MedTrace Logo

LLM-Generated Plain-Language Patient Synopses to Improve Comprehension in Hematology and Oncology (oncOPAL)

NCT07519811 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 150

Last updated 2026-04-16

No results posted yet for this study

Summary

This study tests whether patients with blood cancer or other cancers better understand their medical information when it is rewritten in plain language by an artificial intelligence (AI) system.

When patients are discharged from the hospital, they receive a medical letter summarizing their diagnosis, treatment, and next steps. These letters are often written in technical language that is difficult for patients to understand. In this study, an AI language model running on the hospital's own secure servers rewrites parts of this letter into simpler language. A physician checks the simplified version before the patient receives it.

Patients are randomly assigned to one of two groups. One group receives both the standard medical letter and the AI-simplified version. The other group receives the standard letter only. A separate group of patients who do not speak German well will receive a simplified and translated version.

After reading their letter, all participants fill out a short questionnaire about how well they understood the information. The study takes place at TUM University Hospital (Klinikum rechts der Isar) in Munich, Germany.

Conditions

  • Hematologic Neoplasms
  • Oncologic Disorders

Interventions

OTHER

LLM-Generated Plain-Language Patient Synopsis

A locally implemented large language model (GPT-OSS, on-premise) automatically rewrites selected sections of the hospital discharge letter (Current Status, Medical History, Epicrisis, and Further Management) into plain language. A study physician reviews the output for accuracy before it is provided to the patient. The system is not classified as a medical device and is not used for diagnosis or treatment decisions. No patient data are transmitted to external servers.

Sponsors & Collaborators

  • Technical University of Munich

    lead OTHER

Study Design

Allocation
RANDOMIZED
Purpose
HEALTH_SERVICES_RESEARCH
Masking
SINGLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-04-01
Primary Completion
2026-12-31
Completion
2027-04-01

Countries

  • Germany

Study Locations

More Related Trials

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07519811 on ClinicalTrials.gov