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: 10179033
• 负责人: Kuang-Yi Wen
• 金额: $ 21.88万
• 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 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10179033
• 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 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发生在65岁及以上的患者中。这个年龄组有 发病率增加， 多种共病，老年综合征，工具依赖 活动和日常生活活动，以及反映衰老异质性的多药作用，并影响 老年人的治疗效果。临床指南建议常规使用综合 老年癌症患者的老年评估（CGA），有可能识别和优化非肿瘤治疗 问题和改变化疗决定，提高治疗耐受性。然而，CGA是时间密集型的 由于缺乏足够的医疗资源，让每一个患有癌症的老年人都去看老年病医生是不可行的。 老年病学家G8是一份包含八项内容的调查问卷，被推荐作为一种简短的筛选工具， 通常由具有良好敏感性的卫生保健提供者管理，以识别最有可能 从CGA中受益。然而，G8在常规肿瘤临床实践中的实施情况喜忧参半 由于临床和系统障碍导致次优摄取。为了填补空白我们建议用一个病人- 中介实现方法，特别是采用聊天机器人技术来提供结构化的集合 G8筛选问题，可以模拟现实生活中的对话，如与一个 卫生保健提供者。我们提出的聊天机器人应用程序JeffSeniorChat将促进G8筛选， 支持性和教育性的内容，以解释八国集团工具的目的和好处，每个问题， 帮助回忆的示例和探针，以及G8评估完成后的后续步骤，以及 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，健康传播 实践和我们的初步形成工作，JeffSeniorChat将迭代开发和完善，通过 使用患者利益相关者输入和基于证据的简明语言内容的快速原型制作过程， 可用性测试，以最终确定干预措施。我们会研究 JeffSeniorChat干预针对150名老年人完成G8自我评估和参加CGA 得了癌症总体G8分数将由JeffSeniorChat后端算法自动计算， G8评分≤ 14的患者将被提示在Jefferson的高级成人肿瘤中心安排CGA。 可行性将由预先指定的入组人数和G8完成率确定。可接受性将是 通过参与者报告的干预可信度和满意度来衡量。在G8评分≤ 14的患者中 参考CGA，探索性结果将检查CGA出勤依从性， JeffSeniorChat收集了G8评分，并由CGA确定最终的虚弱指定， 通过随访访谈了解CGA非依从性患者的障碍。将G8分流流程简化为CGA 通过支持聊天机器人的患者自我报告评估和干预， 效率和提高CGA出勤率，最终优化治疗选择和结果。