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

Official title

Prospective Randomized Controlled Trial to Evaluate Locally Implemented Large Language Models (LLMs) for Simplifying Patient Communication in Hematology and Oncology

Overview

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.

Detailed description

Background:

Studies show that up to 40-80% of medical information conveyed during physician consultations is not correctly recalled or understood by patients. This problem is particularly relevant in hematology and oncology, where treatment regimens, prognoses, and side-effect profiles are complex. Large language models (LLMs) have demonstrated the ability to convert medical texts into plain language with high accuracy. However, prospective randomized controlled trials evaluating the clinical benefit of LLM-simplified patient synopses in routine care are lacking.

Study Design:

Prospective, single-center, randomized controlled trial with parallel group design. Randomization is 2:1 (intervention : control) using permuted blocks of variable size (4-6). An additional non-randomized translation arm enrolls patients with insufficient German language proficiency.

Intervention:

The locally implemented LLM system (on-premise, no external data transmission) automatically simplifies the following sections of the discharge letter: Current Status, Medical History, Epicrisis, and Further Management. A study physician reviews and approves the simplified version before it is given to the patient. The system is not classified as a medical device and is not used for diagnosis or treatment decisions.

Endpoints:

The primary endpoint is a comprehension score measured by a 5-item scale (10-point Likert, based on PEMAT), assessing overall comprehension and comprehension of diagnosis, treatment, next steps, and medical terminology. Secondary endpoints include patient satisfaction (EORTC QLQ-INFO25 subscales), subjective uncertainty reduction, format preference, physician review time, correction rate, and translation quality.

Statistical Analysis:

The primary endpoint will be analyzed using a t-test or Mann-Whitney U-test. A clinically relevant difference of 1.5 points on the 10-point scale is assumed. With a standard deviation of 2.5, power of 80%, and alpha of 0.05 (two-sided), 136 randomized patients are required (91 intervention, 45 control). Accounting for a 10% dropout rate, 150 patients will be recruited for the randomized arms, plus 30 for the translation arm (total n=180).

Data Protection:

All data are pseudonymized and stored on secure hospital servers. No patient data are transmitted to external servers or cloud services. The study complies with GDPR.

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.

Primary outcome measures

• Patient Comprehension Score [Time frame: At the time of hospital discharge (Day 0), assessed immediately after reading the synopsis (approximately 15-30 minutes after receipt)]

Secondary outcome measures (5)

• Patient Satisfaction with Information Received [Time frame: Day 0, assessed immediately after reading the synopsis]
• Subjective Uncertainty Reduction [Time frame: Day 0, before and after reading the synopsis]
• Patient Preference for Synopsis Format [Time frame: Day 0, assessed immediately after reading the synopsis]
• Physician Review Time [Time frame: Day 0, recorded at time of physician review]
• Physician Correction Rate [Time frame: Day 0, recorded at time of physician review]

Eligibility criteria

Inclusion criteria

• Age 18 years or older
• Inpatient of the Department of Medicine III (Hematology/Oncology) at TUM University Hospital (Klinikum rechts der Isar), Munich, Germany
• Receipt of a discharge letter including the sections Current Status, Medical History, Epicrisis, and Further Management as part of routine clinical care
• Capacity to provide informed consent
• Written informed consent following the consent procedure

Exclusion criteria

• Cognitive impairment precluding independent assessment of comprehension (e.g., dementia, severe encephalopathy)
• Participation in another study with potential influence on the study endpoints
• Lack of capacity to provide informed consent
• Refusal to participate in the study

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation

Randomized

Model

Parallel assignment

Masking

Single blind

Primary purpose

Health services research

Study locations

Germany · 1 center

• Technical University Munich — Munich

Identifiers

NCT: NCT07519811 · oncOPAL-V1.0

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗