Clinical Research Directory

AI Ambient Scribe in Psychiatry

The goal of this clinical trial is to learn if an artificial intelligence (AI) tool that listens to appointments and drafts clinical notes can help clinicians in psychiatric care. Researchers also want to know if the tool changes how patients experience their care. The study includes mental health care providers (physicians, psychologists/psychotherapists, nurses, and social workers) at Psychiatrie St. Gallen and the adult patients they see in their appointments. The main questions it aims to answer are: * Does the AI tool lower the time clinicians spend writing notes? * Are notes written with the AI tool as good as or better than notes written without it? * Do patients notice a difference in their relationship with their clinician when the tool is used? Researchers will compare clinicians who use the AI tool to clinicians who keep writing notes their usual way, before and during the intervention period, to see if writing time, note quality, and the patient-rated therapeutic relationship differ between the two groups. Clinicians will: Join one of two groups. One group starts using the AI tool on August 17, 2026 (7 clinicians whose devices were delivered late start on November 9, 2026). The other group keeps writing notes the usual way. Both groups will be observed until the end of November 2026 (the 7 late-starting clinicians and additional control data collection until February 22, 2027), with note quality evaluation in February and March 2027. Clinicians who cannot contribute to all measurements may, with their consent, remain in the study for automated measurement of documentation time only. Patients will: \- Be invited to fill out a short, anonymous survey about their experience with their clinician.

A Pragmatic Trial of AI-Assisted Discharge Summary Generation

The goal of this study is to learn whether an artificial intelligence (AI) tool built into the electronic health record helps hospital physicians write discharge summaries. A discharge summary is the document that tells a patient's family doctor and other providers what happened during a hospital stay. The main questions this study aims to answer are: Does access to the AI tool reduce the time physicians spend writing discharge summaries? Does it change the quality of discharge summaries or how soon they are completed? How does it affect physician workload? Physicians will be randomly assigned to either have access to the AI tool or complete discharge summaries as they usually do. Physicians with access can choose whether to use the tool, and they review and edit any AI-generated text before it becomes part of the discharge summary.

Voice Assistant for Outpatient Neurology Visits

Documentation duties account for a substantial portion of an outpatient physician's working time and reduce time available for direct patient interaction. Voice assistants based on automatic speech recognition and large language models are being developed to automate medical documentation across clinical specialties. However, such ambient AI-based services have not been systematically validated in Russian-language outpatient neurology practice integrated with a regional electronic health record platform. This pilot, multicenter, prospective, before-after study evaluated the feasibility and preliminary effectiveness of a voice assistant Service designed to automatically pre-fill the structured outpatient neurology visit protocol in the Moscow regional medical information system (EMIAS). The Service implements a pipeline of streaming speech-to-text transcription, two-speaker diarization, and large language model-based mapping of the dialogue between the physician and the patient onto the fields of the standardized neurology examination protocol. Five neurologists at five outpatient clinics in Moscow participated. The study comprised three stages: (1) baseline timing of consultations without the Service; (2) timing of consultations with the Service after a two-week adaptation period, with parallel evaluation of transcription and pre-fill quality and of physician and patient satisfaction; and (3) statistical analysis. Three hundred twenty consultations were timed (160 per stage). A stratified random sample of 30 audio-recording / generated-protocol pairs was used to evaluate Service quality; free-text fields were rated on a 5-domain Likert questionnaire and on a 10-point visual analogue scale, and binary fields were rated dichotomously to derive sensitivity, specificity, accuracy, Jaccard index, and false-positive rate. Patient satisfaction was assessed by the modified Patient Satisfaction Questionnaire 8 (PSQ-8); physician feedback was assessed by a custom questionnaire (including the Net Promoter Score) and by semi-structured in-depth interviews with thematic analysis using grounded theory. The primary outcomes were the change between stages in (a) the time of focused physician attention to the patient and (b) the time spent filling and editing the protocol. Secondary outcomes addressed total consultation time, transcription quality (Word Error Rate), expert-rated quality of pre-filled fields, patient satisfaction, and physician satisfaction. The study was conducted under the framework of the Moscow Healthcare Department experiment on the use of digital innovation technologies in health care (Order No. 153 of 21 February 2025), and was approved by the local independent ethics committee.