Modern Genomics in conjunction with Patients IP on the value chain
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Modern genomic technologies and health informatics data have immense potential to enhance our understanding of diseases and personal health, but current medical genetics practices struggle to manage the vast amount of genomic data effectively. Innovative informatics approaches are needed to link genomic data with extensive phenotypic information, such as electronic health records, to foster personalized healthcare and patient empowerment. Addressing privacy concerns and reshaping clinical informatics architectures will be crucial for successfully integrating these advancements while navigating ethical and legal challenges.
Modern Genomics in conjunction with Patients IP on the value chain
- 1. Modern genomic technologies and the ease ofobtaining extensive health informatics data hasan unmatched potential to allow deciphering of the many prevalent diseases and disorders. Furthermore, the advent of other high-throughput, large-scale molecular biology assays such as Exome Sequencing, proteomics, metabolomics, and interact-omics, is expected to provide a deeper insight in to an individual’s genome and its relationship to phenotype 1 . In addition, providing yet another layer of refinement are the miniaturized sensing devices that are now easily accessible and affordable for individuals to (remotely) monitor various everyday health measures such as blood pressure or glucose, cholesterol and urinanalysis 2–4 . Also, environmental parameters such as physical activity, food intake, the ambient temperature, presence of pollutants could also be recorded to complete a personalized and public health ensemble 5–7 . All of these data can be considered relevant for understanding the exposome; their integration and combined analysis looks very promising for advancing biomedical research 7–9 . It is now clear that the current practices in medical genetics can’t be efficiently implemented to cope with and analyse the large amount of genomic data, thereby requiring innovative thinking towards new informatics approaches that could efficiently handle the emerging “Big data” trends 2,3,8 . Indeed, a dire disequilibrium exists at present in our ability to sift through the human genome data and our technological prowess to use that information to directly improve individual patients’ and communal health. The traditional phenotyping as conducted in classical epidemiological settings would severely restrict the venues to elucidate accurate genome-phenome associations 10,11 . Therefore, alternate algorithms in health informatics are called for, in order to thoroughly investigate the extensive and dynamic phenotypes, with eventual goal to understand the disease and acquire as much of the genetic variation across the human population as possible. That is, the best utilization of the extensively available, and upcoming, genomics data will only be realized in its full potential upon proper annotation and correlation with detailed parameters of patient phenotypic data 5 . In order to resolve this information effectively, accessible databasescontaining extensive phenotypic information, such as the electronic medical/health records (EHR) and personal health records (PHR),must be linked to genome sequence data 9 . PHR could include essential real-world elements such as filled prescriptions, data collected in patient registries, biometric data from sensors, Internet searches and social media 6,7,12 . Existing clinical informatics architectures must be modified and restructured in a way that would allow creation of a centralized, evidence-based, iterative information flow, culminating in an accessible resource hub that is ideally searchable, annotated and shared across healthcare systems. PHR is a powerful resource that, when envisioned and integrated with secure oversight, could aid in resolving many of the outstanding roadblocks towards a comprehensive personalized health care 13–15 . It
- 2. would empower patients through their active involvement in the management of their own health and foster greater interoperability among health care providers by cutting down the overall diagnosis costs and establishing a shorter, but efficacious, route to successful treatment. Having said that, privacy concerns, accessibility and return of results has led to low-uptake of such an integrated scheme. Perhaps a defined hierarchy of contract and privacy laws, if implemented in a patient-centric approach could pave way for a smoother amalgamation. Wherein, the patient has rights to sell access to their records, rights superseding those of clinicians and payers 16,17 .Furthermore,patient always retains the uncompromised rights to inspect, copy, distribute and amend their own medical records 13–15 . As policymakers, scientists, and the public grapple with the growing data deluge and concerns about privacy, in-depth research is needed to help construct evidence-based models to tackle the intricately sensitive ethical, legal and regulatory issues associated with generating such networks 18–21 . Although, the vast amount of health data generated and stored around the world each day offers significant breakthrough towards tracing real-time epidemiological trends, forecasting disease outbreaks, and developing personalized care; gathering, analyzing, and distributive sharing of health data is time-consuming, labour- some, expensive, and controversial 2,3,8,19 . Perhaps it is now time to move past the classical dichotomy of privacy or data utility and to seize the possibilities of emerging health technologies, processes, and projects 19,20,22,23. Far from being harmful, Patient-controlled health records in collaboration with emerging technologies that work within a defined, over sighted and legislated regulatory framework will facilitate innumerable benefits 13,16,24,25. There are a number of forthcoming statistical solutions that would permit assembly of data sets at a patient level with limited risk to privacy and also delineate the cumbersome contentions of data ownership and access 7–9,12 .
- 3. References 1. Biomedical Informatics. (Springer London, 2014). doi:10.1007/978-1-4471-4474-8 2. Heitmueller, A. et al. Developing public policy to advance the use of big data in health care. Health Aff.(Millwood). 33, 1523–30 (2014). 3. Berger,M. L. et al. Optimizing the leveraging of real-world data to improve the development and use of medicines. Value Health 18, 127–30 (2015). 4. Carroll, R. J., Eyler, A. E. & Denny, J. C. Intelligent use and clinical benefits of electronic health records in rheumatoid arthritis. (2015). at <http://informahealthcare.com/doi/abs/10.1586/1744666X.2015.1009895> 5. Kohane, I. S. Using electronic health records to drive discovery in disease genomics. Nat. Rev. Genet. 12, 417–28 (2011). 6. McWalter, K. & Gaviglio, A. Introduction to the Special Issue: Public Health Genetics and Genomics. J. Genet. Couns. (2015). doi:10.1007/s10897-015-9825-9 7. Roeber, B., Rehse,O.,Knorrek, R. & Thomsen, B. Personaldata: how context shapes consumers’ data sharing with organizations from various sectors. Electron. Mark. (2015). doi:10.1007/s12525- 015-0183-0 8. Huang, T. et al. Promises and Challenges of Big Data Computing in Health Sciences. Big Data Res. (2015). doi:10.1016/j.bdr.2015.02.002 9. Dohan, M. S., Abouzahra, M. & Tan, J. Mobile Personal Health Records: Research Agenda for Applications in Global Health. in 2014 47th Hawaii International Conference on SystemSciences 2576–2585 (IEEE, 2014). doi:10.1109/HICSS.2014.325 10. Hens,K., Nys, H., Cassiman, J.-J. & Dierickx, K. Biological sample collections from minors for genetic research:a systematic review of guidelines and position papers. Eur. J. Hum. Genet. 17, 979–90 (2009). 11. Benjamin, I. et al. American Heart Association cardiovascular genome-phenome study: foundational basis and program. Circulation 131, 100–12 (2015). 12. Medical Informatics, e-Health. (Springer Paris,2014). doi:10.1007/978-2-8178-0478-1 13. Li, J. Ensuring Privacy in a PersonalHealth Record System. Computer (Long.Beach.Calif). 48, 24–31 (2015).
- 4. 14. Carroll, R., Cnossen, R., Schnell, M. & Simons, D. Continua: An Interoperable Personal Healthcare Ecosystem. IEEE Pervasive Comput. 6, 90–94 (2007). 15. Krist, A. H. et al. Engaging primary care patients to use a patient-centered personal health record. Ann. Fam. Med. 12, 418–26 (2014). 16. Hall, M. A. & Schulman, K. A. Ownership of medical information. JAMA 301, 1282–4 (2009). 17. Krist, A. H. & Woolf, S. H. A vision for patient-centered health information systems. JAMA 305, 300–1 (2011). 18. Ethics, Law and Governance of Biobanking.14, (Springer Netherlands, 2015). 19. Pang, T. Genomics for public health improvement: relevant international ethical and policy issues around genome-wide association studies and biobanks. Public Health Genomics 16, 69–72 (2013). 20. Berg, J. S., Khoury, M. J. & Evans, J. P. Deploying whole genome sequencing in clinical practice and public health: meeting the challenge one bin at a time. Genet. Med. 13, 499–504 (2011). 21. Dove, E. S. & Ozdemir, V. All the post-genomic world is a stage: the actors and narrators required for translating pharmacogenomics into public health. Per. Med. 10, 213–216 (2013). 22. Green, E. D. & Guyer, M. S. Charting a course for genomic medicine from base pairs to bedside. Nature 470, 204–13 (2011). 23. El-Sayed, A. M., Koenen, K. C. & Galea, S. Rethinking our public health genetics research paradigm. Am. J. Public Health 103 Suppl , S14–8 (2013). 24. Löhr, H.,Sadeghi, A.-R. & Winandy, M. Securing the e-health cloud. in Proceedings of the ACM international conference on Health informatics - IHI ’10 220 (ACM Press,2010). doi:10.1145/1882992.1883024 25. Van Gorp, P.,Comuzzi, M., Jahnen, A., Kaymak, U. & Middleton, B. An open platform for personal health record apps with platform-level privacy protection. Comput. Biol. Med. 51, 14–23 (2014).