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?
Making information accessible is hard work. Certainly, there are new tools that can analyze massive amounts of data in order to bootstrap information management. However, there’s a point at which human expertise is required.
I just read a report on how and why to model search behavior from Mark Sprague, Lexington eBusiness Consulting, http://msprague.com. Mark has been in the search business as long as I have. He helps organizations understand what their customers are looking for, and what the impact of their information access/search design will have on their customers finding what they are seeking. The report I read discusses a consumer search behavior model he built for the dieting industry. In it, Sprague explains that a good search behavior model starts by gathering data on what users are searching for, but that’s just the beginning. Building a behavior model can affect your information architecture, the content you post on your site, how to incorporate the search terms customers use into the content, which featured topic pages that will attract views, the SEO strategy this model drives, and the changes that will result in existing PPC strategies. Sprague finds top queries, then uses them to generate titles and tags that fit the terms users are searching for—particularly the phrases. He also categorizes queries into a set of high-level topics with subtopics. These categories can and should affect the organization of a Web site, enabling users to browse as well as search.
Sprague has observed that at each stage of the online buying process, from research to deciding to purchasing, the query terms differ. This difference can be thought of as an indication of intent, and it can be used to tailor results for an individual as the user moves from one part of the process to the next. Finally, Sprague uses the terms to perform a cost-benefit analysis to improve SEO.
This thoughtful approach starts with observing user behavior and models the information architecture and Web site to fit—not the other way around. That’s smart, and it’s good business.