Mapping the sociotechnical ecosystem of precision medicine

在《21世纪世纪治愈法案》的激励下， ORIEN和APOLLO等快速增长的数据网络将卫生系统、工业 合作伙伴和研究人员的基因组，生理，诊断，治疗，研究和移动的健康数据 数以百万计的个体。精准医疗是一个社会技术学习卫生系统，由动态， 复杂环境中人员、流程、政策和技术之间的协作关系。在 社会技术系统，技术和社会环境相互建构，因此， 技术通常是构成医疗保健复杂系统的关键组成部分， 是将标准和使用融入实践的社会承诺。例如，学术医学 中心正在采用精准医疗方法和技术，并根据 无论他们的目标是提高快速有效的临床管理、研究性试验的能力，还是 大规模的基础研究。局部目标决定了后续的测序选择（全基因组， 靶向组或整个外显子组），如何测序（文库制备方法，平台，制造商），以及 物流（速度、体积）。这些选择反过来又对在该领域工作的人产生影响， 他们参与的合作进程，他们遵循的政策，以及下游技术发展。 推动精准医疗的尖端科学需要学习卫生系统的基础设施， 多尺度（个人、诊所/实验室、组织和社会）将大数据转化为知识（例如， BD 2K），将新知识付诸实践，并评估随后的健康结果。 近80%的精准医疗项目专注于癌症，35%的精准医疗项目 在学术医疗中心运行。拟议的研究将通过检查 在五个学术医疗中心的精准肿瘤学项目中学习的社会技术生态系统 公立大学能够建立一个网络的网络，进行跨系统分析，以确定可以 通过政策或实践来解决。拟议的项目将绘制学习的社会技术图， 通过识别参与者（人员，组织），技术，流程， 以及在整个学习周期（数据收集、分析、知识生成， 结果评价）和多个尺度（个人、团体（诊所/实验室）、组织和 在全国五个精准健康实践点（目标1）。然后，我们将进行迭代设计 为精确肿瘤学开发社会技术成熟度模型的过程， 描述系统的进展和发展里程碑的方法，包括社会方面 这通常被认为是独立于技术的（目标2）。我们的综合方法将使 无形的角色可见，并澄清监管现代化和数据知情治理的机会。

Large-scale investment in precision medicine incentivized by the 21st Century Cures Act, as well as other rapidly growing data networks of networks such as ORIEN and APOLLO connect health systems, industry partners, and researchers to the genomic, physiologic, diagnostic, treatment, research, and mobile health data of millions of individuals. Precision medicine is a sociotechnical learning health system, defined by dynamic, collaborative relationships between people, processes, policy and technology in complex environments. In sociotechnical systems, both technology and social settings mutually co-construct one another, so while technology is often a critical component of what constitutes a complex system in healthcare, just as important are the social commitments to integrate standards and usage into practice. For example, academic medical centers are adopting precision medicine methods and technologies and are making decisions based on whether their goal is to increase capacity for quick and efficient clinical management, investigational trials, or large-scale basic research. Local goals shape subsequent choices about what to sequence (whole genome, targeted panels, or whole exome), how to sequence (library preparation methods, platform, manufacturer), and logistics (speed, volume). These choices, in turn, have implications for people working in the field, the cooperative processes they engage, the policies they follow, and downstream technological developments. The cutting-edge science driving precision medicine requires infrastructure of learning health systems at multiple scales (individual, clinic/lab, organization, and societal) to transform big data to knowledge (e.g. BD2K), move new knowledge into practice and evaluate subsequent health outcomes. Nearly 80% of precision medicine programs are focused on cancer, with 35% of precision medicine programs being run at academic medical centers. The proposed research will capture this experience by examining the sociotechnical ecosystem of learning in precision oncology programs at five academic medical centers at public universities enabling a network of networks, cross-systems analysis to identify gaps that can be addressed by policies or practices. The proposed project will map the sociotechnical of learning ecosystem for precision oncology by identifying the actors (people, organizations), technologies, processes, and policies that are operating across the learning cycle (data collection, analysis, knowledge generation, outcome evaluation) and functioning at multiple scales (individual, group (clinic/lab), organizational, and societal) in five precision health practice sites nationwide (Aim 1). We will then conduct an iterative design process to develop a sociotechnical maturity model for precision oncology, adapting an informatics approach to describing the progression and developmental milestones of systems to include the social aspects that are often considered separate from the technical (Aim 2). Our comprehensive approach will make invisible roles visible, and clarify opportunities for regulatory modernization and data-informed governance.