Discovering the Value of Imaging: A Collaborative Training Program in Biomedical Big Data and Comparative Effectiveness Research for the Field of Radiology

发现影像的价值：放射学领域生物医学大数据和比较有效性研究的协作培训项目

• 批准号: 9312810
• 负责人: Constantin F. Aliferis
• 金额: $ 16.76万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-30 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9312810
• 关键词: Address; American; American College of Radiology; Area; Big Data; Canada; Caring; Clinical; Communities; Computerized Medical Record; Cost Effectiveness Analysis; Data; Data Analytics; Data Set; Databases; Decision Analysis; Educational Curriculum; Educational Models; Educational Status; Elements; Ensure; Exposure to; Financial Support; Funding; General Population; Goals; Health; Health Care Costs; Healthcare; Hour; Hybrids; Image; Imaging Techniques; Incidence; Individual; Industry; Institute of Medicine (U.S.); Institutes; Learning; Malignant Neoplasms; Measurement; Medical; Medical Imaging; Mentors; Mentorship; Morphology; New York; North America; Outcome; Pathology; Patient Care; Patient Rights; Patient-Focused Outcomes; Patients; Performance; Persons; Positron-Emission Tomography; Problem Sets; Publications; Radiation; Radiation exposure; Radiology Specialty; Research; Research Personnel; Research Project Grants; Research Training; Resources; Roentgen Rays; SECTM1 gene; Scientist; Services; Severity of illness; Societies; Specific qualifier value; Structure; Technical Expertise; Technology; Time; Training; Training Programs; Treatment Effectiveness; United States; United States Agency for Healthcare Research and Quality; Universities; Work; base; big biomedical data; comparative effectiveness; cost; cost effective; effectiveness research; evidence base; gigabyte; imaging study; implementation science; improved; instructor; interest; learning materials; lectures; medical schools; member; next generation; programs; public health relevance; radiologist; skeletal; skills; symposium; systematic review

 DESCRIPTION (provided by applicant): Over the past 50 years, the cost of health care in the United States has dramatically increased, equaling $2.9 trillion and equating to 17.4% of the Gross Domestic Product (GDP) in 2013. Imaging costs have also significantly increased over the past several years, a cumulative 70% between 2000 and 2010. A large portion of this imaging cost increase was due to an increase in advanced imaging such as CT and MR, which more than doubled from 2000 to 2010. This increase has occurred despite the lack of studies documenting improved patient outcomes and value from these imaging studies. This rise in imaging, particularly that of CT, has also led to significant increases in radiation exposure to patients and potential increased incidence of radiation induced malignancies. To ensure the appropriate and cost effective use of imaging ‒ to make sure that imaging is performed the right way, at the right time, for the right patient's ‒ there is a critical need to perform comparative effectiveness research (CER). One reason for the lack of research in imaging CER is that training in CER, and in the use of biomedical big data that is inherent to CER in imaging, is only available to a very small subset of medical imagers, and is available only at significant cost. Creating effective training in CER and big data analytics for the broader community of medical imagers is beyond the capabilities of individual imaging training programs. In order to address the unique and important need for CER and big data training for imagers, we propose a collaborative and broadly accessible tiered program in CER training and the use of biomedical big data. Tier 1 is a set of 8 lectures on the fundamentals of CER and big data to be available online and also presented at the American Institute for Radiologic Pathology, an intense, 1 month training course attended by 90-95% of all radiology residents in the United States and Canada. Tier 2 is an advanced CER and biomedical big data training program including the potential for continued mentorship in CER and the use of big data, directed toward and available to the medical imaging community. To ensure that this program has the greatest impact, we will use a hybrid educational structure with both online and in-person interactive sessions. Our proposal is unique in that it already has the support of the major national imaging organizations (including the American College of Radiology, American Roentgen Ray Society, Radiological Society of North America, Society of Chairs of Academic Radiology Departments, Association of Program Directors in Radiology, Association of University Radiologists, American Society of Neuroradiology, Society of Skeletal Radiology) as well as members of the imaging industry (including Philips Healthcare and Siemens Medical Solutions).

 描述（由申请人提供）：在过去的50年里，美国的医疗保健成本急剧增加，相当于2.9万亿美元，相当于2013年国内生产总值（GDP）的17.4%。在过去几年中，成像成本也大幅增加，2000年至2010年期间累计增加了70%。这一成像成本的增加很大一部分是由于先进成像的增加，如CT和MR，从2000年到2010年增加了一倍多。尽管缺乏记录这些成像研究改善患者结局和价值的研究，但这种增加还是发生了。这种成像的增加，特别是CT成像的增加，也导致患者的辐射暴露显著增加，并可能增加辐射诱发恶性肿瘤的发病率。为了确保适当和具有成本效益地使用成像检查，以确保在正确的时间以正确的方式对正确的患者进行成像，迫切需要进行比较。 有效性研究（CER）。缺乏成像CER研究的一个原因是，CER培训以及CER在成像中固有的生物医学大数据的使用仅适用于非常小的医学成像人员子集，并且仅以显着的成本提供。为更广泛的医学成像人员社区创建有效的CER和大数据分析培训超出了单个成像培训计划的能力。为了满足成像人员对CER和大数据培训的独特而重要的需求，我们提出了一个合作和广泛访问的CER培训和生物医学大数据使用的分层计划。Tier 1是一套8个关于CER和大数据基础知识的讲座，可在线获得，也可在美国放射病理学研究所（American Institute for Radiologic Pathology）进行，这是一个为期1个月的密集培训课程，美国和加拿大90-95%的放射科住院医师参加了该课程。Tier 2是一个高级CER和生物医学大数据培训计划，包括CER持续指导的可能性和大数据的使用，针对医学成像社区并提供给医学成像社区。为了确保该计划产生最大的影响，我们将使用混合教育结构，包括在线和面对面的互动课程。我们的建议是独一无二的，因为它已经得到了主要国家成像组织的支持（包括美国放射学会、美国伦琴射线学会、北美放射学会、学术放射学系主任学会、放射学项目主任协会、大学放射学家协会、美国神经放射学会、骨骼放射学会）以及成像行业成员（包括飞利浦医疗保健和西门子医疗解决方案）。