Implementing Pharmacogenomics in Diverse Health Care Systems

在不同的医疗保健系统中实施药物基因组学

• 批准号: 10302548
• 负责人: Nina Sperber
• 金额: $ 45.51万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-22 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10302548
• 关键词: Academic Medical Centers; Administrator; Adoption; Adverse effects; Affect; Area; Benefits and Risks; COVID-19 patient; Clinical; Complex; Consolidated Framework for Implementation Research; Cost Savings; Costs and Benefits; Coupled; Data; Deimplementation; Development; Drug Evaluation; Drug Prescriptions; Ensure; Evaluation; Foundations; Funding; Genetic; Genomics; Genotype; Goals; Growth; Health Insurance; Health Professional; Health Services; Health system; Healthcare Systems; Hemorrhage; Incidence; Individual; Institution; Interview; Knowledge; Methods; Mission; Multicenter Studies; National Human Genome Research Institute; Oral; Outcome; Pathway interactions; Patients; Persons; Pharmaceutical Preparations; Pharmacogenetics; Pharmacogenomics; Phase; Policies; Population; Protocols documentation; Provider; Psychotropic Drugs; Public Health; Recommendation; Research; Respondent; Sampling; Strategic Planning; Structure; System; Technology; Testing; Theoretical Domains framework; United States National Institutes of Health; Work; base; clinical care; cost; evidence base; genetic information; genetic testing; genetic variant; health care delivery; implementation determinants; implementation outcomes; implementation strategy; implementation trial; improved; inhibitor/antagonist; innovation; mathematical algorithm; mathematical methods; novel; percutaneous coronary intervention; pharmacogenetic testing; precision medicine; programs; provider adoption; relative cost; response; routine care; standard care; tool; uptake

Project Summary There is a growing evidence base to support use of genetic information to optimize prescribing decisions; how- ever, implementation has been slow relative to discovery. Health care delivery systems need pragmatic tools to facilitate use of genetic information for prescribing both within and across clinical settings with anticipated growth of this innovation. The long-term goal is to support precision medicine to improve health services and outcomes at a population level. The overall objective is to identify best practices for health systems to use or integrate (i.e., to implement) genomic information within real-world clinical care settings. The rationale for the proposed research is to identify determinants, strategies, and causal pathways in complex, real-world settings in order to develop evidence-based guidance for implementation. This R21 includes two specific aims: 1) Iden- tify determinants and strategies for implementation of genotype-guided prescribing at the institutional level and 2) Determine causal pathways, i.e., how implementation determinants and strategies work together to affect adoption of genotype-guided prescribing by providers. Our sample will include institutions that have imple- mented genotype-guided prescribing to varying degrees (e.g., beginning phase, preemptive policy) outside of academic research funding. For the first aim, data from semi-structured, in-person interviews with multiple stakeholders (administrators, providers, patients) will be elicited and integrated to obtain their perspectives about factors that affect implementation. Interview questions will primarily derive from two implementation de- terminant frameworks, the Theoretical Domains Framework (TDF) and the Consolidated Framework for Imple- mentation Research (CFIR). In the analysis phase, implementation strategies described by respondents will be identified using the Expert Recommendations for Implementing Change (ERIC) compilation. For the second aim, a novel method, Configurational Analysis (CNA), will be applied to data from Aim 1 to reveal what combi- nations of factors make a difference for provider adoption of pharmacogene test orders. CNA uses a mathe- matical approach to identify minimally necessary and sufficient conditions for intermediate outcomes, such as implementation strategies, and desired endpoints, such as pharmacogene test orders, and has not yet been applied to evaluate implementation of genotype-guided prescribing. Qualitative data from Aim 1 will additionally be used to interpret findings. These qualitative and quantitative data are essential for the selection of imple- mentation strategies and tailoring to different settings. In the short-term, this research will enhance tools for implementing genotype-guided prescribing that have been developed by the NIH Consortium Implementing Genomics into Practice (IGNITE). In the longer-term it will provide foundational information for an implementa- tion trial, in which we could assess relative costs and effects of protocols for implementing genotype-guided prescribing under varying conditions. This contribution is expected to be significant because prescribing and outcomes could be optimized if genetic testing were more widely and appropriately deployed in clinical care.

项目摘要 有越来越多的证据支持使用遗传信息来优化处方决策;如何- 与发现相比，实施一直很慢。卫生保健提供系统需要务实的工具， 促进使用遗传信息在临床环境内和跨临床环境开处方， 这种创新的增长。长期目标是支持精准医疗，以改善卫生服务， 在人口水平上。总体目标是确定卫生系统可采用或 集成（即，以实现）真实世界临床护理环境中的基因组信息。的理由 拟议的研究是在复杂的现实世界环境中确定决定因素、策略和因果路径 以便为执行工作制定循证指南。这一R21包括两个具体目标：1）识别- 确定在机构层面实施基因型指导处方的决定因素和策略， 2)确定因果途径，即，实施决定因素和战略如何共同作用， 采用提供者的基因型指导处方。我们的样本将包括具有简单- 不同程度的精神分裂症基因型指导处方（例如，开始阶段，先发制人的政策）之外 学术研究经费。对于第一个目标，数据来自半结构化，面对面访谈， 利益相关者（管理者、提供者、患者）将被引出并整合，以获得他们的观点 影响执行的因素。面试问题将主要来自两个实施方案， 终止框架、理论领域框架（TDF）和实施统一框架。 心理学研究（CFIR）。在分析阶段，答复者所描述的实施战略将 使用实施变更的专家建议（ERIC）汇编确定。第二 目的，一种新的方法，简化分析（CNA），将应用于目标1的数据，以揭示什么组合， 不同国家的因素对提供者采用药物基因检测指令有影响。CNA使用数学- 确定中间结果的最低必要和充分条件的数学方法，例如 实施策略和期望的终点，如pharmacogene测试订单，尚未 用于评估基因型指导处方的实施情况。目标1的定性数据还将 用于解释调查结果。这些定性和定量数据对于选择简单的 心理战略和适应不同的设置。在短期内，这项研究将加强工具， 实施NIH联盟开发的基因型指导处方 基因组学实践（IGNITE）。从长远来看，它将为实施提供基础信息， 试验中，我们可以评估实施基因型指导的方案的相对成本和效果 在不同的条件下开处方。预计这一贡献将是重要的，因为处方和 如果在临床护理中更广泛和更适当地采用基因检测，就可以优化结果。

项目成果

A Mixed-Methods Protocol to Identify Best Practices for Implementing Pharmacogenetic Testing in Clinical Settings.

• DOI: 10.3390/jpm12081313
• 发表时间: 2022-08-13
• 期刊: JOURNAL OF PERSONALIZED MEDICINE
• 作者: Sperber, Nina R.;Cragun, Deborah;Roberts, Megan C.;Bendz, Lisa M.;Ince, Parker;Gonzales, Sarah;Haga, Susanne B.;Wu, R. Ryanne;Petry, Natasha J.;Ramsey, Laura;Uber, Ryley
• 通讯作者: Uber, Ryley