Digital analysis of clinical trial patient records with LLMs

Introduction

Clinical trial teams want fast, reliable ways to turn patient records into analysis-ready datasets. Yet much of the information that matters—eligibility evidence, adverse event context, and progression details—lives in semi-structured or unstructured text. This post explains why that is hard, where classical code pipelines break, and how large language models (LLMs) can help, along with validation strategies you can put in place today.

The unstructured data challenge

Clinical records combine structured tables (labs, ICD codes) with narrative notes, PDFs, and scanned forms. A common example is trial eligibility: key inclusion or exclusion facts are often documented only in free-text clinic notes or pathology narratives. Published research on oncology trials has shown that the majority of eligibility information is present solely in unstructured notes, forcing teams to use natural language processing to retrieve it. The takeaway is simple: if your pipeline only reads rows and columns, you will miss critical protocol signals.

Why classical code pipelines struggle

Traditional approaches—regular expressions, rule engines, and one-off ETL—tend to be brittle for three reasons:

Language variability and negation make rules fragile across sites and templates.
Temporal reasoning is hard to encode (for example, “no progression within 6 months”).
Maintenance costs grow with each protocol and vendor, while regulators expect traceability when reusing EHR data in trials.

LLM strategies to aggregate records

With proper constraints, LLMs can read messy notes and return structured outputs that slot into analysis datasets.

Schema-guided extraction with terminology normalization Prompt the model to emit strict JSON that matches your target schema (for example, an SDTM-like structure for adverse events or medical history) and then normalize terms to controlled vocabularies such as MedDRA or SNOMED using a terminology service.
Retrieval-augmented generation for evidence-grounded outputs Retrieve the patient’s source snippets (labs, pathology paragraphs, AE narratives) and require the model to cite those snippets when producing summaries or eligibility calls. This improves auditability and reduces hallucinations.
Temporal timeline assembly Orchestrate a two-step flow: first extract dated events, then aggregate by episode (screening, on-treatment, follow-up). This makes it easier to verify washout periods or progression windows that are usually written in prose.

A minimal example of schema-guided extraction with validation:

schema = {
  "type": "object",
  "properties": {
    "eligibility": {"type": "boolean"},
    "reasons": {"type": "array", "items": {"type": "string"}},
    "evidence_spans": {"type": "array", "items": {"type": "string"}}
  },
  "required": ["eligibility", "reasons", "evidence_spans"]
}

resp = llm(prompt, documents=[note_text])
data = validate_json(resp, schema)         # fail closed if invalid
assert all(span in note_text for span in data["evidence_spans"])

Validation and current challenges

Healthcare LLM studies still show uneven evaluation, with many focusing on benchmark questions rather than real-world patient documents. For clinical-trial use, combine:

Gold-standard evaluation against expert-annotated samples with precision and recall.
Clinician-in-the-loop adjudication of disagreements.
Source-grounded audits that capture exact text spans.
Robustness checks across sites and time to catch dataset shift.
Governance: PHI handling, model and prompt versioning, and traceable mappings to analysis datasets.

Summary

Digital analysis of clinical trial records is limited by unstructured text and brittle code. Constrained LLM patterns—schema-guided extraction, evidence-grounded retrieval, and temporal assembly—can turn narratives into compliant, auditable datasets when paired with rigorous validation and governance. Do you need advice or help with a project? EVOBYTE is your partner in managing clinical trial data.