Provider and Patient-generated Remote Oro-Dental Health Electronic Data Capture for Algorithmic Longitudinal Evaluation and Risk-Assessment (PROHEALER)

提供者和患者生成的远程口腔牙科健康电子数据采集，用于算法纵向评估和风险评估 (PROHEALER)

• 批准号: 10449579
• 负责人: Amy Catherine Moreno
• 金额: $ 15.95万
• 依托单位: UNIVERSITY OF TX MD ANDERSON CAN CTR
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10449579
• 关键词: Acute; Address; Adopted; Aftercare; Algorithms; Area; Assessment tool; Award; COVID-19; Cancer Patient; Cancer Survivor; Caring; Chronic; Clinic; Clinical; Clinical Informatics; Clinical Management; Clinical Trials; Collection; Combined Modality Therapy; Communication; Computer Models; Computerized Medical Record; Data; Data Aggregation; Data Collection; Decision Making; Deglutition Disorders; Dental; Dental Care; Dental Informatics; Dental Records; Dentists; Development; Development Plans; Dimensions; Disease; Electronic Health Record; Evaluation; FAIR principles; Fostering; Future; Gingiva; Goals; Head and Neck Cancer; Health; High Prevalence; Individual; Informatics; International; Interruption; Intervention; Knowledge; Machine Learning; Mandible; Measures; Medical; Mentors; Mentorship; Methodology; Methods; Modeling; Monitor; Morbidity - disease rate; National Institute of Dental and Craniofacial Research; Nomenclature; Oncologist; Ontology; Operative Surgical Procedures; Oral; Oral cavity; Oral health; Osteoradionecrosis; Outcome; Outcome Assessment; Patient Outcomes Assessments; Patients; Periodontal Diseases; Population; Prevention; Process; Provider; Public Health; Public Health Informatics; Quality of life; Radiation; Radiation Dose Unit; Radiation Oncologist; Radiation therapy; Readability; Reporting; Research; Research Design; Research Personnel; Risk; Risk Assessment; Selection for Treatments; Series; Severities; Standardization; Statistical Methods; Structure; Survivors; Symptoms; System; Systems Development; Techniques; Time; Toxic effect; Training; Training Activity; Trismus; United States; United States National Institutes of Health; Vendor; Work; Xerostomia; base; care coordination; career development; clinical center; clinical data repository; clinical practice; craniofacial; data exchange; data quality; design; electronic data; evidence base; experience; formative assessment; implementation barriers; implementation efforts; implementation evaluation; implementation outcomes; implementation science; implementation strategy; implementation study; improved; individualized prevention; innovation; interest; interoperability; learning strategy; machine learning method; machine learning model; malignant oropharynx neoplasm; novel; personalized management; prevent; project-based learning; prospective; risk prediction; risk prediction model; survival outcome; survivorship; symptom management; technology validation; tool; treatment optimization; treatment planning

PROJECT SUMMARY/ABSTRACT Research. Oral cavity and oropharyngeal (OC/OPC) cancers afflict more than 53,000 individuals in the United States annually. Despite advancements in oncologic therapies, the majority of patients will experience significant toxicity burden during and after therapy, including moderate-severe xerostomia, dysphagia, reduced mouth opening (i.e. trismus), periodontal disease, and osteoradionecrosis. Remote electronic symptom monitoring through standardized assessment tools for patient reported outcomes (ePROs) is an evidence-based best practice, particularly in the COVID-19 era, yet few clinical practices have demonstrated sustainability of implementation efforts. To date, acute and chronic orodental complications afflicting OC/OPC survivors are largely managed on empirical knowledge with wide inter-provider management variability based on provider experience and available clinical information which is often incomplete, incorrect, or nonexistent. Therefore, standardization of electronic data capture of PROs and objective measures of provider-assessed orodental toxicity severity remains an unmet public health need. Our central hypothesis is that synchronous optimization of machine-readable patient- and provider-generated data collection can be achieved through prioritization of effective implementation strategies for longitudinal oro-systemic ePRO data collection (Aim 1) and creation of novel dental standards for accurate orodental toxicity reporting in both electronic health and dental records (Aim 2). As a subcomponent to Aim 2, we will also design and pilot a novel radiation odontogram to enhance treatment communication between providers. Accurate risk predictions of high-morbidity high-prevalence post-therapy orodental sequelae using high-quality electronic data from Aims 1 and 2 will be incorporated into a statistically robust machine-learning based model (Aim 3). In summary, the PROHEALER proposal fosters innovative and novel informatics approaches for data-driven risk assessment and algorithmic prevention and management of treatment-related oral health diseases afflicting OC/OPC survivors. Career Development & Training. Dr. Moreno's overarching goal is to become an internationally recognized independent research investigator with domain expertise in advanced radiation therapy techniques, clinical informatics and rigorous toxicity modeling methodologies as they pertain to improving patient quality of life and promoting precision prevention and risk-based interventions for orodental complications. This proposal presents Dr. Moreno's 5-year mentored career development plan which includes mentorship from prominent Established NIH Investigators who have committed to overseeing the progress of the proposed projects and Dr. Moreno's overall professional development. The outlined training activities build upon Dr. Moreno's clinical expertise as a Head and Neck Cancer Radiation Oncologist and her prior work in EHR utility enhancement with the inclusion of a comprehensive didactic and project-based curriculum focused on domain knowledge expansion in dental informatics, implementation science, and advanced statistical methods in risk prediction modeling.

项目摘要/摘要 研究。口腔和口咽（OC/OPC）癌症遭受了53,000多人 每年的状态。尽管肿瘤学疗法的进步，大多数患者都会出现重大 治疗期间和之后的毒性负担，包括中度重度静脉炎，吞咽困难，嘴巴减少 开口（即三症），牙周疾病和骨质膜片。远程电子症状监测 通过标准化的评估工具为患者报告的结果（EPRO）是基于证据的最佳 练习，特别是在Covid-19时代，但很少有临床实践表明可持续性 实施工作。迄今 基于提供商 经验和可用的临床信息通常不完整，不正确或不存在。所以， 电子数据捕获专业人士的标准化和由提供者评估的Orodental的客观度量的标准化 毒性严重程度仍然是未满足的公共卫生需求。我们的中心假设是同步优化 可以通过优先级排序来实现机器可读的患者和提供商生成的数据收集 纵向Oro-Systemic Epro数据收集（AIM 1）的有效实施策略和创建 电子健康和牙科记录中精确的发病毒性报告的新型牙齿标准（AIM 2）。作为AIM 2的子组件，我们还将设计和试行一个新型的辐射态图，以增强治疗 提供者之间的沟通。高多发性高尚疗法的准确风险预测 使用来自AIM 1和2的高质量电子数据的Orodental后遗症将纳入统计上 强大的基于机器学习的模型（AIM 3）。总而言之，ProHealer提案促进了创新和 用于数据驱动风险评估和算法预防和管理的新型信息学方法 与治疗有关的口腔健康疾病困扰OC/OPC幸存者。 职业发展与培训。莫雷诺博士的总体目标是成为国际认可的 具有晚期放射治疗技术领域专业知识的独立研究研究者，临床 信息学和严格的毒性建模方法与改善患者生活质量和 促进预防精确的预防和基于风险的干预措施。该提案提出 Moreno博士的5年指导职业发展计划，其中包括来自著名的既定的指导 致力于监督拟议项目的进度和莫雷诺博士的NIH调查人员 整体专业发展。概述的培训活动以莫雷诺博士的临床专业知识为基础 头颈癌肿瘤学家及其先前在EHR实用程序增强方面的工作 综合教学和基于项目的课程，重点介绍牙科领域知识的扩展 风险预测建模中的信息学，实施科学和高级统计方法。