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How to overcome barriers to adopting AI technology in long-term care

A recent survey we conducted at MatrixCare indicated that a large number of healthcare providers believe new artificial intelligence (AI) technology can be used to improve patient outcomes. But when we asked how many have actually started using AI tools, most said they haven’t. I’m going to look at ways to overcome some of the barriers that keep long-term care providers from using AI.

The future of AI in healthcare

AI is already transforming long-term care. From software that can use a phone photo to measure the area and depth of a wound to track healing, to radar that can detect and identify people in a room and watch for falls, there are many AI-enabled tools already available. And more of these tools are coming. AI can empower your clinical staff with easier access to key data, and is already helping improve clinical efficiency and patient and resident outcomes. That means it’s important to understand best practices for implementing new AI technology and learn how your organization can benefit from it.

First things first: Set clear goals

Some organizations may be tempted to implement a new AI tool just for the sake of getting new technology. But the first and most important aspect of adding AI technology is to have clear goals. And these goals should not be technology related. A better approach is to look at your whole organization and set an overarching goal such as reducing falls, improving antibiotic stewardship, or lowering readmissions.

A key part of implementing any new technology is making sure your goals are well defined, achievable, and communicated throughout your organization. Once you’ve clearly identified what you want to achieve, you can apply technology.

The myth of complexity

Another barrier I’ve seen is the concept that AI/ML tools are too complex to implement and use, that they will overwhelm an organization. This is actually not true at all. Good AI-driven software works “behind the scenes,” and should fit seamlessly into your workflow, without adding steps or forcing you to learn an entirely new system.

For example, if you’re a nursing home administrator or manager, when you visit facilities, you may already be using an AI tool that delivers aggregated EHR data so you don’t have to take time to go through individual patient charts. AI technology does all the work in the background, without any need for the user to fully understand exactly how it works. The main thing you, as a user, need to understand is how to incorporate the data provided by AI into your existing workflows. All you need to know is how to take care of your patients and how to gather the information about those patients that will have the most impact on their care.

Another aspect of the complexity myth has to do with implementation. Anyone considering implementing these technologies should ask questions about how the data provided fits into your organization’s workflows, but it’s no more complex than that. As clinical caregivers, the main focus should be on process, rather than the technology behind it, and on ensuring your staff adopts these tools and makes them part of the everyday workflow.

Compliance and liability

I’ve heard concerns about AI tools that aggregate data to help caregivers prevent adverse outcomes such as falls. And there are two things to consider from this angle. First, many AI/ML tools are separate from your main EHR software. The workflows may be seamless, but the information in the AI tool is not auditable. Any recommendations the tool makes and any risk scores it provides are not part of what is considered clinical documentation. So if someone is worried about being held liable to prevent a fall, for example, that data would not be part of the review process.

In fact, I’ve heard from some users of our Clinical Advanced Insights tool that it helps them improve documentation of the care they’ve provided. If a nursing home resident falls, clinicians can open up the AI tool and show all the different risk factors they’ve been monitoring—such as medication, meals, blood pressure—and show that they’ve done the proper trending and assessment to provide the right care at the right time.

Spread the word

The last barrier to overcome after you’ve set goals, defined clear processes and overcome misconceptions is to communicate. It’s important to share successes and results throughout your organization to reinforce that these new tools and technologies really can improve quality of care. And it’s important to do this for the long haul. When you first kick something off, everyone is excited. After six months, the enthusiasm dies down, but you need to keep monitoring and sharing your results so you can say, “As an organization, we’ve reduced resident falls” or whatever you chose as your goal. This is a great way to keep your staff focused on shared goals, and also helps drive improved job satisfaction.

Empowering caregivers

I believe the future of AI holds great things. Imagine intelligent sensors, AI-assisted care, comprehensive coding support, voice-driven documentation, data-driven fall prevention, in-room telehealth and more. We’ll need to communicate and collaborate with regulators, but AI technologies can fill voids we currently see in healthcare. I can see long-term care providers’ experience transformed from one where mundane tasks take up most of their time to one where they can perform at their highest levels, where they’ll have time for comprehensive critical judgment and patient connections. And that’s a future I think we can all look forward to.

Break barriers to AI adoption. Explore how MatrixCare’s Clinical Advanced Insights empowers caregivers with the tools to provide proactive care.

Daniel Zhu

Daniel Zhu comes from a diverse background of clinical experience and technology entrepreneurship. Having spent five years in clinical and clinical research roles with the Alberta health system and the University of Toronto health network, he has diverted his medical expertise to architecting and building technology solutions to optimize health care practice. At various organizations, he has lead engineering teams, product teams, and founded his own natural language processing start-up in the clinical research space. Stepping away from the start-up world, Daniel has spent time as a data consultant for large corporations such as Ford, Co-op, and RBI. Re-entering the health technology field, today Daniel has recently joined the ResMed and MatrixCare team to lead the productization of AI and machine learning capabilities.

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