Advancing Diagnostic Excellence For Older Adults Through Collective Intelligence And Imitation Learning
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 40
- 试验地点
- 1
- 主要终点
- Feasibility of embedding the AI CDSS into a primary care visit
研究概览
简要总结
Older adults commonly experience diagnostic errors that may lead to direct harms and increased healthcare costs. Older adults are especially at risk because of higher rates of comorbidity burden, medical complexity, frailty, and cognitive impairment. An artificial intelligence (AI) clinical decision support system (CDSS) offer a promising approach to promote diagnostic excellence for older adults.
The purpose of this study is to assess the acceptability and feasibility of a new AI CDSS for older adults in primary care. The goal of this AI CDSS is to provide diagnostic support during primary care visits (i.e., help make timely and accurate diagnoses) and support communication amongst patients, doctors, and caregivers about the patient's health.
In this study, participants will use the AI CDSS in a primary care visit and review its suggestions for diagnoses and tests. Afterwards, they will complete a feedback survey and interview where they share their thoughts about and experience using the AI CDSS.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Diagnostic
- 盲法
- None
入排标准
- 年龄范围
- 65 Years 至 —（Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Work in the University of Pennsylvania Health System
- •Work in a primary care setting (i.e., Internal Medicine, Geriatric Medicine, Family Medicine, and Penn Primary Care)
- •Actively treat adult patients who are 65 years old or older
- •65 years of age or older
- •Have an upcoming encounter with a participating primary care clinician
- •Indicate a new or worsening health concern that they wish to discuss at the upcoming encounter
- •18 years old or older
- •Accompanying the participating patient during the encounter with the AI CDSS
- •Identified by the patient as a caregiver
排除标准
- •An individual who meets any of the following criteria will be excluded from participation in this study:
- •Under the age of 18 years old
- •Unable to provide informed consent in the opinion of the investigator
- •Has a "Research Do Not Contact" status in the electronic health record
研究组 & 干预措施
Use of INTERLACE tool in primary care visit
All participants will be asked to use INTERLACE in their primary care visit. The tool will be presented on an handheld electronic device and may be used by patients, caregivers, and primary clinicians at each visit. First, the patient will input their current symptoms into INTERLACE. Then, using these symptoms, latest vital signs, and the patient's medical history, INTERLACE will make suggestions for diagnoses and tests. Patients, caregivers, and clinicians can view and discuss these suggestions together to arrive at a potential diagnosis.
干预措施: Artificial intelligence-based clinical decision support tool for diagnostic support (Other)
结局指标
主要结局
Feasibility of embedding the AI CDSS into a primary care visit
时间窗: From enrollment to the end of the study interview, up to two weeks
Participants will complete a Feasibility of Intervention Measure (FIM) via feedback survey following the primary care visit. Responses are measured on a 5-point Likert scale.
Feasibility of embedding the AI CDSS into a primary care visit
时间窗: From enrollment until the end of the study interview, up to two weeks
Feasibility will also be assessed through brief survey questions asking whether participants experienced any barriers to using INTERLACE, whether it was useful during the visit, and how long it was used (all measured on a 7-point Likert scale), as well as open-ended questions in a semi-structured interview.
Acceptability of embedding the AI CDSS into a primary care visit
时间窗: From enrollment until the end of the study interview, up to two weeks
Participants will complete the Acceptability of Intervention Measure (AIM) via feedback survey after the primary care visit. Responses to the AIM are measured on a 5-point Likert scale.
次要结局
未报告次要终点
研究者
Gary Weissman
Assistant Professor of Medicine and Informatics
University of Pennsylvania