Clinical Documentation

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.

AI-powered tools that automatically document clinical conversations are being adopted rapidly in outpatient settings but have not been evaluated in hospital wards. Existing tools use audio recording only, which cannot capture physical examination findings, procedural observations, or clinical safety behaviours - elements of a ward round that are visible but not audible. This study evaluates an ambient audio-visual (AV) capture system - BlackFrame - that uses both microphone and camera to generate accurate clinical documentation and structured educational feedback in a real inpatient surgical ward setting. Medical students and doctors in training participate in supervised ward round encounters with consenting adult inpatients. The BlackFrame AI platform generates: (a) a structured draft clinical note for the supervising clinician to review and countersign before any use in the patient record; and (b) formative feedback for the trainee, delivered within 30 minutes, covering clinical communication, examination technique, and documentation quality. The study measures whether AI-generated feedback improves trainee clinical performance over a placement, how much documentation time is saved, and whether the system is acceptable to patients and clinicians. No AI-generated text enters the patient record without explicit clinician review and sign-off. All participation is voluntary.