A chatbot-powered G8 screening intervention to facilitate referrals to a comprehensive geriatric assessment among older adults with cancer

由聊天机器人驱动的 G8 筛查干预措施，有助于将患有癌症的老年人转介到进行全面的老年评估

• 批准号: 10459431
• 负责人: Kuang-Yi Wen
• 金额: $ 17.87万
• 依托单位: THOMAS JEFFERSON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10459431
• 关键词: Activities of Daily Living; Address; Adopted; Adult; Aging; Algorithms; American Society of Clinical Oncology; Cancer Patient; Caring; Clinical; Clinical Oncology; Decision Making; Dependence; Education; Educational Materials; Elderly; Enrollment; Geriatric Assessment; Goals; Guidelines; Health Personnel; Heterogeneity; Incidence; International; Intervention; Interview; Knowledge; Language; Life; Malignant Neoplasms; Measures; Mediating; Medical Care Team; Modeling; Morbidity - disease rate; National Comprehensive Cancer Network; Nurses; Oncologist; Oncology; Outcome; Participant; Patient Noncompliance; Patient Self-Report; Patients; Polypharmacy; Predictive Value; Process; Questionnaires; Readiness; Recommendation; Reporting; Resources; Schedule; Screening procedure; Selection for Treatments; Self Assessment; Societies; Specific qualifier value; Structure; Subgroup; Syndrome; System; Technology; Testing; Text Messaging; Time; Training; Treatment outcome; Triage; Work; acceptability and feasibility; age group; brief screening; cancer diagnosis; cancer therapy; care coordination; chatbot; chemotherapy; clinical practice; communication behavior; comorbidity; data sharing; design; digital health; evidence base; experience; follow-up; frailty; health belief; health communication; health information technology; human old age (65+); implementation barriers; implementation intervention; improved; innovation; older patient; patient engagement; patient subsets; prototype; remote consent; risk perception; satisfaction; screening; therapy design; tool; treatment optimization; uptake; usability

The majority of cancer diagnoses and morbidity occurs in patients age 65 and over. This age group has increased incidence of by multiple co-morbid conditions, geriatric syndromes, dependence in instrumental activities and activities of daily living, and polypharmacy reflecting the heterogeneity of aging and influence the treatment outcomes for older adults. Clinical guidelines recommend the routine use of a Comprehensive Geriatric Assessment (CGA) for older cancer patients with the potentials to identify and optimize nononcologic issues and alter chemotherapy decision and improve treatment tolerance. However, a CGA is time intensive and it is not feasible for every older adult with cancer to be seen by a geriatrician given the shortage of geriatricians. G8, an eight-item questionnaire, has been recommended as a brief screening tool which is usually administered by a health care provider with good sensitivity to identify patients who are most likely to be benefit from a CGA. However, the implementation of the G8 in routine oncology clinical practice has mixed results with sub optimal uptake due to clinical and system barriers. To fill the void, we propose using a patient- mediated implementation approach, specifically adopting the Chatbot technology to delivering a structured set of G8 screening questions that can simulate the content experienced by real-life conversation such as with a health care provider. Our proposed Chatbot application, JeffSeniorChat, will facilitate G8 screening with supportive and educational content to explain the purpose and benefit of the G8 tool, each question with examples and probes to help with recall, and next steps after the G8 assessment is completed and how the results might impact their cancer treatments. Guided by the Health Belief Model, health communication best practices and our preliminary formative work, JeffSeniorChat will be iteratively developed and refined through a rapid prototyping process using patient-stakeholder inputs and plain-language evidence-based content and usability testing to finalize the intervention. We will examine the feasibility and acceptability of the JeffSeniorChat intervention targeting G8 self-assessment completion and CGA attendance in 150 older adult with cancer. Overall G8 score will be automatically calculated by the JeffSeniorChat backend algorithm and patients with G8 scores ≤ 14 will be prompted to schedule a CGA at Jefferson’s Senior Adult Oncology Center. Feasibility will be determined by pre-specified enrollment and G8 completion rate. Acceptability will be measured by participant reported intervention credibility and satisfaction. Among patients with G8 scores ≤ 14 referred to a CGA, exploratory outcomes will examine CGA attendance compliance, predictive value of the JeffSeniorChat collected G8 score with the final frailty designation determined by the CGA, and perceived barriers among CGA non-adherent patients via follow-up interviews. Streamlining G8-triage process to a CGA through the Chatbot-enabled patient self-reported assessment and intervention should improve clinical efficiency and improve CGA attendance rate, ultimately optimizing treatment selection and outcomes.

大多数癌症诊断和发病率发生在65岁及以上的患者中。这个年龄段有 增加事件的事件 多种合并症，老年综合症，依赖性 日常生活的活动和活动，以及一多兵，反映了衰老的异质性和影响 老年人的治疗结果。临床指南建议常规使用综合 老年癌症患者的老年评估（CGA），具有识别和优化非营养的潜力 问题并改变化学疗法决策和改善治疗耐受性。但是，CGA是时间密集的 鉴于短缺 老年医生。 G8是一项八项调查表，已被建议作为一种简短的筛选工具 通常由卫生保健提供者管理良好的敏感性，以识别最有可能的患者 受益于CGA。但是，在常规肿瘤学临床实践中实施G8的实施不同 由于临床和系统障碍而引起的最佳摄取结果。为了填补空白，我们建议使用患者 介导的实施方法，专门采用聊天机器人技术来提供结构化集合 G8筛选问题可以模拟现实生活对话所经历的内容，例如 医疗服务提供方。我们提出的聊天机器人应用程序Jeffseniorchat将有助于G8筛选 支持和教育内容以解释G8工具的目的和好处，每个问题都带有 示例和问题以帮助召回，以及G8评估完成后的下一步以及如何完成 结果可能会影响他们的癌症治疗。在健康信念模型的指导下，健康沟通最好 实践和我们的初步形成性工作，Jeffseniorchat将通过迭代和完善 使用患者利益相关者的投入和基于循证的循证内容的快速原型制定过程， 可用性测试以最终确定干预措施。我们将研究 Jeffseniorchat干预措施针对G8自我评估完成和150名老年人的CGA参加 癌症。总体G8分数将由Jeffseniorchat后端算法自动计算，并且 G8分数≤14的患者将提示在杰斐逊高年级成人肿瘤学中心安排CGA。 可行性将由预先指定的入学率和G8完成率确定。可接受性将是 通过参与报告的干预信誉和满意度来衡量。在G8分数≤14的患者中 转介到CGA，探索结果将检查CGA出勤率的合规性，预测价值 Jeffseniorchat通过CGA确定的最终脆弱设计收集了G8评分，并感知到 CGA非依从患者之间的障碍通过后续访谈。将G8-Triage过程简化为CGA 通过支持聊天机器人的患者自我报告的评估和干预应改善临床 效率并提高CGA出勤率，最终优化治疗选择和结果。