The AI Revolution Is Here – Are You Prepared?
We’ve been inundated with stories about AI. It’s in the news every day, usually positioned as either the savior of humankind or its demise. Like any radical technological development, the truth lies very much in the middle of those two extremes. Understanding it, and what it means to the healthcare industry, is imperative in being ready for the future. We at U.S. HealthTek have been on the forefront of this from the beginning, already using it successfully in many instances for a greater good in terms of both efficiency and profitability. Our team has attended numerous conferences on the topic, and we’ll be providing more information to better inform and understand it all (join our mailing list for updates). This is an introduction to that series, so let’s begin with some basics.
AI is not “one thing.” It is many things, there are many aspects to it, and it can operate on many levels. This is key to understanding how it can be leveraged to a specific healthcare organization’s advantage. Narrow AI, also known as Weak AI, is a branch of AI that performs specific tasks in a narrowly defined field, and is void of general cognitive ability. Generative AI, or GenAI, which we’ve been hearing a lot about lately, is a type of Narrow AI. This is what is already all around us now, but especially for this industry, needs specific guidance from qualified professionals to be effective. There are other more theoretical forms of AI that aren’t yet a reality, so for now, we only work with what frameworks we know are proven.
Understanding the pitfalls of Narrow AI. AI will often return wrong or misleading results when there is insufficient training data to feed it. In many business situations, especially healthcare, incorrect assumptions spewed out as fact can have a devastating effect. The model must be properly trained, and trained by experienced professionals who understand how to avoid situations of responses provided as fact when they are not.
AI is taught using data. This data is information that already exists in cyberspace, and it matters how we tap and utilize that data. You don’t want to just plug into “all” of it, and a company may have four or five different “use” cases from each main department and customer service is certainly one of them. In that case, we want to be able to pull data more quickly for customers asking basic questions and thus use AI to free up staff. In doing this, we can learn where most of our “pain” points are, and what customers are asking based on AI activity. Beyond that example, we also want to better mine the data we have. A simple example of this is knowing which clients are the most loyal to your business, and which services are especially popular and profitable. Those are just two use cases where you need data compiled in a very specific way, but we can use language models to ask those questions, and to translate those questions into accurate information.
Prep properly for AI systems. We need to prepare, as the prerequisites for initiating AI are complicated. There are procedures that must be put into place. When implementing a system, we compile your data, centralize it, and fit you on the right platforms operationally to allow AI to work and crunch that data so that it’s primed for success.
Our process involves working through “proof of concept.” This means the solution needs to be derived through an initial pilot project which demonstrates the concept is feasible. Once successful on a small scale, it is scaled up successfully when in the right hands. We’ll start with questions and conversations with your internal team to ensure we’re clear on the fundamentals, which can be a heavy lift, but we have the experience and knowledge to achieve it.
AI doesn’t have to cost a lot of money. This does not have to be a major investment of millions of dollars; be wary of anyone who presents it to you that way. Also, don’t think it’s “almost free” and just requires a basic subscription of one of the increasing numbers of AI providers. We have found a middle ground where we can take what companies have already developed, restructure the infrastructure, and mold it together so that they can then leverage AI for specific use cases.
AI is not an end goal. We at U.S. HealthTek have always focused on the problem, and then built a solution. So, the focus is not just on AI as an end but positioned as a powerful instrument seen through the lens of seeking the right solution. The more successful conferences we’ve attended have placed AI in the context of questions like, “What is it you’re trying to solve?” and then “How does AI fit into that equation?”
AI is a powerful revolution, but it’s not an endeavor to take lightly, as we at U.S. HealthTek fully understand. Our position is that it is best-handled and implemented by an organization that has a great understanding and deep experience with medical-related data. It requires being a strategic partner in helping executives identify opportunities in leveraging it properly for each organizations business goals, both short-term and long-term. There’s an education, exploration, and guiding process between these partners that will be crucial in achieving clearly defined goals.
In articles to come, we’ll deep dive into specifics and I’ll share details from some of the conferences I attend, plus what we’re learning from projects and research on this topic. Most of all, know that we are in front of this revolution and will keep you informed of all the developments that happening on an almost daily basis. We’ll find the right solutions together.