Three Principles for Bringing Augmented Intelligence to Life Sciences

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In today’s healthcare system, we are not getting the value for the costs we are spending. It’s a complex issue with an equally complex set of potential solutions. But as the industry shifts to value-based care, we’ve reached a tipping point.

It is important, more now than ever, that all players within the healthcare ecosystem are rowing together in the same direction. There needs to be better use of data to drive better decision-making, both across healthcare and more broadly throughout life sciences.

Years before joining the Watson Health team, I sat in a strategy meeting to discuss the future of IBM; it was in this meeting that IBM Watson Health came to life. To this day, we are guided by our mission to improve lives and give hope. Through artificial intelligence (AI) – or as we prefer to call it, augmented intelligence or cognitive technology – and with three guiding principles in mind, that’s becoming a reality.

The elements that are essential to usher AI into the life sciences are purpose, transparency and skills.

Purpose

First, there needs to be purpose – a reason to act. IBM’s contribution in the life sciences space spans the drug development lifecycle, helping – not replacing – humans to drive improvements in four major areas. Through Watson for Drug Discovery and collaborations with top pharmaceutical companies, we are accelerating a process that currently takes a staggering 10 years and $2.6B to complete. For example, working with researchers at the Barrow Neurological Institute in Phoenix, Arizona, IBM Watson Health discovered five never-before linked genes to ALS, also known as Lou Gehrig’s disease.

ALS has no known cause and only one FDA-approved medication on the market. The discovery gives researchers the insight they need to determine what to target when developing drugs to treat ALS. Additionally, at Canada’s Toronto Western hospital, IBM helped identified 52 approved drugs that could potentially be used to treat Parkinson’s, with 16 of those therapies having never before been linked to treatment for the disease. Parkinson’s is also the focus of an ongoing IBM-Pfizer collaboration using the Internet of Things to enable remote monitoring and measurement of health and quality of life for patients suffering from the disease.

AI is also improving the antiquated, inefficient clinical trials process that has plagued the industry for decades. IBM Watson Health is using technology to aggregate and analyze real world evidence – such as clinical claims data, patient data and even endogenous data like the weather – and applying cognitive to ensure that only the best drug therapies go to market. Additionally, we’re working with industry partners to transform the pharmacovigilance process, collaborating with companies like Celgene to improve patient monitoring capabilities and transform patient safety during the drug discovery process.

Transparency

With augmented intelligence, we can now see things that we could never see before and do things in a much more efficient way. Up to 80 percent of clinical information today exists in an unstructured format, such as diagnostic reports or a clinician’s notes in a patient’s electronic medical record (EMR). Add to that the fact that there are an estimated 8,000 medical journals published every day and it’s no surprise that the industry is suffering from data overload.

At IBM Watson Health, we see enabling and enforcing transparency as part of our role in improving healthcare – using AI and cognitive capabilities to bring insights to the forefront and make value-based care possible. With better visibility and insights come better decisions.

Skill

For any measurable or meaningful healthcare improvement to be possible, it requires skill. Whether a patient or caregiver, AI helps ensure they have the information needed to make the best decisions possible. AI is not technology replacing humans – it’s technology helping humans deliver or receive better care.

It’s also important to remember that AI platforms must be built within the context of the problem they are solving and in collaboration with industry experts. Both cognitive systems and end users must be trained together as part of a symbiotic relationship. No one technology or person can or should do it alone. Companies must be prepared to invest in training users as much as they are training the system itself.

What’s perhaps most exciting is the power that augmented intelligence has to improve so many parts of the healthcare ecosystem. From oncology and genomics, to value-based care, government and more, the industry must focus on converging data and insights from across the healthcare ecosystem to support the bigger role in improving healthcare together. At the end of the day, the biggest opportunities will come from data and be powered by collaboration.

What discovery is just around the next cognitive riverbend? Stay tuned.