The future for artificial intelligence (AI), predictive analytics, and machine learning in the healthcare space is bright and expansive. In the past few years alone, the news has been littered with incredible advancements and problems solved through the leveraging of new technology.
With that in mind, here’s the obvious follow-up question—how does a healthcare organization turn the benefits possible with AI from just “something they read about in medical journals” to something that is real, attainable, and capable of producing measurable and meaningful results within their organization?
Thankfully, with the right steps, this accessibility is attainable. The very first step in the process of turning AI into a reality starts with identifying which problems and opportunities within the organization could and should be solved through AI.
Here are suggestions to help identify key problems to solve with AI.1
Begin By Creating a Master List of Issues
Most healthcare leaders have a good pulse on the pressing issues within their organizations. And while this is great, that list doesn’t necessarily translate over to the list of issues that can be tackled by AI. Not all problems are strong candidates. Some issues revolve around intangibles driven by factors that are too challenging to measure. Other issues may require solutions where developing technological fixes might not be cost-effective.
There are two key takeaways here. Number one—start with the problem first. An intimate understanding of the desired end state and what you’re trying to solve ensures that you don’t end up with technology that produces results, but results that don’t actually do you much good.
Number two—take the time to identify which problems are good candidates for AI intervention. Data is the lifeblood of predictive analytics, machine learning, and AI. If the factors affecting the issue don’t lend themselves well to data collection, it might be a bad fit. Additionally, consider the cost vs. benefit analysis. Is the cost of researching and developing an automated and data-driven fix worth the weight of the problem?
Properly Categorize and Prioritize
Once you have a list of potential problem candidates, it’s time to start categorizing and prioritizing. One of the best ways to do this is to look at the groups affected by each issue. Separate these groups into one of three categories based on what level of technological innovation support they’re currently receiving—underserved, overserved, or inappropriately served.
Why is this important? The success and failure of new technological solutions relies heavily on the affected group’s willingness to adopt. AI and data-driven technologies almost never come out of the box perfectly. They require a creative space to research, develop, and adapt.
Underserved groups are much hungrier and open to committing the space and focus needed to foster success. And if this is one of the first times you’re making an investment into new AI technology, that may be the best place to start. Success there can supply the backing needed to secure buy-in from the less-eager parts of the organization.
Consider the Connection to the Strategic Perspective
The other piece of the puzzle when it comes to prioritizing problems is how each problem ties into the overall strategic perspective of the organization. Would solving the problem have a marked impact in line with the corporate and P&L strategies already in place? Even more so, how tangibly does the team experience the problem? What’s the degree of the problem and the level of threats that come with it if left unaddressed?
It’s a challenging debate, but an important one. Developing AI and predictive analytics solutions is resource-intensive. By taking the time to fully flesh out this debate, resource allocation can be set to give an organization the best chances of success. And since technological growth is exponential, this is imperative from day one.
1Net Health Webinar, Modern Healthcare Innovation Leaders, 2021
Modern Healthcare Innovation Leaders
How Top Health Systems Plan and Execute Innovation