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Innoplexus: Leveraging Big Data and AI to Build the Future of Healthcare

Expert insights from Gunjan Bhardwaj, CEO & Co-Founder of Innoplexus

Innoplexus: Leveraging Big Data and AI to Build the Future of Healthcare 01/11/2017
Copyright: 1971yes / 123RF Stock Photo

AI is reaching into new industries all the time. Healthcare is a prime example of an industry ripe for disruption, and I wanted to get a pulse on how AI and Big Data are driving the change. The following is an interview I had with Gunjan Bhardwaj, CEO and founder of Innoplexus AG, an artificial intelligence provider in the life sciences and financial sectors.

1. What’s the history of Innoplexus? Where and how did you begin?

Innoplexus was founded in 2011 by myself and my CTO Gaurav Tripathi in India, though we’ve always had a global focus, as we serve clients around the world. We’d observed that a majority of data in the life sciences and healthcare field was delivered through outdated consulting models. Experts would curate information and then sell it to clients at incredibly high prices.

While reference data like this can be helpful in simple industries, it simply wasn’t good enough for industries as complex as lifesciences, pharm, healthcare and others. As a result, researchers and professionals were turning to outlets like Google to help find information, but this presented problems with data accuracy. It also meant that researchers had to be precise with queries, which isn’t always possible depending on the kind of information you are looking for. We decided that AI and machine learning technology could be applied to the problem, to help develop an entirely new model. We set out to become offer an automated Data as a Service (DaaS) model, to increase access to data at more attainable pricing.

We also set out to provide Analytics as a Service solutions to help deliver continuous insights, customized to each client's’ needs. This kind of customization was impossible through older, manual approaches to data, and AI was the key that helped open the door to providing these services. Our end goal was and still is, to make data more useful for decision making, and there are few decisions more important that the ones made daily in the lifesciences.

2. What specific problem are you solving? How do you solve it?

We’re working to democratize data, by leveraging the latest in AI and machine learning technologies. We work with companies in the life sciences, and financial sectors to help them leverage the wealth of unstructured data that may have been unattainable for them in the past.

In the life sciences, researchers often need to seek outside information to help them test theories and speed up the development of new treatments. In most cases the data they have access to is whatever archival system their organization has previously paid for, meaning they might be missing out on the most current and contextualized information assets. The myriad of medical, patient, and research findings are often unattainable, or if accessible they are spread across so many resources that it’s impossible to effectively search for them.

We’re working on democratizing that sea of data, not only by making it navigable for researchers, but also by automating its use, making it easier to identify key insights in a fraction of the time it used to take. One of the biggest ways we help achieve that goal is through our platform iPlexus. iPlexus is able to crawl the wealth of data from all kinds of sources, whether they be web or enterprise, and it aggregates those data sets for specific use cases. In turn, it is able to analyze for patterns, entities and research that might be valid or helpful for the researchers. We call this process CAAV or  Crawl, Aggregate, Analyze, Visualize. The AI behind this methodology can help triangulate data on known therapeutic techniques, drugs, and diseases, while making it easier for the user to turn those insights into action.

3. What’s the future of AI for Healthcare, Pharma, etc.?

We think this technology could revolutionize the way we tackle problems with health and well being. Instead of relying on outdated resources, modern researchers will have instant access to the most current research in existence, but they’ll also have powerful AI that can point them toward information that might be relevant to their research. Our hope, is that as our platform serves more clients, that more and more researchers will get access to the information they need to develop things like drugs, new therapies, and related treatments at a much faster rate.

We also think that companies and nonprofits in the life sciences will begin to increase their budgets for IT as the need for innovative systems becomes more prevalent. The good news is the days of needing massive IT budgets to excess the latest and greatest tools is going away. With integrated platforms, prices are becoming more accessible, and services more customizable for every size organization. As small and midsize organizations gain access to the same research tools, we can expect a rapid acceleration in the pace of innovation for life sciences, healthcare and pharma, which will help make patients more healthy, while helping companies stay profitable.

4. Why is this space ripe for disruption?

Anytime an industry has problems that aren’t solved by existing solutions that means disruption is right around the corner. When it came to the life sciences, we found that data was inaccessible, at a time when technology is making data easier to collect, archive and interpret than ever before. That’s why we decided to apply new approaches to the problems facing this industry, using the latest in AI and machine learning technologies that have helped revolutionize other industries.  

The same is true in the financial sector. The number of challenges this industry faces on a daily basis is growing, and financial services companies need the support of AI and other intelligent machines to help them engage with those challenges.

This demand for sophisticated intelligence solutions has resulted in a new wave of innovation, leading to faster development of new drugs, better business forecasting, and improved efficiency of healthcare organizations. If this wave continues we can expect the use of these technologies to help make entire markets more efficient, reducing the amount of time it takes to identify new areas for growth, and increasing the number of new therapies researchers can deliver to patients in need.

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