NHSR Evaluation of AI-Generated Documentation Software to Improve Physician Documentation Efficiency and Reduce Burnout
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 入组人数
- 105
- 试验地点
- 2
- 主要终点
- Pajama Time
研究概览
简要总结
The goal of this rapid, randomized quality improvement trial is to learn if implementing generative AI scribe software can enhance physician documentation efficiency and reduce burnout in outpatient providers at Children's Healthcare of Atlanta facilities. The main questions it aims to answer are:
- Do AI scribes have significant benefits in terms of physician burnout, clinical efficiency, patient experience, and business efficiency?
- Does one vendor outperform another in these measures?
The investigators will compare providers using DAX Copilot and Abridge AI scribe software to a control group using traditional documentation methods to see if AI scribes improve documentation efficiency and reduce burnout.
Participants will:
- Be randomized to one of two AI scribe vendors or control
- Intervention participants may be crossed over to the other vendor mid-trial.
- Collect patient experience scores pre- and post-intervention
- Complete surveys on burnout, efficiency, and fulfillment.
详细描述
Background: While electronic health record (EHR) systems have contributed to advances in patient safety and quality of care, they have also been associated with a significant increase in documentation burden, contributing to burnout among clinicians. This is particularly true for physicians with insufficient time for documentation. In some cases, it has resulted in a reduction in appointment slots to allow for additional documentation time, which in turn decreases patient access to care and physician productivity.
Artificial intelligence (AI) scribes use visits recorded with verbal patient/parental consent and leverage generative AI to create note sections in near-real time that the provider can use and edit as they see fit. In addition, it allows providers to continue to use their documentation templates while adding the generative AI to "smart sections" within their note. This approach has the potential to substantially reduce documentation burden while maintaining documentation preferences of many providers
. This rapid, randomized quality improvement trial aims to assess whether the implementation of generative AI software for documentation can enhance physician documentation efficiency and reduce burnout. It also aims to determine which of two vendors is most effective overall and cost-effective for the health system.
Objectives Quality Improvement Global Aim: To increase provider documentation efficiency and reduce provider burnout related to documentation burden.
Children's Operational Goal: Determine if the cost of ambient AI scribe products (DAX Copilot and Abridge) is justified by reduction in proxies for physician burnout and/or could be offset by seeing more patients in the same time period to improve revenue and patient access.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Crossover
- 主要目的
- Health Services Research
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •1. Provider (MD, DO, APP) in an outpatient practice at Children's Healthcare of Atlanta in a specialty supported by integrated ambient AI scribe software
- •Includes Urgent Care and Emergency Department
- •Excludes providers working in hospital-based clinics due to lack of availability of workflow integration into the EHR
- •Does not currently utilize an in-person human scribe
- •High clinical workload during pilot period
- •Agrees to use the Children's EHR mobile application (Haiku) on their personal device.
- •5. Agrees to offer use of the ambient AI scribe software for at least 75% of patient visit encounters for the duration of the project period
排除标准
- 未提供
结局指标
主要结局
Pajama Time
时间窗: 12 months prior to study start through study completion (total 16 months)
The average number of minutes per scheduled day spent in charting activities outside 7 AM to 5:30 PM on weekdays, time outside scheduled hours on weekends, and time on unscheduled holidays
Time in Notes per Appointment in Minutes
时间窗: 12 months prior to study start through study completion (total 16 months)
The Epic Signal Metric "Time in Notes per Appointment" in minutes.
次要结局
- Professional Fulfillment(At baseline, prior to crossover at 2 months (for those who do crossover) and at study completion (4 months).)
- Burnout(At baseline, prior to crossover at 2 months (for those who do crossover) and at study completion (4 months).)
- Manual Note Contribution(12 months prior to study start through study completion (total 16 months))
- AI Scribe Adoption(Study enrollment through study completion (4 months) for intervention groups)
- Patient/Family Satsifaction(5 visits actively solicited at baseline and 5 visits actively solicited at study completion (4 months))
- wRVU per visit(12 months prior to study start through study completion (total 16 months))