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. 2025 Jul 23;9(4):e70027.
doi: 10.1002/lrh2.70027. eCollection 2025 Oct.

Advancing the science of genomic learning healthcare systems

Teri A Manolio  1 , Renee Rider  1 , Carol J Bult  2 , Rex L Chisholm  3 , Patricia A Deverka  4   5 , Geoffrey S Ginsburg  6 , Eric D Green  1 , Gail P Jarvik  7 , George A Mensah  8 , Jahnavi Narula  1 , Erin M Ramos  1 , Mary V Relling  9 , Dan M Roden  10 , Robb Rowley  1 , Noura S Abul-Husn  11   12 , Adam H Buchanan  13 , Christopher G Chute  14 , Guilherme Del Fiol  15 , Gai Elhanan  16 , Susanne B Haga  17 , Rizwan Hamid  18 , Carol R Horowitz  19 , Peter J Hulick  20   21 , Cynthia A James  22 , Janina M Jeff  23 , Bruce Korf  24 , Latrice Landry  25 , Deven McGraw  26 , Howard L McLeod  27 , Nancy J Mendelsohn  28 , Travis Osterman  29 , Casey Overby Taylor  30 , Daryl Pritchard  31 , Heidi L Rehm  32   33 , Krystal S Tsosie  34 , Jason L Vassy  32   35   36 , Karriem Watson  37 , Ken Wiley Jr  38 , Marc S Williams  13
Affiliations

Advancing the science of genomic learning healthcare systems

Teri A Manolio et al. Learn Health Syst. .

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.

Keywords: genomic medicine; genomics; informatics.

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Conflict of interest statement

Noura Abul‐Husn is an employee and equity holder of 23andMe and serves on the scientific advisory board for Allelica. Adam Buchanan owns an equity stake in MeTree and You, Inc. Janina M. Jeff is employed by Illumina. Nancy Mendelsohn was an employee of Optum Health at the time this work was conducted. Travis Osterman receives research funding from GenomOncology and advises eHealth Technologies and MD Outlook. Daryl Pritchard is employed by the Personalized Medicine Coalition. Heidi Rehm receives research funding from Illumina and Microsoft and is a compensated member of the Genome Medical Scientific Advisory Board. Carol Bult, Rex Chisholm, Christopher Chute, Guilherme Del Fiol, Patricia Deverka, Gai Elhanan, Geoffrey Ginsburg, Eric Green, Susanne Haga, Rizwan Hamid, Carol Horowitz, Peter Hulick, Cynthia James, Gail Jarvik, Bruce Korf, Latrice Landry, Howard McLeod, Teri Manolio, George Mensah, Jahnavi Narula, Erin Ramos, Mary Relling, Renee Rider, Dan Roden, Robb Rowley, Casey Overby Taylor, Krystal Tsosie, Jason Vassy, Karriem Watson, Ken Wiley, and Marc Williams have no conflicts to declare.

Figures

FIGURE 1
FIGURE 1
Example of EHR algorithm providing actionable information on possible genetic diagnosis of kidney disease at the point of care, with additional information available if desired. (M. Williams, personal communication).
FIGURE 2
FIGURE 2
Example of genomic report visualization that can be communicated at the point of care to users (both clinicians and patients) with limited knowledge of genomics.
FIGURE 3
FIGURE 3
Longitudinal multi‐omics and wearable data enabled deep phenotyping for precision health. Omics and non‐omics data across times (T1–Tn) could be integrated using machine learning and deep learning approaches to predict disease risk, subtyping, biomarker discovery, molecular insights, and response to treatment, among others.
See this image and copyright information in PMC

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