Clinical trial · Observational
Ambient AI Documentation in Oncology Consultations
Feasibility Study of Ambient Artificial Intelligence Documentation Systems for Oncology Consultations at the National Cancer Center
- Source
- ClinicalTrials.gov
- Retrieved
- Oct 1, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20261001-000001
Summary
Brief summary (as posted)
This study tests whether an artificial intelligence (AI) system can help doctors write medical notes during cancer consultations. After a cancer patient is seen by a multidisciplinary team (a group of specialists who decide on the best treatment), they have a consultation with their oncologist to discuss the treatment plan. During this consultation, doctors must write detailed medical notes, which takes significant time and effort. In this study, an ambient AI system called VOCALI is used during oncology consultations at the National Cancer Center in Vilnius, Lithuania. Before the consultation begins, the doctor loads relevant pseudonymized clinical data from the electronic health record (such as diagnosis and treatment plan) into the system. During the consultation, VOCALI records the conversation (with the patient's consent) and combines it with the loaded clinical data to automatically generate a draft medical note in Lithuanian. Patients may ask the doctor to stop the recording at any time. The doctor then reviews the draft, makes any necessary corrections, and approves the final document. The AI does not make any medical decisions - the doctor is always responsible for the final note. What the study measures: The study has three main goals: * To assess the quality of the AI-generated notes, rated by two independent reviewers using the PDQI-10, a standardized documentation quality instrument translated into Lithuanian using forward-backward translation * To measure the time doctors spend reviewing and approving the AI-generated draft * To monitor safety - whether any AI errors could affect patient care The study also collects information on doctor workload, ease of use of the system, and patient satisfaction with the consultation. Who can participate: Adult patients (age 18 or older) attending a primary oncology consultation at the National Cancer Center in Vilnius, Lithuania, after a multidisciplinary team decision, who speak Lithuanian and provide written consent. Study details: 250 patients will be enrolled over 12 months. Each participant attends one consultation and completes a short satisfaction questionnaire (about 5 minutes). No additional visits are required. There is no comparator group - all participants receive the AI-assisted documentation. This is the first study to evaluate ambient AI documentation in the Lithuanian language and in the Lithuanian oncology setting.
Conditions
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Artificial Intelligence in Medicine | — | UNRESOLVED | — |
| Clinical Documentation | — | UNRESOLVED | — |
| Neoplasms | Neoplasm | ONTOLOGY_EXACT | 0.90 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Ambient AI documentation system | Device | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Oncology patients undergoing AI-assisted consultation
- description
- Adult oncology patients attending a primary oncologist consultation at the National Cancer Center following a multidisciplinary team (MDT) decision, during which the VOCALI ambient AI documentation system is used to generate a draft clinical note (E025 form).
- interventionNames
- Device: Ambient AI documentation system
Primary outcomes (3)
- measure
- Documentation Quality - AI-Generated Clinical Documentation Quality (PDQI-10)
- timeFrame
- Per consultation, throughout the enrollment period (up to 12 months)
- description
- Mean score assessed by two independent raters using the Physician Documentation Quality Instrument (PDQI-10), covering 10 domains (accuracy, completeness, usefulness, organization, clarity, conciseness, synthesis, consistency, data accuracy, and bias) on a 1-5 Likert scale.
- measure
- Documentation Efficiency - Documentation Completion Time
- timeFrame
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * Age 18 years or older at the time of signing the informed consent form * Primary oncologist consultation at the National Cancer Center following a multidisciplinary team (MDT) decision, with diagnosis and treatment plan established at the MDT meeting * MDT protocol available in the electronic health record (EHR) * Signed informed consent form, including consent to audio recording of the consultation Exclusion Criteria: * Absence of MDT protocol in the EHR * Diagnosis or treatment plan not yet established * MDT recommended additional diagnostic workup (treatment plan not yet defined) * Palliative care consultation (not active oncological treatment planning) * Non-Lithuanian speaking patients * Cognitive impairment affecting ability to understand study information * Hearing impairment affecting participation in verbal consultation
References
Publications (5)
- BACKGROUNDOlson KD, Meeker D, Troup M, Barker TD, Nguyen VH, Manders JB, Stults CD, Jones VG, Shah SD, Shah T, Schwamm LH. Use of Ambient AI Scribes to Reduce Administrative Burden and Professional Burnout. JAMA Netw Open. 2025 Oct 1;8(10):e2534976. doi: 10.1001/jamanetworkopen.2025.34976. PMID 41037268
- BACKGROUNDSasseville M, Yousefi F, Ouellet S, Naye F, Stefan T, Carnovale V, Bergeron F, Ling L, Gheorghiu B, Hagens S, Gareau-Lajoie S, LeBlanc A. The Impact of AI Scribes on Streamlining Clinical Documentation: A Systematic Review. Healthcare (Basel). 2025 Jun 16;13(12):1447. doi: 10.3390/healthcare13121447. PMID 40565474
- BACKGROUNDDraper TC, Cox T, Lamb-Riddell K, Moretti LA, McCormick J, Trowell S, Kiely J, Luxton R. Clinical AI Scribes in primary care: accuracy, error severity and implications for clinical practice. BMJ Digit Health Ai. 2025 Sep 28;1(1):e000092. doi: 10.1136/bmjdhai-2025-000092. eCollection 2025. PMID 42712320
- BACKGROUNDStults CD, Deng S, Martinez MC, Wilcox J, Szwerinski N, Chen KH, Driscoll S, Washburn J, Jones VG. Evaluation of an Ambient Artificial Intelligence Documentation Platform for Clinicians. JAMA Netw Open. 2025 May 1;8(5):e258614. doi: 10.1001/jamanetworkopen.2025.8614. PMID 40314951
- BACKGROUNDPalm E, Manikantan A, Mahal H, Belwadi SS, Pepin ME. Assessing the quality of AI-generated clinical notes: validated evaluation of a large language model ambient scribe. Front Artif Intell. 2025 Oct 22;8:1691499. doi: 10.3389/frai.2025.1691499. eCollection 2025. PMID 41199808