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。这个年龄段的人有 通过以下方式增加发病率 多种合并症，老年综合征，对仪器的依赖 日常生活能力和活动能力，以及反映老龄化异质性的多药并影响着 老年人的治疗结果。临床指南建议常规使用综合型 老年癌症患者识别和优化非肿瘤性疾病的老年评估(CGA) 改变化疗决定，提高治疗耐受性。然而，CGA是时间密集型的 而且，由于缺乏足够的医疗资源，让每个患有癌症的老年人都去看老年医生是不可行的。 老年病医生。G8是一份包含八个项目的问卷，已被推荐作为一种简短的筛选工具，它是 通常由具有良好敏感性的医疗保健提供者管理，以确定哪些患者最有可能 受益于CGA。然而，G8在常规肿瘤学临床实践中的实施情况喜忧参半 结果由于临床和系统障碍，摄取率不太理想。为了填补这一空白，我们建议使用一个病人- 中介式实现方法，特别是采用聊天机器人技术来交付结构化集合 在G8筛选问题中，可以模拟现实生活中的对话所经历的内容，例如使用 保健医生。我们建议的聊天机器人应用程序JeffSeniorChat将促进G8筛查 支持性和教育性内容，解释G8工具的目的和好处，每个问题 帮助召回的示例和调查，G8评估完成后的下一步，以及如何 结果可能会影响他们的癌症治疗。以健康信念模式为指导，做好健康传播 实践和我们的初步形成工作，JeffSeniorChat将通过一个 使用患者-利益相关者输入和基于证据的通俗易懂的内容和 可用性测试以最终确定干预措施。我们会研究这项计划的可行性和认受性。 针对150名老年人的G8自我评估完成和CGA参与的JeffSenorChat干预 得了癌症。G8总分将由JeffSeniorChat后端算法自动计算，并 G8评分为≤14的患者将被提示在杰斐逊老年成人肿瘤学中心安排CGA。 可行性将取决于预先指定的招生人数和八国集团的完成率。可接受性将是 以参与者报告的干预可信度和满意度来衡量。在G8评分的患者中，≤为14 参考CGA，探索性结果将检查CGA出勤率合规性， JeffSeniorChat使用CGA确定的最终脆弱性名称收集G8分数，并认为 CGA未依从性患者通过随访访谈中的障碍。将G8-分类流程简化为CGA 通过启用聊天机器人的患者自我报告评估和干预应改善临床 提高治疗效率，提高CGA出席率，最终优化治疗选择和结果。