Release date: 2016-11-16 At the recent Fast Company Innovation Festival, a group of experts in the medical industry discussed in depth the development potential and problems of the new personalized and targeted healthcare model. Niven R. Narain, CEO of biopharmaceutical company Berg Health, talked with industry experts from Mount Sinai and Columbia University. In fact, precision medicine is an all-encompassing term that broadly refers to treating patients in a more personalized and targeted way, instead of using the traditional one-size-fits-all approach to treating disease. Earlier this year, the US federal government announced an investment of $215 million in precision medicine. Narain pointed out, “This is an unprecedented change. But the paradox is that while biomedical research continues to transform and progress, the economic pressure on patients is increasing.†The biopharmaceutical company Berg Health is located in Boston, behind which is the Silicon Valley real estate developer and billionaire Carl Berg. The company hopes to reduce the cost and time of drug development through fusion genomics technology. The company has been developing this project for ten years, costing more than $3 billion, using a combination of genomics, proteomics, and artificial intelligence to find new drug targets. Berg Health hopes to combine these tools to provide patients with affordable new drugs. For Berg Health, the potential barriers seem to be endless: in existing health care, it is very difficult to bring together different types of data, and regulatory agreements are an obstacle to getting around. Most hospitals still charge by service rather than by treatment, which means that hospital inspections are expensive and cumbersome. Regarding the development of precision medicine and potential obstacles, the following experts discussed the following: data collection All participating experts agree that patient data collected through genomics is not entirely useful. Eric Schadt, director of the Ikan Institute for Genomics and Multiscale Biology in Mount Sinai, believes that medical research should find better ways to get in touch with patients. In order to develop a drug efficacy prediction model, he believes that researchers need to obtain relevant examinations and medication data from the patient's electronic medical records, laboratories. They should also be able to access their user devices and access their health data through services such as the Apple HealthKit service. The key to getting patient data is to give it a certain value in exchange for access to its health data. Shatt pointed out that most of us are willing to exchange personal information with Google in exchange for search engines and email services. “The content in our Gmail is more personal than the content in the medical record, so we must also find a balance in medicine to provide patients with a more accurate service value so that they can agree to exchange personal medical care,†said Schatt. data. The policy advantage of obtaining patient data is that the current institutional review board and the regulatory body's opinion on agreeing to data sharing is clearer, but there is still much work to be done. Eric Nestler, a professor of neuroscience at Mount Sinai, points out that we need more substantive legal policies to protect patients from discrimination. For example, data sharing should prevent patients from being discriminated against in terms of medical insurance and long-term care. Do we need more new drugs? Another question raised by experts at the meeting was about the development of new drugs: do we need to use new treatments to deal with the disease, or should we concentrate on using existing drugs. Andrea Califano, head of the Department of Systems Biology at Columbia University, points out that new algorithms that match patients' tumors with optimal drug treatment are being used more by researchers. He said, "We have collected thousands of various research compounds and matched them by this algorithm." Califano said that some cancer patients only need a single drug treatment to be effective. This is also what he believes is the recent development direction of precision medicine. He said, "We don't need to develop too many new drugs, we can treat different cancers." But Narain believes that we need to re-examine existing drugs and develop new drugs. His company is trying to find new therapies through a differentiated approach, which he calls "Biology First." The new drug development process begins with the extraction of biological data from tumor samples and healthy tissue samples, and then uses artificial intelligence algorithms for drug experiments. Data operability Participants agreed that collecting patient data is a huge challenge. Data stored in medical files in various forms is not easy to store or share. For people in the medical research department, this is a nightmare. Part of the reason for this is that medical institutions such as hospitals do not want patients to flow to other medical institutions for medical treatment. When asked if this status could be resolved in the near future, or whether the health information could be standardized, the participating experts did not show the optimism they deserved. Saudi Arabia pointed out that “it seems quite far away.†At the same time, experts also believe that artificial intelligence is not the first method to solve problems quickly. 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