In this episode of "A Duty to Act," hosts Jennifer and Josh reflect on the impact of their previous episodes, including discussions on cancer in the fire service and secondary traumatic stress. They discuss their goals for the next 25 episodes, which include exploring topics such as lifeguards, smoke jumpers, and tribal EMS. They also delve into the importance of data-driven decision-making and building a culture of data in public safety organizations. Join them on this journey of discovery and insight into the world of emergency services.
In this episode, Jennifer Darling and Josh discuss the progress and impact of their podcast, "A Duty to Act." They reflect on the most impactful episodes so far, including the Des Moines episode and the discussion on secondary traumatic stress with Pat Ellis. They also mention the importance of addressing cancer in the fire service and their plans to cover topics such as lifeguards, smoke jumpers, and tribal EMS. Josh shares his experience in building a data science course for EMS and fire leadership, emphasizing the importance of thinking like a data scientist and using data to make meaningful changes in departments. They discuss the value of a data-driven culture and the need for curious minds to analyze and interpret data accurately.
The Des Moines episode and the discussion on secondary traumatic stress have been the most impactful episodes so far.
Cancer in the fire service is an important issue that needs to be addressed.
Lifeguards, smoke jumpers, and tribal EMS are potential topics for future episodes.
Building a data-driven culture is crucial for making informed decisions and improving departments.
Curiosity and asking the right questions are essential for effective data analysis.
"The Des Moines episode is such an important issue." - Josh
"I hadn't thought about the life and death of it. I had thought about the working environment and longevity and recruitment and retention." - Jennifer
"Everybody wants to grow up to be an influencer. When I was a kid, everybody wanted to grow up to be an astronaut or a firefighter or a race car driver." - Josh
"Everybody's got an ear to ear smile when they get to tour an ambulance or a fire truck." - Jennifer
"Looking at the numbers without context may tell a completely different story than having that kind of context added into it." - Josh
Don't miss this engaging episode of "A Duty to Act" as Jennifer Darling and Josh discuss the impact of their podcast and the importance of data-driven decision-making in public safety. Tune in to gain valuable insights and stay informed about the latest trends in the industry.
[TRANSCRIPT]
0:00:01 - (A): Welcome to a duty to act with your host, Jennifer darling.
0:00:11 - (B): Hey, everybody. This is Jennifer with a duty to act podcast. I am sitting here alongside Josh and ready to go with our 25th episode recap. Josh, how do you think it's going?
0:00:24 - (C): I think it's going. I think it's going very well.
0:00:27 - (B): Yeah.
0:00:28 - (C): Yes. We're growing and that's pretty cool. And we're talking to interesting people and hopefully, hopefully having some impact, which is pretty awesome.
0:00:40 - (B): Do you have any vote for which episode has been the most impactful?
0:00:51 - (C): I'd like to hope that it would be the Des Moines episode. Then again, I wonder about the episodes, about hiring and with Ryan Coffey and the interview practices and stuff. And then also the, what I call the Fab four when you were hanging out, talking to the new recruits, because that gets people in the job. Right? Helps people get hired. But then again, I'd like to think that the Des Moines, it's just cancer in the fire service is such an important issue.
0:01:30 - (C): So I'd like to think that that would be most impactful.
0:01:34 - (B): I don't know, but I've thoroughly enjoyed every episode that we've done. I think the two most impactful in the past 15 episodes really have been Des Moines and also talking to Pat Ellis about secondary traumatic stress. And the reason why I think those were the most impactful or more impactful is because I did not see those episodes coming. I have not ever had a lecture on secondary traumatic stress. I've been to classes and critical incident stress debriefing, but it was just a fresh look at it.
0:02:12 - (B): And I think it came at a time that I was really receptive for a fresh look at stress. And I also think that the Des Moines episode sort of took me by surprise, because when I think about making a difference in people's lives, when I think about the aim that we set out, when we set out to do the podcast, I hadn't thought about the life and death of it. I had thought about the working environment and longevity and recruitment and retention, very much on the business side of things. And I kind of didn't really have sight over what some people are dealing with. So that was a great episode for me.
0:02:54 - (C): Yeah, it's surprisingly prevalent. I just last week was in Tucson and met a retired firefighter with pancreatic cancer. And just, he was a part of a workshop that I was doing. And it was interesting. We got to talking about the Des Moines episode a little bit and what they did to try and push the legislation forward and things. It was a good conversation.
0:03:25 - (B): Yeah, very good. Stories keep coming out of the woodwork about cancer. I know that cancer cluster isn't the right word. There's a very specific definition for that. But that's what I'm going to call it right now for these cancer clusters departments that have a handful of people battling cancer all at the same time. There's actually one very close to home and another one further south, a couple hours south of us that are interesting. So I hope to spend a little bit more time there.
0:03:57 - (C): Yeah, I hope so.
0:04:00 - (B): Yeah, absolutely. So what are the targets? What are the goals? What are the aims for the next. Let's see next 25 episodes, because that's when we're going to meet again and do this.
0:04:13 - (C): A million listeners.
0:04:14 - (B): A million listeners, which we're not there.
0:04:16 - (C): Yet, but, you know, someday.
0:04:18 - (B): But we're closer than we were at ten.
0:04:20 - (C): A journey of a thousand miles begins with one step. So. Yeah. Okay.
0:04:25 - (B): So a journey of a million listeners begins with a handful. A handful. All right. We've also had some really nice bump up in our numbers over the past 20. Sorry, 15 episodes. That's been really great to watch.
0:04:41 - (C): Yeah. And like you said before, I like watching the map.
0:04:44 - (B): Yeah. I think that's my favorite part.
0:04:47 - (C): New cities and new countries.
0:04:48 - (B): So what if we added geographically?
0:04:51 - (C): Oh, off the top of my head, I don't like. Tokyo is in there. And Hawaii.
0:04:58 - (A): Yeah, Hawaii.
0:04:59 - (C): There's a couple more around Europe and stuff. It's just been fun.
0:05:02 - (B): We got Russia.
0:05:04 - (C): Yeah. Moscow.
0:05:05 - (B): Moscow.
0:05:06 - (C): Putin's listening.
0:05:07 - (B): There you go.
0:05:09 - (C): Or some sort of Spotify bot. I don't know.
0:05:14 - (B): Let's not talk about.
0:05:15 - (C): It was Moscow. It was Moscow.
0:05:17 - (B): All right, well, very cool. Yeah. We have had a few high highs, not just steady climb, but a couple of, really peaks that we've scaled with just a single episode, which was fantastic.
0:05:30 - (C): Yep.
0:05:31 - (B): And that's been fun to watch.
0:05:33 - (C): Yeah, it's pretty cool. This is a fun adventure.
0:05:36 - (B): Okay, so what topics would you like to see us cover in the next 25 episodes?
0:05:45 - (C): You know, we talked about lifeguards. We still haven't been able to pin down a lifeguard. And I'd love to talk to somebody. I've had three random run ins with smoke jumpers or smoke jumper recruits, and I'd love to talk to some of those folks. That'd be interesting.
0:06:05 - (B): Okay.
0:06:07 - (C): And maybe even some of the support group, because that's, you know, the folks.
0:06:11 - (A): Who go out and do the wildfires.
0:06:12 - (C): Then there's that whole crew of people who are on the ground cooking breakfast for them. And stuff. That might be an interesting group of folks to talk to as well.
0:06:23 - (B): Yeah. And you put out some feelers already, too. You've reached out to an explorer group.
0:06:28 - (C): Mm hmm.
0:06:29 - (B): Where was that? Do you remember? Oh, was that the Hawaii?
0:06:32 - (C): No, no, I don't remember. They didn't reach back out. I'll have to dig through my emails again. But that was. That was pretty interesting.
0:06:39 - (A): There's.
0:06:39 - (C): Yeah, yeah, there's a. It was like middle school.
0:06:43 - (B): Yeah.
0:06:44 - (C): Middle school explorer program kind of thing. And it was a brand new program. I'll have to look through my email because that was. That was actually pretty fun.
0:06:51 - (B): Yeah, that was. That looked really interesting. And I like the idea of learning more about these creative programs where they're getting kids even younger. You know, they're not just targeting high school career fairs, but actually trying to get the kids younger. I'm convinced that that's really where we need to work on this. I know. I talked to Richmond fire. Sorry, Richmond ambulance authority. About that briefly.
0:07:16 - (B): Just really getting more youth, getting access to kids. That doesn't sound right. But getting into the school, making fun.
0:07:27 - (C): Firefighting. Cool again.
0:07:28 - (B): Yeah. Making law enforcement and ems and just attracting people.
0:07:32 - (C): Well, when I was a kid, everybody wanted to grow up to be an astronaut or a firefighter or a race car driver. Now everybody wants to grow up to be an influencer.
0:07:40 - (B): That's pretty funny, actually. I had an interesting experience at work a couple weeks ago. There was science night in Mount Vernon, which I did not know what that meant. I thought it was a science fair, and they asked us to be there with the fire engine in the ambulance. We weren't able to spare a fire engine that night, but we went with the ambulance. It was not a science fair. It was not a career fair. It was not just high school. It was all of the people in the community, all of the kids, I mean, very small toddlers, up to college age kids coming through and getting in the back of the ambulance and looking around, and we tried to cover everything. This is the science of how body temperatures works, and this is the science of hydraulics with our stretcher.
0:08:29 - (B): It was really interesting, and I'd like to spend some time doing more of that stuff, getting more insight into what people are doing that works, to bring the people into the back of the ambulance and bring the people to the field.
0:08:45 - (C): Yeah. I have yet to meet anybody who, you know, man, woman, young, old, doesn't matter if they're. If they get to tour an ambulance or a fire truck or sit in the driver's seat or something like that. Everybody's got an ear to ear smile.
0:08:59 - (B): Yeah, it's.
0:09:00 - (A): It's just. It's just cool.
0:09:02 - (B): Yeah.
0:09:02 - (C): Like, I don't. I don't care who you are.
0:09:03 - (A): It's cool.
0:09:04 - (B): Yeah. I think we have to do more of that.
0:09:06 - (C): Yeah.
0:09:07 - (A): Yeah.
0:09:07 - (B): Absolutely. Let's see. So we've got smoke jumpers. We've got some more explorer programs. We'd like to get into the lifeguarding and just understand more about that. Oh, coroner's office. I have some feelers out for coroner office. I've spoken to a trauma surgeon with multiple military still, so multiple tours of duty. He's going to come out. I have a medical program director coming out and talking about bias and the way it influences the way we care for patients.
0:09:42 - (B): And we're looking into bomb squad.
0:09:45 - (C): Yeah. And we were just talking today about native.
0:09:51 - (B): Oh, yeah, tribal.
0:09:52 - (C): Tribal ems and stuff like that. So just because, you know, it's a different. Different system, funded differently, all that sort of stuff. Might be interested to chat with them.
0:10:02 - (B): Yeah.
0:10:03 - (C): Yeah. See what kind of programs they have and stuff.
0:10:05 - (B): And all of these. I think the best part is basically all of these are all over the country. I just love that, the reach that we've been able to have talking to people, trying to set up our recordings with them from different time zones. It's actually. It's a little bit of a hassle and also a genuine pleasure knowing that we're able to talk to people across the country.
0:10:27 - (C): Yeah, that's been great.
0:10:30 - (B): Very cool. So I want to say that I have a pet project, and I want to ask you about your pet project, but I want to talk about mine first because mine hasn't gone anywhere yet. I really want to spend time talking about creative funding solutions for the things that we're doing. And, of course, not just funding the organization, that's a completely different beast, but actually funding these public education initiatives, funding explorer programs and school science fairs and so on. I want to look into grants and help people understand grant funding and what's out there. So that's the pet project that I want to work on.
0:11:10 - (B): I want to talk about what you've been working on, because you just spent five months on a project and you've finally gotten it done, and it's awesome. Tell me about the project that you just worked on.
0:11:22 - (A): I built a data science course for EMS fire leadership.
0:11:31 - (B): All right, so what is a data science course? What is the value? Like, what was that all about?
0:11:40 - (A): I wanted to build a course that would go beyond teaching EMS and fhir professionals about how to use Excel, but to teach them how to think like a data scientist, how to take raw data and translate it into an insight, and then use that insight to create a meaningful report and then use that meaningful report to or case argument to make real world change in their departments. So that could be like, well, in the course, we end up making a case for new apparatus and new personnel.
0:12:35 - (B): Okay, so I'm going to give you three examples of data and you're going to kind of help me pull them apart. Okay?
0:12:43 - (A): Okay.
0:12:43 - (B): So the first data I have is there's a national statistic that says the most common cause of household fires are kitchen fires.
0:12:54 - (A): Sure.
0:12:55 - (B): Okay. There's other data about fire deaths indicating that men and elderly are more likely to die in house fires.
0:13:05 - (A): Okay.
0:13:06 - (B): Okay. So are they all cooking when they die?
0:13:10 - (A): Maybe. I mean, there's a correlation between eating cheese before bed and dying tangled in your bread sheets. So your question is more about causality than correlation. There may or may not be causality. The question need to ask is why for each of those independently. And if you arrive at an answer that marries the two, then sure, then it's causal. Otherwise, no.
0:13:50 - (B): Okay. And so that's how a data scientist thinks. You look at the data and you say, okay, these happen to occur simultaneously. This data arises and simultaneous data we find. But it doesn't necessarily mean that all the old men are in the kitchen cooking, setting them on fire and then dying in those kitchen fires.
0:14:11 - (A): Yeah.
0:14:11 - (B): Okay. Do you think there's a lot of value in collecting data from a small department and comparing it to national data like that? Or do you think it's better if the small departments just collect the data and compare it to their own internal numbers?
0:14:30 - (A): Yes, you should do both, but you shouldn't get hung up on the comparison. It's good to know the comparison, because it's just good to have that kind of context to see how you compare. And if you're terrible, well, then maybe you should ask, why are we terrible compared to these other cities? And if you're amazing, then maybe you should contact those other cities and offer help. But what's more important is that you are tracking your own trends year over year, and that you're setting goals, making a plan, and then measuring year over year, quarter over quarter, whatever the case may be, to see if you are trending in the correct direction.
0:15:21 - (A): That's. That's way more important than comparing, I don't know, Mount Vernon, Washington, to, I don't know, New York City.
0:15:33 - (B): Okay.
0:15:34 - (A): Yeah.
0:15:35 - (B): All right, so I have a terrible data story to share with you. It's a terrible data story. It's got a terrible ending.
0:15:40 - (A): Okay.
0:15:41 - (B): Okay. I know of a small town that decided to add on first response ALS. This is something that they decided to do. They were well meaning and best of intentions and all the good things in mind. And when they decided to add on first response ALS, the data that they looked at was about call volume. And that's a great thing to look at. I mean, you want to put the resources where the calls are, but there was never any deeper analysis of that. And so the call volume, they looked at where they were busiest, where the most calls came in.
0:16:19 - (B): And it was sort of interesting because from the outside perspective, they put the first response unit in a residential, commercial, and interstate area, like an area where busy traffic. So, yeah, there's the car crashes, there's some residences. There's a lot of businesses. And those businesses are open Monday through Friday. So this is where they put the unit, instead of putting the unit where there was a cluster of nursing.
0:16:47 - (B): And of course, we know that nursing homes are the bread and butter of all the calls. It's a bunch of elderly people. They're all living in the same place. So a high number of people with lots of comorbidities, risks, and problems like that. Sure.
0:17:01 - (A): And old guys catching the houses on fire, apparently.
0:17:03 - (B): And the old dudes are setting the houses on fire. That's right. In the kitchen. So I'm thinking there's a lot of toast burning. But anyway, the decision was made to put the first response ALS unit in the area of town based on call volume. And it came down to the fact that there was a super user in that area. There was a frequent caller who would call one to three times every day. That caller, that single caller, skewed the numbers so dramatically just for calls coming in, not actually for things like time spent on the ambulance. I mean, if you have a frequent caller, you go over you, I don't know, pick them up off the floor or reassure them or give them a quick ride to the hospital.
0:17:48 - (B): It's very different than the calls where you spend a half an hour on scene and you have a 20 minutes transport. So based on this one statistical anomaly, this one person, they made this decision to put resource there. And again, I want to say the department had the best of intentions. They were using their data brain, the data brain that we have organically, and they made that decision. Interestingly, that person was struck by a train, and that call volume dropped to almost nothing.
0:18:26 - (B): And yet they had used that statistic to make that decision. So tell me about the holistic approach that we should take with data.
0:18:42 - (A): You're sort of faced with two problems or two general solutions at a department. When you have a data analysis. When you're trying to approach data analysis, either you have one person do it, or you have you open it up and build data driven culture and open it up for the folks who work there to kind of crowdsource the problem, so to speak, or crowdsource the solution. One thing that comes to mind with this particular issue is clearly, they looked at call volume, but they probably didn't look at the.
0:19:34 - (A): What is the devil in the details here when it comes to call volume, is like, what are the types of calls that are coming in and cutting those by, and having a curious mind to cut those things by, let's say, address or. Or coordinates or by name, those kinds of things, and to really kind of drill down, because the devil's in the details and those kinds of things. When you see such an anomalous, when you see such an anomaly, and what often happens is that you have one person who has limited knowledge and limited time, and they are trying to find a solution, and they come across a number that makes them, you know, makes their adrenaline fire, and then they run off and say, here's what we need to do.
0:20:36 - (A): We need to spend, you know, $250,000 on a rig, and we need to hire new people. When, if they took a. If they had the opportunity to take a moment and step back and ask why? And to dig down into the data and be patiently curious, they would find that there is a superuser there. And sometimes that requires more brains, which is why I advocate for this data driven approach or the crowdsourcing approach, particularly around the fire stations, just asking the guys around the kitchen table, because they could probably just point to that particular individual in this particular case.
0:21:28 - (A): But having more brains is really, really important, and it does a couple of things, is that, like, you have these different perspectives where two, you could have two or three people looking at the same. The same set of data, and they will all ask different questions and not necessarily come to different conclusions, but find different things in the data. And that's really important, because then you have a greater exploration of what is actually behind this current phenomenon. But then we're sort of talking about culture building, which is different from the data course.
0:22:10 - (B): So when you say crowdsourcing, you think that that's the right way to do it. Crowdsourcing isn't necessarily something that you do where you post something up on a user blog and you ask people to chime in on it. You're talking about crowdsourcing, like, gather your people, get your stakeholders involved.
0:22:32 - (A): Yeah. And start asking why? And just saying, hey, you know, what do you all think about this? There's something going on. We notice something weird. We're not sure what's going on. Can we get two or three people to sort of dig around and see what they find? Right. And that just comes with experience. And having been patiently digging through data, like panning around for gold many, many times, it's a muscle that needs to be practiced to dig around, recognize trends, start to recognize patterns, and then start to take notes and come up with insights that are meaningful and useful.
0:23:25 - (A): And that's hopefully what I hope people get out of the course. It's not just how to use Excel, which I teach, how to use some vlookups and stuff like that, and some of the more complex things and also the simple things. But more importantly, it doesn't matter what tool you're using. If you don't know, if you haven't practiced asking the right questions, then.
0:23:50 - (C): Excel.
0:23:51 - (A): Tableau, it doesn't really matter. Right.
0:23:53 - (B): Okay. You mentioned about a data minded culture. You said that a couple times to me in the past. You talk about getting your people within the agencies, whatever the service that you're in, to be thinking with a data mindset. Does that mean that you want them to be looking at things with, with analysis in mind, or do you want them to be looking at things in terms of what questions to ask?
0:24:27 - (A): It's kind of both, really. Sometimes you don't know the question. Sometimes you discover a thing, and then you sort of formulate the question there. And sometimes you go into it with a question in mind or hypothesis in mind, but other times you pan around and you discover something, and then the question comes to mind, and then the hypothesis starts to form. But the data driven culture is about having a certain level of openness, where people have the freedom to look at the data and to analyze the data and to talk to each other about the data and to present findings at all levels and the freedom to have that data questioned.
0:25:26 - (A): And that's really, really important because we need to have the sanity check. If you come to me with a spike in call volume like we're talking about in a meeting, a data driven culture will have, you know, half the hands in the room should go up in that meeting, and every one of those should be saying, should be asking the question, why are you sure that that number is correct? And why? And you know what I mean? Whereas someone where they don't have a data driven culture, they might just look at that number for what it is and.
0:26:06 - (A): Or take it at face value and then proceed, which seems to be what happened. And it's a tricky thing to do to build this kind of culture where it's genuine and it's not artificial. It can be kind of a tricky thing to do, but it also turns out to be really, really valuable because you can reduce turnover pretty dramatically, increase the quality of care, you can implement new programs, all that kind of stuff.
0:26:41 - (A): And there's a lot that can be done, but there's. But there's a culture that has to be built around it.
0:26:53 - (B): I have a situation that comes up when I'm teaching where it's just a thing, when you're an EMT or paramedic, but when you start off, they tell you that if your patient is stable, you should take their vital signs every 15 minutes. And if your patient is unstable, you should check their vital signs every five minutes.
0:27:15 - (A): Okay.
0:27:17 - (B): The problem is you don't know if your patient is stable until you have a trend of vital signs.
0:27:21 - (A): Sure.
0:27:22 - (B): So I think if you have one set of vital signs, you have one set of vital signs. If you have two sets of vital signs, you have two sets of vital signs. If you have three sets of vital signs, you have a trend.
0:27:33 - (A): Yeah, basically.
0:27:35 - (B): And so that's how it works with data. When we're collecting it for our department, when we're thinking about the number of house fires that occur on 4 July, we can say living in an area where fireworks are used, we can say that last year we lost three houses or had house fires, three different houses. The year before, there were none. And the year before that, there was one.
0:28:08 - (A): Sure.
0:28:09 - (B): What does that, does that give us enough for a trend? Can we come up with data like that? What's a better tool or a better way to do that?
0:28:15 - (A): That's exactly the right tool. Yeah. It doesn't matter if it's vital statistics or if it's house fires. Data is data. It's just that the numbers mean different things. They have different contexts and they're attached to different things. But a trend is a trend is a trend, I guess. And the issue is, is that trend consistent or inconsistent? And what is the story behind that, behind the change? So in your example, you had year one, there was one fire.
0:28:49 - (A): Year two, there was zero fires. Year three, there was three fires. You know, I'd want to. I'd want to overlay a couple of things there. It's like, well, what was the weather? Since we're talking about 4 July, we're talking about use of fireworks, then that means people are going outside and doing things. Was there a nasty rainstorm in year two and nobody wanted to go outside, so there was less people outside lighting fireworks? You know what I mean?
0:29:22 - (A): It's like that particular thing, what you just said doesn't really describe a trend for me because it goes up and down, then up. If it went up, up, up, then sure, that's a trend. Or if it went down, down, down, that's a trend. Sure. But it was sort of a little bit of a jagged line. It doesn't make a whole lot of sense. But then there's this whole layer of context that needs to be added to it. It's like was year three when there's three fires, which some people might say a 300% increase. Right. But we're really just talking about three fires.
0:29:54 - (A): Was it particularly dry? Maybe. Is there a huge spike in population growth at the same time going on? Have there been a lot more houses built? Like, there's a lot of things that maybe go into, and sometimes those things interact with one another. You know, if you have greater population, more houses, and it's super dry, and people are running around you using fireworks and drinking, that's probably a recipe for disaster.
0:30:28 - (B): Sure. You know, that's an interesting thought there. You actually just mentioned drinking. And I wonder, bringing in a correlation about was there a spike in drinking and driving incidents on that day? Were there an increased number of arrests and taking the data from fire and correlating it with data from EMS, like drunk driving acts, you know, patients drunk or ill from that, and then correlating that with law enforcement, whether there were more acts of violence or something like that. At the same time, was there more unrest?
0:31:05 - (A): And were the house fires that you're talking about actually caused by fireworks?
0:31:09 - (B): Yeah.
0:31:10 - (A): Cause there's also people barbecuing, so they could have been like. Just because there's use of fireworks doesn't necessarily mean that. Just like the old men in the house burning down, right? Yeah, they are. Not necessarily. We have to investigate to find if there's causation.
0:31:26 - (B): Sure.
0:31:27 - (A): Yeah.
0:31:28 - (B): It seems that it's very unsettling to be a data scientist because, I mean, your whole job is about not taking things at face value. Your whole job is about looking at things, not taking them at face value, and then looking for patterns to emerge. And not knowing where those patterns are going to emerge and not necessarily having the patterns that you predict emerging. Is that accurate?
0:31:53 - (A): Yeah. It also turns out that everything you know is wrong.
0:31:58 - (B): All right.
0:31:59 - (A): Yeah. I mean, it's like when you're a data scientist and then you watch the news and you hear these numbers being tossed around on the news then, and then you're always like, well, are you sure? And then if you do a little bit of investigation, you turns out, no, like, these numbers aren't often wrong.
0:32:19 - (B): Yeah.
0:32:20 - (A): You know, so it's like, and it's because there's a certain level of context and nuanced thinking that is required. And most people, most people don't have that kind of time or attention or they don't know what they don't know. And that's the point of the data course, is to try and help people discover, to help them cultivate that layer of thinking and not just the technical knowledge of building a report and using Excel. But how do they have an analyst muscle? How do they start to exercise the analyst muscle?
0:33:03 - (B): All right, so your data course is designed to help public safety professionals, and you did focus on EMS data, but public safety professionals think more like a data scientist.
0:33:16 - (A): Yeah.
0:33:16 - (B): Which is just going to leave them feeling really unsettled.
0:33:20 - (A): I mean, maybe. I think it's pretty wonderful. And, you know, I facepalm myself a lot.
0:33:27 - (B): Yeah, yeah.
0:33:29 - (A): But whatever.
0:33:31 - (B): Now. Okay, well, no, I think it's really valuable. I think in our future episodes, I mean, that's what this episode is about, is really looking for the next, the next 25 that we're going to do. I think maybe we take some time to grab a couple of statistics and apply them to some of the communities that we've gotten to know. For example, we had the wonderful episode with Des Moines, Iowa. Let's look at some cancer statistics. Let's try and come up with some analysis there that helps them with their cause, helps all of us with that cause, honestly.
0:34:09 - (A): Yeah.
0:34:10 - (B): And maybe some statistics about community risk reduction. For example, why are the men burning up in their kitchen fires?
0:34:20 - (A): Yeah.
0:34:20 - (B): And not that that's happening, of course, and maybe some more about one other thing that we can look at is how we manage to move the needle in public service. For example, the fires on the 4 July may have gone up or down for a lot of reasons, but did they happen to go down? Because we had a fire marshal on duty, we patrolled and went out. There were proactive, handing out non incendiary toys to the children.
0:34:57 - (B): And then the year that we had three fires. We didn't have the budget for that.
0:35:01 - (A): Yeah, that's exactly. And that's going back to that layer of context. You need to. Looking at the numbers without context may tell a completely different story than having that kind of context added into it.
0:35:15 - (B): Yeah. All right. And we should crowdsource for that. We should get our brightest and best minds involved. Not just the one person doing it, but seeking out that information from the whole gang.
0:35:32 - (A): Yeah. You know, and I don't even think it necessarily has to be the brightest and best. I think it's just a curious mind.
0:35:37 - (B): A curious mind. I like that.
0:35:39 - (A): Yeah. They just need to be curious. They just need to be asking why and willing to dig around. That's really it.
0:35:46 - (B): So curious minds to crowdsource the information, creating a culture of data and really just getting the data. Getting the data. Looking for those patterns to emerge. Asking why.
0:36:02 - (A): Yeah. Yeah. And sort of, you just made me think about the unit hour utilization. So there's this whole section dedicated to unit hour utilization. And you reminded me of this because you're talking about, like, thinking about things differently. The unit hour utilization is something that people are talking a lot about these days in fire and ems. It's like, well, is that the right number to use? And is the current calculation the right calculation?
0:36:38 - (A): I say maybe, and I propose a different unit hour utilization calculation, which I think is maybe a bit more effective.
0:36:48 - (B): So anyway, some more unsettling information there.
0:36:52 - (A): Yeah, yeah.
0:36:53 - (B): All right. Okay, so we're gonna meet back up in 25 more episodes. At episode 50, we're gonna do a recap. Maybe we'll have, you know, party favors and horns to blow or something like that. At that time. How's that sound?
0:37:11 - (A): Sounds like fun.
0:37:11 - (B): That'll be episode 75, 0500. At episode 50. Not in 50 episodes. At episode 50. 50, yeah. Somehow in my brain, that just got confused. Okay, yeah.
0:37:24 - (A): So 1025. 50, and then 100.
0:37:26 - (B): All right.
0:37:27 - (A): And then 500. Then 1000.
0:37:30 - (B): Okay. But by what? Do we have a special one when we hit a million listeners? Cause that's your goal. A million listeners.
0:37:38 - (A): I think we throw in the can. At that point, we're just like, all right, we're done. I think we're done.
0:37:42 - (B): We hit a million listeners. When we leave, we drop the mic.
0:37:44 - (A): Yeah, I think so.
0:37:45 - (B): Yeah, I think you're thinking too small.
0:37:50 - (A): All right.
0:37:52 - (B): All right. Well, I am thoroughly enjoying this, and I thank you for helping me with this journey.
0:38:00 - (A): It's my pleasure.
0:38:01 - (B): Okay, awesome. Thanks, everybody. Let us know if you're interested in learning about something, hearing more about something we've already talked about. Honestly, let us know if you're interested in being on the show, because odds are we're interested in talking to you. Be safe.