AI Real-World Evidence Analyst Interview Questions

A great answer explains that RWD is the raw data from EHRs, claims, registries, etc., while RWE is the clinical evidence derived from analyzing RWD, and that the distinction matters because data alone is not evidence-it requires rigorous analytical design.

Should cover EHRs (rich clinical detail but missing data and unstructured text), administrative claims (large populations but coding inaccuracies and no clinical granularity), and patient registries (disease-specific depth but limited generalizability and potential selection bias).

Should explain that OMOP standardizes disparate healthcare data sources into a common structure, enabling reproducible multi-site studies, and mention OHDSI's open-source toolchain.

A strong answer covers that ICD-10 codes are standardized diagnosis codes used for billing and clinical documentation, that they can identify disease cohorts in claims data, but that coding practices vary and misclassification is common.

Should explain that sicker patients receive more aggressive treatments, making naive comparisons misleading, and that methods like propensity score matching are used to address this bias.

Should cover new-user design, active comparator selection, inclusion/exclusion criteria, washout periods, outcome ascertainment via ICD codes, propensity score estimation and matching, and sensitivity analyses.

A great answer discusses creating a gold-standard annotated test set, calculating precision/recall/F1 at the mention and document level, assessing generalizability across clinical specialties, and comparing against manual chart review.

Should explain that PSM creates matched pairs reducing sample size, IPTW reweights the full cohort for balance, and that IPTW preserves sample size but can produce extreme weights requiring truncation or stabilization.

Should describe how the period between cohort entry and treatment initiation is misclassified, discuss time-dependent exposure classification, and mention landmark analyses or Mantel-Haenszel methods.

Should cover MCAR/MAR/MNAR assumptions, multiple imputation methods, complete case analysis risks, and how missingness can introduce selection bias that undermines causal estimates.

Should mention prompt engineering with clinical context, few-shot examples, output parsing and validation, hallucination mitigation through grounding in report text, and comparison with rule-based and fine-tuned approaches.

Should explain that STROBE provides a checklist for transparent reporting of observational studies, covering title, methods, results, and discussion sections, and that adherence increases reproducibility and regulatory credibility.

Should cover that data provenance tracks the origin, transformations, and lineage of every data point, that regulators require audit trails, and that poor provenance undermines study credibility and reproducibility.

Should discuss mapping to a common data model like OMOP, reconciling terminology differences using concept mapping tables, handling temporal alignment, and validating the merged dataset against source records.

Should explain that clinical endpoints measure how patients feel, function, or survive, while surrogate endpoints substitute for these, and that AI NLP can extract endpoints from unstructured notes where structured data is incomplete.

Should discuss combining outcome modeling with propensity score estimation, the double robustness property where consistency holds if either model is correct, and assumptions of unconfoundedness, positivity, and SUTVA.

Should cover the generalized random forest framework, splitting criteria based on treatment effect heterogeneity, CATE estimation for individual patients, variable importance for identifying effect modifiers, and honest estimation to avoid overfitting.

Should mention E-values, Rosenbaum bounds, quantitative bias analysis, and negative control outcomes or exposures, and explain how to visualize results as the threshold at which the conclusion would change.

Should cover vector embedding of guideline documents, chunking strategies, retrieval ranking, citation-backed generation, confidence scoring, human-in-the-loop validation, and comparing outputs against source documents.

Should describe the OMOP federated analysis approach, common protocol distribution, site-level execution, meta-analysis of aggregate results, handling of site-level heterogeneity, and privacy-preserving techniques.

Should cover stratified performance evaluation, examining training data representation, adversarial debiasing techniques, calibration across subgroups, and the clinical impact of differential extraction rates on downstream evidence quality.

Should address data volume and noise, missingness patterns, selection bias in device adoption, calibration of continuous signals to clinical events, regulatory validation requirements, and integration with traditional EHR data.

Should explain that negative controls are exposures or outcomes with no known causal link, that observed associations should be null, and that LLMs can help identify candidate controls from literature while empirical calibration uses their distribution.

Should cover nested cross-validation to avoid data leakage, bootstrap optimism correction, temporal validation strategies, transportability analysis for external sites, and calibration plots versus discrimination metrics.

Should discuss the legislative mandate to evaluate RWE for label expansions, the FDA's pilot programs, how AI accelerates endpoint extraction and causal analysis, and the emerging need for AI model validation documentation in regulatory submissions.

Should cover study design considerations given claims-only data, using proxy measures for disease severity (biologic-naive status, prior therapy lines), propensity score methods, sensitivity analyses for unmeasured confounding, and supplementing with NLP on linked EHR notes if available.

Should discuss multilingual model options, translating reports to English for extraction, collecting Spanish-language annotation data, adjusting confidence thresholds, and transparently reporting performance by language to stakeholders.

Should cover data quality assessment, determining whether the issue is systematic or random, excluding or flagging affected records, assessing impact on sample size and representativeness, and documenting the decision in the study report.

Should explain that SMD thresholds are guidelines not absolute rules, discuss whether the residual imbalance is clinically meaningful, offer to add regression adjustment on top of matching, and present results from alternative specifications as sensitivity analyses.

Should discuss investigating study design differences, population heterogeneity, outcome definitions, time periods, and potential biases in both studies, then presenting the discrepancy transparently and discussing it in the evidence synthesis.

Should identify this as a form of selection bias or conditioning on a collider, discuss time-indexed modeling approaches, consider competing risks, and explain the bias introduced by the 3-month requirement.

Should cover Bayesian methods for small samples, borrowing strength from related populations, exact matching or full matching instead of 1:1, descriptive and exploratory approaches, and honest limitations reporting.

Should discuss absolute versus relative effect measures, minimal clinically important differences, number needed to treat, patient-reported outcome perspectives, and presenting results in clinically interpretable units rather than relying solely on p-values.

Should cover geographic aggregation to higher levels, using area-level deprivation indices instead of specific ZIP codes, differential privacy techniques, synthetic data approaches, and consulting with privacy officers.

Should discuss conducting a code mapping analysis to compare pre- and post-change algorithms, performing sensitivity analyses stratified by time period, using chart review to validate a sample, and documenting the issue transparently.

Should cover data access from S3/HealthLake, preprocessing and de-identification, tokenization with BioBERT tokenizer, fine-tuning on annotated clinical data, post-processing with rule-based normalization to RxNorm, evaluation, and deployment via SageMaker endpoint.

Should describe document loading and chunking, vector store indexing, retrieval-augmented generation for structured extraction, output parsing into comparable metrics, and a comparison module that highlights concordance and discordance.

Should cover dbt models for data transformation, Snowflake for centralized or federated data storage, Python analysis scripts called from dbt or orchestrated by Airflow, environment configuration per site, and CI/CD for pipeline testing.

Should describe using ATLAS for cohort definition, Achilles for data characterization, ROhdsiWebAPI for programmatic study execution, and the CohortMethod package for propensity-score-adjusted comparative effectiveness analysis.

Should cover fine-tuning a sentence transformer for semantic similarity, building a SNOMED CT concept index, candidate generation through approximate nearest neighbor search, re-ranking with a cross-encoder, and handling of abbreviation and synonym normalization.

Should cover ingesting reports from FDA FAERS or EudraVigilance, real-time NLP extraction, disproportionality analysis (PRR, ROR) with rolling time windows, alert thresholds, dashboard integration via Streamlit, and human review workflows.

Should discuss data preparation for the library's expected format, choosing between S/T/X-learner and causal forest, handling computational complexity with subsampling or distributed computing, validating with placebo tests, and interpreting CATE estimates.

Should cover few-shot prompt design with clinical examples, Pydantic model for structured output validation, handling edge cases like PRN medications, error handling and retry logic, comparing LLM output against pharmacy records for validation.

Should describe defining data quality expectations for clinical data, running automated validation suites, storing results for trend analysis, building Streamlit visualizations showing quality metrics over time, and alerting on degradation.

Should cover MLflow for experiment tracking and model registry, Docker containerization, CI/CD with GitHub Actions, A/B testing or shadow deployment, performance monitoring with data drift detection, and retraining triggers.

A strong answer shows proactive detection, clear communication to stakeholders, transparent documentation of the issue and its impact, and a systematic approach to resolution or sensitivity analysis.

Should demonstrate ability to translate technical concepts into clinical language, use visual aids or analogies, check for understanding, and adapt communication style to the audience's background.

Should show intellectual honesty, respect for clinical expertise, willingness to re-examine methods, presenting evidence transparently rather than deferring to authority, and collaborative resolution.

Should demonstrate prioritization frameworks, transparent communication about capacity, proactive risk escalation, and ability to maintain quality standards under pressure.

Should mention concrete habits like following specific journals, attending conferences, participating in OHDSI or ISPE communities, hands-on experimentation with new tools, and structured reading routines.