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

Abstract

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.

Keywords: Clinicogenomic registries; Data infrastructure; Health equity; Interoperability; Precision medicine.

Fig. 1

Healthcare access, equity, and informed consent are all challenges with precision medicine implementation that improved data governance with digital tools can address. Graphics by Sara Nuttle

Fig. 2

Numerous companies are developing technology platforms for clinicogenomic data stewards (including patients and research participants) and data consumers to discover, connect, and transact. Graphics by Sara Nuttle

Fig. 3

Computational tools and data science can augment drug discovery and drug development by enriching the story of both the disease and the drug or therapeutic strategy. Caution is warranted as biased and nonrepresentative data can lead efforts in directions that are not generalizable or repeatable. Graphics by Sara Nuttle

Fig. 4

Health equity and access are long standing social challenges intersecting with a new challenge, patient agency in health data in an increasingly connected healthcare ecosystem. Graphics by Sara Nuttle