Karthik Sivashanker, MD: [00:31] A lot of the core folks that you need to engage in—and I'm really speaking to the quality safety leaders right now—if you were trying to do this work, you know, from scratch, a lot of the folks you'd want to engage are here. They're the teams we've asked you to bring. So it's you, it's your program manager, it's your allies and patient family relations, it's community population health, in the ENI, etc. But there's other folks that are going to need to engage in the coming months and weeks, actually, and those are some of the folks on the right side and that's not even an exhaustive list. So you may need to connect with and engage your IT or data teams, you may, it probably should be engaging your patients as this work unfolds and trying to see how you can center their voices. There might be times we actually need to talk to your financial leaders about things. So as we're going along, we'll try to point you in some directions, but I want you all to be thinking about: who in my institution do I need to be engaging? Very specifically, who is the person? And that's one of the things you're going to start mapping out as a team. Next slide.
[01:45] The other thing is, we want you to start small and build from there. So there's many different places you may be tracking data. Start in one place, get it right in that one place first, and then expand. So we can take this approach implemented really robustly in one space, like patient safety, and then we can spread it to experience and HR and other areas. So just want to make that explicit that this work can have ripple effects, and that is the goal, but start small, start focused. Next slide.
[02:21] So these are some of the things we'll go over. We'll go over the equity enhanced high reliability algorithm that we used at the Brigham. I want to put out a disclaimer: this is SG Collaboratives' intellectual property. So we're sharing it with their permission. What that means is that you may have your own algorithm or your own, you know, just culture approach. So we're not trying to say use this algorithm, what we're really going to be pointing to is, how would you take these prompts or this language and integrate it into whatever workflow you're using? Okay. Same thing with RL Solutions, your reporting solution, your demographic case tracking, you know, we use RL Solutions, you might use something else. So we're not saying use RL Solutions, we're saying how would you integrate equity into your reporting solution, into your demographic, or into your case tracking system, etc.
[03:22] So this is an example of an algorithm. And prior to us starting to do this work, there was nothing about equity in any of this. And that was one of the first places we started. And so if you click again, you can see how we started to just very simply embed language throughout it. You see system inequities—keep clicking, please. You see in inequities in the human performance level, and the personal human performance level. Keep clicking. And so this was kind of a high-level overview, but you can see all the different places that we methodically inserted language. Now, what I'm going to say is, it's one thing to put it into your algorithm. It's another thing completely to actually start to do this work, right? Algorithms, people look at it, and then they put it in their desk. So that's where I'm going to really handed off to Karen and Karen, I'd love for you to share a little bit about, how do teams start? You know, we can start here in a concrete place, and it's a nice place to start, but then how do you translate this to actually having it embedded in the work as an everyday part of what we do in our culture? In our meetings? Can you just describe that journey and what we learned?
Karen Fiumara, PharmD, BCPS, CPPS: [04:41] Sure, so I think that—and I'm not sure how every organization is set up—but most of the hospitals and the me's that I talked with at other organizations, most folks have a standardized methodology by which they review and analyze when things go wrong. And so to that end, at the Brigham our standardized methodologies outlined here, you might be like, “Oh, my gosh, this is super, super complex.” It's actually, a really, really simple four step process. One, you start with the risk, then you think through, what are the systems? And do you have reliable systems? You think through the human performance things that could contribute to a bad outcome. And then you think through the behaviors. And the whole focus is sure you can do you have to first start with updating your policy or your algorithm, make sure that you have that standard knowledge set for your team and for the organization moving forward.
[05:41] But then the real secret sauce is how does that get translated into your day-to-day performance? How does that get translated into actually operationally looking at events? And what I would say is: really a completely different lens. And again, it's—I use the word embarrassing, because it is super embarrassing to think that you know, I'm a safety specialist and my craft, my science is supposed to be or is really thinking about what are the things that drive and contribute to when things go wrong? And before that conversation with Karthik, inequities wasn't on my radar. And so making sure that with everything that we do, and any case that you look at at the system level, you're thinking about inequities, and how does structural racism play in? And what are the structural racist barriers that you can really try to modify, if possible, to make the outcomes for our patients better moving forward? Similarly, you know, again, inequities and in human performance, and what when do things…when is our human performance really being affected by inequities that we see in our patients, and similarly our behaviors?
[07:04] So it starts with the policy update, embedding everything into your systems and getting your taxonomy right, and then the really hard work and the lift is the training, the education, the practice, getting people that look like me, and others really comfortable with this language and comfortable talking about these issues, which culturally for some folks that might work for you, it could, culturally feel really, really off putting. And I think I'm fortunate in that I come from you know, a big Italian family where we kind of talk about everything and, you know, nothing's taboo and kind of throw everything in your—I think we, culturally in my family, we just kind of lead with whatever and who we are and how we're feeling. But there are so many folks that worked for me and with me over the years that that's just, that's just not natural. And so really, you know, Karthik uses the word creating that container and that safe space, where you can get folks to really be open and curious. And ultimately, the thing that was most important is I really impressed upon the team. We are not doing our work, we are missing everything, unless this is embedded in everything. And so, I think that that's really, really critical to moving this work forward.
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