Dan Abdul, VP of technology at UnitedHealth Group (a large US healthcare company) presented at AIIM 2018 on driving intelligent information in US healthcare, and how a variety of AI and machine learning technologies are adding to that: bots that answer your questions in an online chat, Amazon’s Alexa telling you the best clinic to go to, and image recognition that detects cancer in a scan before most radiologists. The US has an extremely expensive healthcare system, much of that caused by in-patient services in hospitals, yet a number of initiatives (telemedicine, home healthcare, etc.) do little to reduce the hospital visits and the related costs. Intelligent information can help reduce some of those costs through early detection of problems that are easily treatable before they become serious enough to require hospital care, prediction of other conditions such as homelessness that often result in a greater need for healthcare services. These intelligent technologies are intended to replace healthcare practitioners, but assist them by processing more information faster than a person can, and surface insights that might otherwise be missed.
Abdul and his team have built a smart healthcare suite of applications that are based on a broad foundation of data sources: he sees the data as being key, since you can’t look for patterns or detect early symptoms without the data on which to apply the intelligent algorithms. With aggregate data from a wider population and specific data for a patient, intelligent healthcare can provide much more personalized, targeted recommendations for each individual. They’ve made a number of meaningful breakthroughs in applying AI technologies to healthcare services, such as identifying gaps in care based on treatment codes, and doing real-time monitoring and intervention via IoT devices such as fitness trackers.
These ideas are not unique to healthcare, of course; personalized recommendations based on a combination of a specific consumer’s data plus trends from aggregate population data can be applied to anything from social services to preventative equipment maintenance.
We’re in a different business and technology environment these days, and a recent survey by AIIM shows that a lot of people think that their business is being (or about to be) disrupted, and digital transformation is and important part of dealing with that. However, very few of them are more than a bit of the way towards their 2020 goals for transformation. In other words, people get that this is important, but just aren’t able to change as fast as is required. Mancini attributed this in part to the escalating complexity and chaos that we see in information management, where — like Alice — we are running hard just to stay in place. Given the increasing transparency of organizations’ operations, either voluntarily or through online customer opinions, staying in the same place isn’t good enough. One contributor to this is the number of content management systems that the average organization has (hint: it’s more than one) plus all of the other places where data and content reside, forcing workers to have to scramble around looking for information. Most companies don’t want to have a single monolithic source of content, but do want a federated way to find things when they need it: in part, this fits in with the relabelling of enterprise content management (ECM) as “Content Services” (Gartner’s term) or “Intelligent Information Managment” (AIIM’s term), although I feel that’s a bit of unnecessary hand-waving that just distracts from the real issues of how companies deal with their content.
He went through some other key findings from their report on what technologies that companies are looking at, and what priority that they’re giving them; looks like it’s worth a read. He wrapped up with a few of his own opinions, including the challenge that we need to consider content AND data, not content OR data: the distinction between structure and unstructured information is breaking down, in part because of the nature of natively-digital content and in part because of AI technologies that quickly turn what we think of as content into data.