Implementing Precision Medicine: Determinants of Adoption in Community Oncology

实施精准医学：社区肿瘤学采用的决定因素

• 批准号: 10115125
• 负责人: Shellie Dawn Ellis
• 金额: $ 22.2万
• 依托单位: UNIVERSITY OF KANSAS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-15 至 2024-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10115125
• 关键词: Address; Administrator; Adoption; Agreement; American; Artificial Intelligence; Attention; Behavioral; Cancer Patient; Code; Communities; Community Clinical Oncology Program; Community Practice; Consumption; DNA Sequence Alteration; Data; Data Set; Development; Diagnosis; Disease; FDA approved; Frequencies; Goals; Human; Individual; Institutes; Interview; Investments; Kansas; Knowledge; Link; Machine Learning; Malignant Neoplasms; Maps; Memory; Molecular Profiling; Motivation; Natural Language Processing; Oncologist; Ontology; Outcome; Pharmaceutical Preparations; Physicians; Precision Medicine Initiative; Prevalence; Privatization; Provider; Quality of life; Regulation; Research; Research Personnel; Resources; Societies; Somatic Mutation; Speed; Structure; Surveys; System; Techniques; Testing; Theoretical Domains framework; Time; Training; Translations; Work; algorithm training; base; cancer care; cancer genome; cancer genomics; clinical practice; comparative effectiveness trial; evidence base; experience; feasibility testing; feature extraction; genome sequencing; health care settings; implementation barriers; implementation process; implementation science; implementation strategy; innovation; precision medicine; psychologic; randomized trial; rapid diagnosis; skills; success; tool; tumor; uptake

ABSTRACT Precision medicine has enormous potential to change cancer outcomes for >500,000 Americans annually by targeting the genetic mutations of their tumors with FDA-approved drugs known to more effectively treat their disease. Thus, accelerating the use of cancer genomics is a national priority with combined public and private investment topping $8 billion a year. Despite high significance and investment, uptake of precision medicine in clinical practice is low. Tumor genome sequencing is not widely used and treatments based on molecular profiling are infrequently implemented. Implementation science is an emerging field which offers a theoretically-informed, evidence-based approach to accelerate the translation of evidence into practice, but has yet to be applied to precision medicine and lacks tools to rapidly diagnose organizational challenges to innovation adoption. Using this approach, we have identified a number of critical gaps in current research on the barriers to precision medicine adoption. Focusing on the needs of community oncologists, who deliver the majority of cancer care in the US, we will: 1) Survey oncologists to identify precision medicine adopters, assess community oncologists' motivations for innovation adoption, and evaluate the degree to which precision medicine aligns with community practice. 2) Conduct linked, semi-structured qualitative interviews of physicians, staff and administrators involved in precision medicine implementation. Using the Theoretical Domains Framework, we will identify constructs key to implementation success and further describe the strength, frequency, and type of implementation strategies used by successful organizations. 3) Assess the feasibility of using natural language processing to more rapidly diagnose adoption and implementation barriers. We will apply an ontology of barriers and facilitators to data collected in Aim 2 and three extant qualitative datasets exploring innovation adoption. We will develop and train an automated feature extraction system to code data sets and compare congruence of results from human coding and artificial intelligence. This work is expected to advance precision medicine, implementation science and cancer outcomes. Aim 1 will allow the first estimate of precision medicine adoption in community oncology. Barriers to precision medicine implementation identified in Aims 1 and 2 will be mapped to strategies known to be effective in addressing them, enabling a randomized trial of the comparative effectiveness of precision medicine implementation strategies. Ontologies and machine learning developed in Aim 3 will contribute to a larger machine learning effort launched this year to expedite the selection of effective, tailored implementation strategies. Ultimately, this work is expected to expedite society's return on investment in the precision medicine initiative and contribute to cancer patients' longer survival and enhanced quality of life.

摘要 精准医疗具有巨大的潜力，每年可以改变超过50万美国人的癌症结果， 用FDA批准的药物靶向他们肿瘤的基因突变，这些药物已知能更有效地治疗他们的肿瘤。 疾病因此，加速癌症基因组学的使用是国家的优先事项， 每年投资超过80亿美元。尽管有很高的意义和投资， 临床实践水平低。肿瘤基因组测序没有被广泛使用，并且基于分子谱的治疗 很少执行。实施科学是一个新兴的领域，它提供了一个理论上知情， 以证据为基础的方法，以加快将证据转化为实践，但尚未应用于 精准医疗和缺乏工具来快速诊断创新采用的组织挑战。使用 这种方法，我们已经确定了一些关键的差距，在目前的研究障碍，精度 医学收养关注社区肿瘤学家的需求，他们提供了大部分的癌症护理， 美国方面，我们会： 1)调查肿瘤学家，以确定精准医学采用者，评估社区肿瘤学家的动机， 创新采用，并评估精准医疗与社区实践的一致程度。 2)对参与的医生、工作人员和管理人员进行联系的、半结构化的定性访谈， 精准医疗的实施使用理论领域框架，我们将识别结构 实施成功的关键，并进一步描述实施的力度、频率和类型 成功的组织所使用的策略。 3)评估使用自然语言处理来更快速地诊断采用情况的可行性， 实施障碍。我们将把障碍和促进者的本体应用于目标2中收集的数据， 三个现存的定性数据集探索创新采用。我们将开发和培训一个自动化的 特征提取系统，用于对数据集进行编码并比较来自人类编码的结果的一致性， 人工智能 这项工作预计将推动精准医学，实施科学和癌症结果。目标1将 允许对社区肿瘤学中的精准医学采用进行首次估计。精准医疗的障碍 目标1和2中确定的执行工作将与已知能有效解决这些问题的战略相对应， 能够对精准医疗实施策略的比较有效性进行随机试验。 Aim 3中开发的本体和机器学习将有助于启动更大的机器学习工作 今年，我们将加快选择有效的、有针对性的执行战略。最终，这项工作是 预计将加快社会对精准医疗计划的投资回报，并有助于癌症 患者的生存期更长，生活质量更高。