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Silva P, Dahlke DV, Smith ML, Charles W, Gomez J, Ory MG, Ramos KS. An Idealized Clinicogenomic Registry to Engage Underrepresented Populations Using Innovative Technology. J Pers Med 2022; 12:jpm12050713. [PMID: 35629136 PMCID: PMC9144063 DOI: 10.3390/jpm12050713] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 04/18/2022] [Accepted: 04/26/2022] [Indexed: 11/26/2022] Open
Abstract
Current best practices in tumor registries provide a glimpse into a limited time frame over the natural history of disease, usually a narrow window around diagnosis and biopsy. This creates challenges meeting public health and healthcare reimbursement policies that increasingly require robust documentation of long-term clinical trajectories, quality of life, and health economics outcomes. These challenges are amplified for underrepresented minority (URM) and other disadvantaged populations, who tend to view the institution of clinical research with skepticism. Participation gaps leave such populations underrepresented in clinical research and, importantly, in policy decisions about treatment choices and reimbursement, thus further augmenting health, social, and economic disparities. Cloud computing, mobile computing, digital ledgers, tokenization, and artificial intelligence technologies are powerful tools that promise to enhance longitudinal patient engagement across the natural history of disease. These tools also promise to enhance engagement by giving participants agency over their data and addressing a major impediment to research participation. This will only occur if these tools are available for use with all patients. Distributed ledger technologies (specifically blockchain) converge these tools and offer a significant element of trust that can be used to engage URM populations more substantively in clinical research. This is a crucial step toward linking composite cohorts for training and optimization of the artificial intelligence tools for enhancing public health in the future. The parameters of an idealized clinical genomic registry are presented.
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Affiliation(s)
- Patrick Silva
- Health Science Center, Texas A&M University, 8441 Riverside Pkwy, Bryan, TX 77807, USA; (J.G.); (K.S.R.)
- Correspondence: ; Tel.: +1-979-436-9055
| | - Deborah Vollmer Dahlke
- School of Public Health, Texas A&M Health Science Center, 212 Adriance Lab Rd., College Station, TX 77843, USA; (D.V.D.); (M.L.S.); (M.G.O.)
| | - Matthew Lee Smith
- School of Public Health, Texas A&M Health Science Center, 212 Adriance Lab Rd., College Station, TX 77843, USA; (D.V.D.); (M.L.S.); (M.G.O.)
| | - Wendy Charles
- BurstIQ, 9635 Maroon Circle, #310, Englewood, CO 80112, USA;
| | - Jorge Gomez
- Health Science Center, Texas A&M University, 8441 Riverside Pkwy, Bryan, TX 77807, USA; (J.G.); (K.S.R.)
| | - Marcia G. Ory
- School of Public Health, Texas A&M Health Science Center, 212 Adriance Lab Rd., College Station, TX 77843, USA; (D.V.D.); (M.L.S.); (M.G.O.)
| | - Kenneth S. Ramos
- Health Science Center, Texas A&M University, 8441 Riverside Pkwy, Bryan, TX 77807, USA; (J.G.); (K.S.R.)
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