patients. 

In this context, teams risk: 

Understanding these challenges 

Defining patient populations, following patients over time, and linking outcomes and cost are all essential to generating evidence suitable for regulatory, HTA, and payer review. In practice, each of these steps is constrained by limitations in traditional real‑world data sources.

Incomplete inputs to patient population definition

Defining the patient population is often the first limitation. Inclusion criteria frequently depend on detailed clinical characteristics such as disease severity, co‑morbidities, or biological markers. Generalist datasets, such as claims databases, do not contain this level of detail, making it difficult to identify the exact patients of interest. This becomes more complex when cohort definitions rely on laboratory values, genomic data, or imaging information, which are often unavailable or not structured for precise cohort selection. Teams therefore iterate on cohort definitions across datasets, with limited consistency between analyses.

Limited longitudinal visibility across care settings

Building a longitudinal view of the patient journey remains challenging. Claims data may capture activity across providers but lacks clinical depth, while medical records and EHR datasets provide richer clinical information but are typically limited to a single institution or care setting. Patients cannot be consistently followed from diagnosis through treatment and outcomes when care spans multiple providers.

Missing clinically-relevant outcomes

Measuring outcomes introduces further constraints. Many clinically relevant endpoints are not consistently captured in structured datasets or are only available in unstructured formats that are difficult to access at scale. Teams may rely on proxy measures or incomplete data, which weakens the clinical validity of the analysis.

Fragmented views of cost and healthcare resource utilization

Estimating cost and healthcare resource utilization requires access to data beyond a single source. When patient data is not linked across care settings, cost estimates are partial or based on assumptions, increasing complexity and reducing confidence in the results.

Together, these limitations make it difficult to generate real‑world evidence that is consistent, comprehensive, and defensible during external regulatory, HTA, and payer review.

What market access teams need

To generate evidence that can be used in filings and submissions, teams need to work from a consistent patient population, rather than redefining cohorts across datasets.

A consistent, well‑defined patient population

To generate evidence that can be used in filings and submissions, teams need to work from a consistent patient population, rather than redefining cohorts across datasets. This requires applying precise inclusion and exclusion criteria, including clinical, biological, and demographic characteristics, and maintaining those definitions across analyses.

Longitudinal visibility across care settings

Teams need to follow patients longitudinally across care settings, from diagnosis through treatment and outcomes, without reconstructing the patient journey manually from multiple sources. This is essential to accurately characterize treatment pathways and assess outcomes over time.

Integrated analysis of outcomes and cost

Outcomes and cost must be analyzed together. This requires linking clinical data with healthcare resource utilization across settings so that cost estimates reflect actual care delivery rather than partial or proxy measures. Results must be comparable across patient sub‑groups, treatment pathways, and geographies.

Explainable and defensible evidence

The evidence must be explainable and defensible. Teams need to document how patient cohorts were defined, how data was sourced and processed, and how analyses were conducted, so results can be reviewed and validated during external review.

Together, these requirements define what is needed to generate regulatory‑grade real‑world evidence that can support filings, submissions, and external review with confidence.

Grounding evidence in real-world patient data 

At BC Platforms, we enable market access teams to generate real-world evidence that can be used directly in regulatory, HTA, and payer submissions. 

Teams access clinically rich, multi-modal patient data curated to address specific research questions, built through our global data partner network spanning more than 150 partners across 35+ countries and over 187 million patient lives. 

Based on defined inclusion and exclusion criteria, we create custom patient cohorts integrating structured and unstructured data, including electronic health records, laboratory data, imaging, and genomics where relevant. 

Patient data can be analyzed longitudinally across care settings, from diagnosis through treatment and outcomes. Clinical outcomes can be linked with healthcare resource utilization, allowing cost to be assessed in the context of actual care delivery. 

All data is accessed through BC Mosaic, our trusted research environment (TRE), where de-identified patient-level data can be analyzed in a secure and compliant workspace aligned with regulatory frameworks such as GDPR and HIPAA. The platform includes integrated analytical tools and high-performance computing capabilities, enabling efficient analysis of large-scale, multi-modal datasets. By making data available in a secure research environment, we enable teams to efficiently work with complex data sets and collaborate across organizations and borders – reducing iteration, improving consistency across analyses, and enabling teams to generate evidence that can be clearly explained, justified, and defended during dossier reviews. 

Where needed, our Scientific Consulting team supports study design, cohort definition, and analysis to ensure that results are methodologically sound and aligned with reviewer expectations. This includes generating outputs that can be directly used in submission materials and supporting responses to questions during dossier review.

Our Technology Consulting team can help with other key activities that might be needed, including data migration, integration and mastering; AI model development and / or validation; system implementation and optimization, and more. 

Why BC Platforms

BC Platforms enables teams to build evidence from a single, purpose-built patient cohort defined up-front based on the requirements of the submission. We source and harmonize clinically rich, multi-modal data directly from hospitals across our global data partner network, covering over 187 million patient lives in more than 35 countries. 

This ensures that key clinical variables, including disease severity, co-morbidities, and biological markers, are captured alongside treatment and outcomes, without requiring reconciliation across multiple datasets. 

Through BC Mosaic, teams access data in a secure environment where analyses can be performed consistently on a traceable dataset. Because the data spans multiple care settings, teams can reconstruct patient pathways and analyze outcomes alongside healthcare resource utilization and cost. 

Data provenance is preserved from the original source through harmonization, allowing teams to document how data was collected, transformed, and analyzed when responding to reviewer questions. 

Conclusion

Generating evidence for reviewer submissions requires more than combining datasets and analyses. It requires the ability to define clinically meaningful patient populations, capture real-world treatment pathways, and link outcomes with cost in a way that can be clearly explained and justified. 

By working from a single, purpose-built cohort sourced from clinical practice, teams can reduce uncertainty, improve consistency, and build evidence that is aligned with reviewers’ expectations from the start.