Healthcare Data Layers

Healthcare Data Layers #

1️⃣ Data Sources (Raw Data & Collection Level) #

These are the foundational data sources used in healthcare analysis, originating from clinical trials, hospitals, insurance claims, and patient records.

  • Clinical Data (RCTs, EHR, OMOP, CDM) – Structured, controlled, and often randomized data used for regulatory and research applications.
  • Real-World Data (RWD: EHR, Claims, Registries) – Observational and confounded, requiring advanced causal inference methods to extract meaningful insights.
  • Relationship: Clinical Data is typically highly structured and standardized, whereas RWD is heterogeneous, requiring bias correction.

2️⃣ Data Management & Standardization (Processing & Infrastructure Level) #

This layer ensures that raw clinical & real-world data are cleaned, structured, and made interoperable for analysis.

  • Healthcare Informatics – The framework for data integration, ETL processes, standardization (OMOP, FHIR, CDMs), interoperability, and terminology mapping (SNOMED, LOINC, ICD).
  • Healthcare Informatics acts as a bridge between data collection (clinical & RWD) and analytics.
  • Without informatics, AI models and statistical analyses would lack clean, structured, and standardized data.

3️⃣ Data Analytics & Decision Intelligence (AI & Statistical Analysis Level) #

This layer applies statistical, machine learning (ML), and deep learning (DL) models to structured and unstructured healthcare data for actionable insights.

Traditional Data Science & Statistical Analysis (Used for both Clinical & RWD)

  • Biostatistics, Bayesian Methods, Survival Analysis, Causal Inference (PSM, DAGs, DiD)
  • Used to control bias, estimate treatment effects, and generate regulatory-grade evidence (RWE).

AI in Healthcare (Machine Learning & Deep Learning Applications)

  • Supervised Learning (Logistic Regression, Decision Trees, Random Forests)
  • Deep Learning (CNNs, Transformers, NLP, Reinforcement Learning)
  • Model Interpretability (SHAP, LIME) and AI Fairness (Bias Mitigation)

Relationship:

  • AI & ML rely on structured, clean data (from Healthcare Informatics) and leverage Clinical Data & RWD to generate predictions and automate decision-making.
  • Statistical analysis methods (causal inference, survival analysis) are critical for ensuring valid results before AI is applied.