Day 1: Precision Medicine Meeting
Author: Alex Chang
During the Precision Medicine meeting, hosted by FDLI and Genentech, key panel speakers shared their perspectives on how recent innovative technology will be impacting healthcare. The speakers were Michael Blum, Karen Corallo, Joseph Fireman, Kai Peters and Katja Schulze.
It's still a young and growing field populated by buzz words such as Artificial Intelligence and Machine Learning. If we dial it back a bit, it's the utilization of current data analytics technology into the health industry. If Artificial Intelligence scares you, don't worry. I don't think that it's any time soon that we replace our doctors with robots.
After hearing from the panelists, the concerns we have were: the ethical issues with data privacy and with cybersecurity. What was news to me until this meeting was the National Institute of Health's All-of-Us Initiative. This initiative is an effort to gather data from no fewer than one million people, taking into account their biology, lifestyle and environment, to further scientific advancement in precision medicine. This raises legal and ethical issues as it pertains to informed consent and the ownership of your data. Data privacy regulations such as the EU General Data Protection Regulations (GDPR) and the California Consumer Privacy Act is the step towards the right direction in safeguarding one's privacy. I envision that there will just be additional methods to de-identify patient data such that the patient identity and privacy will be protected. Even though Big Data is new to the healthcare industry, the protection of patient health information isn't.
Some other discussion was the possibility of having a government-funded organization to function as a central repository to handle all the data mining tasks and making the data readily available to the public. Tasks such as collecting, cleaning, identifying and verifying data are costly but the growing concern is that with the absence of a centralized repository, one will question the consistency and veracity of the data. There's no arguing that there's a lot of data out there. The challenge is analyzing and choosing only the meaningful data for your data analysis.
This meeting was only an hour and it really piqued my interest. I'm interested in learning more from tomorrow's meeting on Digital Health.