We can all use a good personal assistant, one that keeps our health in mind, not just our appointments. This assistant needs to understand who we are today: our current state of mind, our location and our preferences. Recommendations on how to keep fit in July won’t work in January if you are snowed in with the flu. Instead, we need a sympathetic advisor who urges chicken soup instead of cookies, and suggests a hot shower, a nap and perhaps some gentle stretches for the aches.
This post may seem a far cry from our normal focus on cognitive computing, but in fact, it showcases one of the major leaps forward that cognitive computing will promote: true individualized recommendations that are presented within the framework of who you are, where you are, how you’re feeling, and what you like to do. Over the last two years, healthcare in particular has moved into the world of big data in order to provide individualized recommendations that are backed up with sound evidence. From cancer diagnoses to congestive heart failure, vast amounts of data have been mined to uncover new treatments or prevent hospital readmissions.
Cognitive computing is also moving into disease prevention. Welltok® rather than focusing on disease and diagnoses, has developed a Health Optimization Platform™, CaféWell®, to help healthcare plans, providers and employers keep consumers healthy and reward healthy behavior. The platform is a well-integrated combination of curated health and nutrition information and social and gaming technologies that drive consumer engagement.
To deliver more individualized health programs, Welltok partnered with IBM Watson in 2014 to add cognitive computing capabilities, thereby a personalized experience for consumers. The CaféWell® Concierge application powered by IBM Watson learns constantly from its users, so that it evolves to offer better, more appropriate suggestions as each individual uses the system. Jeff Cohen, Welltok’s co-founder and lead for their IBM Watson project, tells us that their goal is to make their existing platform more intelligent about each member’s health conditions and context. CaféWell strives to answer the question, “What can I do today to optimize my health?” for each of its members.
To accomplish this goal, Welltok starts with good information on health, exercise and nutrition—from healthcare systems and well-respected structured and unstructured data sources. It factors in individual information about health status, available benefits, demographics, interests and goals. The IBM Watson technology parses and processes this information to find facts, patterns and relationships across sources, using a proprietary Welltok approach. Welltok also adds its taxonomy of healthcare concepts and relationships. Then it creates question-answer pairs to train the system. These query-answer pairs are a key ingredient to help Watson enrich implicit queries. Welltok also provides navigation so that users don’t get lost as they seek answers. Free-flowing dialog between the user and the system is one of the earmarks of a cognitive application, but users need hints and choices in order to avoid frustration. Welltok provides these, constantly updating and retraining the system as it learns to predict pathways through the information. The information is filtered for each member’s health plan coverage and individual profile. Cognitive computing also incorporates temporal and spatial facets, so that the recommendations are suitable for the user’s time and place. This all eliminates information dead ends because it prevents inapplicable information from being displayed.
In addition to relevance, members are given incentives to participate and they are rewarded as they pass certain milestones. More importantly, the system learns their preferences and what motivates them to be healthy. For example, if you are only interested in exercising in groups, that’s what will be recommended, but if you prefer walks in the woods, you’ll instead get tips, perhaps, on places to walk or find mileage and terrain for common routes.
The Welltok use of cognitive computing has all the earmarks of a cognitive system. It’s dynamic and it learns. It parses both information sources and the user’s situation deeply, and matches the individual to the information and the recommendations. It is interactive, and it devours data—the more, the better.
One of the most fertile areas of development for cognitive applications is in this area of intelligent personal advisors. Suggestions for actions that are tailored to who you are make it more likely that you will try them. Now, where did I put the chicken soup?
Big Data and Cognitive Computing: The Next Industrial Revolution? updates the trends we covered in The Answer Machine, published by Morgan & Claypool last year. This webcast on Jan. 30, 2014 was given to the Cornell Entrepreneur Network, but was open to all. You can listen to the recording at https://cornell.webex.com/cornell/lsr.php?RCID=616468230cc9b30a45ddd07d778325e2.
In updating the book, we found that the nascent trends we discussed in 2012 have quickly exploded. Applications that aggregate information and integrate technologies are becoming common. Task-centered design is almost a requirement. The market, driven by the buzz around big data, and bombarded by information has started to demand what vendors foresaw: there’s immense value in putting together the pieces from disparate sources, and we need help in doing this. IBM’s Watson may have been the first to define cognitive computing, but we see others positioning themselves in this marketplace as the interest grows. We’ll be covering some of these new companies in the months ahead.
During the past year, as we work with vendors and technology buyers, we have found that one of the most difficult concepts to get across is probabilistic computing. Where does it fit in the current IT landscape? Does it replace traditional BI? We expect to explore this topic also in the coming months. Please contact me directly if you’d like to discuss it in depth, or to schedule a briefing for your company. I can be reached at email@example.com.
Breakthroughs in technology are sometimes less about the underlying technology than they are a leap in understanding how people need to use technology. The iPod and its ecosystem, for instance, create a synergy between a handy gadget and the music and content people want to carry around and listen to. By understanding that people want to download, listen to, and share music easily without having to shift from one application to another, the iPod and its successor devices changed our use of content and upended whole industries. Bo Begole’s book, “Ubiquitous Computing for Business”, emphasizes that designing an application with the task—the context—as a starting point trumps starting with a technology if you want people to adopt that software. Business applications have lagged behind consumer applications in ease of use, but sooner or later, what we learn in the consumer space infuses new business interaction designs.
Today’s launch of Emu is another consumer breakthrough that will have broad implications in the business arena as well. Consider the awkwardness of organizing an evening with friends. Email or texting to find out if they are available. Having everyone check one or more calendars. Merging the answers. Then agreeing on time and place. Then seeing if restaurants are available and if that time will fit with the movie schedule. A familiar and time consuming process. What emu does is technically difficult but, on the surface, simple and obvious. It lets you stay in SMS as you arrange a date, time and location. It checks calendars, available times for favorite restaurants, movie times. Then it makes reservations, and even shows where you all are on a map as you start to converge on the location.
Yes, I know the founders of emu, but beyond that, I am taken with this application because it fits squarely into the trend of simple, usable applications that save time and hide technical complexity. Check it out at emu.is.