Layperson-Friendly AI Translation of Medical Documents to Improve Doctor–Patient Communication: Protocols for the AI-INFOCARE and AI-MEDTALK Randomized Controlled Trials
Notice bibliographique
Résumé
BACKGROUND: Many patients struggle to understand referral letters and discharge summaries; low health literacy is prevalent, and short consultations limit explanations. Large language models (LLMs) can translate clinical jargon into layperson language, but their impact on doctor-patient communication in real care remains untested. OBJECTIVE: This study aims to determine whether providing an artificial intelligence (AI)-generated, medically validated layperson-language translation of key medical documents before a consultation improves the quality of doctor-patient communication. METHODS: We plan to conduct two single-center, parallel-group randomized controlled trials in neurosurgery: AI-INFOCARE (outpatients prior to treatment discussions) and AI-MEDTALK (inpatients at surgical discharge). Adults (aged 18 years or older, German-speaking, and able to consent) are randomized 1:1 to receive either (1) an AI-generated layperson-friendly summary of their referral or discharge document in addition to usual care or (2) usual care only. Summaries are produced with a Claude-based system (Simply Onno GmbH) and undergo human verification against the source document using a predefined checklist. The primary outcome is patient-rated interaction quality (Fragebogen zur Arzt-Patient-Interaktion/Questionnaire on the Quality of Physician-Patient Interaction) immediately postconsultation. Secondary outcomes are perceived autonomy support (brief Health Care Climate Questionnaire), self-rated understanding (5-point item), physician-rated encounter difficulty (Difficult Doctor-Patient Relationship Questionnaire, 10-item), and consultation length (minutes). Randomization uses a computer-generated allocation list with concealed, sequential assignment at the point of inclusion; recruiting clinicians have no access to the sequence, and analysts are blinded to group codes. Fidelity is assured by standard operating procedures, rater training, and periodic dual review with interrater agreement estimates. Safety monitoring defines information-related adverse events (eg, distress requiring unplanned clinical support or clinically relevant inaccuracies) and includes an independent safety overseer. Ethics approvals were obtained in February 2025 from the ethics committee of Technische Universität Dresden, and both trials are registered in the German Clinical Trials Register. RESULTS: Each trial targets 300 participants (150 per arm), providing greater than 80% power for an effect size d≈0.4 on the primary outcome (α=0.05). Outcomes are assessed immediately after the index consultation, with no additional follow-up planned in this protocol. We hypothesize that providing layperson-friendly summaries will significantly improve patients' understanding and satisfaction with information, foster a more autonomy-supportive communication climate, and reduce physicians' perceived difficulty in the encounter without unduly prolonging consultation time. Neither study received external funding. Both trials are currently in the recruitment phase, with patient enrollment scheduled to begin in May 2025 and expected to conclude by February 2026. Results are anticipated to be published in summer 2026. CONCLUSIONS: These pragmatic randomized controlled trials test a scalable AI intervention to strengthen understanding and interaction quality without adding clinician burden. If effective, AI-assisted layperson summaries could be integrated into routine workflows to advance health literacy and patient-centered care. TRIAL REGISTRATION: Deutsches Register Klinischer Studien DRKS00036810; https://www.drks.de/search/de/trial/DRKS00036810 and Deutsches Register Klinischer Studien DRKS00036814; https://drks.de/search/de/trial/DRKS00036814. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/77204.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,055 | 0,094 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,002 |
| Méta-épidémiologie (sens large) | 0,008 | 0,008 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,007 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,034 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».