Clinical Validity & Real-World Evidence Consulting
Endpoint adjudication, phenotyping validation, RWE study design, and clinical AI validation — by a physician-scientist with 150+ publications and 18 international guideline citations.
The Clinical Validity Gap in Real-World Evidence
Real-world evidence is reshaping regulatory submissions, formulary decisions, and clinical practice guidelines. But RWE companies routinely ship cardiovascular studies that contain foundational clinical errors invisible to data scientists and statisticians — errors that a physician-scientist can identify in hours but that cost months to correct once embedded in analytical pipelines:
- Phenotyping misclassification — algorithms built from claims and EHR data encode billing behavior rather than clinical diagnosis, systematically misclassifying patients with atrial fibrillation, heart failure, and acute coronary syndromes
- Medication discontinuation errors — gap-based assumptions ignore prescription refill patterns, sample dispensation, and formulary switching, producing artificially elevated discontinuation rates
- Outcome misclassification — composite endpoints conflate events with meaningfully different mechanisms and clinical significance
- Systematic structural errors — embedded in the most common analytical pipelines in the industry, not edge cases
- AI algorithm misalignment — AI and ML models deployed in clinical workflows without physician-scientist review of clinical logic, feature selection, and outcome definitions
The FDA's Real-World Evidence Program and the EMA's evolving framework have materially raised the bar for methodological transparency and clinical defensibility. Regulatory agencies now question phenotype construction, exposure ascertainment, and outcome adjudication that most RWE teams cannot adequately answer without physician-scientist input.
Consulting Services
AI-HEART Lab brings physician-scientist oversight directly into the RWE infrastructure review process. Engagements span four domains, each designed to catch clinical validity errors before they propagate into regulatory submissions, publications, or clinical decisions.
Clinical Validation of Phenotyping Algorithms
Phenotyping algorithms are the foundation of every RWE study — and the most common source of systematic error. Algorithms built from claims codes and EHR data frequently encode billing behavior rather than clinical diagnosis, producing cohorts that are structurally biased from inception.
- Review case identification logic against published validation studies and clinical diagnostic criteria
- Identify systematic misclassification patterns invisible to non-clinicians
- Recommend specific code additions, exclusions, and sensitivity analyses
- Deliverable: Written validation report with actionable recommendations
Endpoint Adjudication Consulting
Independent endpoint adjudication is required by the leading cardiovascular journals and expected by FDA and EMA for pivotal studies. Dr. Chaudhary currently serves as Chair of the Endpoint Adjudication Committee for the WARRIORS Trial, a multinational prospective cardiovascular outcomes trial led by Imperial College London.
- Endpoint adjudication committee chair and member services
- Charter development and adjudication criteria definition
- Composite endpoint coherence review — ensure each component is ascertained by the most defensible available method
- Meet adjudication standards expected by top-tier cardiovascular journals (JACC, Circulation, European Heart Journal)
- Review of AI-assisted adjudication systems for clinical appropriateness
Study Design Review for Real-World Evidence
Corrections cost hours upstream — months downstream. Study design review catches methodological issues before they are embedded in analytical code and regulatory documents.
- Population selection and comparator definition
- Confounding variable specification and time-zero alignment
- Outcome definition and ascertainment methodology
- Alignment with FDA RWE framework and EMA methodological guidance
Clinical AI Validation
Healthcare AI systems — from clinical decision support to AI-assisted endpoint adjudication — require physician-scientist oversight that bridges clinical expertise and computational methodology. This service provides the expert clinical judgment that automated tools alone cannot deliver.
- Validate AI/ML model outputs against clinical ground truth
- Review feature selection and outcome definitions for clinical appropriateness
- Evaluate AI-assisted clinical workflows before deployment
- Physician-scientist oversight for regulatory submissions involving AI
For automated, continuous auditing of AI-patient conversations and clinical documentation, see our VIGIL — Clinical AI Audit.
Who This Is For
Clinical validity consulting for the organizations where methodological rigor has the highest stakes.
For CROs and Data Analytics Companies
Phenotyping algorithms built without physician review face increasing regulatory pushback. Clinical validity gaps discovered late cost months of rework and jeopardize submissions.
- Phenotyping algorithm validation against clinical diagnosis criteria
- EAC chair with publication record for multinational trials
- Clinical validity review before regulatory submission
- AI-assisted analytics require physician-scientist validation
For Pharmaceutical and Biotech Companies
RWE studies increasingly support regulatory submissions, formulary decisions, and health policy. The methodological bar is rising — and the consequences of clinical validity failures are measured in years and regulatory letters.
- RWE study design built to meet FDA and EMA standards
- Post-market evidence generation with defensible methodology
- AI tools in clinical trials require independent clinical validation
- Medical affairs oversight with KOL-level credentialing
For Health Systems and Academic Medical Centers
Health systems deploying AI tools and conducting population health research need physician-scientist oversight that bridges clinical practice and computational methodology.
- AI vendor evaluation before clinical deployment
- Population health analytics phenotyping validation
- Research protocol review for grant submissions
- Clinical AI governance with physician-scientist input
Why a Physician-Scientist-Engineer
Large CROs offer scale — networks of 1,000+ experts across therapeutic areas. AI-HEART Lab offers depth: a single physician-scientist who bridges clinical medicine, published research, and machine learning engineering. For engagements where the methodology must hold up to regulatory and editorial review, depth matters more than breadth.
Dr. Chaudhary can review a phenotyping algorithm's clinical logic and its computational implementation. He can chair an endpoint adjudication committee and evaluate the AI system assisting adjudication. He can assess whether an RWE study design is clinically defensible and whether the ML model powering it is methodologically sound. That combination — clinical authority, research credibility, and engineering depth — is what CROs assemble across multiple consultants but rarely find in one.
Most AI consulting firms hire engineers who consult clinicians. This is a clinician who mastered engineering. That directionality matters: the starting point is clinical expertise and guideline-level publication authority, extended by computational capability — not the reverse. When your deliverable must convince an FDA reviewer, a Circulation editor, or a Joint Commission surveyor, the credential behind the recommendation is as important as the recommendation itself.
The Regulatory Imperative
The methodological bar for real-world evidence and clinical AI is rising across every major regulatory body. Organizations that invest in clinical validity infrastructure now will be positioned when requirements become mandatory.
FDA Real-World Evidence Program
- 21st Century Cures Act mandates FDA evaluate RWE for regulatory decisions — increasing standards for phenotype construction, exposure ascertainment, and outcome definitions
- Good Machine Learning Practice (GMLP) principles require ongoing monitoring and validation of AI systems used in clinical contexts
EMA and International Standards
- EMA's evolving RWE framework demands methodological transparency and reproducibility that many analytical pipelines cannot currently demonstrate
- ICH E8(R1) guidelines on study design increasingly reference real-world data quality and clinical validity requirements
Clinical AI Oversight
- ONC HTI-1 — transparency requirements for AI used in certified EHR systems
- California AB 3030 — disclosure requirements for AI in clinical communications
- Colorado AI Act — impact assessments for high-risk AI systems including healthcare
Organizations that build clinical validity infrastructure now will be positioned when these requirements become mandatory.
Credentials
The work speaks through publications, guideline citations, and active leadership roles in clinical research.
150+ Publications
Peer-reviewed across cardiology, thrombosis, and AI
h-index 27
2,975 citations across indexed platforms
18 Guideline Citations
AHA, ESC, ACC/AHA, SCAI, HRS
Practicing Cardiologist, FACP, FACC
Staff cardiologist, VA Pittsburgh Medical Center
Georgia Tech MSCS
Machine learning specialization
Kelley MBA
Indiana University; Lean Six Sigma Green Belt
CMO Experience
Chief Medical Officer, AI health technology
Training
Johns Hopkins University (residency, postdoctoral research) → Mayo Clinic Rochester (faculty) → UPMC (cardiology fellowship, NIH T32 in AI-enhanced cardiovascular medicine) → Georgia Tech (MSCS, machine learning) → Kelley School of Business (MBA, Lean Six Sigma)
Leadership
- Chair, Endpoint Adjudication Committee — WARRIORS Trial, Imperial College London
- Guest Editor — Journal of Clinical Medicine, AI in Cardiovascular Procedures
- Peer review — Circulation, European Heart Journal, JACC Advances, npj Digital Medicine, BMJ Open
Frequently Asked Questions
What is clinical validity consulting for real-world evidence?
Clinical validity consulting assesses whether real-world evidence studies produce clinically defensible conclusions. This includes validating phenotyping algorithms against clinical diagnosis criteria, reviewing endpoint definitions for clinical coherence, and ensuring outcome ascertainment methods meet the standards expected by FDA, EMA, and top-tier journals.
What does an endpoint adjudication committee do?
An endpoint adjudication committee (EAC) provides independent physician review of clinical events in trials to ensure consistent event classification per protocol-defined criteria. Services include charter development, adjudicator training, quality monitoring, and regulatory-grade documentation. Independent adjudication is required by most major cardiovascular journals and regulatory agencies.
How do you validate phenotyping algorithms?
Validation involves cross-referencing algorithm logic against clinical diagnosis criteria, reviewing sensitivity and specificity against chart-review gold standards, identifying systematic misclassification patterns (e.g., billing codes that encode insurance behavior rather than clinical diagnosis), and recommending specific code additions, exclusions, or sensitivity analyses.
What is the difference between RWE and traditional clinical trials?
Real-world evidence uses observational data from EHRs, claims databases, and registries, while traditional clinical trials use randomized, controlled protocols. RWE offers larger populations and real-world generalizability but requires more rigorous methodology to control confounding. Both are increasingly used together in regulatory submissions.
How does FDA evaluate real-world evidence?
Under the 21st Century Cures Act, FDA evaluates RWE for data relevance, reliability, and regulatory standards. The agency scrutinizes phenotype construction, exposure ascertainment, outcome definitions, and confounding control. FDA’s RWE framework and GMLP principles are raising the bar for methodological transparency.
What clinical AI validation services do you provide?
Clinical AI validation includes assessing algorithm performance against clinical ground truth, reviewing feature selection for clinical appropriateness, evaluating AI-assisted endpoint adjudication systems, and providing physician-scientist oversight for healthcare AI deployment. For automated, continuous auditing, see VIGIL — our Clinical AI Audit.
What qualifications should a clinical validity consultant have?
Ideal qualifications include active board certification in a relevant specialty, a substantial peer-reviewed publication record with guideline citations, methodological training in epidemiology and biostatistics, and computational skills for reviewing algorithm logic. The combination of clinical authority and technical depth is essential for regulatory-grade work.
How do I engage a physician-scientist consultant?
Engagement begins with a discovery call to scope the project, followed by a written proposal with deliverables and timeline. The physician-scientist conducts the review and delivers a written report with actionable recommendations. Follow-up support is available for implementation and regulatory preparation.
Can you serve as endpoint adjudication committee chair?
Yes. Dr. Chaudhary currently serves as Chair of the Endpoint Adjudication Committee for the WARRIORS Trial, a multinational prospective cardiovascular outcomes trial led by Imperial College London. Services include charter development, adjudicator training, quality monitoring, and regulatory-grade documentation.
What therapeutic areas do you cover?
Primary expertise spans cardiovascular medicine including atrial fibrillation, heart failure, acute coronary syndromes, VTE, and lipid disorders. Methodological expertise in phenotyping validation, endpoint adjudication, and study design transfers across therapeutic areas. Expanding into digital health and AI-augmented clinical trials.
Start a Conversation
Whether you need endpoint adjudication committee leadership, phenotyping algorithm validation, RWE study design review, or clinical AI validation — the conversation starts with a discovery call. No commitment, no sales pitch — just a physician-scientist assessing whether the engagement makes sense for your study.
Explore our free clinical tools, try the VIGIL — Clinical AI Audit for healthcare AI quality assurance, or view Dr. Chaudhary's publications.