NeoplasmsNVC-AI-DOCNCT07850700
Ambient AI Documentation in Oncology Consultations
An observational study of Neoplasms and Clinical Documentation, run by National Cancer Center Affiliate of Vilnius University Hospital Santaros Klinikos at 1 site in Lithuania.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Neoplasms, Clinical Documentation, Artificial Intelligence in Medicine
- What is being tested?
- Ambient AI documentation system
- Type of treatment
- Medical device
- How many people
- 250 participants
- Where
- 1 siteLithuania
- Study dates
- 1 Nov 2026to 31 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adult oncology patients attending a primary oncologist consultation at the National Cancer Center (Vilnius, Lithuania) following a multidisciplinary team (MDT) decision, with diagnosis and treatment plan established.
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Lithuania1 site
National Cancer Center Affiliate of Vilnius University Hospital Santaros Klinikos
Vilnius, Vilnius City, LT-08660
Ernestas Sileika, MD, +37060950402, ernestas.sileika@nvc.santa.lt
Ernestas Sileika, MD
About this study
Official title: Feasibility Study of Ambient Artificial Intelligence Documentation Systems for Oncology Consultations at the National Cancer Center
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.
Read more
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.
Read the detailed description
BACKGROUND Electronic health record (EHR) documentation imposes a substantial administrative burden on physicians and is a major contributor to professional burnout. Studies consistently show that clinicians spend up to half their working time on computer-based documentation, leaving less than 30% of the working day for direct patient contact. Ambient AI documentation systems have shown reductions in documentation time and cognitive load across multiple health systems in the United States and Europe, with AI-generated note quality approximating that of physician-authored notes as measured by a modified Physician Documentation Quality Instrument (PDQI-9). No studies have evaluated ambient AI documentation in the Lithuanian language or in the Lithuanian oncology setting.
STUDY DESIGN Prospective, single-group, single-center feasibility study conducted at the National Cancer Center (NVC), Vilnius, Lithuania. No randomization or control arm.
AI DOCUMENTATION SYSTEM The VOCALI ambient AI system is activated at the start of each eligible consultation. Before the consultation, the physician loads pseudonymized clinical data from the EHR (diagnosis, MDT decision, treatment plan) into the system. The system audio-records the consultation in real time, integrates the preloaded clinical data, and generates a structured draft of the E025 outpatient clinical note in Lithuanian. The physician reviews, edits if necessary, and approves the final document in the EHR. The system does not make autonomous clinical decisions.
PROCEDURES
- Informed consent obtained before consultation, including consent to audio recording.
- PSQ-18 patient satisfaction questionnaire completed by participants on the same day post-consultation (~5 min).
- PDQI-10 documentation quality instrument rated by two independent raters per AI-generated note.
- NASA-TLX physician workload questionnaire administered at baseline (T0), 1 month (T1), and end of enrollment (T2).
- SUS system usability scale completed by physicians at T2.
- Documentation time and draft editing extent (% characters changed) logged automatically by the system.
- Safety incidents reported by physicians within 3 working days; monthly safety review meeting held; immediate notification of the NVC Deputy Director for Medicine and the ethics committee required for level-5 (critical) incidents.
SAMPLE SIZE 250 participants will be enrolled over a 12-month period. As a feasibility study, sample size was determined by practical feasibility and literature precedent rather than formal power calculation. With n = 250, proportions can be estimated with a 95% CI of approximately ±3-4%; if no critical incident is observed, the upper 95% confidence limit is 1.2% (rule of three). Approximately 20% data loss is anticipated.
STATISTICAL ANALYSIS R ≥4.0 or SAS ≥9.4. Descriptive statistics for all outcomes. PDQI-10 mean score with 95% CI and domain-level analysis; documentation time as mean with 95% CI and median with IQR; SUS mean with 95% CI; PSQ-18 subscale means. Inter-rater reliability assessed by rWG coefficient (target ≥0.7). Critical incident rate reported with 95% CI using the Clopper-Pearson method. NASA-TLX change (T2-T0) analyzed by paired t-test or Wilcoxon signed-rank test depending on distribution. All inferential tests are descriptive in intent; results interpreted via confidence intervals.
DATA PROTECTION All participant data are pseudonymized immediately upon enrollment. Audio recordings and transcripts are stored in the VOCALI provider's secure infrastructure under a data processing agreement per GDPR Article 28; the provider has no access to identifiable participant data. Data retained for 5 years after study completion.
- Being tested
- Ambient AI documentation system
- Keywords
- Clinical documentation, Ambient artificial intelligence, AI scribe, Oncology consultation, Feasibility study, Physician burnout, Natural language processing, Electronic health records, Lithuanian language
- Medical subject headings
- Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Ambient AI documentation systemDevice
Also called VOCALI
An ambient artificial intelligence documentation system that records the consultation audio, integrates relevant pseudonymized clinical data from the electronic health record (EHR), generates a speech transcript, and produces a draft clinical note (E025 form) for physician review and approval. The system does not make autonomous clinical decisions. The physician reviews, edits if necessary, and approves all AI-generated content before it is saved to the EHR.
| Study group | Role | What they receive |
|---|---|---|
| Oncology patients undergoing AI-assisted consultation | 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). Receives: Device: Ambient AI documentation system |
What is being measured
The primary outcome is the main question the study is designed to answer.
Documentation Quality - AI-Generated Clinical Documentation Quality (PDQI-10)
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.
Measured: Per consultation, throughout the enrollment period (up to 12 months)
Documentation Efficiency - Documentation Completion Time
Time (in minutes) from opening the AI-generated draft to final approval in the electronic health record (EHR), automatically recorded by the system.
Measured: Per consultation, throughout the enrollment period (up to 12 months)
Safety - AI System Incident Rate
Frequency of AI documentation incidents classified by a 5-level severity scale (1 = insignificant, 2 = minor, 3 = moderate, 4 = major, 5 = critical). Incidents reported by physicians within 3 working days. Critical incident (level 5) rate reported with 95% confidence interval using the Clopper-Pearson method.
Measured: Per consultation, throughout the enrollment period (up to 12 months)
Secondary outcomes (4)
Physician Subjective Workload (NASA-TLX)
Physician-reported workload assessed using the NASA Task Load Index (NASA-TLX), covering 6 dimensions (mental demand, physical demand, temporal demand, performance, effort, frustration). Change from baseline (T0) to end of enrollment (T2) analyzed by paired t-test or Wilcoxon signed-rank test.
Measured: Baseline (T0), 1 month (T1), and end of enrollment period (T2, up to 12 months)
System Usability (SUS)
Physician-rated system usability assessed using the System Usability Scale (SUS), a 10-item questionnaire scored 0-100.
Measured: End of enrollment period (T2, up to 12 months)
AI Draft Editing Extent
Proportion of characters modified between the AI-generated draft and the final approved document, expressed as percentage. Automatically calculated per consultation.
Measured: Per consultation, throughout the enrollment period (up to 12 months)
Patient Satisfaction with Consultation (PSQ-18)
Patient-reported satisfaction assessed using the Patient Satisfaction Questionnaire (PSQ-18), an 18-item instrument. Results reported as subscale means.
Measured: After each consultation, throughout the enrollment period (up to 12 months)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Sep 2026
- Study start1 Nov 2026 (estimated)
- Primary completion1 Nov 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (5)Papers the sponsor has linked to this study.
- Other trials for NeoplasmsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 250 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Cancer Center Affiliate of Vilnius University Hospital Santaros Klinikos
- Sponsor type
- University, hospital or other
- Collaborators
- UAB Vocali
- Responsible party
- Principal investigator, Ernestas Sileika, National Cancer Center Affiliate of Vilnius University Hospital Santaros Klinikos
- Investigators
- Ernestas Šileika, Principal investigator, National Cancer Center Affiliate of Vilnius University Hospital Santaros Klinikos
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07850700
- Sponsor's protocol ID
- NVC-2026-001
- Other IDs
- 2026/5-1769-1212
- Acronym
- NVC-AI-DOC
- Status last verified
- Sep 2026
- First submitted
- 23 Sep 2026
- Last updated
- 30 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared. The ethics-approved protocol and the informed consent do not provide for transfer of individual data to third parties, in accordance with the EU General Data Protection Regulation (GDPR) 2016/679 and Lithuanian data protection legislation. The data include consultation audio recordings and clinical documentation. Only aggregated results will be published in scientific journals and presented at conferences.
Publications
Olson 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.
PubMed 41037268Sasseville 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.
PubMed 40565474Draper 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.
PubMed 42712320Stults 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.
PubMed 40314951Palm 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.
PubMed 41199808
Study at a glance
From the registry record, last updated by the sponsor on 30 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Feasibility Study of Ambient Artificial Intelligence Documentation Systems for Oncology Consultations at the National Cancer Center
- ClinicalTrials.gov ID
- NCT07850700
- Acronym / sponsor ID
- NVC-AI-DOC / NVC-2026-001
- Keywords
- Clinical documentation, Ambient artificial intelligence, AI scribe, Oncology consultation, Feasibility study, Physician burnout, Natural language processing, Electronic health records
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 250 participants (estimated)
- Lead sponsor
- National Cancer Center Affiliate of Vilnius University Hospital Santaros KlinikosUniversity, hospital or other
- Collaborators
- UAB Vocali
- Study start
- 1 Nov 2026 (estimated)
- Primary completion
- 1 Nov 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 30 Sep 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
BACKGROUND Electronic health record (EHR) documentation imposes a substantial administrative burden on physicians and is a major contributor to professional burnout. Studies consistently show that clinicians spend up to half their working time on computer-based documentation, leaving less than 30% of the working day for direct patient contact. Ambient AI documentation systems have shown reductions in documentation time and cognitive load across multiple health systems in the United States and Europe, with AI-generated note quality approximating that of physician-authored notes as measured by a modified Physician Documentation Quality Instrument (PDQI-9). No studies have evaluated ambient AI documentation in the Lithuanian language or in the Lithuanian oncology setting.
STUDY DESIGN Prospective, single-group, single-center feasibility study conducted at the National Cancer Center (NVC), Vilnius, Lithuania. No randomization or control arm.
AI DOCUMENTATION SYSTEM The VOCALI ambient AI system is activated at the start of each eligible consultation. Before the consultation, the physician loads pseudonymized clinical data from the EHR (diagnosis, MDT decision, treatment plan) into the system. The system audio-records the consultation in real time, integrates the preloaded clinical data, and generates a structured draft of the E025 outpatient clinical note in Lithuanian. The physician reviews, edits if necessary, and approves the final document in the EHR. The system does not make autonomous clinical decisions.
PROCEDURES
- Informed consent obtained before consultation, including consent to audio recording.
- PSQ-18 patient satisfaction questionnaire completed by participants on the same day post-consultation (~5 min).
- PDQI-10 documentation quality instrument rated by two independent raters per AI-generated note.
- NASA-TLX physician workload questionnaire administered at baseline (T0), 1 month (T1), and end of enrollment (T2).
- SUS system usability scale completed by physicians at T2.
- Documentation time and draft editing extent (% characters changed) logged automatically by the system.
- Safety incidents reported by physicians within 3 working days; monthly safety review meeting held; immediate notification of the NVC Deputy Director for Medicine and the ethics committee required for level-5 (critical) incidents.
SAMPLE SIZE 250 participants will be enrolled over a 12-month period. As a feasibility study, sample size was determined by practical feasibility and literature precedent rather than formal power calculation. With n = 250, proportions can be estimated with a 95% CI of approximately ±3-4%; if no critical incident is observed, the upper 95% confidence limit is 1.2% (rule of three). Approximately 20% data loss is anticipated.
STATISTICAL ANALYSIS R ≥4.0 or SAS ≥9.4. Descriptive statistics for all outcomes. PDQI-10 mean score with 95% CI and domain-level analysis; documentation time as mean with 95% CI and median with IQR; SUS mean with 95% CI; PSQ-18 subscale means. Inter-rater reliability assessed by rWG coefficient (target ≥0.7). Critical incident rate reported with 95% CI using the Clopper-Pearson method. NASA-TLX change (T2-T0) analyzed by paired t-test or Wilcoxon signed-rank test depending on distribution. All inferential tests are descriptive in intent; results interpreted via confidence intervals.
DATA PROTECTION All participant data are pseudonymized immediately upon enrollment. Audio recordings and transcripts are stored in the VOCALI provider's secure infrastructure under a data processing agreement per GDPR Article 28; the provider has no access to identifiable participant data. Data retained for 5 years after study completion.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Oncology patients undergoing AI-assisted consultation | – | Device: Ambient AI documentation systemAdult 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). |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult oncology patients attending a primary oncologist consultation at the National Cancer Center (Vilnius, Lithuania) following a multidisciplinary team (MDT) decision, with diagnosis and treatment plan established.
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (7 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Ambient AI documentation systemAlso: VOCALIAn ambient artificial intelligence documentation system that records the consultation audio, integrates relevant pseudonymized clinical data from the electronic health record (EHR), generates a speech transcript, and produces a draft clinical note (E025 form) for physician review and approval. The system does not make autonomous clinical decisions. The physician reviews, edits if necessary, and approves all AI-generated content before it is saved to the EHR. | Device | Oncology patients undergoing AI-assisted consultation |
Primary outcome
Documentation Quality - AI-Generated Clinical Documentation Quality (PDQI-10)
Time frame: Per consultation, throughout the enrollment period (up to 12 months)
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.
Documentation Efficiency - Documentation Completion Time
Time frame: Per consultation, throughout the enrollment period (up to 12 months)
Time (in minutes) from opening the AI-generated draft to final approval in the electronic health record (EHR), automatically recorded by the system.
Safety - AI System Incident Rate
Time frame: Per consultation, throughout the enrollment period (up to 12 months)
Frequency of AI documentation incidents classified by a 5-level severity scale (1 = insignificant, 2 = minor, 3 = moderate, 4 = major, 5 = critical). Incidents reported by physicians within 3 working days. Critical incident (level 5) rate reported with 95% confidence interval using the Clopper-Pearson method.
4 secondary outcomes
Physician Subjective Workload (NASA-TLX)
Time frame: Baseline (T0), 1 month (T1), and end of enrollment period (T2, up to 12 months)
System Usability (SUS)
Time frame: End of enrollment period (T2, up to 12 months)
AI Draft Editing Extent
Time frame: Per consultation, throughout the enrollment period (up to 12 months)
Patient Satisfaction with Consultation (PSQ-18)
Time frame: After each consultation, throughout the enrollment period (up to 12 months)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Sep 2026.
- Background: PubMed 41037268Olson 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.
- Background: PubMed 40565474Sasseville 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.
- Background: PubMed 42712320Draper 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.
- Background: PubMed 40314951Stults 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.
- Background: PubMed 41199808Palm 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.
- Individual participant dataSharing planned: No. Individual participant data will not be shared. The ethics-approved protocol and the informed consent do not provide for transfer of individual data to third parties, in accordance with the EU General Data Protection Regulation (GDPR) 2016/679 and Lithuanian data protection legislation. The data include consultation audio recordings and clinical documentation. Only aggregated results will be published in scientific journals and presented at conferences.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Ernestas Šileika principal investigator
National Cancer Center Affiliate of Vilnius University Hospital Santaros Klinikos
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Neoplasms and Clinical Documentation, run by National Cancer Center Affiliate of Vilnius University Hospital Santaros Klinikos at 1 site in Lithuania.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 30 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Lithuania. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Ernestas Sileika, MD (+37060950402, ernestas.sileika@nvc.santa.lt), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Documentation Quality - AI-Generated Clinical Documentation Quality (PDQI-10) (time frame: Per consultation, throughout the enrollment period (up to 12 months)). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07850700, which also lists 5 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Sep 2026.