{"id":17491,"date":"2021-04-20T09:38:43","date_gmt":"2021-04-20T13:38:43","guid":{"rendered":"https:\/\/healthit-gov.go-vip.net\/buzz-blog\/?p=17491"},"modified":"2026-01-06T05:44:44","modified_gmt":"2026-01-06T05:44:44","slug":"health-it-advances-precision-medicine","status":"publish","type":"post","link":"https:\/\/healthit.gov\/blog\/insights-updates\/health-it-advances-precision-medicine\/","title":{"rendered":"Health IT Advances Precision Medicine"},"content":{"rendered":"\n

Precision medicine creates <\/span>treatment<\/span> and prevention strategies <\/span>that meet patient needs by considering individual variability in their genome, environment, and lifestyle. While precision medicine can revolutionize health care, it requires access to an unprecedented volume and variety of data, and an infrastructure that rapidly brings new discoveries to bear.  <\/span>Recently<\/span>,<\/span> we published an article in the <\/span>Journal of the American Medical Informatics Association<\/span><\/i><\/a> <\/span><\/i>that describes health IT\u2019s vital role in precision medicine based on key insights from a series of <\/span>ONC projects<\/span><\/a> supporting the Precision Medicine Initiative.<\/span> <\/span><\/p>\n\n\n\n

Four key health IT needs for precision medicine are:<\/span> <\/span><\/p>\n\n\n\n

    \n
  1. D<\/span>et<\/span>ermining requirements for data<\/span> <\/span><\/li>\n\n\n\n
  2. Building robust infrastructure<\/span> <\/span><\/li>\n\n\n\n
  3. I<\/span>ntegrating precisi<\/span>on medicine into clinical care<\/span> <\/span><\/li>\n\n\n\n
  4. Developing relevant policies<\/span> <\/span><\/li>\n<\/ol>\n\n\n\n

    Data Requirements<\/span><\/h2>\n\n\n\n

    Electronic health data are widely available thanks to the prevalence of health IT, but not all data are standardized or include\u00a0the\u00a0metadata needed\u00a0for precision medicine. To increase the availability of rich\u00a0data sets, ONC\u2019s projects\u00a0developed methodologies\u00a0for collecting and exchanging\u00a0diverse data types\u00a0using\u00a0standardized APIs.\u00a0\u00a0<\/span><\/p>\n\n\n\n

    These projects also <\/span>highlighted data quality issues such <\/span>as accuracy, consistency, completeness, and usefulness. In particular<\/span>,<\/span> <\/span>these projects<\/span> <\/span>highlighted <\/span>a trade<\/span>–<\/span>off between richness of dat<\/span>a and simplicity of standards. <\/span> <\/span><\/p>\n\n\n\n

    Standardizing data generally results in improved data quality and consistency, but the use of data standards does not always result in data standardization due to inconsistent implementation of current standards and the use of proprietary standards, creating a <\/span>constant <\/span>need for harmonization.<\/span> <\/span><\/p>\n\n\n\n

    Robust\u00a0Infrastructure<\/span><\/b><\/h2>\n\n\n\n

    Precision medicine requires robust health IT infrastructure and additional functionality may be needed to capture, access, and share data in an interoperable manner. <\/span> <\/span><\/p>\n\n\n\n

    The projects<\/span> focused on building<\/span> functionality <\/span>that allowed participants<\/span> to capture data<\/span> from outside clinical care,<\/span> access data from other informatio<\/span>n systems,<\/span> and advance interoperability through <\/span>the <\/span>use of standardized functions<\/span>.<\/span> <\/span><\/p>\n\n\n\n

    Projects revealed that testing and validation activities are important for successfully implementing new functionality, but these activities can often be <\/span>resource-intensive<\/span>. The projects also revealed other infrastructure considerations and challenges. For example, firewalls <\/span>impede<\/span>d<\/span> access to data sources such as devices that reside<\/span>d<\/span> outside the firewall, and some sites did not have access to adequate data storage and management resources.<\/span> <\/span><\/p>\n\n\n\n

    Clinical Integration<\/span><\/b><\/h2>\n\n\n\n

    Several projects tested the ability to integrate precision medicine data into clinical systems. The projects reinforced the importance of clinical workflow consideration but <\/span>also <\/span>the <\/span>need <\/span>to augment data with educational materials to understand and communicate genomic information.<\/span> <\/span><\/p>\n\n\n\n

    Genetic test results shared in PDF format were not easily integrated into an EHR. Clinical and laboratory information systems had difficulty accommodating complex genomic use cases, forcing development of customized implementation guides which <\/span>limit<\/span>ed<\/span> the guides\u2019 utility across provider organizations.<\/span> <\/span><\/p>\n\n\n\n

    The projects <\/span>also <\/span>revealed the importance of using standards that are device or wearable agnostic, which facilitates integrating data from such devices into clinical care<\/span>.<\/span> <\/span><\/p>\n\n\n\n

    Policy<\/span><\/b>\u00a0Development\u00a0<\/span><\/h2>\n\n\n\n

    As data are collected from individuals, there are <\/span>new <\/span>policy considerations for privacy and security, ethics, and data s<\/span>haring. <\/span>If highly identifiable patient information would be shared with different clinical entities and researchers, this may create privacy risks. <\/span> <\/span><\/p>\n\n\n\n

    Even though projects used<\/span> <\/span>existing<\/span> standards<\/span>,<\/span> <\/span>authenticating<\/span> users and devices across multiple settings remains a challenge<\/span>. A<\/span>lign<\/span>ing<\/span> security and permission<\/span>s<\/span> across <\/span>disparate <\/span>systems often resulted in<\/span> time-consuming<\/span> connection problems.<\/span> <\/span> <\/span><\/p>\n\n\n\n

    Projects also identified concerns <\/span>about <\/span>industry-wide use of access control and security standards, as well as the need for clarity <\/span>on <\/span>how genomic data<\/span> <\/span>are protected under current policies and legislation<\/span>.<\/span> <\/span><\/p>\n\n\n\n

    Continuing Progress<\/span><\/b> <\/span><\/h2>\n\n\n\n

    ONC\u2019s projects have begun to address health IT precision medicine needs.  However, gaps remain in routinely collecting diverse data types and anticipating future data needs; quality and completeness of collected data; broad use of relevant standards; use of tools for implementation testing and validation; and use of privacy preserving techniques.<\/span> <\/span><\/p>\n\n\n\n

    ONC<\/span> is advancing high-priority <\/span>health data <\/span>standards <\/span>and developing a better understanding of the current API and app ecosystem. <\/span>ONC\u2019s <\/span>21<\/span>st<\/span> Century Cures Act Final Rule requires the adoption of the <\/span>United States Core Data for Interoperability<\/span><\/a> (USCDI) standard, which will help improve standardized data sharing needed for precision medicine. Recent <\/span>LEAP in Health IT<\/span><\/a> awards will <\/span>address research functionality through open, FHIR\u00ae-based health IT tools and platforms that will benefit precision medicine.  <\/span> <\/span><\/p>\n\n\n\n

    ONC will continue to work in close collaboration with<\/span> stakeholders at<\/span> <\/span>All of Us <\/span><\/i>Research Program<\/span><\/a> <\/span>and NIH broadly as <\/span>NIH is encouraging use of FHIR (and USCD<\/span>I<\/span>)<\/span><\/a> <\/span>in research applications. <\/span> <\/span><\/p>\n\n\n\n

    Read the latest article in the Journal of the American Medical Informatics Association, <\/span><\/i>Meeting the Challenge: Health IT\u2019s Essential Role in Achieving Precision Medicine<\/span><\/i><\/a>.<\/span><\/i> <\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

    Precision medicine creates treatment and prevention strategies that meet patient needs by considering individual variability in their genome, […]<\/p>\n","protected":false},"author":619,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_selected_menu":"","_show_breadcrumbs":"true","_blog_show_featured_image":false,"footnotes":""},"categories":[1],"archived-category":[708],"featured":[],"topics":[408,418,424,422,417],"class_list":["post-17491","post","type-post","status-publish","format-standard","hentry","category-insights-updates","archived-category-precision-medicine","topics-care-continuum","topics-interoperability","topics-scientific-initiatives","topics-standards-technology","topics-usability-provider-burden"],"acf":{"blog_authors":[{"blog_author_profiles":"173423"},{"blog_author_profiles":"198667"},{"blog_author_profiles":"198709"},{"blog_author_profiles":"198708"},{"blog_author_profiles":"198667"}],"hp_news_hide":false},"yoast_head":"\nHealth IT Advances Precision Medicine - ONC Blog<\/title>\n<meta name=\"description\" content=\"Explore how health IT supports precision medicine by standardizing data, enhancing infrastructure, and integrating genomic insights into clinical care. 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