Services
Healthcare AI & Data Consulting
Strategic guidance on applying artificial intelligence and machine learning to healthcare data for clinical and operational insights.
AI and machine learning are transforming healthcare, but successful implementation requires more than data science skills. It demands deep understanding of clinical workflows, healthcare data structures, regulatory constraints, and the practical challenges of deploying models in clinical environments. I provide consulting services that bridge the gap between AI capabilities and healthcare realities. From data pipeline architecture and feature engineering with clinical data to model deployment strategies that integrate with EHR workflows, I help healthcare organizations move from AI experimentation to production value. My focus is on practical, explainable, and clinically validated AI applications, not hype.
Schedule a Consultation8+
Healthcare AI Projects Advised
20+
Clinical Data Sources Integrated
15+
Data Pipeline Architectures Designed
What I Deliver
Clinical Data Pipeline Architecture
I design data pipelines that extract, transform, and load clinical data from EHR systems, claims databases, and IoT devices into analytics-ready formats while maintaining data lineage and quality controls.
Healthcare Feature Engineering
I help data science teams understand and engineer features from complex healthcare data including lab results, medication histories, diagnosis codes, clinical notes, and vital sign time series.
CDS Integration Strategy
I architect the integration between AI models and clinical workflows using CDS Hooks, SMART on FHIR, and custom EHR integrations so that model outputs reach clinicians at the right moment in their workflow.
Regulatory & Ethical Framework
I help navigate the regulatory landscape for healthcare AI, including FDA software as a medical device guidelines, bias assessment frameworks, and the explainability requirements for clinical AI tools.
De-identification & Privacy
I implement de-identification pipelines against the method selected by your privacy team. Expert Determination requires a qualified expert; engineering controls alone do not establish HIPAA compliance.
Why Work With Me
Clinically Relevant Insights
AI models informed by deep clinical domain knowledge produce insights that are actionable in the care delivery context, not just statistically interesting but clinically meaningless.
Faster Path to Production
My experience with healthcare data structures and EHR integration means your data science team spends less time understanding the data and more time building models that deliver value.
Compliant by Design
Every data pipeline and model deployment strategy I design accounts for HIPAA, FDA, and organizational privacy requirements from the outset, avoiding costly compliance remediation later.
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Frequently Asked Questions
What types of healthcare AI use cases do you support?
I support a range of healthcare AI use cases including clinical risk prediction (readmission, sepsis, deterioration), population health stratification, operational optimization (scheduling, resource allocation, length of stay), clinical documentation improvement with NLP, and diagnostic support systems. My role is typically at the data engineering and clinical integration layer rather than the core model development, though I collaborate closely with data science teams.
How do you handle the complexity of clinical data for AI models?
Clinical data is messy, sparse, and semantically complex. I address this by building robust data pipelines that handle missing values, normalize terminology across different coding systems, temporal alignment of events, and proper handling of clinical context. For example, a lab value means different things depending on the clinical context, the units used, and the reference ranges, and I ensure these nuances are captured in the feature engineering process.
What about FDA regulations for healthcare AI?
If your AI model makes clinical decisions or recommendations, it may be classified as a Software as a Medical Device by the FDA. I help you understand where your use case falls on the regulatory spectrum, from clinical decision support that is exempt from FDA oversight to locked algorithms that require premarket submission. I also help implement the documentation and validation practices that support regulatory submissions.
Can you help integrate AI model outputs into our EHR?
Yes. This is one of my core strengths. I design integration patterns that deliver AI-generated insights directly into the clinician workflow within the EHR. This might be through CDS Hooks that display risk scores at the point of care, SMART on FHIR apps that present more detailed model outputs, or custom EHR integrations that write model results back as clinical observations or alerts.
How do you ensure patient data privacy in AI projects?
I implement data governance frameworks that include role-based data access, HIPAA-compliant de-identification for model training datasets, audit logging for data access, and secure computation environments. For projects requiring real PHI, I ensure proper BAA agreements are in place and that data handling follows minimum necessary access principles. I also evaluate synthetic data generation and federated learning approaches when applicable.
Exploring AI for Your Healthcare Organization?
Let me help you develop a practical AI strategy grounded in real healthcare data, clinical workflows, and regulatory reality. No hype, just actionable plans.