These trends include big data, cloud computing, and the use of wearable devices to better record information and turn it into knowledge. Storing and coding data electronically helps with easy retrieval and access, leading to faster and better diagnoses and decisions in a clinical setting. The concerns and challenges that need to be addressed now revolve around data integration and standardization.
Q: What are some of the recent trends in bioinformatics and data analysis?
A: One of the biggest trends in informatics is big data. The cost of computing has gone down dramatically, and the cost of computing infrastructure is at its all-time low. As a result, you can compute a lot more genomic data and do a lot more sequencing much faster, which was cost-prohibitive two or three years ago. The cost of storing petabytes (forget terabytes) of this data is also much lower. With big data, everything is getting recorded electronically— electronic medical records, electronic health records, etc. There is now a new protocol in place called ICD-10. It codes each and every data point being collected in the lab, at a clinical site, or at a hospital. For example, if I went to my physician for a sore throat, he would enter a code for sore throat into the system. Nothing is written down on a piece of paper anymore—it is all going into an electronic system, into one central database. So if someone wants to do a macro-level analysis on all the sore throats happening in New Jersey, they could do that because everyone suffering is coded under the same ICD-10. With all this data becoming available, and with cheaper and faster computing, the two have come together to create big data.
The other big trend is cloud computing. Cloud computing and companies like Amazon have completely changed the computing space. You can turn on a grid cluster in Amazon with a thousand servers in 30 seconds, and when you are done, you can turn it off. You only pay for the hours you use it, and that’s your total cost. You don’t have to buy servers and all that infrastructure, and you don’t have to hire people to manage it. Amazon is definitely leading the way in cloud computing.
The third big trend that I see is the use of wearable devices that can collect and share data on the internet. They call this the internet of things (IoT). The Fitbit is the most consumer-aware product, and they are coming out with improved versions that can monitor variables like blood sugar levels and more. The trend so far is that all these devices are external to your body but measure things within your body. The next generation of devices will be located inside your body. The pacemaker was one of the first adopters, but soon there will be more and more devices in your body measuring stuff within. These are trends that are happening not specifically in the clinical space, but they are definitely going to impact it.
Q: Are pharma and biotech companies actively using cloud computing and services from companies like Amazon? Are there no concerns around data security and privacy?
A: Yes, pharma companies are using cloud computing and services from companies like Amazon. When working in the cloud, the data is never made public. If I were to rent those thousand servers for three hours, my data would always stay in that environment and that infrastructure for the time that I use it. After that, it would be wiped out. You pay only for what you use. Amazon, from a security perspective, is probably the most secure environment—even compared with what a biopharma company may have on its own. I think from an infrastructure perspective, and from everybody I have talked to, security has never been a primary concern with cloud computing. The primary concern was that such an infrastructure did not exist. Now that it exists, people are using it all the time.
Q: What about trends in data storage and remote access?
A: That trend in remote access of data by using smartphones and iPads already exists. From a technology perspective, I am now looking at what will start to make a difference in five to ten years. In terms of data storage, an image—even a photograph—takes up a lot of space. Your typical photograph is 300 megabytes of space. Now the quality of images is changing. You have high-definition images. You have images that not only provide a two-dimensional, but also a 3-D view. That requires more space, but at the same time, for $50 you can now get a ten-terabyte hard drive. So I think that with all this data available, research could happen much faster, whether it is pure or applied research. It is going to be much easier to start and change your research direction. In a way, I think it is going to level the playing field quite a bit.
Q: With these changes in clinical informatics, what changes are you seeing on the regulatory side?
A: I think, from a regulatory perspective, the concern always has been how good the data is, whether it is reliable, and whether it can be used in approving anything for the consumer market. IoT just makes it more concerning, now that data can be captured not just by humans, but also by devices. For instance, in clinical trials, you measure a patient’s outcome at a clinical site, and then someone from the site collects that data and reports it. That data is then used as part of the data analysis and reported to the FDA. That may now change, because you may have a patient in a clinical trial wearing a device, and that device is now directly providing data for analysis and reporting. So from a regulatory agency perspective, you are going to be wondering what device is being used, and whether there is any manipulation of that data. That standardization of data and device does not currently exist. This new technology leading the charge right now sounds great, but can it be used in the real world for decision making?
Q: What are the biggest challenges you are facing right now, and where do you see the gaps?
A: There is a lot of experimentation going on today, especially with technology. The challenge is in knowing whether this is a new fad or if it is something that will sustain over time. The triedand- tested method of the past is being challenged, but we don’t know if that challenge is real or not. There is so much happening with technology. Every day something new comes up, and how to keep yourself updated is a tough challenge.
Q: Are there any challenges with data integration or interpretation, or have those issues been resolved?
A: Those challenges exist even today, and they will continue to be a challenge in the future too. The standardization of data is always a big challenge. There are so many devices today, and each device comes up with its own standard. So how do you make sure that your Android-based device is using the same standard as your iPhone or your Fitbit? That issue has always been there, and with more data and more devices, it has ballooned into something even bigger.
Q: Looking ahead at these trends, what should lab managers be investing in, in terms of technology and training?
A: Devices that are being used are getting a lot more sophisticated, and from a pure lab perspective, I think all these devices will someday become internet-friendly. What does that mean for the lab technician? The technician will require a lot more training. I think bioinformatics and the ability to analyze data is a growing field right now. Being able to analyze 3-D images and multiple images will become increasingly important.
Q: How do you keep yourself updated with technology and developments in informatics?
A: Going to conferences, researching online, and keeping track of a few key players, like Amazon, Apple, and Google, is very important. There are a lot of start-ups that are shaking up the market, and it’s important to take a look at them too. I think attending conferences and checking on your peers to see what they are doing is useful. Academia tends to be more on the leading edge of technology and much nimbler at adopting new technologies. Compared with the biopharma industry, I see academia being further ahead, and the industry then catching up in various ways.
