Watson is IBM’s big bet on AI, and healthcare is a prime domain for present and future applications. We take an inside look at Watson, why and how it can benefit healthcare, and what kind of data is used by whom in this process.
IBM’s big bet on Watson is all over the news. This week’s World of Watson event helped bring Watson to the limelight, with attendees from 110+ countries. If numbers impress you, Ginni Rometty’s number-dropping in WoW’s keynote should leave you impressed indeed.
Watson is meant to be positioned as the leader in a market worth $32 billion (cognitive systems), help organizations make better decisions worth an estimated $2 trillion, and make a difference in the lives of nearly one billion people through its 700 clients.
200 million of these people are consumers, and another 200 million are patients, but according to Teva, one of IBM’s major partners in healthcare, the “consumerization” of healthcare is the driving force behind its ongoing transformation: consumers expect to get everything here and now, in a way that is convenient, affordable, transparent and adjusted to their needs. They will not accept healthcare they do not understand, costs too much, and requires them to leave the comfort of their home too often.
Social determinants of health
Spyros Kotoulas, research manager for IBM Health and Person-Centric Knowledge Systems, says “we are therefore moving from treating a set of problems to treating a person. Traditionally, IT in healthcare is there to (a) record and share information and (b) provide tools to help users make better decisions, based on clinical evidence.
There is a critical, and increasingly visible, gap between what health outcomes are expected, based on the patient’s clinical evidence, and observed outcomes. This gap is known as the social determinants of health: a person’s socioeconomic status, family situation, social context, etc. These play a huge role in health outcomes.
Social determinants play a key role in people’s well-being, and big data enables us to keep track of them. Image: Schroeder, SA
The next logical step is to build systems that account for these social determinants, decision support systems that are based on a broader set of criteria and a broader set of tasks. For example, doing deep personalization of care plans, based on what has worked in the past for similar patients or guiding health professionals to seek the information that will make the biggest difference in their decisions.”
IBM’s core skillset is in computer science and AI, and Kotoulas manages a team of researchers and engineers with AI-related backgrounds (semantic web, deep learning, machine learning, ranking and recommendation, natural language processing, and healthcare decision support).
Social determinants, however, are a concept that needs social and medical science to be utilized, therefore a highly interdisciplinary approach is taken, working closely with domain experts and customers in order to validate the effectiveness of approaches.
For example, according to Kotoulas, “IBM is involved in the ProACT project, working closely with experts in psychology, nursing, and primary care, as well as customers, to develop and validate a new paradigm for integrated care. This paradigm integrates advanced analytics and IoT to advance self-management of multiple morbidities in the elderly population and spans the home, community, and secondary care environment as well as healthcare and social care.
Experts in healthcare play a much more important role than domain experts in traditional [business intelligence], as the domain is much more challenging, and maintaining a ‘human touch’ is critical. Through recent acquisitions (Explorys, Phytel, Truven, Merge), a significant number of health professionals have joined the ranks of IBM, in addition to world-class health innovators such as Paul Tang and Kyu Rhee.”
Using streaming IoT data for healthcare
For Kotoulas, “the key factors for health are clinical, socio-economic, and medical care related. For each of these, you can have data on multiple levels. Aggregate data such as neighborhood poverty levels (socioeconomic), or health services (quality of care) are not sensitive and relatively easy to get (e.g. from the census).
That data has been shown to play an important role in health outcomes, but to better understand a person, their own situation needs to be understood, as well as that of their social context (family members, informal carers, community members).”
Healthcare needs a wide array of data sources to function, and streaming IoT data is valuable for getting to-the-minute insights. Image: IBM
Healthcare experts like Teva can gather huge amounts of data from all kinds of sources, including biosensors. Biosensors help learn more about patients, and Watson enables them to understand their condition and make suggestions.
Kotoulas says, “IoT is a key ingredient to deliver on the promise of truly integrated care. What IoT brings to the table is the ability to continuously monitor patients in their own environment.
This has many advantages: readings at home may be different from readings in a formal care environment, the frequency in which we can get information is much higher, it gives a sense of empowerment and independence to patients and, in many cases, it is cheaper — what’s not to like?
Making sense of IoT data, and particularly multiple concurrent streams, is something that can come in waves. You can get some very useful data very easily, like mobility and step counts which are readily accessible from smartphones.
Some data requires additional inference, for example detecting social isolation from multiple streams. There are also streams that are much harder to interpret and need specialized hardware, combining multiple signals at real-time to monitor some conditions.”
Watson is accessible as a collection of services running on the IBM Cloud, and several of the services in Watson Developer Cloud originate from IBM Research. But Watson’s underpinnings are a topic in and by itself, so stay tuned for more.