How to Access Hospital Data for Machine Learning
Every ML engineer in healthcare asks the same question
"How do I actually get access to real hospital data to train my model?" The question is simple. The answer has historically been: you can't — unless you're a well-funded pharmaceutical company, an academic researcher with an IRB-approved study, or willing to spend 12-18 months negotiating data use agreements that may still fall through.
This guide covers every legitimate pathway to access hospital data for machine learning, ranked by practicality, compliance posture, and ML utility.
Pathway comparison: hospital data access for ML training
Approach
Public datasets (MIMIC, ChestX-ray14, UK Biobank)
Verdict
Good for prototyping. Insufficient for production models. Dataset populations do not match your target deployment population. Limited modality diversity. No longitudinal data.
Approach
Academic research partnership
Verdict
Requires IRB approval, faculty co-investigator, defined research protocol, and publication commitments. Data access is restricted to the approved research question. Cannot be used for general-purpose model development or commercial products.
Approach
Data use agreement (DUA) with hospital
Verdict
Legally binding. Data exports to your infrastructure under specified terms. Creates PHI custody liability. Months of negotiation. Limited to specific use cases. Data must be destroyed or returned after agreement term. If you build a model, the hospital may claim ownership of IP developed from their data.
Approach
Synthetic data generation
Verdict
Useful for pipeline testing. Cannot capture real clinical distributions, rare disease patterns, temporal dynamics, or real-world confounders. A model trained on synthetic data must still be validated on real data — which brings you back to the access problem.
Approach
Data marketplace / data broker
Verdict
Platforms like Datavant, HealthVerity, and Komodo Health aggregate and license de-identified patient data. Useful for analytics and real-world evidence. Limited for ML training: de-identification strips temporal resolution from dates, truncates zip codes, and removes free text. The features you need are often the features de-identification destroys.
Approach
Compute-to-data (Rapha Protocol)
Verdict
The model trains locally at the hospital under SGX/TDX hardware attestation, OPA policy enforcement, and network air-gap isolation. Only trained weights leave. No PHI export. No data custody transfer. No re-identification risk. Per-job USDC settlement with cryptographic proof receipts.
Step-by-step: accessing hospital data through Rapha Protocol
- Register for API access at rapha.ltd/early-access. Submit your company profile, model architecture, target modality, and compute budget.
- Receive developer API key after application review. The secure API authenticates your requests and manages ZK-TLS identity verification.
- Upload model artifact through the Secure Compute Console. Submit model code (PyTorch, TensorFlow, JAX), dataset profile, output policy, and USDC budget.
- Escrow USDC into RaphaClearingVault on Polygon mainnet. Funds are held until training completes and proof is verified.
- Rapha routes your job to a configured hospital edge node matching your dataset profile. The Network Orchestration Hub verifies SGX/DCAP and TPM attestation before routing.
- Model trains locally inside the hospital's SGX/TDX enclave. Rust kernel air-gap severs WAN during training. Go OPA guard enforces your output policy. RaphaDataLoader counts records for settlement.
- Receive trained weights — the model, fine-tuned on real clinical data. Plus training metrics, proof receipt, and settlement confirmation on Polygon.
At no point in this workflow do you receive raw PHI, DICOM pixels, FHIR bundles, EHR rows, or patient identifiers.
Rapha Protocol is private-alpha. Model training depends on configured hospital nodes matching your dataset profile. Modality availability varies by deployment. Production access requires signed agreements, institutional approval, and applicable BAA/DPA analysis.