Hi everyone, my name is Gemma, I'm on the marketing team here at Sage, and welcome! Thank you so much for joining us today in our live webinar on how senior living communities are using AI to prevent falls. For anyone who isn't familiar with us today, Sage is an AI-powered nurse call and fall management platform. I'm joined today by Alex, our General Manager of Detect here at Sage. Alex is a resident expert on this topic, and she's going to walk us through how Sage helps communities like yours get ahead of falls before they happen. As she walks us through, feel free to drop in your questions in the Q&A box below, and I'll let her take it from here. Thank you, Alex.

Terrific, thanks so much, Gemma. Good morning, or good afternoon, depending on where you are. As Gemma mentioned, I'm the General Manager of Detect here at Sage, and I've had the real privilege of working with a lot of our clients as they look to solve problems and make sure the quality of care for their residents is as high as it can be.

As we've been working with our clients, not just bringing them the system but partnering with them, they often told us: this is great data, this is great information, but to really solve the problem of falls, I need to understand the event. It became clear to us that allowing a fall to be witnessed by a clinician was a wonderful way to give them the full picture of what was going on with that individual, and how to create an intervention that would give better quality of care and prevent or reduce future falls.

We know how important this is. Falls are the leading cause of injury and liability in this industry. They carry a high rate of claims and have an incredible impact on an individual's quality of life, their care, and can have long-lasting consequences depending on the event itself. Our clients wanted to understand: how does technology help us? How can we utilize AI and other emerging technologies to do this better?

For those less familiar with Sage, our point of view is to create a comprehensive ecosystem, because it's not just about one event. The way you get ahead of problems is by understanding when there are changes in an individual as they're happening in real time.

As opposed to legacy solutions, which involve a lot of paper and manual work, or point solutions that hone in and excel at just one thing, at Sage we believe you need an understanding not just of one moment, but of everything leading up to it. The power of AI for our clients comes from having all of that context.

We serve the entire continuum of care. Our core product handles nurse call, we have our fall management product, and we have a product for skilled nursing. Covering that entire range means we can gather inputs from every aspect of an individual's care to build a clear picture.

So how does AI fit in? Our AI monitors when others can't, acting as a consistent presence to identify: is there an on-the-ground event? Is this something clinicians need to know about? Does this resident need help from the care team?

The Sage Detect device monitors in the background. When it identifies that a resident needs help, such as a fall, it sends an alert to the care team to go check on them right away. At the same time, because the system has been monitoring continuously, the AI grabs a video clip from the 15 minutes before the event and serves it up in real time to the clinical leader. As they respond to the event and decide whether the resident needs to go to the ER, they can actually watch what happened.

All of this data feeds into our Insights tool, where AI helps surface trends and patterns, which I'll show you in a minute.

To summarize the workflow: our device detects someone on the ground, a notification goes to the care team, the team confirms they're responding, and then we determine whether it was actually a fall. If so, it goes into our fall review process. The appropriate clinical leader confirms the fall and also receives an additional 15-minute video clip following the first one, so they have both the fall witnessed for the incident report and a view of how their team responded. That way, if adjustments are needed to improve response, they have the information to have those training conversations.

That video clip is delivered in real time, embedded in the alert, but only for permissioned clinical leaders. This is meant for root cause analysis, so it's important it reaches the right person at the right time to inform their decisions.

I'll go through and show you what this looks like.

Here's the Sage Care app that the care team uses on the ground. I'm logged in as a clinical leader, and I have a couple of escalations, including a fall alert I triggered earlier here in our lab, where we set up and test our products. Right before this session, I went in and fell in front of the Sage Detect device so I could walk you through the experience.

You can see the alert came in and tells me which room to go to. What we find is that by having AI trigger that alert, care teams know to prioritize it because it's a potential fall. In communities that have both Sage Nurse Call and Detect, teams respond to Detect alerts 50% faster. Getting to the room quickly has a tremendous effect on reducing fall-related injury. Residents trust that someone is coming to help them, so they're willing to wait, even though we're really only talking about a minute or so before someone arrives to assess the situation.

When I open the alert, I can see critical information about the resident pulled over from the EHR. As the clinical leader, I also see the video of the event embedded right there in the product. I can claim the alert, which lets my team know I'm on it and taking accountability.

When I play the video, it loads blurred by default, to make sure I'm in an appropriate setting to view it, in keeping with HIPAA. I can unblur it to get a clear picture, and enlarge it. You can see the room in our lab where this happened.

Watching the footage, I can see what led up to the fall, what the resident was doing, whether they were reaching for something, and I can zoom in for a closer look. You can see me step over, and then fall to the ground. I didn't hit my head. I can zoom in to check my positioning, and use that same lead-up footage to see if there's any indication of a larger problem.

After completing my assessment and helping the resident up safely, I resolve the alert. I press a button in the room to confirm someone responded in person, resolve it as a fall, add any notes, and submit.

At that point, it shows up again for the clinical leader or manager in the fall review. From there, I can reopen it and watch the video if I didn't do so in real time.

It's that ability to witness the fall that enables the interventions which ultimately reduce falls for our clients. We want all the appropriate clinical staff to have this important data, which also eases the burden of filling out incident reports since everything is right there in front of them.

I confirm that yes, there was a fall, and an incident report is filed. That pushes to our analytics and makes sure everything matches up, so there's never any question in the event of an audit or other review.

One of the most important things when you're dealing with video and monitoring technology, and a lot of what our AI empowers us to do, is understanding that a resident needs help even when someone can't physically be in the room with them. We want to make sure residents' privacy is respected and that everything is handled securely.

For us, that means the resident needs to provide consent and opt in. We do not have a live video feed available for anyone to just watch.

That's the role of the AI layer: to handle this respectfully by capturing and sharing only the clips relevant to the event itself.

Only clinically appropriate staff are permissioned to see those videos, and we maintain a retention policy that matches each client's needs. By default, that's about 30 days, though it can be shorter or longer depending on the use case.

Everything is HIPAA compliant. We work with third parties who independently verify that our systems are securely stored and encrypted, both at rest and in transit, confirming not just that we say we do this, but that our systems are actually built to do it.

Data is valuable, but this is where a second layer of AI comes into play.

The way to truly get ahead of a fall is to understand when changes in an individual put them at higher risk than before.

This is where Sage really shines: by bringing together all of this data on an ongoing basis, whether from the care team responding to alerts, logging care throughout their shifts, or responding to a fall event, it all flows into our Insights platform.

Knowing how busy everyone is, we make sure this data is actionable. We have clinical experts who support onboarding and are always available to validate that the insights driven by our AI and analytics are valid and worthwhile.

We look to our clients to bring the clinical expertise to build and implement the interventions that create real change and prevent future events.

But we want to keep it usable and simple, surfacing exactly what teams need in the moment so they can take action, which is what actually prevents the fall.

We provide summaries and ongoing support from our team so we can adjust as needed and make sure clients are getting exactly what they need, when they need it most.

I want to close by speaking about one of our clients, an early adopter of our product who helped us understand this program and how to use these different capabilities to build a program that reduces falls, allows for faster response times that reduce fall-related injury, and cuts down on hospitalizations and ER visits.

We worked with them on their initial community to refine these workflows and ensure the interventions were actionable and actually being put into place.

With the success of that first program, we expanded to the rest of their portfolio, and months later, we're excited to see that it's held up.

This wasn't a one-time result: month over month, we continue to shrink the repeat-faller cohort and reduce the number of falls and associated injuries as their teams get this valuable information.

We have a couple of questions from folks. Thank you so much, Alex, for walking us through that, it was amazing. Let's get right into the questions.

First question, from Tara: How does Detect integrate with our EHR?

Great question. Right now we have integrations with all, or most, of the major EHRs, and that data is being pulled over. We also have a few EHRs where we have a two-way integration set up, so we're able to push data back, whether that's fall data or other routine care. Depending on your EHR, about 80% of the time we can build toward that two-way integration if it doesn't already exist.

Amazing, thank you. Next, from Andrea: What hardware do we need to install this?

Good question. For Detect, there's the Detect camera itself, which goes into resident rooms. You'll also need Sage buttons, which are used to confirm events. For the full nurse call suite, we also use that button in wearable form, as pendants, and can place call buttons in rooms and common spaces as well.

Amazing. And Monica asks: How long does implementation typically take?

We can move very quickly. If a client's existing call system is on its last legs, or has already failed, we can do the whole implementation in about two weeks. Generally, though, we like to give it 30 to 45 days, depending on what's going on at that community. We don't want to overwhelm them, but we do take on as much of the work as possible to make it as easy as we can for them.

Cool, amazing. Next, from Sandra: <strong>How would this convince families that it's better than a Ring camera? How does the Detect camera compare?</strong>

Great question. It's really difficult for a human to sit and watch all the time. With a smart camera, you can drop in and see what's going on, or set up alerts. But the difference is having a device specifically designed to detect a fall event and immediately alert the care team, who are on-site and able to respond.

As a family member, of course you want to be involved and know what's going on with your loved one, but the reason your loved one is in a community is so the care team can respond quickly and give them the care they need. That's exactly what Sage is built and set up to do.

Thank you so much. One last question, from Steven: <strong>How do staff feel about being captured on video, especially while providing normal care?</strong>

Great question. Unless there's an event, no video of staff caring for a resident is shared or reviewed.

If there is an event, like a fall, it triggers an alert and that video is captured, but only in that instance.

Staff generally understand that the benefit of knowing when someone is on the floor is essential, it allows them to do their jobs better.

What gets captured is a very brief moment around the event itself, and it also means that when staff respond appropriately and with compassion, that gets seen too.

We're big believers in celebrating when staff do a great job, not just using this as a tool to flag where improvements are needed.

Thank you so much, Alex, for answering everyone's questions, and I know there are a few our team will follow up with you on directly. Thank you for joining our live webinar today, and thank you again for all the work you do every day keeping residents safe, supported, and cared for. Sage was built because we believe the people doing that work deserve tools that make it easier, not harder. For any questions we didn't get to today, our team will follow up, and a recording will also be available on demand. We hope to catch you in a future session. Thank you so much!

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