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Advancing the science of genomic learning healthcare systems.

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Title: Advancing the science of genomic learning healthcare systems.
Authors: Manolio, Teri A.1 (AUTHOR) manolio@nih.gov, Rider, Renee1 (AUTHOR), Bult, Carol J.2 (AUTHOR), Chisholm, Rex L.3 (AUTHOR), Deverka, Patricia A.4,5 (AUTHOR), Ginsburg, Geoffrey S.6 (AUTHOR), Green, Eric D.1 (AUTHOR), Jarvik, Gail P.7 (AUTHOR), Mensah, George A.8 (AUTHOR), Narula, Jahnavi1 (AUTHOR), Ramos, Erin M.1 (AUTHOR), Relling, Mary V.9 (AUTHOR), Roden, Dan M.10 (AUTHOR), Rowley, Robb1 (AUTHOR), Abul‐Husn, Noura S.11,12 (AUTHOR), Buchanan, Adam H.13 (AUTHOR), Chute, Christopher G.14 (AUTHOR), Del Fiol, Guilherme15 (AUTHOR), Elhanan, Gai16 (AUTHOR), Haga, Susanne B.17 (AUTHOR)
Source: Learning Health Systems. Oct2025, Vol. 9 Issue 4, p1-9. 9p.
Subject Terms: *Best practices, Patient care, Synchronization, Acquisition of data, Clinical decision support systems, Medical genomics, Cooperation
Abstract: Introduction: Identifying key characteristics of exemplar genomic learning healthcare systems (gLHS) and knowledge gaps that can be explored by collaboration among them is likely to accelerate the sharing of best practices and generation of evidence that informs the use of genomics in clinical care. Methods: Deliberations of an expert group convened by the National Human Genome Research Institute (NHGRI) supplemented by relevant literature. Results: Recent advances in genomic data standardization, automated clinical decision support, increased interoperability, and improved genomic technologies have enabled the development of several robust gLHS. They remain concentrated in major academic centers, however, and operate largely independently. Sharing their methods and tools would increase access to these innovations and advance the field. Several gLHS have expressed willingness to collaborate in a coalition designed to gather, evaluate, and disseminate best practices and development needs. Such a coalition has recently been formed under the leadership of NHGRI. Conclusion: Increased collaboration, interoperability, and sharing of genomic information and strategies across gLHS can help define, refine, and disseminate best practices. Such cooperation can improve genomic variant curation and interpretation, diagnostic accuracy, evidence generation, and ultimately patient care through seamless integration of research as an integral component of good clinical care. [ABSTRACT FROM AUTHOR]
Copyright of Learning Health Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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PubType: Academic Journal
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  Data: Advancing the science of genomic learning healthcare systems.
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  Data: <searchLink fieldCode="AR" term="%22Manolio%2C+Teri+A%2E%22">Manolio, Teri A.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> manolio@nih.gov</i><br /><searchLink fieldCode="AR" term="%22Rider%2C+Renee%22">Rider, Renee</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bult%2C+Carol+J%2E%22">Bult, Carol J.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chisholm%2C+Rex+L%2E%22">Chisholm, Rex L.</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Deverka%2C+Patricia+A%2E%22">Deverka, Patricia A.</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ginsburg%2C+Geoffrey+S%2E%22">Ginsburg, Geoffrey S.</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Green%2C+Eric+D%2E%22">Green, Eric D.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jarvik%2C+Gail+P%2E%22">Jarvik, Gail P.</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Mensah%2C+George+A%2E%22">Mensah, George A.</searchLink><relatesTo>8</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Narula%2C+Jahnavi%22">Narula, Jahnavi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ramos%2C+Erin+M%2E%22">Ramos, Erin M.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Relling%2C+Mary+V%2E%22">Relling, Mary V.</searchLink><relatesTo>9</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Roden%2C+Dan+M%2E%22">Roden, Dan M.</searchLink><relatesTo>10</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Rowley%2C+Robb%22">Rowley, Robb</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Abul‐Husn%2C+Noura+S%2E%22">Abul‐Husn, Noura S.</searchLink><relatesTo>11,12</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Buchanan%2C+Adam+H%2E%22">Buchanan, Adam H.</searchLink><relatesTo>13</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chute%2C+Christopher+G%2E%22">Chute, Christopher G.</searchLink><relatesTo>14</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Del+Fiol%2C+Guilherme%22">Del Fiol, Guilherme</searchLink><relatesTo>15</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Elhanan%2C+Gai%22">Elhanan, Gai</searchLink><relatesTo>16</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Haga%2C+Susanne+B%2E%22">Haga, Susanne B.</searchLink><relatesTo>17</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Learning+Health+Systems%22">Learning Health Systems</searchLink>. Oct2025, Vol. 9 Issue 4, p1-9. 9p.
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  Data: *<searchLink fieldCode="DE" term="%22Best+practices%22">Best practices</searchLink><br /><searchLink fieldCode="DE" term="%22Patient+care%22">Patient care</searchLink><br /><searchLink fieldCode="DE" term="%22Synchronization%22">Synchronization</searchLink><br /><searchLink fieldCode="DE" term="%22Acquisition+of+data%22">Acquisition of data</searchLink><br /><searchLink fieldCode="DE" term="%22Clinical+decision+support+systems%22">Clinical decision support systems</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+genomics%22">Medical genomics</searchLink><br /><searchLink fieldCode="DE" term="%22Cooperation%22">Cooperation</searchLink>
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  Data: Introduction: Identifying key characteristics of exemplar genomic learning healthcare systems (gLHS) and knowledge gaps that can be explored by collaboration among them is likely to accelerate the sharing of best practices and generation of evidence that informs the use of genomics in clinical care. Methods: Deliberations of an expert group convened by the National Human Genome Research Institute (NHGRI) supplemented by relevant literature. Results: Recent advances in genomic data standardization, automated clinical decision support, increased interoperability, and improved genomic technologies have enabled the development of several robust gLHS. They remain concentrated in major academic centers, however, and operate largely independently. Sharing their methods and tools would increase access to these innovations and advance the field. Several gLHS have expressed willingness to collaborate in a coalition designed to gather, evaluate, and disseminate best practices and development needs. Such a coalition has recently been formed under the leadership of NHGRI. Conclusion: Increased collaboration, interoperability, and sharing of genomic information and strategies across gLHS can help define, refine, and disseminate best practices. Such cooperation can improve genomic variant curation and interpretation, diagnostic accuracy, evidence generation, and ultimately patient care through seamless integration of research as an integral component of good clinical care. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Learning Health Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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