Walton Nephi A, Hafen Brent, Graceffo Sara, Sutherland Nykole, Emmerson Melanie, Palmquist Rachel, Formea Christine M, Purcell Maricel, Heale Bret, Brown Matthew A, Danford Christopher J, Rachamadugu Sumathi I, Person Thomas N, Shortt Katherine A, Christensen G Bryce, Evans Jared M, Raghunath Sharanya, Johnson Christopher P, Knight Stacey, Le Viet T, Anderson Jeffrey L, Van Meter Margaret, Reading Teresa, Haslem Derrick S, Hansen Ivy C, Batcher Betsey, Barker Tyler, Sheffield Travis J, Yandava Bhaskara, Taylor David P, Ranade-Kharkar Pallavi, Giauque Christopher C, Eyring Kenneth R, Breinholt Jesse W, Miller Mickey R, Carter Payton R, Gillman Jason L, Gunn Andrew W, Knowlton Kirk U, Bonkowsky Joshua L, Stefansson Kari, Nadauld Lincoln D, McLeod Howard L
Intermountain Precision Genomics, Intermountain Healthcare, Salt Lake City, UT 84107, USA.
Department of Pediatrics, University of Utah, Salt Lake City, UT 84108, USA.
J Pers Med. 2022 Nov 8;12(11):1867. doi: 10.3390/jpm12111867.
The clinical use of genomic analysis has expanded rapidly resulting in an increased availability and utility of genomic information in clinical care. We have developed an infrastructure utilizing informatics tools and clinical processes to facilitate the use of whole genome sequencing data for population health management across the healthcare system. Our resulting framework scaled well to multiple clinical domains in both pediatric and adult care, although there were domain specific challenges that arose. Our infrastructure was complementary to existing clinical processes and well-received by care providers and patients. Informatics solutions were critical to the successful deployment and scaling of this program. Implementation of genomics at the scale of population health utilizes complicated technologies and processes that for many health systems are not supported by current information systems or in existing clinical workflows. To scale such a system requires a substantial clinical framework backed by informatics tools to facilitate the flow and management of data. Our work represents an early model that has been successful in scaling to 29 different genes with associated genetic conditions in four clinical domains. Work is ongoing to optimize informatics tools; and to identify best practices for translation to smaller healthcare systems.
基因组分析的临床应用迅速扩展,使得基因组信息在临床护理中的可得性和实用性不断提高。我们开发了一种利用信息学工具和临床流程的基础设施,以促进在整个医疗系统中使用全基因组测序数据进行人群健康管理。尽管出现了特定领域的挑战,但我们最终形成的框架在儿科和成人护理的多个临床领域都能很好地扩展。我们的基础设施与现有的临床流程相辅相成,并受到了医护人员和患者的欢迎。信息学解决方案对于该项目的成功部署和扩展至关重要。在人群健康层面实施基因组学,需要使用复杂的技术和流程,而当前的信息系统或现有的临床工作流程并不支持许多卫生系统采用这些技术和流程。要扩展这样一个系统,需要一个由信息学工具支持的坚实临床框架,以促进数据的流动和管理。我们的工作代表了一个早期模型,该模型已成功扩展到四个临床领域的29种不同基因及相关遗传疾病。目前正在进行工作以优化信息学工具,并确定向较小医疗系统推广的最佳实践。