2022 Franz Edelman Competition: Gobierno de Chile

Ashley Kilgore:

This podcast is part of a special series featuring the 2022 finalist teams for the INFORMS Franz Edelman Award for achievement in advanced analytics, operations research, and management science, the most prestigious award for achievement in the practice of O.R. and advanced analytics. For more than four decades, the Edelman Award has recognized contributions that are transforming how we approach some of the world’s most complex problems. Finalists for the Edelman Award have contributed to a cumulative impact more than $336 billion since the award’s inception, as well as countless other non-monetary benefits. The winner of this year’s award will be announced to the 2022 INFORMS Business Analytics Conference held April 3rd of the fifth in Houston, Texas.

Ashley Kilgore:

Joining me for this episode are Andrés Couve, the Chilean minister of science technology, knowledge and innovation, and Leonardo Basso, professor with the Universidad de Chile in the civil engineering department and director of the complex engineering systems institute to discuss the finalist entry from the team representing the country of Chile. During the COVID 19 crisis, the Chilean ministries of health and sciences partnered with the complex engineering systems institute and telecom company, Intel, to develop innovative methodologies and tools that placed operations research and analytics at the forefront of the battle against the pandemic.

Ashley Kilgore:

These innovations have been used in key decisions that helped shape the strategy against the virus, including tools that shed light on the actual effects of lockdowns in different municipalities, and over time helped allocate limited intensive care capacity, allowed multiplying the testing capacity, provided on the ground strategies for the active search of asymptomatic cases based on anonymized mobility data, and implement the nationwide serology surveillance program that greatly influenced Chile’s decision regarding booster doses and provided valuable insight to the rest of the world.

Ashley Kilgore:

Andrés, Leonardo, thank you for joining me. I’m really looking forward to sharing your team’s work with the INFORMS community.

Andrés Couve:

Thank you very much. It’s a great pleasure to be here with you in this podcast today. So thank you very much for the invitation.

Leonardo Basso:

Yeah. So here, thanks a lot for having us.

Ashley Kilgore:

Looking back to the start of the pandemic, could you share a snapshot of how its impact unfolded in Chile?

Andrés Couve:

A few things that I think would be important to share with you today. The first one was that the pandemic started very quickly. It was a very rapid emergency. And that was a timeframe which was very different from the normal timeframes that we plan for. For example, a young minister of science. So first, it was very rapid. Second, it was extremely unpredictable. It was very difficult to know what was going to happen the next day, the next week, the next month. Then it was at a very large scale. I think none of us were used to react to emergencies of this scale at this very sort of rapid pace. And then it became very obvious from the very beginning that it was impossible to tackle from a single point of view, and that it required coordination and it required cooperation.

Leonardo Basso:

Yeah. What I can add to that is that this unfolded very fast. I recall vividly being in a conference in January and then coming back to Chile and getting almost, the next day, messages from the same colleagues saying this is bad, this is going to be very important. One of the important things about how things unfold is that, because we were a couple of weeks, if you want, after what was happening in Europe, we were getting heads-up saying, this is what’s happening. This is what we’re facing right now. And so the sense was urgency, things were running fast and we needed solution fast. And so I guess how impacted until it was this is new and nobody knew what to do. And so the impact in my side, on the scientific community was what do we do now? How can we help? That was the first shock.

Ashley Kilgore:

What different organizations came together to help develop the tools and methodologies necessary to shape the strategy for combating the coronavirus in Chile?

Andrés Couve:

The Ministry of Science is a new institution in Chile. We’re about three years old now. So it is a challenge to incorporate research institutions and how the country prepared to tackle the pandemic. So I would say universities, centers of excellence, of course, the entire health system really got ready from the very beginning. But it was not only research institutions, private companies, different types of … Private associations also got involved from the very beginning. And then it required a coordinated effort from the public sector.

Andrés Couve:

What I think became really clear with COVID 19 was that it was not only a health issue, it was a biological crisis, and a biological crisis needed a different approach to tackle it. So it was not just, for example, the Ministry of Health. It was also the Ministry of Interior. It was also the Ministry of Science. It was also the Ministry of Economy. And so it really required that research institutions, private institutions, companies, industry, and the public sector really joined forces to tackle it from the very beginning, because it became very clear that the impact was in many different areas of our daily living.

Andrés Couve:

I think in terms of government, it teaches us that when it comes to science the government doesn’t have the … or funding research is not the only responsibility of a government. It also needs to orient the science, provide strategic guidelines, and it also needs to coordinate the public, the private and the research sectors. So I would say different actors acting very rapidly and with the high sense of collaboration and single purpose.

Leonardo Basso:

Yeah. Of course, the number of people involved in any institutions, involved in facing a crisis like this is huge. Perhaps one of the things that is interesting for readers of this podcast is how you get engineers and people working in analytics and operations research involved in something like this.

Leonardo Basso:

What I can add to what the minister was saying before is that early in the pandemic, in March, we at the institute ask ourselves, how can we engineers help for these crisis? That’s how we got involved. We actually start meeting with people from the health system, of course, and asking, what do you want us to do other than, of course, doing models? Which is something that we can do, and we can try to predict how the pandemics is going to move, but we wanted to do more. We wanted to bring analytics and operations research into the ground, into a real application. That’s how we got involved with the Ministry of Science, who early called on upon scientists to try to help.

Leonardo Basso:

We reached to the public sector, to Intel, the largest telecom company, because the first effort we required was, what is the effect of lockdowns. That was the first instrument we had at hand. We needed to know, we needed to have eyes on the ground trying to look at what were the effects of the lockdowns. And so this is how the institutions that are applying for this price got together. We signed this collaboration agreement with Intel and Intelution, their digital solution brand, and Easy, the institute start working on data and start working on, first of all, getting this information out. Our lockdown was effective. Are we getting the results we want and what can we do to do better?

Leonardo Basso:

Of course, a government people on the ground, healthcare workers that are on our side, analytics and with the private sector in this case the telecom company to try to bring information into decision-making.

Andrés Couve:

Leo, maybe I can add that we had a group of scientists working on data. It was about the health system data mostly. And then a few scientists suggested to include the telecom data and to establish the connection with the telecom companies. At the very beginning, I would say that it was not intuitive. We were like, “Why would we include telecom companies in understanding the pandemic?” We thought we need to understand the intensive care units. We need to understand how the virus is … or how different variables are affecting its transmission. But then it became very evident that the telecom was an important player. And so I would say that was very unpredictable as well, or at least non-intuitive, that we had incorporate a sector, which was not obvious at the beginning.

Leonardo Basso:

I absolutely agree. It was not obvious to them this. This required, and this is different than other applications where you have probably one institution working with different departments inside. This required a huge amount of discussion, conversations to try to get this collaboration agreement going. Here, the role of the Ministry of Science is huge, but we were able to get this company convinced. And boy, they did work. They put their best scientists to work with us in universities. And as I’m going to tell you later with the way we used our analytics, it paid off. It was a good call and absolutely not intuited. The good thing is that we made it early, March, April, when the first case until it was late February.

Ashley Kilgore:

Now, what were some of the unique challenges facing those involved in this effort?

Andrés Couve:

From government, it became very clear, very early, how difficult it was to incorporate evidence and incorporate the research capacities into decision-making and tackling the pandemic. It might sound easy, but it’s not. It’s not easy to generate evidence and then to use that evidence for a process of decision-making during a crisis. That means that scientists had to work hand in hand with politicians. Different committees were established to provide the evidence to government for decision-making in many different areas, but it was not easy in terms of, for example, estimating the future of the pandemic, estimating the need for the health system to grow.

Andrés Couve:

When it started to happen, I think we had another amazing challenge, which was credibility, because people were very skeptic about what was happening and how … And this I think happened in every country. The people were very skeptic about how governments were managing the pandemic. So it required a huge effort to provide a sense of trust and credibility, and that need to be done very, very rapidly. So I think from our point of view, from government, I think one of the unique challenges was to basically design mechanisms to incorporate the best and the latest scientific evidence into decision-making.

Andrés Couve:

And this is again, and I want to insist on the timing, because the timing was very different to what scientists are used to, in terms of providing evidence for decision-making, which usually takes months or years. And here we had to provide the evidence in days or sometimes the same day. So we had to gather the evidence that was produced internationally, and also the evidence that was being produced in Chile in different topics, and then use for processes of decision-making. So I would say in terms of government, that was one of the greatest challenges. I would say incorporate the evidence and also at the same time do that very well so that it would add credibility to the management of the pandemic.

Leonardo Basso:

Yes, indeed. Urgency is the first challenge that we think of. It defines the way we work during the pandemic, and it is so different than what we are used to do, as opposed to other applications of analytics that can be planned carefully. Here, we have to deal with an always changing situation where needs appeared suddenly, and analytics solution had to come swiftly. We needed to respond quickly and convincingly, so that our tools and what we were posing was useful.

Leonardo Basso:

Perhaps just to give you a couple of examples, when we needed information on the effect of lockdowns, we didn’t have three months to get this information out. We needed to work with the data. We needed to process this huge amount of data into something that was useful, and then that could provide information. We needed that within two weeks.

Leonardo Basso:

Later when we were facing the first wave and ICU capacity was somewhat getting closer to 100%, it got up to 95%. When you needed to forecast what’s going to happen in one week, and the minister converged to this, we had a few hours to come up with a prediction. That is something that I can be proud to say that we did. In 24 hours, we were able to provide a good estimate of how many beds will be needed in the country, region by region in the next seven and 14 days. So urgency is the first challenge.

Leonardo Basso:

Coordination was another challenge. This is because there was a large number of different professionals with very different backgrounds and working in different institutions that needed to be coordinated. We had engineers from different institutions with training in data science and modeling. You have public officials in terms of adapting the environment for this project to be implemented in practice. You have healthcare professionals that formed the frontline workforce. They were stressed, they were working on the ground. They didn’t have a lot of time to hear from scientists. And then you have the public relation teams from many different institutions that need to be coordinated.

Leonardo Basso:

I guess another challenge that I want to stress is communication. The minister has suffered this more than me, but you were communicating things that were … when you’re talking about the number of beds that remain in a region, or whether people are complying or not with lockdowns. It’s all information that was very delicate and needed to be communicated, because if you don’t communicate it well, then you lose trust. And that is the one thing that you need to keep trust. This on our side, on the side, this side, this required working in ways that we are not used to. And so that was challenging, but I guess it was successful.

Leonardo Basso:

So as you can see actually, a number of challenges which appeared when you are working on such a national level, you need to convince the people on the street, you need to convince healthcare workers, you need to convince the authorities, you need to be able to be talking the same language and be convinced that what you are doing is useful and all this against running clock.

Andrés Couve:

I think that’s very interesting, Leo, because those three things that you mentioned, I think credibility, scale, speed, and I would add as you said before, the need of multidisciplinary teams. So I would say credibility, scale, speed, and collaboration between multidisciplinary teams, I think were great challenges that had to be tackled very, very early on.

Andrés Couve:

I would add the other one, what we talked about before, which is to include points of view to include areas which were not intuitive at the beginning. I mean, we were all thinking about diagnostics, we were all thinking about vaccines, we were thinking about beds. And it was not immediately obvious, at least to us at the Ministry of Science, that specific topics of operation research that had to do with mobility, with telecom companies, with, I don’t know, large scale predictions for testing, really we’re very novel and we’re very innovative in the way that I think government works traditionally, which is through the Ministry of Health to tackle emergencies of this kind. I don’t know if it’s the COVID itself or the global time in which this happened, which really required a response, which was of a different kind altogether.

Ashley Kilgore:

Could you share what were the different ways that O.R. and analytics were implemented to support this effort?

Leonardo Basso:

So the Chilean strategy to contain the pandemic had, if you want, three strategic pillars, contagion prevention, a national centralized management of critical beds, and of course an early vaccination rollout. At the core of the collaboration between the Ministry of Health, the Ministry of Science and Tele and I was the collection, processing and analysis of massive amounts of critical data. That data, specifically mobility at the beginning was crossed with all the data that the Ministry of Science was making public available at the GitHub. I have to say here that in a recent nature piece, the Ministry of Science GitHub was recognized as one of two leading examples of how to make data publicly available to deal with the pandemic.

Leonardo Basso:

And so where is the data science? Where’s the analytics at the beginning? At the beginning it was about lockdowns. So what we needed to do was to deal with all these telecom data, which was massive, and to come up with ways to show and understand mobility. In many countries, scientists were using Google mobility data. That was not enough for understanding in detail what was going on with lockdowns.

Leonardo Basso:

What we did was with Intelution here and the team that [inaudible 00:26:18] lead, we were able to construct very granular data on mobility. We were able to shed light on something that at the beginning was not clear. Compliance was very different in different parts of cities. That is something that we didn’t know and helped to make different decisions. We were able at this point also by looking at the data and running econometrics models and statistical methods to understand that mobility played a huge role on how the pandemic advanced, how it moved, if you want, from one place to the other.

Leonardo Basso:

But one of the things that we learned there was that it was not only about how much and how people move, but it was also about from where and to where they were moving in the sense that there was more risk in some movements, depending on how many active cases you had in different parts of cities. And so we learned that somehow you were able to predict where to find people that might be in danger when moving. That led us to the second application which was after the first wave passed.

Leonardo Basso:

This is probably true for almost every country. Huge first wave, lockdowns. At some point, things get better. When things were getting better, the question was how can we keep testing? How can we test now that things are a little bit better? But you do want to test. You want to stop contagion before they happen, that requires trying to find asymptomatic people, but those people they were moving, they were working again.

Leonardo Basso:

And so we come up with this idea of mixing information on cases geographically, and the way people moved to try to help the Ministry of Health in their efforts to find asymptomatic cases. This was called the active search for asymptomatic cases. What we did there was come up with this model that would help every authority in the country, regional authority, to try to place their efforts in the best part in the public space. This is exactly like saying, if you want to try to go and catch people that are infected by … But they do not know, just go and put your little testing station in these places. So there was a huge effort, and here I have to recognize the people working in the Ministry of Health, because this was a joint effort by mixing these two very different data sets into one single model that was helping do this.

Leonardo Basso:

So we had a quite complex application of analytics and data science that allowed us to have in Chile, a very successful active search for asymptomatic cases. Almost a third of the cases every day are asymptomatic cases. And then what happened was, when we were looking at this data, and the minister may recall that we were presenting this. We were very excited. When we were collecting this data about asymptomatic cases, we realized that we were testing in just a few points of the country, but gathering surprisingly good sample in terms of their statistical … how representative it was of different regions. And because vaccine rollout was starting, and Chile had a multi-platform strategy, we realized very soon that we needed to create our own data. Data will not come from other places because we were, if you want, mixing vaccines very early so that we can vaccinate very fast.

Leonardo Basso:

And so we come up with these, if I’m allowed to be technical one second, these integer programming solutions to try to now not test, but to gather information on IgD response to vaccines. We were running this along the country dynamically deciding where to test for a vaccine response in the general population. So as to have very early information on what was the immune response, the first one at least, IgD, to the vaccination strategy that we were doing. Because again, urgency means here that if you wait to have all the data, perhaps is too late, we needed the data earlier. We started first with the Ministry of Science and BHP who were the first ones to, if you recall, Andrés, to give us tests for the pilot. And then we went internationally with this model and we were able to gather information on immune response to vaccine very early. This was pivotal information to start the third dose approach in Chile, which was very early in the world.

Leonardo Basso:

I’ve been dragging for some time, but of course the application of machine learning models to try to predict ICU bed occupancy in the next week, that was huge. This is pure applied analytics against the clock when you were counting beds. So that application is straightforward. It sounds very simple. It was a lot of work. It was quite difficult also because you knew what you were dealing with, here is, can we get enough beds out there for these people who are getting sick in the first and second waves?

Andrés Couve:

Just to add one more thing. So I think the all the sort of topics of the operation research that Leo has just mentioned, and the effort that the different universities and the institute in collaboration with academia, sorry, in collaboration with industry made. So thinking about the question of how they were implemented, and I think they could be implemented because the country has made an effort for decades also to build a very robust system for healthcare.

Andrés Couve:

So basically, research and the institute and the Ministry of Science were working together, but we were working on very solid ground with the Ministry of Health, so that the solutions that the institute provided could be implemented because the substrate that the health system had provided for decades allowed it. This became very clear for vaccination, for testing, for the hospital or the hospital system with the ICU sort of units integrating both public and private.

Andrés Couve:

And so I think the effort that we made coordinating academia really were much more successful because we had a very good substrate. Of course the substrate had more to do with health issues, with vaccines, with diagnostics, with PCR, with treatment. But this new layer of analytics, of projections, of basically including mathematics in how to fight a pandemic was very new, but it really benefited from the healthcare system.

Leonardo Basso:

Yeah. That’s a very good point minister. The implementation side of it was huge. Still has a decentralized management system of this healthcare, it has health services, 29 of them. You needed to find a way so that this tool could be used on the ground. There were hundreds of people using these tools in different parts of the country. This is something that has to do with the challenges. I go back to this, when you’re talking to say the minister or the undersecretary, and you convince them about that this is a tool that can be useful, and they provide critical input for saying, “You know what? This is not going to work this way. Why don’t we try this other thing.” But then you need to move to the ground, and then you need to talk to the people that are going to have to work with this. They have little time, of course, it’s a pandemic. This communication with them, trying to explain how the tool works, it was and still it’s a great experience.

Leonardo Basso:

From time to time, we had these seminars where we showed them that we had results that this was working, that you got something that was important for the country, that you were saving lives. And that permanent, if you want, summing-up saying analytics and engineering is helping you. It builds this trust within this very different people working on this and allows you to go on for two years. I guess this is not unknown to anyone. People are tired and they no longer have time. So keeping the spirits up by showing the actual results is something that was very important for, again, a word that we have been using a lot for trust, not only for trust of the public, but for trust of the system, for trust of the first line of healthcare workers.

Ashley Kilgore:

Combating the COVID-19 pandemic was obviously a multifaceted effort. One that is still underway throughout much of the world. Could you share what the impact has been in Chile as a result of the implementation of these or, and analytics tools and methodologies?

Leonardo Basso:

So probably the most obvious way to answer this is, did you avoid people getting sick? Were you able to save lives? One of the things that this application made us do was quantify this and to try to come up with ways to say, “Okay. Now breathe for a second and try to tell us, what did you achieve with this?” We made this effort, and we quantified the impact in a very conservative way and in a less conservative way. And to summarize it in one sentence, at least 2,000 lives were saved by analytics tools and methodologies. That is about 5% of the death toll in Chile. This is being very conservative.

Leonardo Basso:

And if I can emphasize this, because it surprised me, 2,000 people is something that it’s hard to grasp. These are just not numbers, 2,000 people that saved their lives because you were able to come up with a model and then together with government and the ministry applied it, it’s something that is impressive when you summarize it in that way. There is a lot of other impacts that you can next talk about. We learned how to bring analytics into public health. This is something that we’re not going to stop doing now. It’s something that we learned and that we will keep doing we hope long after the pandemic is gone. But if you allow me, Ashley, to say what was the impact here, analytics saved lives. That is the one sentence that I want to stress

Andrés Couve:

Maybe complimenting that and I agree completely. I think there’s another word which apart from saving lives it’s very obvious now. It’s not at the beginning. We understand things that we didn’t understand then, and we have a better comprehension of some things that no one in the world knew. When we started this, no one in the world knew the effect of lockdowns. No one in the world knew how were lockdowns going to be respected by different socioeconomic backgrounds in a large city like Santiago. Really no one knew what the are consequences of a lockdown were. Maybe we had some historical data of lockdowns from different eras, but not in this era where large cities, complex cities, globalization, were also a very different background to previous.

Andrés Couve:

So I think we understand now things that we didn’t understand then. I think lockdowns is clearly one of them, and that came from understanding the data that the institute provided with telecom companies. That was very innovative also globally. So if one reads literature now, there’s some examples, but they’re not many examples. So this was very new and very unique that was done here in Chile. The other thing is that we have one of the most successful vaccine campaigns in the world. At one point we had to decide whether we would go or not for the third dose after the second dose was applied. It was a consensus around the world that two doses were okay. But now we’re thinking, okay, we’re leading the vaccine strategy globally. So do we go, or don’t we go for a third dose? And there, for example, the studies of cell prevalence that the institute provided were absolutely key to understand that immunity decreased with time. That time was not very long.

Andrés Couve:

So we were talking about three, six months and again, very rapid and that was very, very good evidence that these studies provided at a key moment where the world had to decide whether more doses were required or not. And then I would add a third thing, which is, we’ve talked about it before, but it’s estimated the critical unit beds where the institute provided weekly reports and sometimes biweekly reports. So on a Monday, and then on a Wednesday we would get the report on the number of beds that would be required in the next week, and the next two weeks throughout the country separated by region.

Andrés Couve:

They were not the only ones doing it. So the Ministry of Health was also doing it, but we were permanently contrasting both projections, both estimations. And so it really acted as a backup to make sure that we were sort of in the right ballpark, that we were in the range. That was also very important because in general, these estimations we know that they’re difficult. So we decided to work with a shorter timescale of a couple of weeks, but it provided a permanent evidence and that could be contrasted to more traditional ways that the Ministry of Health estimates, the better occupancy. So that was also very clear as an impact of the work that the institute carried out

Leonardo Basso:

If I can add something else. This is about what are the impacts of this implementation of our analytics? One of the things that I believe it’s a huge impact is that all the things that we now know, and that we were able to do as a team, and by team, I mean, government scientists and private companies, is something that we now know is not going to stop. We’ve learned things. We’ve learned that we can do some things and that will continue and will be changing in the future to something that is similar, but not exactly the same. For instance, every winter and as in many countries, critical beds start running out. You do know that because it depends on what viruses you have, how many vaccines you have put for these other diseases.

Leonardo Basso:

So are we going to be able to tell how the system is going to look winter after winter? And this is something that from the ministry it’s something that will be useful. So what we learned here can be used in the future in that sense. The applications of mobility, for sure, this is something that’s not going to stop until is very interested and open to keep working on different applications. We have now a second agreement that is not only about pandemics now, but it’s about creating products and value. The undersecretary of public health says we do have problems trying to find cases of other diseases. Can you help us to try to do that better?

Leonardo Basso:

And so I would say that one of the impacts that the work in this pandemic has is that the applications of our analytics we’ll keep going, they’re going to be transferred to other areas. And we do hope that this is going to be transferred not only in Chile, but in other parts of the world. We’ve learned things here that we’ve shared with the world, both through government communication, but also through academic communication. I’m very proud to say that we have at least five published papers on these. I never thought that engineers will publish in Lancet together with MDs. But that is something that we’ve learned and is something that will keep happening.

Andrés Couve:

That’s a very good point, Leo, that when research and researchers get involved in public policy, my view after the pandemic is that that allows them to do better science, not worse science, but better science and more science. I think that is also a good lesson for the entire scientific community. This requires the best available science and that means that the scientific community can keep doing what it normally does, which is aim for excellence in science. And that has, as you say, it’s clearly proven by the fact that these studies have been published in top journals around the world.

Ashley Kilgore:

Andrés, Leonardo, I want to thank you both again so much for joining me and sharing a look inside your team’s finalist project. I wish you and the rest of your team the very best of luck in the 2022 Franz Edelman Competition. Before we wrap up, is there anything else you’d like to share with our listeners?

Andrés Couve:

One cannot consider that we’ve been talking about very, very sophisticated solutions, research of excellence. And that is coming from the far South, is coming from a country like Chile, which is removed from the hubs of science and technology in North America, Europe, and Asia. That tells us that this country which is far away really has an outstanding talent. I think that’s also that this nomination is helping to showcase that a small country which believes in science and technology that has talent can really make a difference to its population and to the world.

Andrés Couve:

And then that is also the result of a very sustained effort that we’ve made over many, many years and decades of funding and supporting science and technology through, for example, centers of excellence, like the institute. We’re now seeing the impact of that investment, which I think is very clear.

Andrés Couve:

And then finally, Leo, mentioned that before, but I cannot end this conversation without mentioning again, but we built an extraordinary database for COVID 19, which was recognized in nature this week as one of the prime examples of databases around the world. And again, from a far country, from a country, which is removed from the hubs of science and technology. I think we’ve shown that we can really compete with the best scientists, the best research institutions to incorporate evidence into decision-making, install capacities, and still do excellent scientific research. So thank you very much for the invitation.

Leonardo Basso:

What I would like to stress is that I, of course, follow all Franz Edelman nominees and their work. What I would like to stress is that this application is about cutting edge innovations that were needed very urgently and that required for them to be implemented, the will and work of so many different people for this to be implemented nationally. It is an effort of so many with different backgrounds, people that were exhausted and tired, but who eventually believed that this could be useful, and it was.

Leonardo Basso:

And so it is an application that is quite different from others, from the more common things that we usually hear. And it is I believe, and I feel that people feel it this way. It’s a huge recognition to so many people that over two years have been working against these virus that has changed so many lives. And so I’m happy and proud and thankful for the nomination because it allows us to show and tell all these people that their efforts are recognized.

Ashley Kilgore:

Want to learn more, visit resoundinglyhuman.com for additional information on this week’s episode and guest. The podcast is also available for downloader streaming, from Apple Podcasts, Google Play, Stitcher, and Spotify, wherever you listen. If you enjoy Resoundingly Human, please be sure to leave review to help spread the word about the podcast. Until next time, I’m Ashley Kilgore, and this is Resoundingly Human.