Real-World Data & Market Intelligence Software

EXPLAINER

From Patient Data to Commercial Decision-Making

Real-world data in life sciences spans claims records, electronic health records, lab results, genomics, and payer data — all generated outside the controlled conditions of clinical trials. The analytical challenge is integrating these heterogeneous sources into coherent, decision-ready insights at scale. Teams working in commercial strategy, medical affairs, and clinical development increasingly rely on purpose-built platforms to navigate this complexity.

At the patient level, longitudinal data across care settings reveals how disease progresses, how treatment patterns shift, and where gaps in care exist — intelligence that informs both market positioning and trial design. In oncology specifically, the intersection of clinical, genomic, and outcomes data is reshaping how sponsors identify eligible patients, build synthetic cohorts, and generate post-approval evidence.

On the commercial side, understanding provider behavior, referral networks, and patient population characteristics is foundational to targeting strategy. Tools in this domain bring together disparate data assets — often with AI-driven analytics — to help teams move from raw data to actionable intelligence across the full product lifecycle.

SUBDOMAINS

Real-World Evidence Software by Specialisation

Healthcare Professional (HCP) & Market Targeting Analytics

Tools that aggregate and analyze healthcare provider profiles, patient claims, and market data to enable provider identification, audience segmentation, territory optimization, and commercial go-to-market strategy in life sciences.

Explore

Oncology Real-World Data (RWD) & Trial Intelligence

Tools that leverage real-world oncology data -- including EHR, claims, and genomics -- to support patient identification, cohort building, clinical trial recruitment, and evidence generation for drug development and commercial decision-making.

Explore

Patient Journey Analytics Platforms

Tools that aggregate and analyze real-world patient-level data across claims, EHR, lab, and payer sources to generate commercial, clinical, and market insights across large patient populations.

Explore

PROBLEMS SOLVED

Real-World Evidence Software: Common Challenges

Fragmented patient data across care settings: Patient journeys span multiple providers, payers, and labs, making it difficult to reconstruct complete longitudinal records without integrated data platforms.
Identifying trial-eligible patients at scale: Locating patients who meet specific clinical or genomic criteria across real-world datasets is time-consuming without dedicated cohort-building infrastructure.
Opaque HCP prescribing and referral patterns: Commercial teams lack visibility into provider behavior and network dynamics, limiting the precision of targeting and territory planning efforts.
Slow evidence generation post-approval: Building real-world evidence for regulatory or payer submissions requires structured access to outcomes data that few organizations can assemble internally.
Misaligned market sizing and segmentation: Estimating addressable patient populations or provider segments without claims and EHR data routinely leads to inaccurate forecasts and misallocated resources.
Poor signal from undifferentiated market data: Generic market research rarely captures the disease-level specificity needed for credible commercial or clinical strategy in competitive therapeutic areas.

USE CASES

Real-World Evidence Software Use Cases

Commercial launch planning in new indications: Brand teams use patient-level claims and EHR data to define the addressable market and prioritize provider segments ahead of a product launch.
Oncology cohort building for trial recruitment: Clinical operations teams use genomic and EHR data to identify and pre-qualify patients meeting trial eligibility criteria before site activation.
Generating real-world evidence for payer submissions: HEOR and medical affairs teams assemble retrospective patient cohorts to demonstrate comparative effectiveness for formulary or reimbursement negotiations.
Territory design and field force optimization: Commercial analytics teams use provider-level data to align sales territories with actual prescribing volume and patient population distribution.
Tracking treatment patterns after approval: Medical affairs teams monitor how an approved therapy is being used in real-world practice compared to label indications and clinical trial populations.
Competitive landscape analysis by disease area: Strategy teams use market intelligence platforms to assess prescribing trends, patient share, and competitive dynamics within a specific therapeutic area.

VENDOR EVALUATION

Evaluating Real-World Evidence Software: Key Questions

What real-world data sources are included, and how frequently are they refreshed?

How are patient records de-identified and linked across claims, EHR, and lab datasets?

Does the platform support custom cohort definitions using clinical or genomic criteria?

What is the geographic and care-setting coverage of the underlying patient population?

How are AI-derived insights validated against clinical ground truth or published benchmarks?

HOW TO CHOOSE THE RIGHT SOLUTION

Is Real-World Evidence Software Right for Your Team?

: Your team makes decisions based on patient population size, treatment patterns, or disease epidemiology drawn from real-world sources.
: You need to identify, segment, or prioritize healthcare providers based on prescribing behavior, patient volume, or network affiliation.
: Your organization is generating real-world evidence for regulatory, payer, or publication purposes using retrospective patient data.
: You are designing or recruiting for clinical trials and need to validate feasibility or locate eligible patients using EHR or claims data.
: Your commercial or medical affairs strategy depends on understanding how a therapy is used outside of controlled trial conditions.

TOOLS IN THIS CATEGORY

Example Tools On Our Platform

TriNetX LIVE
Real-world data cohort building and analysis across 280M+ patients in 20+ countries for clinical trial design and optimization.
Visit tool page
Rapid Data Products
Curated disease cohorts with rich phenotypes and genomic data, delivered in days instead of months.
Visit tool page
Catalyst AI
AI-powered Real World Evidence generation combining automated EMR and claims data extraction with 21 CFR Part 11 compliant eClinical infrastructure.
Visit tool page
SOPHiA DDM for Multimodal
Integrate genomic, imaging, clinical, and biological oncology data to generate novel insights and accelerate precision medicine research through AI-powered harmonization and predictive analytics.
Visit tool page
Horizon Datascapes
Real-world evidence generation from 4M+ patient journeys for oncology research, configurable across cohorts, variables, cadence, and geography.
Visit tool page
Basil MedTech Intel - Commercial Intelligence
Real-time regulatory and market monitoring for medical device competitors, approvals, clinical trials, and post-market events.
Visit tool page