Health equity innovation in precision medicine: data stewardship and agency to expand representation in clinicogenomics

被引：0

作者：

Patrick J. Silva ^{[1
]}

Vasiliki Rahimzadeh ^{[2
]}

Reid Powell ^{[3
]}

Junaid Husain ^{[1
]}

Scott Grossman ^{[2
]}

Adam Hansen ^{[4
]}

Jennifer Hinkel ^{[5
]}

Rafael Rosengarten ^{[6
]}

Marcia G. Ory ^{[7
]}

Kenneth S. Ramos ^{[8
]}

机构：

[1] School of Medicine,Texas A&M Health

[2] Texas A&M Institute for Bioscience and Technology,Department of Environmental and Occupational Health, Center for Population Health and Aging

[3] Baylor College of Medicine,undefined

[4] Greater Houston Healthconnect,undefined

[5] Merck and Co.,undefined

[6] Geneial,undefined

[7] The Data Economics Company,undefined

[8] Genialis,undefined

[9] Alliance for Artificial Intelligence in Healthcare,undefined

[10] Texas A&M University School of Public Health,undefined

[11] Texas A&M System,undefined

来源：

Health Research Policy and Systems | / 22卷 / 1期

关键词：

Health equity; Precision medicine; Clinicogenomic registries; Data infrastructure; Interoperability;

D O I：

10.1186/s12961-024-01258-9

中图分类号：

学科分类号：

摘要：

Most forms of clinical research examine a very minute cross section of the patient journey. Much of the knowledge and evidence base driving current genomic medicine practice entails blind spots arising from underrepresentation and lack of research participation in clinicogenomic databases. The flaws are perpetuated in AI models and clinical practice guidelines that reflect the lack of diversity in data being used. Participation in clinical research and biobanks is impeded in many populations due to a variety of factors that include knowledge, trust, healthcare access, administrative barriers, and technology gaps. A recent symposium brought industry, clinical, and research participants in clinicogenomics to discuss practical challenges and potential for new data sharing models that are patient centric and federated in nature and can address health disparities that might be perpetuated by lack of diversity in clinicogenomic research, biobanks, and datasets. Clinical data governance was recognized as a multiagent problem, and governance practices need to be more patient centric to address most barriers. Digital tools that preserve privacy, document provenance, and enable the management of data as intellectual property have great promise. Policy updates realigning and rationalizing clinical data governance practices are warranted.

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