Ignite Creation Date:
2026-03-26 @ 3:16 PM
Ignite Modification Date:
2026-03-30 @ 1:16 AM
Study NCT ID:
NCT07475104
Status:
ENROLLING_BY_INVITATION
Last Update Posted:
2026-03-16
First Post:
2026-03-11
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Redesigning Surgical Care for Patients in Wisconsin
Sponsor:
University of Wisconsin, Madison
Organization:
Study Overview
Official Title:
Redesigning Surgical Care to Support the Health Outcome Goals and Care Preferences for Older Adults: Better Conversations for Better Informed Consent
Status:
ENROLLING_BY_INVITATION
Status Verified Date:
2026-03
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
This study evaluates strategies to train surgeons to use Better Conversations, an evidence-based communication framework designed to improve informed consent by helping patients understand the goals of surgery, the downsides of treatment, and what to expect. Better Conversations supports deliberation, patient preparation, and alignment of decisions with patient goals, addressing known shortcomings in traditional informed consent.
The purpose of this study is to compare two methods of surgeon training: (1) training delivered by an education specialist using audit and feedback, and (2) training supported by computerized automation that identifies elements of Better Conversations in de-identified transcripts of surgical consultations. The central question is whether the automated training program is non-inferior to the specialist-delivered program.
Approximately 60 surgeons from two academic health systems will be randomized to one of these training approaches. Each surgeon will complete a didactic session, have outpatient surgical consultations audio-recorded for feedback, and complete assessment recordings after training. Patients of enrolled surgeons will complete surveys before and after their surgeon's training to evaluate patient-reported communication outcomes.
Findings from this study will assess the effectiveness, feasibility, and acceptability of automated training and support the development of a larger pragmatic study to evaluate the broader effects of Better Conversations on patient outcomes.
The purpose of this study is to compare two methods of surgeon training: (1) training delivered by an education specialist using audit and feedback, and (2) training supported by computerized automation that identifies elements of Better Conversations in de-identified transcripts of surgical consultations. The central question is whether the automated training program is non-inferior to the specialist-delivered program.
Approximately 60 surgeons from two academic health systems will be randomized to one of these training approaches. Each surgeon will complete a didactic session, have outpatient surgical consultations audio-recorded for feedback, and complete assessment recordings after training. Patients of enrolled surgeons will complete surveys before and after their surgeon's training to evaluate patient-reported communication outcomes.
Findings from this study will assess the effectiveness, feasibility, and acceptability of automated training and support the development of a larger pragmatic study to evaluate the broader effects of Better Conversations on patient outcomes.
Detailed Description:
This study evaluates two training strategies to help surgeons use Better Conversations, an evidence-based communication framework that improves informed consent by supporting patient understanding, deliberation, and preparation for surgery. Prior work by the investigative team shows that Better Conversations addresses shortcomings in traditional consent practices, aligns with quality improvement standards, and is well-received by surgeons and patients.
This is a non-inferiority trial comparing (1) audit-and-feedback training delivered by an education specialist and (2) an automated training approach that uses natural language processing and active machine learning to identify key elements of Better Conversations in de-identified transcripts of surgical consultations. Automated output is reviewed and finalized by the education specialist before being shared with surgeons. The hypothesis is that automated training will be non-inferior to specialist-delivered training, with a 5% non-inferiority margin.
The study also evaluates feasibility and acceptability of training a large number of surgeons across two institutions. Feasibility is defined as training at least 85% of surgeons to competence. Acceptability will be assessed using surgeon surveys and exit interviews, and patient-reported outcomes. Patients will complete validated surveys (Feeling Heard and Understood; CAHPS Surgical Care items) before and after their surgeon's training. A subset of patients will participate in interviews regarding their consultation experience. A separate group of patients will consent to have their consultations audio recorded solely for surgeon training or assessment purposes.
Sixty surgeons from UW Health and the Medical College of Wisconsin will be randomized, stratified by site, to one of the two training groups. All surgeons will begin with a 30-minute didactic session, followed by 10 training recordings with corresponding feedback, and 5 assessment recordings used to evaluate competence. Consultations will be audio recorded, transcribed, and de-identified prior to feedback and analysis. Competence will be evaluated using a standardized adherence checklist developed by the study team, assessing both the presence and quality of required communication elements.
This study will generate estimates of within- and between-surgeon variation in communication performance and patient-reported outcomes. These data will inform analytic planning and power calculations for a future pragmatic trial examining the broader impact of Better Conversations on patient decision making, satisfaction, and health outcomes.
This is a non-inferiority trial comparing (1) audit-and-feedback training delivered by an education specialist and (2) an automated training approach that uses natural language processing and active machine learning to identify key elements of Better Conversations in de-identified transcripts of surgical consultations. Automated output is reviewed and finalized by the education specialist before being shared with surgeons. The hypothesis is that automated training will be non-inferior to specialist-delivered training, with a 5% non-inferiority margin.
The study also evaluates feasibility and acceptability of training a large number of surgeons across two institutions. Feasibility is defined as training at least 85% of surgeons to competence. Acceptability will be assessed using surgeon surveys and exit interviews, and patient-reported outcomes. Patients will complete validated surveys (Feeling Heard and Understood; CAHPS Surgical Care items) before and after their surgeon's training. A subset of patients will participate in interviews regarding their consultation experience. A separate group of patients will consent to have their consultations audio recorded solely for surgeon training or assessment purposes.
Sixty surgeons from UW Health and the Medical College of Wisconsin will be randomized, stratified by site, to one of the two training groups. All surgeons will begin with a 30-minute didactic session, followed by 10 training recordings with corresponding feedback, and 5 assessment recordings used to evaluate competence. Consultations will be audio recorded, transcribed, and de-identified prior to feedback and analysis. Competence will be evaluated using a standardized adherence checklist developed by the study team, assessing both the presence and quality of required communication elements.
This study will generate estimates of within- and between-surgeon variation in communication performance and patient-reported outcomes. These data will inform analytic planning and power calculations for a future pragmatic trial examining the broader impact of Better Conversations on patient decision making, satisfaction, and health outcomes.
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: