5 considerations when starting a healthcare AI strategy Depending on your company and role within healthcare, you’re either researching AI, experimenting with AI, or scaling up your approach. But regardless of where you are in your AI journey, there are a number of questions to answer and items to mull over. Here are some key considerations to weigh as you embark upon creating your AI strategy. 1. An AI strategy is now a non-negotiable AI isn’t the Segway. There’s no longer a question of if AI will change the way we do things; the question is how. With that in mind, it’s important to take the technology seriously, and to spend the requisite time understanding its capabilities, limitations, applications, and potential risks, and to align it to your key business initiatives. In healthcare, automation is all but a necessity these days, in great part thanks to staffing challenges. AI, when used appropriately, can increase employee productivity and efficiency, and help combat some of what causes employee burnout. It’s important to consider the jobs that need to be done in your organization, which elements of them are tedious and repetitive, and whether automation can help. As Mark D. McDonald, senior director at technology research firm Gartner told The Wall Street Journal: “We can participate and learn how to use this technology or we can ignore it. The choice is yours but it’s here to stay, and it is going to make a big difference in how we do things.” 2. Choose the right initiative for AI support Think about your company’s or department’s most important initiatives, and consider the ways in which automation might be applied to support those. For example, if you’re focused on reducing costs, revenue cycle management could be an area to focus. How can automation help reduce AR days? How can AI help keep staffing and staff-training costs in check? How can AI help make existing staff more efficient? Ensuring a successful AI strategy begins with choosing the right area of your business to apply the technology. And options exist even for organizations looking for low-risk entry points. If adopting AI solutions related to patient care gives you pause, automation of back-office tasks – like Infinitus provides – could be a better fit. 3. Creating an in-house solution vs. working with a technology partner Some organizations are in a strong position to create their own AI solutions. Some aren’t. There’s no right answer, but there is the right answer for your organization. Unfortunately, making the wrong decision can be costly. To decide which path to take, it’s important to understand what, exactly, is the problem you’re attempting to solve. Is there an algorithm that could solve it? What data would you need? How would your staffing needs change? Does a solution to solve this problem already exist? Once your solution is live, do you have the resources and talent to support and maintain it over time? What’s the time needed to realize value with the solution? In most cases, healthcare organizations are likely to benefit from working with external partners – they’re better resourced with talent and infrastructure, and should be able to scale as your needs grow and change. But it’s important to consider the pros and cons of each approach as you build out your strategy. 4. How PHI, data security, and compliance requirements are handled In healthcare, regulations add an additional layer of complexity to any AI application, and figure into the kind of large language models that make the most sense for specific use cases. If you go the route of working with an external solution, it’s critical to understand how they handle PHI, how they protect data, the guardrails they keep in place, and how they ensure compliance with regulations like HIPAA. At Infinitus, for example, we remove PHI from all materials our machine learning models are trained on, as a guardrail to reduce the risk of bias and ensure no sensitive information is used in inappropriate or unexpected ways. We are also committed to staying HIPAA and SOC II certified. When developing your AI strategy, be sure you’re able to thoroughly understand how these matters are handled, and can effectively explain such strategies to stakeholders across your organization. 5. How to measure success This is a big one. You’ll want to track ROI, of course, but as with any new technology, there can be upfront costs and time before you start to see significant return on your investment. As you build out your healthcare AI strategy, you will want to know the metrics most important to your business, how you’ll track them, and even when you should expect to see positive returns. The true ROI of implementing AI at your organization won’t be as simple as the cost of deployment and the resources saved. If you go the route of working with an external AI solution, they should be able to help you understand the complete picture. But regardless, you’ll need to develop success metrics to evaluate whether your strategy is having its intended impact. Getting started on your healthcare AI journey If you’ve been charged with finding ways to use AI for healthcare administration, we’ve got you covered. From partial call automation to end-to-end, Infinitus has a solution for you. Learn more about what’s possible with automation, and how we’re unlocking time for busy healthcare workers – not replacing them. Talk to an Infinitus team member today.