In the near future, genome sequencing, among other biological measures, will be as routine as X-rays and cholesterol testing. The challenge, though, will be accurately interpreting the vast amount of data and effectively using it to guide decisions about health care.
In a position statement published in Hepatology, Mayo Clinic researchers layout perspectives of various stakeholders (e.g., providers, payers, governments, and health care institutions) on the clinical questions to be answered using big data in genomics and how innovative analytical methods such as machine learning may help with interpretation.
"Accumulation of big data is increasing at an unprecedented pace in medicine — doubling the sum of medical knowledge every 73 days in 2020 compared to every 50 years in 1950," says Konstantinos Lazaridis, M.D., the Everett J. and Jane M. Hauck Associate Director of Mayo Clinic's Center for Individualized Medicine and co-author of the article. Dr. Lazaridis is the William O. Lund, Jr. and Natalie C. Lund Program Director for Clinomics.
In the article, Dr. Lazaridis and co-author Arjun Athreya, Ph.D., M.S., an electrical and computer engineer within Mayo Clinic's Department of Molecular Pharmacology and Experimental Therapeutics, explore the complexity of generated data, the level of sophistication in analytical approaches, and the degree of interpretation from the different methods.
"As computing technologies evolve, we have to jointly address the complexity posed by medical data, emphasizing the need for close collaborative partnerships," says Dr. Athreya. "We need to gather additional information and apply analytical innovations to be able to uncover answers for patients and improve health outcomes."
The review summarizes the importance and challenges of managing and analyzing big data for patients, providers, health care institutions, payors, and government agencies.
Read the rest of the article on the Center for Individualized Medicine blog.
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