Skill Guide

Real-world evidence (RWE) analytics from claims and EHR datasets

The systematic analysis of patient-level data from insurance claims and electronic health records (EHRs) to generate insights on treatment effectiveness, safety, utilization, and cost in real-world clinical settings.

This skill is highly valued because it enables evidence-based decisions on drug development, market access, and payer reimbursement by supplementing traditional clinical trials with real-world patient outcomes. It directly impacts business outcomes by informing pricing strategies, identifying patient populations, supporting label expansions, and mitigating post-market safety risks.

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9.2 Avg Demand

20% Avg AI Risk

How to Learn Real-world evidence (RWE) analytics from claims and EHR datasets

Focus on: 1) Understanding data structures - claims (CPT, ICD-10, NDC, revenue codes) vs. EHR (diagnoses, labs, notes). 2) Foundational epidemiology concepts (incidence, prevalence, survival analysis, bias/confounding). 3) Basic data privacy and compliance (HIPAA, de-identification, IRB requirements).

Move to practice by: 1) Conducting cohort studies using common data models (OMOP, Sentinel) with SQL/Python. 2) Applying propensity score matching and inverse probability weighting to address confounding. 3) Avoid common mistakes like immortal time bias, selection bias from incomplete data capture, and misclassifying exposures/outcomes from claims codes.

Master by: 1) Designing multi-database federated analyses and interpreting conflicting results. 2) Aligning RWE generation with regulatory (FDA guidance) and payer evidence needs (ICER, AMCP dossiers). 3) Building and mentoring cross-functional teams of epidemiologists, data scientists, and clinicians.

Practice Projects

Beginner

Project

Cohort Identification and Descriptive Analysis

Scenario

You have a de-identified claims dataset. Identify patients with Type 2 Diabetes (T2D) initiated on Metformin vs. a GLP-1 agonist. Describe baseline characteristics and 1-year healthcare utilization.

How to Execute

1) Use ICD-10-CM codes (e.g., E11.x) and NDC codes to define the cohorts. 2) Write SQL queries to extract demographics, comorbidities (using Elixhauser comorbidity index), and baseline costs. 3) Generate descriptive statistics (means, medians, frequencies) and compare groups using t-tests or chi-square. 4) Document data quality issues (missing data, coding errors).

Intermediate

Project

Comparative Effectiveness Analysis with Causal Inference

Scenario

Evaluate if a new biologic reduces hospitalization risk for rheumatoid arthritis patients compared to a traditional DMARD, using a large EHR database. Control for confounders.

How to Execute

1) Define treatment cohorts, inclusion/exclusion criteria, and follow-up periods. 2) Construct a propensity score model including demographics, disease severity (e.g., DAS28 scores from labs/notes), and prior treatments. 3) Apply matching or weighting, then run a Cox proportional hazards model to estimate hazard ratios. 4) Conduct sensitivity analyses (e.g., E-value) to assess unmeasured confounding.

Advanced

Project

Multi-Database RWE Study for Regulatory Submission

Scenario

Lead the design and execution of a post-authorization safety study using three disparate real-world databases (one claims, one EHR, one registry) to assess a cardiovascular drug's risk of hepatotoxicity.

How to Execute

1) Standardize data using a common data model (OMOP CDM) or create a federated query framework. 2) Develop a protocol with pre-specified analysis plans, endpoint definitions (e.g., ALT >3x ULN), and power calculations. 3) Implement harmonized cohort definitions and run parallel analyses. 4) Synthesize results, assess heterogeneity (I²), and prepare a clinical study report following STROBE guidelines for submission to health authorities.

Tools & Frameworks

Software & Platforms

SQL (for data extraction/manipulation)R/Python (statistical analysis, e.g., survival, glm)OMOP Common Data ModelATLAS/OHDSI tools

SQL is non-negotiable for querying large datasets. R/Python are used for advanced analytics and visualization. The OMOP CDM and its suite (ATLAS) enable standardized, reproducible research across institutions.

Statistical & Epidemiological Frameworks

Propensity Score MethodsInverse Probability of Treatment Weighting (IPTW)Time-to-Event (Survival) AnalysisTarget Trial Emulation

These are the core methodological tools to reduce confounding and bias in observational data. Target Trial Emulation is a gold-standard framework for designing RWE studies to mimic randomized trials.

Data & Compliance Standards

HIPAA Privacy RuleCDISC/CDASH standards for clinical dataFDA Guidance on RWE

Mandatory for ensuring data use is legally compliant and that findings are formatted for regulatory review. Understanding FDA guidance is critical for designing studies intended to support labeling changes.

Interview Questions

Answer Strategy

The interviewer is testing methodological rigor and awareness of observational study limitations. Use a structured response: Identify major biases (selection, confounding, immortal time, measurement), then state mitigation strategies for each. Sample answer: 'Key biases include confounding by indication (sicker patients get drug A), immortal time bias from misaligning treatment start, and outcome misclassification from claims codes. I would mitigate by: 1) Using propensity score weighting with a rich set of covariates including prior healthcare utilization. 2) Using a new-user design with strict cohort entry criteria to handle immortal time. 3) Validating outcome algorithms (e.g., for major bleeding) against chart review or prior literature.'

Answer Strategy

This tests critical thinking and communication skills. The strategy is to compare study designs, not just results. Sample answer: 'First, I would systematically compare the two studies on patient population, exposure measurement, follow-up duration, and outcome definitions. The discrepancy likely stems from differences in generalizability (real-world vs. trial-eligible patients) or residual confounding. I would communicate to stakeholders that RCTs demonstrate efficacy under ideal conditions, while our RWE study reflects effectiveness in a broader, more complex population. Both are valuable, and the difference itself informs our understanding of treatment use in practice.'