Riding the Wave-Project Management for Emergency Managers · 2026-08-05 · 43 min
Key moments - from our scoring
Substance score
75 / 100
Five dimensions, 20 points each
Emergency managers struggle to demonstrate value when their core mission is preventing events that never happen. Eric Saylors, Fire Chief of El Cerrito Kensington Fire Department with 30 years in fire service and author of "Quantifying How Homeland Security Adds Value," tackles this fundamental measurement challenge. He reframes emergency management using a medical analogy - white blood cells versus red blood cells - to explain why outcome-focused metrics matter more than activity counts. Saylors advocates for the threat-consequences-vulnerabilities framework from homeland security, arguing that while threats and consequences lie outside emergency management's direct control, vulnerabilities are where intervention happens. He demonstrates the impact of reframing through a real example: Sacramento's $130 million fire department budget defending an $87 billion economy - a 1:670 ratio that shifts stakeholder perception entirely. The discussion covers credible methods for measuring avoided losses using case studies and expert causal chain analysis rather than statistical proof, long-term project storytelling strategies that anchor elected officials to prevention goals despite election cycles, and leveraging AI tools like economic impact modeling to support data-driven narratives. Saylors emphasizes that technical findings must translate into shared values around community resilience across siloed departments - law, finance, public works, law enforcement.
Use counterfactual analysis: construct credible causal chains showing what would have happened without intervention, supported by expert analysis and case studies. Assign numerical value (lives, infrastructure, economic loss) to the avoided consequence to create defensible estimates, acknowledging that all models are approximations designed to provide directional compass, not perfect certainty.
The threat-consequences-vulnerabilities (TCV) model from homeland security: threats and consequences exist independently, but vulnerabilities - the weak points in systems - are what emergency managers actually control and should measure reduction against.
Reframe the risk equation: show the economic asset being protected divided by the response cost (e.g., $87 billion economy defended for $130 million annually) and tell specific human stories about what happens when response capacity is cut, since elected officials remember narratives, not data.
Metrics must have clear linkage to community resilience outcomes (lives saved, property protected, risk reduced), not just activity volume; ask the right question upfront during project initiation to ensure you're measuring what matters.
Dig down to shared values that transcend departmental lanes - ultimately everyone's mission is protecting the people in the community, and disasters prove that siloed thinking fails; frame emergency management as protecting all their constituents.
Our reviewer’s read on each dimension, with quotes from the episode.
The episode delivers substantial, non-obvious frameworks for measuring emergency management value, particularly the distinction between outputs and outcomes (red blood cells vs. white blood cells analogy), the risk formula (threats + consequences + vulnerabilities), and the counterfactual methodology for proving avoided losses. However, the conversation meanders with repetition of core points and some throat-clearing, particularly in the latter half around AI applications where concepts are restated rather than deepened.
We can't really measure their outputs because they're not running around producing things. But what they are doing is they are in the background and they're observing and they're coding and they're watching and they're preparing
risk has been defined in the domain of homeland security as a mix of threats, consequences and vulnerabilities. Now that becomes incredibly useful for us when we are talking about prevention and what our value of work is, because we don't necessarily manage threats
The episode presents genuinely fresh thinking on emergency management value measurement, particularly the counterfactual/causal chain methodology and the reframing of EM as outcome-focused rather than output-focused. The white blood cell analogy is creative. However, the storytelling/narrative framing and the general deployment of AI for cognitive load reduction are somewhat familiar concepts, and the episode doesn't aggressively push against conventional wisdom.
So I think a real good way to think about this in an analogy is to think about white blood cells versus red blood cells in your system
the person that dials 911 that pulls the 5 year old out of the pool doesn't know any of this. All they know is that they need help right now
Eric Saylors is highly credentialed and operationally relevant: Fire Chief with 30+ years in service, prior 25 years at Sacramento City Fire Department, doctorate in leadership, authored research on homeland security value quantification and active shooter studies, and actively shapes product strategy at HEN Technologies. He speaks from lived experience managing million-dollar budgets, conducting multi-year research studies (active shooter study with 200 drills), and testifying to elected officials. This is a genuine practitioner-expert, not a thought leader or consultant.
Dr. Eric Saylers is fire chief of El Cerrito Kensington Fire Department. And, and he has over 30 years of experience in the fire service. He previously spent 25 years with the Sacramento City Fire Department. He's the author of Quantifying a How Homeland Security Adds Value
I did over 200 live drills
The episode provides concrete examples and numbers in places (the $87 billion Sacramento economy defended by $130 million budget, $20 million budget cut analysis, Galveston seawall, Oakland Hills fire hydrant adapter failure, 200 active shooter drills, 60-victim mass casualty math problem, Pulse nightclub, Las Vegas 800 victims in 10 minutes) but lacks specificity in other areas. The AI application discussion is vague on actual metrics produced; the active shooter study findings mention the child/adult distinction but don't provide quantified outcome differences; and much of the strategic framing remains at conceptual rather than granular execution level.
$130 million a year. But you're defending an $87 billion economy
the Eaton fire is about to happen and this is what it's going to do, what would you do now? Right. What would, what steps would you take now instead of trying to figure this out halfway through? Right. If I told you 9,000 homes, 19 lives
Host questions are reasonably structured and follow logically from prior answers, creating coherent topic progression (outputs vs. outcomes → measuring value → long-term projects → data/dashboards → stakeholder alignment). However, the host rarely pushes back, challenge claims, or probe deeper when assertions warrant scrutiny. When Eric makes significant methodological claims (e.g., proving counterfactuals through causal chains, the AI orientation model), the host accepts and moves on rather than testing rigor. The conversation reads as collaborative exploration rather than rigorous examination.
Got it. So when developing a prevention or preparedness project, what measures do you think should be identified during that project initiation
So one aspect of this is that technology today, AI is like on steroids now, where we can generate large amounts of operational data
Computed from the transcript - who did the talking, and the words that came up most.
Summary In this episode, I spoke with Fire Chief Dr. Eric Saylors. Dr. Saylors has spent 30+ years asking a question most of the profession struggles with: how do you prove the value of a disaster that never happened? In this episode he lays out a practical answer - define risk as threats, consequences, and vulnerabilities, recognize that vulnerability is the only one you actually control, and then get honest about what's sitting on the roulette table. Dr. Saylors shares the moment he reframed his department's budget for a skeptical mayor, why avoided-loss estimates rest on causal chains and case studies rather than statistical proof, and why arguing only for your own budget costs you credibility. He also covers the two-part story that moves elected officials, where AI genuinely helps with mass-casualty math, the Oakland Hills hydrant-adapter failure, and his work at HEN Technologies helping incident commanders get unstuck at "orient" in the OODA loop. Time Stamps 01:13 Intro and guest background 02:25 Outputs vs. outcomes - how should we define success? 06:15 What should you measure at project initiation? 10:45 How do you make an avoided-loss estimate credible?
Transcribed and scored by The B2B Podcast Index.
Speaker A: Yeah, it's $130 million a year. But you're defending an $87 billion economy. Now look at me. $87 billion economy for $130 million a year. That's your response model that you're putting forth to defend that thing. That reframing of the consequences changes entire outlook on what was happening. Because that's what's sitting on the roulette table. And you're coming at it with $130 million. And if that $130 million fails, you're going to lose that infrastructure. You've proven it over and over again through floods and fires and natural disasters. That's what's coming off through that. In a world filled with chaos and a myriad of risks, there is opportunity. You are listening to Riding the Wave Project Management for Emergency Managers where we discuss how we adapt and rise above those rolling waves of hazards and threats
Speaker B: we face and rise to the top.
Speaker A: And now your host, the president of Pinnacle Performance management, Andrew Boyarsky.
Speaker B: Dr. Eric Saylers is fire chief of El Cerrito Kensington Fire Department. And, and he has over 30 years of experience in the fire service. He previously spent 25 years with the Sacramento City Fire Department. He's the author of Quantifying a How Homeland Security Adds Value, Sense Making and Risk Assessments and a seven Year Active Shooter Study. He holds a doctorate in leadership from the University of Southern California and he also serves as a product lead for HEN Technologies. And we'll get into that a little bit later. First of all, before I call you Dr. Eric or Dr. Saylors or Eric, what do you prefer?
Speaker A: Eric works because the doctor chief thing just starts to get mangled and I've never really changed much through the ranks of the education. I'm still just Eric. So thank you very much. I appreciate it. Eric is perfect.
Speaker B: Thank you for joining the Riding Way podcast. I do want to mention I highly urge my listeners in public safety and emergency management to, to go to the articles, the thesis, and also the other podcast interviews that Eric has done. And I will put some links to them in the podcast notes. So let's get into it. Emergency managers can easily count plans completed, people trained, exercises conducted. Those are the outputs, right? And not necessarily outcomes in terms of capacity building towards what we would consider community resilience. How should we define project or program success when the real objective is fire prevented, deaths or injuries avoided and um, community risk that's reduced?
Speaker A: I think that is critical to understanding what we do and why we exist is the outcomes that we want to get from our response model, right? So when we're talking about emergency managers, we are looking at potential events, we are coding ourselves for them, we are creating training for them. And then if the event happens, the outcome is really what we want. So we never cause the event, but what we want to do is we want to inject ourselves in the chain of events and stop the next terrible thing from happening. Because all of these things that we are tasked with stopping is not things that we've chosen, right? Nobody ever chose to have to deal with these things. Things, but they're going to happen. And our outcome is how did we lessen that impact on the community as much as possible. So I think a real good way to think about this in an analogy is to think about white blood cells versus red blood cells in your system. So when you are looking at red blood cells, they're a great production system. They carry oxygen around. We usually measure them at 90, 98, 99% oxygen capacity. They're doing their job. That's a very good business way to look at things. That's a very good business way that measures outputs, right? And we can look at those red blood cells and say, hey look, they're dropping down to 80%. This is really dangerous. The human organism is at danger of dying here. However, there's this other system that runs in parallel right next to it called the white blood cells. Now you can't really measure their outputs because they're not running around producing things. But what they are doing is they are in the background and they're observing and they're coding and they're watching and they're preparing and foreign proteins are coming into the system. They're saying, hey, here's a foreign protein, we need to watch out for this. Let's build a response model for it. Right? Let's get ready for that. Now when you look at white blood cells, it's all about the outcomes. Did you survive the flu? Did you survive that bacteria that came into your system? Now it's going to take a ton of energy and a lot of resources to get that virus beat down. But that is a positive outcome that you're looking for out of your white blood cells. And when we're talking about emergency managers, it's very similar. We are sitting back and we're thinking, okay, what's coming? Natural disasters, man made disasters, fires. What are we going to do? What can we bring to the table to stop this? And now what are the outcomes that we want? We can't have a total loss of the Organism, which means you can't have the whole city burned down. You can't lose the entire region. Right. This isn't going to come out with an ultimate win. Win, that's awesome. But what we want is, we want ultimate survivability. So those outcomes is a much more useful way to look at what our goals are when we're talking about risk and why we develop these plans and why we have response models.
Speaker B: Got it. So when developing a prevention or preparedness project, what measures do you think should be identified during that project initiation so that the team is not trying to prove value value after the work is completed? Right. We can think about some of the projects that might be applications, they might be exercises that we're involved with to justify why we did them. Right.
Speaker A: Yeah. It comes down to the definition of terms and how you use those terms to frame what you're trying to do. So the definition of risk is one that we tend to struggle with a lot because it's a word that gets thrown out there a lot. But what does it mean? And then how do you use it? And unfortunately it has multiple definitions dependent on the domain that you're in. And sometimes we'll pull it from different domains. Now, if we're to talk specifically, risk has been defined in the domain of homeland security as a mix of threats, consequences and vulnerabilities. Now that becomes incredibly useful for us when we are talking about prevention and what our value of work is, because we don't necessarily manage threats. Threats aren't something that we do. That is something that exists. Whether it's the potential for a dam collapse or an act of shooter or a massive fire, that's a threat that's coming our way. We also don't necessarily manage consequences, but we would like to measure them. Um, we can measure them in lost life, we can measure them in dollar amount of infrastructure. But consequences are really important to us now. Vulnerabilities, that's where we actually operate. Right. We can't eliminate threats. We frequently can't move the consequences, but we can reduce vulnerabilities. And when we talk about building a prevention plan, that's where it should land is how do we reduce these vulnerabilities? You can never drive risk to down to zero, but you can start to put blocking nodes in place to reduce that. So for example, the, the public has a hard time really understanding what this means. And I had this, this moment in one of my chief's vehicles. I was driving around with our, uh, mayor at the time, Mayor Steinberg, and He was stating how expensive the fire department was and how much of the budget that it ate up. And at the time, this is ah, a quite a few years ago, at the time it was $130 million a year. And I looked over and I said, yeah, it's $130 million a year. But you're defending an $87 billion economy. Now that's a word, $87 billion economy for $130 million a year. That's your response model that you're putting forth to defend that thing that reframing uh, of the consequences changed his entire outlook on what was happening. Because that's what's sitting on the roulette table. And you're coming at it with $130 million. And if that $130 million fails, you're going to lose, you're going to lose that infrastructure. You've proven it over and over again through floods and fires and natural disasters. That's what's coming off the roulette table.
Speaker B: Yeah, it's evident from LA wildfires, LA city, county. I'm not blaming the folks that are there, but uh, if you look at prevention, mitigation, things that, you know, in terms of some of the lessons learned that came out of there on the after action reports, I think it, it bears that out. Uh, those are really good points.
Speaker A: I think I want to play off that because that's such an incredible observation. Right. Is that if you got to go back in time and you could tell your elected officials, your leaders, hey, the Eaton fire is about to happen and this is what it's going to do, what would you do now? Right. What would, what steps would you take now instead of trying to figure this out halfway through? Right. If I told you 9,000 homes, 19 lives, right. And an event that lasts for weeks with a massive hazmat, what type of response model would you put it at that point? Would you be complaining about your cost of your emergency managers, your fire department, your police department? Right. Same with the palace party. So I love that, that idea of a counterfactual because that is what looking at risk, that lens is, hey, this is actually what's at risk. And just to say, well, it's never happened before really has no relevance in what the future looks like when you're talking about the threats coming.
Speaker B: Yep, yep. So as you just mentioned, I want to build on that in this question. Avoiding losses are, uh, is inherently difficult to prove because we're measuring something that did not happen. As you stated just now, a counterfactual so what data, uh, comparison methods or assumptions make an avoided loss estimate credible? And what practices can undermine credibility?
Speaker A: So you have to look at this, this real challenge in statistics that says that it's impossible to prove the negative. That is a good statement, although it's not entirely accurate, right? Because it's in the domain of statistics. And we don't live within the domain of statistics. We live within the domain of reality. And there is some phenomenology that is real that I just necessarily don't need a mathematical model to prove, right? If I jump off a roof, I'm going to hit the ground. I don't need statistical evidence to prove that. So once you get out of that mindset and you say, okay, look, we do have case studies and we have expert analysis that can tell us this is what would have happened had we not intervened, right? These are the chain of events, these causal chain of events that would have happened simply because the system was interconnected. Now, when you are working with that methodology, you can say, yes, I now have consistently built a chain of events that is hard for any reasonable person to deny. I can now start to assign value to that, right, numerical value, whether it's on critical infrastructure, whether it's on buildings, whether it's on the statistical value of life, and I can come back with some credible numbers. Now, that is a very good process that works well. And I will say, quoting the famous statistician George Box, all models are wrong. Some are useful, right? All of this modeling is not perfect. It was never designed to be perfect. Perfect is the enemy of good. But what it's designed to do is to give you a compass of what you're looking at.
Speaker B: Right?
Speaker A: Are we talking about $87 billion? Are we talking about $87 billion million dollars?
Speaker B: Right.
Speaker A: When this dam collapses, I can put a, uh, general guardrails around what the consequences of this are going to be. Is it going to be perfect? No. But I wasn't looking for perfect. I was looking for good. So I can make a decision. That's the decision I want. Now, what undermines that credibility is that if you start to overplay those counterfactuals or you jump out of a validated methodology to try and support a narrative, right? And you don't ever want to do that. What you want to do is you want to seek the truth and support the truth. You don't ever want to seek a narrative. So I'll say, uh, when I started this process in 2012, looking at fire departments, I had just experienced a $20 million cut out of my fire department. And I asked this question to myself. $20 million cut out of the fire department. Are we going to lose $50 million in additional property loss because of this reduction? And if so, can I prove that? But I also was very honest with myself. I said, look, if you cut 20 million out and you lose 50 million, that's a bad decision. If you got 20 million out, you only lose 10 million. That's actually a good decision. I understand that. Right. I m am with that. M. I'm with that. My first degree is in finance. Makes sense to me. So to go at it from a very honest, like, I want to seek the truth instead of support a narrative that this is always better is the right way to do it. If you are always arguing for yourself, I think you are going to undermine credibility. Right. You've got to come forth and say, look, there's a point at which we are going to have to right size emergency management response models. Right. They can't actually bankrupt every entity that supports them. So let's figure out how to right size them and what that means to the community. That will build you credibility, especially when you're able to talk about what the actual potential losses are.
Speaker B: Got it. Uh, many emergency management programs deliver benefits across several years or even decades. As a good example of this, the seawall that was built in Galveston took decades to build. And clearly they had the massive hurricane, that historical hurricane in 1900 or so that killed. Estimates vary from 8,000 to 16,000 of that event. But the leaders of the time knew they were going to build it. They started building it took decades. Now, of course, they're looking at how they can raise it because floodwaters and probabilities have increased. They have had additional flooding in Galveston. So it needs additional work. But while grants, budgets, elected officials operate on shorter cycles, where we expect results sooner, how can project managers establish meaningful interim milestones that demonstrate progress before the ultimate risk reduction outcome may be visible?
Speaker A: So this comes down to human communication in the form of storytelling. This is so critical to those of us that become the leaders of these organizations, understanding that we are dealing with humans and humans are emotional beings. They're not computers. And they are on short cycles. Right. They're on from one election cycle to the next. They are bound by their constituents. And a lot of times in that short cycle, they want short term outcomes. So you are now up against this emotional creature that is on a short term cycle, and you have to tell them a very compelling story. That story comes in Two parts. It comes in the actual data that you bring forth and then the real life case study events that you can plug together to help explain how consequential this is. So when you're talking about things like long term infrastructure projects, all humans suffer from something called relevance bias, right? It is. They'll forget about the long term. They're going to be wrapped up in what is right now. And it is your job to bring them back to what the goals of that long term infrastructure projects are. And you have to package it in a way so that they can sell it to their constituents because ultimately that's what they're bound to. So it's a little bit like giving them something that they can use for themselves. It has to come in a way of a story. So I've testified before the state assembly a few times in California, multiple topics. One of them was actually on wall times for ambulances and transport times. And when I am talking to elected officials that are ultimately controlling large budgets and deciding on long term infrastructure projects, I will package the story into two things. I'm going to give you the data and then I'm going to tell you what it means. So for instance, in this one testimony, I said, look, our response times in this downtown area went from six minutes to more than 20 minutes because of this thing. And that might not mean much to you, but let me tell you what that means. On this date, a 10 year old child was struck by a car in this environment and that child ultimately died because we had to wait over 20 minutes for an ambulance three districts over to come over when the kid was actually hit two blocks away from the hospital. So we have data that tells you what it is. Now I'll give you the case study.
Speaker B: Now.
Speaker A: Funny thing is that they're not going to remember the data. That's not going to stick in the human mind. What is going to stick in the human mind is that story, right? That one event. Because they're going to be able to picture it, feel it, know what it means. And now they're going to be able to relay that story on and when I'm talking about a project and a problem that has been going on for 15 years and it's going to go on for another 15 years unless we start to take action. I need them to remember it and I get them to remember it by that story.
Speaker B: Okay, I guess we can avoid the whole how do we do this with, through the political system at this point? Because that can be a major challenge on its own, whether it's state, federal, local. It's easy to say, okay, well we have a budget deficit this year. We're not going to invest in whatever mitigation measures or preparedness measures that we might be investing in.
Speaker A: M yeah, when that happens, and that will happen to every leader, your job is to tell them what it will cost. Absolutely. You're always going to go through expansion and contraction phases. You go through a construction phase, that's fine. My job is tell you what it's cost. You're going to close these stations, you're going to reduce these positions. I totally understand that. This is what it's going to cost you.
Speaker B: So one aspect of this is that technology today, AI is like on steroids now, where we can generate large amounts of operational data and then we can sift through that data to try to render meeting. But data does not automatically produce better decisions or enhance performance and improved outcomes. How should an emergency manager determine which measures belong on a program dashboard that has clear linkages to community resilience, mitigation, prevention, preparedness, and which are really just interesting activity counts that have limited value in measuring impacts?
Speaker A: It's a great question. So when we think of AI in its form right now, uh, as sometimes it's just a really advanced search engine that is able to reproduce text, it is incredible for generating a lot of data if you ask it the right questions. And it is really all about asking the right questions. Now. The fact that you now suddenly have access to that much data so quickly allows a storyteller to use it effectively. But you still have to make, you still have to be that good decision maker. So for instance, wrapped up in a conversation just recently about the area that I am the fire chief of, uh, which is one of the highest risk areas in the nation. It suffers from earthquakes, landslides and the highest risk wildfire in the nation. And I am talking to elected officials, I can quickly QAI and just get a lot of information that is accurate on the fact that basically I'm looking at a $1.2 billion economy and 5 square miles data like that, my retail sales and millions of what is done in the retail environment. Right. How many homes I have in the value of all those homes which turns into billions of dollars. I can pull all that information in very quickly, but I now have to be the storyteller to explain it and make it uh, relevant to the person that's receiving it. And I also have to be able to ask the right question to whatever. I hate to use the word AI, but I will to Whatever. Whatever. AI. Ah, I'm asking, right? Whatever model I'm asking, I have to be able to ask the right question. And that becomes really the principle of the problem is that question. So if I roll myself back to 2012, the question that I was asking was how, um, much did we save with our actions versus how much was lost? Now that, to answer that question took a lot of work. I did it for years in my agents that I worked in, the return on investment for that fire department was over 2000%. With 19 billion saved a year. That took a lot of work. Now with AI, I can actually ask the right question and condense that work down really quickly. So one of the most rewarding parts of my life is that I am now developing an AI model that can look at a fire instantly and then tell me the economic impact of that fire around it and model it, model it based on winds, model it based on fireflow, and get numbers back quickly. Before, without AI, that would have taken me months to get to, and now I can do it very quickly. So it is an incredible tool, but you still have to be asking the right question so that as you go forward to explain why we are reducing risk, what our ultimate goals for outcomes are, they'll land correctly.
Speaker B: So one aspect of this is that, uh, preparedness and prevention projects often require support from fire departments. Public works, planning, finance, law, law enforcement, community organization. I say emergency managers because my primary audience is emergency managers, but it applies to public safety as well. We have to work with our mutual partners in emergency services, whatever they may be, and also importantly, elected leadership and other stakeholders within government and also partners outside. How can a project manager translate technical findings into a value proposition that different stakeholders will understand and support? You mentioned public, but, uh, you mentioned a little bit about using examples and things like that. But for some of them, it's like, what's in it for me if I'm in the law department or I'm in the, I don't know, accounting department or whatever.
Speaker A: You've got to dig down to those ultimate shared values that everybody has. And you will get to. You dig deep enough. We get so lost in the lane that we live in and the methodologies that we're applying that we sometimes forget why we're here. Right? And if you can dig down to look, what really matters is the people. If you're in a region or a community, what really matters is the people that live here. Whether you're law enforcement, whether you're public works, whether you're emergency manager, whether you're fire. It is about the people that live here. And if we can come on in an agreement on that and work from that principle, then we can bring our methodologies together. But if we fail at this, that it'll be the people that live here that ultimately suffer. And we prove that time and time again with disasters that weren't managed as well as they could have been.
Speaker B: Right?
Speaker A: And that is critical to bringing people together and getting them out of their siloed thinking and their lanes about how they're going to get done, what they need to get done, and reminding them. Um, man, in real life, I just had a very similar conversation about something like this and a little bit of a contention between multiple agencies coming together. And my response to the people in the room was the person that dials 911 that pulls the 5 year old out of the pool doesn't know any of this. All they know is that they need help right now. Right? And they don't know that somebody stabbed somebody else in the back and that somebody else stole from somebody else's budget and that there's a, uh, that there's been a tit for tat war going on for 15 years between these two entities. They don't know any of that. And ultimately I don't want to know any of it right now either. What I want to know is when that five year old comes out of the pool, does the fire department, does the EMS agency, does the medical director, does the hospital agency? Do all of these entities, does law enforcement get there in time? Do all these entities come together to make sure that we get the ultimate outcome that we want?
Speaker B: I think what you've been saying has been very practical. But I also want to boil it down to my emergency management audience after listening to this podcast, what several steps can they take to begin measuring the value of an existing program that they have? Preparedness mitigation, uh, community risk reduction project that currently reports only on the activities.
Speaker A: So I think one of the most important things you can do is look at your consequences and uh, consider what is actually at risk in your region, the things that you're talking about. And if you want to talk about using AI, get an advanced model and ask it basic prompted questions. What is the total value of all the residential property in my area? What is the total value of all the commercial property? What is the total value of all the economic impact so that you can start to understand what is on the roulette table for you, understand the consequences of your actions. That is going to help you A, uh, ton. As you start to develop plans that what I just talked about was just tangible property. But then I encourage you to look at life and not just the statistical value of life, but also your vulnerable populations and what is it going to mean to them based on the outcome for your plans. When you get through that process, process, you're going to have a better understanding of what your mission is. So, for example, we talked about the seven year active shooter study that I did. I did over 200 live drills. And you are paring it down to what is the important question to ask for the local emergency manager. So I worked closely with my medical directors and emergency managers. Discussing this isn't something that just touches the fire department. This just tears through the entire community.
Speaker B: Yeah.
Speaker A: And you come across this interesting phenomena that when you were talking about responders and what they're going to do or not do. The difference between a mass killing that involves adults and the difference between a mass killing that involves children is two totally different things. We may like to bucket them into the same thing, but they are two totally different things. They turn into an ethical question and dilemma for the responders. And you're going to have different behaviors now from emergency managers who are writing plans and writing contingency plans and are saying, okay, we need to coordinate the whole region together and we're going to contact all these hospitals and the hospitals are going to get a census of how many beds they have and where they can take patients and then they're going to send it back and we're going to have to have all these answered questions before patients move. I'm going to say all of that is going to break immediately you inject kids into the problem. Because now these emergency responders are going to do exactly what they need to do. They're going to get them to definitive care, care, and all your plans are going to unravel on you. So to have that side of foresight to ask the right question, what is going to happen in this, in a real world versus my theoretical one, is really going to help you prepare for the next disaster. Because those plans unravel so quickly if you didn't ask the right question.
Speaker B: Absolutely. Yeah. Yeah. I. It's relevant because I don't know how many times I encountered folks who were like, this is the plan. And I said, well, but how was it developed? Did you actually engage with these folks? Did you. Just a simple example like you just stated. Have you gone out to the school and who is the school custodian and is the door Going to be locked on a weekend you can't get in. If you need to open up as a shelter, it's a very practical matter. And if it's not, if someone hasn't really assessed it and engaged folks, then, hey, you're going to have something that's not realistic in a way, you're making
Speaker A: all these assumptions that in your imagination may not play out true. And I just want to, I want to point on a real life, a real life example. The Oakland Hills fire, right? It was national news. It was devastating. It was a fire that came over ridgeside into the town of Oakland and tore through a bunch of homes. And one of the findings was that the agencies that came in to help couldn't hook to any of the hydrants. Oakland had specific threads on their hydrants. So this funny thing is that you look back at it and you go, oh my God, how would you come up with this plan? However, the planners said, we have these special threads. It's not a problem. We're going to have all these adapters, right? We're going to make all these adapters and the adapters will be stored in this warehouse. And when the supporting crews come in, they'll get the adapters and then they'll be able to hook to the hydrants, right? All the rigs from Southern California that are driving 400 miles, they're going to get these adapters and hook. The hydrants will be fine. However, it was a civilian that had access to the warehouse. And guess who gets evacuated during major disasters is civilians, right? So those adopters never came out. And although it was a really neat theoretical plan, I don't think they asked the right question. I don't think they asked the right people. I don't think they actually walked their way through the scenario of what would happen next. They kind of just took the easy button and the outcome, um, was a disaster. So that is just one case study of just highlighting exactly what you said. Did you actually engage with the right people? Did you ask the right questions? Or did you just let your assumptions
Speaker B: move forward and you need to. I think from what I have got, you know, in the interviews that you've done and things you've written, is really walking through the line and drilling these things out. You said you run conducted 200 drills for active shooter and you tested the actual practices in a simulated environment. You created the nightclub environment with lights and flashing and people yelling and screaming and things like that. That's the reality, right? And peaks are peaks. The folks are not going to go in with everything laid out nice and neatly for them to be able to prepare. They're going to go in with their bag and they're going to be going in there cold, so to speak. They might be trained and ready to attack the problem, but that's the reality. And I'm a big believer in drilling and exercising in a really closely simulated environment as much as you can without putting people, of course, in harm's way.
Speaker A: I think that what you just said is brilliant, is that, uh, you've got to actually use the case study and then you have historical events that you can rerun again and see how you would do and find your gaps. But here's one of the things I love about the current environment that we're living in now is there's a lot of opportunities to fix it. So let's talk about AI again, as I'll use that term, and let's talk about that active shooter event. And let's say, all right, look, let's identify what humans are good at and what they're not great at. And an active shooter, something like the Pulse nightclub or really Las Vegas, and a large scale thing where you start to get numbers. They present a math problem that humans aren't necessarily good at. And I'll play it out like this. You got 60 victims, legitimate number. You can put two victims per ambulance, right? So you need a minimum of 30ambulances. Not many regions are going to throw out 30ambulances on the table for you, right? You're probably going to get 15, half of the number that you need, which means some are going to have to hot lap, which means they have to go to the hospital and come back and they're going to have to go to different hospitals. You can have to make critical decisions on who's going to drive two hours out of the region, who's going to drive Code 3 to the nearest hospital and come back. This is a really hard math problems that humans are not great at. Working with my AI partners in our emergency management software, we've built out the math to say, boom, AI can do this very quickly. Here's all the hospitals in the region. You have 60 victims. 20 of them have non compressible injuries. They need definitive care. They go here, here's the ambulances that hot lap. Here's the ambulances that drive two hours out of the region. Here's the people that get on helicopters and go two hours out of the region for a 30 minute flight. And it can bang out that transportation plan for you. Solving that Math problem in a few seconds. Human being in gross neurological default is going to really struggle with that. However, what that allows the human being to do is while the AI is running, the background is that unloads the cognitive load for the human being to do what it's superpower is the human being superpower is emergent ideas. Emergent ideas to impossible problems. Right. I call it the stick and duct tape solution. You gotta stick, duct tape, solve this problem. Humans do that, right? They're amazing. Hey, we can't get access to the backside of the building. Cut the fence. Uh, I don't know how to cut the fence. Pull it down with your ambulance. They come up with these great ideas.
Speaker B: Yeah.
Speaker A: That allows the human being to solve that problem. It allows the AI to solve the complicated math problem. And the ultimate outcome is that you don't have 20 people bleeding to death on the ground waiting for transportation. Right. You have people getting the surgery on time. You have less chaos. It's not going to be chaotic, but you have less chaos than what you had at the Aurora theater shooting, where most of your victims go in the back of cop cars. Right. You have less chaos than what Las Vegas produced, which is 800 victims in less than 10 minutes. People being loaded into the back of pickup trucks. You're not going to solve all that, but you're going to take a better chunk out of it.
Speaker B: Yep, yep. So I want to talk a little bit about some of your work at Hang Technologies, because it's related to this. So you're leveraging a lot of the great research that you've done within, uh, the company as a product lead. So if you could just talk to us a little bit about how you're taking this and really making it applicable to, I would say, emergency services, fire services, et cetera.
Speaker A: Yeah. So let's talk about incident management and what happens to the human being when they put it. They put in charge of a large incident. There's a few things that happen emotionally and cognitively. One, you realize that you don't have a lot of control of what is going to outcome on this, this incident. But you are ultimately going to be held accountable. Right. Anything that goes right, you don't get any credit for everything that goes wrong. You're going to be held accountable for whether you had any control over it or not. So you step into that environment as a human being accepting that it's a little overwhelming. Right. You also know that there is going to be more information coming at you than you can process. We really only do Three to five variables at a time as a human being. And we're going to have 20 things thrown at us all at once, right? This. This thing called relevance realization. I steal it from John, the ability to throw 20 things out on the table, pick out the ones that are relevant and focus on those and let the other 17 things sit. So that's what happens when you step in as a human being. Now, the opportunity at Ken with the founder that I've been in relationships since the beginning in six years is when he is developing sensors and automatic controls for incident management. And AI is coming up. I say, you know what? We can actually use the transformer paper, the actual background, how our AI works right now. And we can reduce those 20 things that come in a human being with some priors down to three. So I can do a real quick relevance realization on the data that's coming to you and say, look, Incident Commander, here's the point of things.
Speaker B: Boom.
Speaker A: I am going to pick the signal out from the noise based on the priorities that you're in, and I'm going to give you the three things that you need to solve right now. Those other 17 things, although they seem critical, they can wait. Don't go down on those rabbits. I can use an AI to do that. I can use an AI to do that. I can get a human being to go through the OODA loop faster. So let's talk about that before I use any unknown acronyms, right? This is a model that is wrong but useful on how humans process information. It was developed by a combat fighter, an aircraft. Right? And UDA is Observe, Orient, Decide, Act. Uh, and you go through loop again. So you observe, you see what's going on. You orient to what's happening. You decide. Now, I've been teaching Incident Command for years. I've been writing after action reviews, and I'll tell you where most incident commanders fail is orient. They observe, they look out and they go, holy crap, what is this? Okay, I see it. Then they try to orient. And when they try to orient, they usually get paralyzed by information. It just comes at them too much. They get stuck in an emotional state. They go into gross neurological default, right? And then they will use cognitive dissonance, which means they will force the incident into something they're familiar with, ignoring it for what it really is. Right? So in the fire world, we'll typically ignore the HAZMAT or the mci and it'll run the fire, even though the HAZMAT of the MCI is a real problem. Right? And then they'll make a bad decision, they'll take back action, and they'll loop again, and they'll loop again, and they'll loop again, and it'll produce funerals. So the AI and the ability to do that relevance realization lands in the orientation world. Observe. Now, I'm going to help you orient. I know what your SOGs are. I know what your priorities are, and I know what's coming in on you on the radio traffic. I've transcribed it all, I know, seen it all. And now I'm going to help you orient. You are not dealing with a fire right now. You are dealing with hazmat that is burning. That is a different problem right now. That is isolate, deny entry. That is not fire, attack. Right. That's just. That's just one scenario of an example. But the AI allowing the incident commander to orient, then make a good decision, and then make a good action is an incredible, incredible use of the technology that is that, uh, really 12 months ago didn't exist. Now I can do it. I have a battalion chief working in my old agency in Sacramento. He drives the hazmat calls. He has a model that he runs, and he says, this is the problem I've got. I've got a leak from a chlorine tank on a rail car in this area. Start the incident action plan for me. And while he's driving, the AI will basically bang out the format of an IAP forum, which me used to take me 20 minutes at the back of my car, right? So it gives them a head start. And that is just an incredible opportunity at him to stop going to funerals for needless actions and to try and save more lives so that incident commander can use their superpower of emergent ideas. But we're going to help them orient to actually what is actually in front of them right now so they can make a good decision and a good action.
Speaker B: I want to thank you very much, Eric, uh, for sharing your thinking on this, your expertise, and it's been a pleasure. And the only thing that I would say is I wish I had a little bit more time. I want to be respectful of your time, but I do appreciate you coming on the podcast.
Speaker A: Oh, uh, thank you so much for having me. It was a ton of fun. Really excited and honored to speak with you, so thank you.
Speaker B: Dr. Eric Saylors is fire chief of the El Cerrito and Kensington Fire Department. He has over 30 years of experience in the fire service, previously worked with the Sacramento Fire Department, and he's also the author of a number of theses and articles. Again, as I said at the top of the podcast, do recommend I will include some links in the show notes. He's also product lead for HEN Technologies. Again, thank you so much for coming on. Thank you. You've got mitigation projects, grant applications, training and exercises to deliver, but keeping all of it moving on time on budget with the team you have. That's where Good Intentions Stall Project Management for Emergency Managers Workshop gives you the practical tools to manage, scope, schedule, budget and deliver. Built specifically for how you work. Walk in with your real projects or walk out with a real plan. Taught by practitioners who've led responses from 911 to Sandy to Covid workshop options from one to four days in person or online, it's time to move from planning to done. Visit pm4em.com to learn more.
Speaker A: You've been listening to Riding the Wave, hosted by Andrew Boyarski, President of Pinnacle Performance Management and Clinical Associate professor in Emergency and Project Management at NYU and John Jay College.
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