35 Burst results for "Mehta"

"mehta" Discussed on Money For the Rest of Us

Money For the Rest of Us

05:22 min | Last week

"mehta" Discussed on Money For the Rest of Us

"Welcome to money for the rest of us. This is a personal finance show on money. How it works, how to invest it and how to live without worrying about it. I'm your host, David Stein and today is episode for 11 and Camden and I are having a discussion with a super smart investor Asha Mehta on emerging markets and frontier markets. She is a quantitative investor. We'll start with having Canada introduced Asha and what we're covering today. And then we'll get into the interview just a note I'm recording this introduction separate from Camden's recording of ashes bio because he's traveling in Japan currently. We actually held the interview while he was in Japan. I was in the U.S. and Asha. I'm not sure where she was. So we'll go ahead and get started with introduction and then we'll jump right in to the interview. I think you'll enjoy it because I definitely learned a lot about what's going on with emerging and frontier markets and quantitative investing. Asha Metta, CFA is the founder and chief investment officer of global delta capital. Her thematic focus includes emerging and frontier markets and sustainable investing. She was previously an investment banker at Goldman Sachs and lead portfolio manager and director of responsible investing at Acadian asset management. Early in her career, she conducted microfinance lending in India. She has traveled to over 80 countries and lived in 6. Asha was named one of the top ten women in asset management by money management executive and profiled as a brilliant quant by Forbes magazine. Asha is an active advocate of financial literacy and financial empowerment. She is a supporter of several related organizations, including compass working capital and 100 women in finance. Our discussion focuses on subjective versus objective decision making when investing, the importance of data and what we need to understand about emerging and frontier market performance. Let's jump in. Well, we're really, I'm really excited to have this opportunity to talk with you and I know that David is as well, found the book to be very interesting. I think it's amazing just the sheer breadth and depth of experiences that you've had just across investing in different markets in different countries. And reading through the book, I think what I found very interesting was just, like I said, the amount of experiences. And my first question was kind of about objective versus subjective decision making. In quant investing, there is you have a lot of data, the goal is to make very objective decisions based on kind of the breadth of data and looking at all the different markets. Though I found it interesting in the book with all of your experiences, it felt like you did always have this opportunity to go to the country you're going to invest in. And to me, travel and those experiences are always very subjective. They're passed through our personal lens. And I was just wondering how our subjective experiences interplay with an apparently objective decision and is it possible to make a truly objective decision by itself in your experience? Camden, thank you. Thanks for reviewing the book. I'm honored, delighted, little bashful. That monthly, mostly just thrilled that you've read the materials and thanks for having me on this session, excited to be here with you and your listeners today. And thank you for a very provocative question. I think you're right. And it highlights some of the most relevant issues in this environment, which I characterize as really the era of big data, data is readily available to cost of technology as plummeted. I see across the investors I speak to, fundamental investors who are rapidly moving towards sophisticated quantitative techniques data driven techniques to adopt into their processes.

Asha David Stein Asha Mehta Camden Asha Metta global delta capital Acadian asset management Japan CFA Goldman Sachs Forbes magazine Canada U.S. India David
"mehta" Discussed on AI in Business

AI in Business

05:55 min | 8 months ago

"mehta" Discussed on AI in Business

"And business podcast, and it's Monday. So those of you who've been with us for a while already know what's going on here. On Monday we cover our AI success factors series. These are short ten to 15 minute episodes focused on a particular enterprise success story with a named client where we discuss the one thing that made that project a success. The goal with the series is that no matter what industry you are in, the factors that lead to a genuine ROI in the enterprise, which is hard to achieve, by the way, are relatively similar across industries. Concerns around data teams collaboration. And today we have an episode that touches on a number of those common themes with some details it should be immediately actionable for our listeners. We've covered many manufacturing use cases over the years. We've had big folks like IBM on. We've talked about manufacturing for electronics. We've talked about manufacturing for car parts. We've never talked about the manufacturing of steel, but as it turns out, just the structural steel market alone is a global market worth hundreds of billions of dollars a year, and the plants that make steel, just like any other manufacturing firm, need to make sure that they're being efficient about their output and throughput. Our guest is Nick kunze Mehta, who is the CEO and founder of falconry falconry as a Bay Area based AI services firm in the Bay Area, focusing primarily on heavy industry. And what you're going to learn in today's episode is the extreme importance of subject matter expertise. Not only from the perspective of the vendor, genuinely knowing and understanding the workflows problems and industry that they're operating in. But what it looks like to pull in a champion who has even more of that subject matter expertise to shepherd a project through to success. I like this episode because we've probably said a hundred times on this show that it is absolutely critically important that subject matter experts who understand AI conceptually be involved in AI projects. It is more than just data scientists that make this stuff come to life and turn into value. And in today's episode, that core message comes in in spades with a great client success story in the again steel manufacturing space. So without further ado, I want to dive into this AI success factors episode. This is Nick coons with falconry here in the AI and business podcast..

Nick kunze Mehta falconry falconry AI services firm Bay Area IBM Nick coons
Judge rejects effort by Trump to toss Jan. 6 lawsuits

AP News Radio

00:56 sec | 10 months ago

Judge rejects effort by Trump to toss Jan. 6 lawsuits

"A federal judge has rejected former president Donald trump's efforts to toss out lawsuits claiming his actions led to the January sixth twenty twenty one insurrection at the U. S. capitol I'm Ben Thomas with the latest two capitol police officers and democratic congressman Eric Swalwell filed a lawsuit arguing that then president his son Donald Trump junior and attorney Rudy Giuliani made false and incendiary allegations of fraud and theft in in direct response to trump's express calls for violence a pilot mob attacked the U. S. capitol U. S. District Court judge Amit Mehta dismissed the charges against the younger trump and Giuliani saying their speech was protected by the first amendment but he said the former president's remarks were plausibly words of incitement not protected he added only in the most extraordinary circumstances would a president's speech not be protected but this is that case Ben Thomas Washington

Donald Trump U. S. Capitol Eric Swalwell Ben Thomas U. S. Capitol U. S. District C Amit Mehta Rudy Giuliani Giuliani Ben Thomas Washington
Facebook Owner Meta Sees Biggest Ever Stock Market Loss

The Charlie Kirk Show

01:28 min | 10 months ago

Facebook Owner Meta Sees Biggest Ever Stock Market Loss

"Virginia. Hey, Charlie, what's going on with Facebook? I see that their stock is really down what caused it. Well, this is the front page of The Wall Street Journal actually today. Mehta is the new name of their company. Their plunge rattles lofty tech shares. Quote, Facebook parents company met up platforms shed more than 230 $1 billion in market value. A one day loss that is the biggest ever for a U.S. company, an increases pressure on a stock market, long powered by tech shares. Stocks priced way down beyond perfection. Faces scrutiny, Facebook, parent, company has record fall. More than $230 billion the biggest ever for a company ever for one day. Biggest drop. Meta platforms gave a disappointing financial forecast, helping the major indexes snap a four session winning streak. The tech heavy NASDAQ composite index dropped 3.7% PayPal Holdings and Spotify technologies also spooked investors in recent days when the payment giants lowered its 2022 profit outlook and the streaming company elected not to provide annual guidance. So Facebook seemed like it was this untouchable company for quite a while. But they are just completely and totally collapsing the stock is down and it's up a little bit. Actually it's backed down 21% in the last 24

Facebook Mehta The Wall Street Journal Charlie Virginia Paypal Holdings Spotify Technologies U.S. Giants
"mehta" Discussed on Code Story

Code Story

04:43 min | 1 year ago

"mehta" Discussed on Code Story

"And you know I think there's so many good examples out there constantly from small to meet him to large companies. But I think more than anything, really listen to what they're saying. But not only that, try to figure out what's the underlying driver behind that. What's maybe making them do that? Because that might help point to the right direction of the insights. They might be seeing themselves. Well, we talked about a mistake earlier, with a little bit different spin. If you could go back to the beginning, what would you do differently or where would you consider taking a different approach? Probably thought about getting this product out sooner than the now because I think the opportunity is always good, but I think there's always the thing about timing. But, you know, it's always easier to connect the dots going backwards, right? It's just harder to connect to going forward. But that's what insights come in as well as if you can have those insights to connect them a little bit going forward that will help you. But I would definitely work on some timing things that I probably would have done a little bit differently. Well, last question, darshan. So you're getting on a plane, and you're sitting next to a young entrepreneur who's built the next big thing. They're jazzed about it. They can't wait to show it off to the world. Can't wait to show it off to you right there on the plane. What advice do you give that person having gone down this road a bit? I would ask them actually a question that or where are there with their product market fit? And oftentimes they're they haven't done enough of that to find out because you know I've been in the position where you're so excited about your product and your technology and everything, you just want to get it out, but one thing I've learned is one way or another, your marketplace is gonna speak to you, right? Whether you seek it or not. And so the choice isn't whether they'll speak to you. Your choice ultimately is, do you want to listen sooner or later? You know, do you want to listen before you spend a lot of money developing, producing marketing and all that? Or do you want to do it after you've done all of that? And so again, that's really to the timing. I would say, you know, do a little bit of insight gathering and testing early on so that you can make those pivots and so on sooner so that when you actually do launch fully with the full arsenal, your chance of being successful of exponentially increased. That's great advice. Well darshan, thank you for being on the show today. Thank you for telling the creation story of eye research. Now, thank you very much. Enjoy talking with you and it's a great conversation. Thank you for that. And this concludes another chapter of code story. Code story is hosted and produced by Noah laptop. Be sure to subscribe on Apple podcasts, Spotify, or the podcasting app of your choice. Support the show on Patreon dot com slash code story for just 5 to ten bucks a month. And when you get a chance, leave us a review. Both things help us out tremendously..

darshan Spotify Apple
"mehta" Discussed on Code Story

Code Story

08:07 min | 1 year ago

"mehta" Discussed on Code Story

"So I just thought about this. I mean, for me, I think in any business, if any business can save someone time, make it easy or make it much more affordable. If you can do any one of those things, your chance of being successful are good. However, if you can do two or three of those things, I think you either can two extra three extra chance of being successful. But in the plus one is if you can evoke an emotion with your product or service, I think you can have maybe 6 X or ten extra 12 X, I don't know, it depends on the emotion, right? But I think that can make a big difference in terms of your product's success in the future. As well as I think I will also expedite and help your marketing of the product as well. If there's something that makes people say, wow, that's really cool because that's when they're likely to share it and mention. Steve Jobs, I think did this with the iPod when it first came out. He was in a position to recognize that the price of hard drives are coming down. But he also knew that he could put it onto a package and give it one benefit that everyone could understand immediately, and that is you can now take a thousand songs in your pocket. I mean, there weren't the first MP3 player, but now the thought of you'd be able to take a thousand songs in your pocket. It was pretty amazing. But then he threw one little wow factor on top of that as well, which was that flywheel. He was like, how cool is that? You can fly through your music super fast and it doesn't even seem like it's a thousand songs you're going through. Yeah, I remember that from a job standpoint and saying that the thousand songs in your pocket. He was a master at kind of coining those phrases to really evoke that emotion and cult following. I will say. And I think that's what he was trying to do. I mean, he knew the benefit that he's going to drive. And it was a benefit that you and I would instantly recognize, right? And he took he took the iPod out of his pocket, right? And I said, now you can take a thousand songs in your pocket. And that was a huge benefit because back then, if you remember, we were like, how many CDs am I gonna take with me or have, you know? I can't take my entire collection with me so I had to pick and choose, right? And then you have to switch them around and change them and stuff. But then that was an instant benefit, but then that wow factor was that flywheel on top of that was like, oh wow, this is really cool. Well, let's flip the script a little bit. So tell me about a mistake you made and how you and your team responded to it. There were times when I got a little too complicated with it. I did have it a problem one time with some developers where they were trying to hold some code hostage and stuff. I learned to get better at all of that. And so you learned, you know, things that you're going along, so yeah, I've learned, don't worry about the mistakes. Those are the problems. If you don't learn from your mistakes, that's the problem. You're gonna make mistakes. And part of it is it's good if you're making mistakes because you're actually doing something different. If you're doing the same thing or the thing you always know, you're not really stretching yourself. You're not what I would say striving and thriving. Well, how are you using all of the insides that you've gained and for lack of a better word insights? That you've gained from your methodologies with eye research. How are you applying those things? Yeah, I like creating so I've actually done everything that I've been doing for clients and I decided to create a product come out and that's where I've developed a mobile app recently called connect quick. And that was born from a pain point that I had had and I said, you know, can I come up with a solution? And I think this is one of the things I realized about myself going back to the early beginnings of doing all of this. And why even got into consulting and branding and marketing advertising because I just really like solving problems. I don't mind problems. I just love solutions. And so I try to find ways if I can come up with solutions to things. And that's what kind of drove me towards my research. It's also extremely towards connect quick. Tell me about connect quick. So what is the product and what does it do and how can people access it? It's actually available now on the App Store. It's called connect quick, and that's QI K and this is an app borne from the idea. We're actually the pain point that I had when I go to conferences. And I'd walk away from these conferences with a stack of 30, 40, 50 cards. And I'm like, oh my God, am I really gonna scan all these or type all these in? It's not the time. So I said, there's got to be a better way to do this in a digital world. And so an idea that's been kicking around on my head for a while, but then I actually found a team that I could work with and that's again finding the right people and then I said, I think I can work with a team that's going to help me solve this. And so basically what this app does allows you, for example, if you are not you and I were to meet in person or even online, I can show you my QR code, which is a personalized QR code with your own picture if you want. We just basically hold up your phone in the camera mode and it'll instantly take your contact information and put it into your context. No typing, no scanning, nothing. And so that's the immediate benefit of the app, but then being a believer of conversations having more engaging interactions, the app also goes further in giving you the opportunity of three different profiles from a personal business and a custom profile because we think that today you and I wear multiple hats. And you know, one business card just doesn't convey all of that. So with this app, it's a much deeper way of giving contact information. Again, it's whatever contact information you want to give. It's up to you. But you can instantly quickly give it to other people. For example, there's a couple of things in there that what I call a conversations starters. One is a feature called wanderlust. You can actually put in there, places you've been, would like to go in your favorites around the world or even locally. And that's something like so if you and I were to change exchange information and say, oh no, wow, you really traveled quite a bit. I'd love to hear more about it. Those are the kind of things that an app like this could help you do, so connect quickly, but also have more deeper engaging interactions. From my understanding, you know, all that you've done with eye research, you've become a expert in the field. And from my understanding, you've written a book as well, right? Correct. Yeah, it's called getting to aha, why today's insights are tomorrow's facts. And basically the premises that were all looking for ways to differentiate. And we're in a very hyper competitive environment in business these days. Because you're no longer just competing with the person down the street, or even in the same state or the next state, in many cases, you may be competing with someone around the world. But there's still plenty of opportunity to differentiate. And so how do you find that insight to differentiate? And I would say the keys just all around you is just having these conversations with your customers and even your employees to find out ways that you can really make a difference and differentiate. And so that's a real believer of insights can make a big difference. It's kind of the difference between in hockey. Do you want to be with a puck is or do you want to be where the puck is headed? And that's where these insights come into play. If you think about, if you look at the world around you, there's many things we take for a fact. It's always existed. That's not the case. At some point, someone had an insight that led to that product or service being created. So in the book, I'm sure you explain this, but what sort.

Steve Jobs App Store hockey
"mehta" Discussed on Code Story

Code Story

03:51 min | 1 year ago

"mehta" Discussed on Code Story

"This episode is brought to you by CTO dot AI. You guys know that I interview a lot of great builders on this show. In one of the most important aspects of a great code story episode, is how a team works together to continuously deliver a great product. And not only just a great product, but one that will scale to meet growing demand. It's easy for growing teams to get overwhelmed by you know it. Complex tools complex tools can be a major source of frustration across a team to spend all of your time managing tools instead of building great products. Meet CTO dot AI. CTO dot AI is a workflow automation platform that simplifies developer operations, so you're growing team can improve their delivery velocity and hit their launch dates. What I love about the platform is that it doesn't matter your experience level. You can be a junior dev, you can be a senior dev, it doesn't matter. The platform allows any developer to build powerful workflow tools and share them across their team. You can do this using their services, pipelines, commands, and insights tools to create your singular workflow in a powerful way. You can easily release code anywhere directly from slack. It's where you live anyway. Automate live previews of new feature changes, and measure your integration, your ci CD, cadence, and stability of all of your products. So who uses CTO dot AI? The best and most sought after startups in the land. CTO AI has helped fast growing startups who've raised over half a $1 billion in funding to scale their software delivery workflows. Find out how they can help your team work flow smarter, not harder. By visiting go dot CTO dot AI slash coat story. This episode is brought to you by courier. Your application speaks to your users with notifications. But what do you do when your users each respond better to a different channel? Building the event triggers is annoying enough. But when you have to build templates for multiple channels track deliverability and performance and manage granular user preferences, you end up with overwhelming complexity that distracts your team from your core product. That's why courier builds its API and notification system as a surface. Courier is the fastest way to design, manage and orchestrate all of your applications, notifications using a simple API. The UI is a powerful drag and drop editor to help you build and send templates over any channel while giving your users full control over their own preferences. Plugin providers like Twilio, SendGrid, mailgun, and Firebase, to send email, SMS push in app, or even direct messages like slack. WhatsApp, or MS teams. Get started today with 10,000 notifications for every month. No credit card needed. Just go to courier dot com slash code story. That's, IER dot com slash code story. Again, starting from the basics, just having simple conversations with your customers, this could just be one on one conversations. But don't forget nowadays, even if you're not willing to have the conversation, your customers are having conversations about you anyways. From comments to post to reviews, and those are also great places to mine for insights and data and information. This methodology really allows everyone to participate. And as a result, we see that the transcripts from these sessions are really much longer and have much more information because you're actually hearing from everyone. My.

Heinicke, Washington spoil Newton's homecoming 27-21

AP News Radio

00:43 sec | 1 year ago

Heinicke, Washington spoil Newton's homecoming 27-21

"Cam Newton returned to bank of America stadium for the first time as Panthers quarterback but it was overshadowed by Washington's Taylor Heinicke who threw for two hundred and six yards and three touchdowns in a twenty seven twenty one win hi Nicky Mehta scrambling forcing to completion to tight end John Bates and a fourth down run on a bootleg to set up the go ahead field goal those plays coming back football you can just try to make something happen trying to find a little opening or little leverage on somebody and give a guy shot so night I remember doing that stuff and I was a kid on the backyard halftime Packer game so didn't did provide some big moments throwing two touchdowns and running for a twenty four yard score but he conceded he needs time to master the playbook I'm Ben Thomas

Taylor Heinicke Nicky Mehta Cam Newton Bank Of America John Bates Panthers Washington Football Packer Ben Thomas
"mehta" Discussed on Journey to Wherever

Journey to Wherever

04:56 min | 1 year ago

"mehta" Discussed on Journey to Wherever

"The thought of between now. And then i right now. I'm say shared with the idea. I wanna do it right now but the problem is chew weeks time. It's like these switch goes off. And i'm like what am i looking at next and obsess over something else short term and. It's a pain in the ass. Now he's point where it's making me irritated because at the in a multi potential. Ah yes at the at the need. At the inner need to switch so many times the benefit is i learned so much and get exposed to lots of different things but the annoying parties i find difficult to deploy patients that long for something so detailed when i'd like to so i look at this guy told me. He climbed the wall. And i envy. He's ability to obsessive from over one meter of a rock face. That's hundreds of meters tall high heel obsess over that one mehta for months To be able to tunnel your will down onto a mehta sector of a rock and analyze every grew knowing that once. You've got impossible that you've still got four hundred six hundred meters more to gal that yeah. I can't comprehend that level of patients patients in detail. And i envy that and i would be willing to if i could switch that on obsessed to the point that it could be unhealthy and sacrifice knowing that i get that glory if i could so i think it's data is but i tell who would. I think you're a detailed enough person to be able to do. It is an advice for you accord. I think you definitely definitely detailed enough. But i'd have to have a big enough reason to do it for that long. I think almost you must have to do a bit of a reverse. A gd self. You must have to like like there's a term. It's like to be obsessed about something. You almost have to like. Repeat it at so many times that you hate you love of that makes sense so you'll love the idea of being obsessed but you know one point in time because of your that in multiple essential on you. It's going to switch what you need to do. Is the bit that you hate. Which is this is the palm off the switching bid in order to get to the angle if that makes sense. Yes musk yoga. You're gonna get that constant drip back of haven. How about this haven..

mehta
"mehta" Discussed on #hottakeoftheday

#hottakeoftheday

02:43 min | 1 year ago

"mehta" Discussed on #hottakeoftheday

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I think <Speech_Male> chained industry <Speech_Male> three times in. <Speech_Male> I don't know twenty seven years. <Speech_Male> I would <Speech_Male> vish changing <Silence> more like five times <Speech_Male> <Speech_Male> <SpeakerChange> So a <Silence> little bit of that <Speech_Male> <Speech_Male> having faith <Speech_Male> and patients that <Speech_Male> always do <Speech_Male> workout. I <Speech_Male> have been impatient in <Speech_Male> the past and have been <Speech_Male> a little quick <Speech_Male> to <SpeakerChange> <Silence> jump the gun as a say. <Silence> But i would <Speech_Male> say <Speech_Male> get broader <Speech_Male> get exported <Speech_Male> as far smarter <Silence> people than yourself <Speech_Male> all <Speech_Male> the time. Strive <Speech_Male> for that <Speech_Male> and have <SpeakerChange> the patience <Speech_Male> to <Speech_Male> come <Speech_Male> new process things <Silence> and take time. <Speech_Male> <SpeakerChange> I <Speech_Male> love that advice. <Speech_Male> And i love. <Speech_Male> I love your construct <Speech_Male> that could <Speech_Male> things ultimately <Speech_Male> happened <Speech_Male> Good people <Speech_Male> which closes <Speech_Male> us with our boy. <Speech_Male> Jon rahm who <Speech_Male> leading the tournament. <Speech_Male> Two weeks ago by six <Speech_Male> strokes was informed <Speech_Male> as he walked off <Speech_Male> the eighteen th green that <Speech_Male> he had failed the covert <Speech_Male> protocol in spite <Speech_Male> of four <Speech_Male> negative tests <Speech_Male> and not having any symptoms <Speech_Male> and he was forced to <Speech_Male> withdraw from a tournament. He <Speech_Male> absolutely <Speech_Male> one hundred percent could've <Speech_Male> one and would have <Speech_Male> won and instead <Speech_Male> of him getting mad <Speech_Male> instead of getting frustrated <Speech_Male> instead <Speech_Male> of him having a tantrum. <Speech_Male> He redoubled his <Speech_Male> positivity and <Speech_Male> this weekend <Speech_Male> fist pumped after birdie <Speech_Male> on eighteen. Eighteenth win <Speech_Male> the us open so <Speech_Male> good things come <Speech_Male> to good people <Speech_Male> as long as you. Just wait <Speech_Male> i to have <Speech_Male> more faith in humanity <Speech_Male> especially after <Speech_Male> this conversation <Speech_Male> and should be. I really appreciate <Speech_Male> you joining. I <Speech_Male> know we had some <Speech_Male> of this conversation last <Speech_Male> week. And i <Speech_Male> i had to share it with <Speech_Male> everyone because i think it's fascinating <Speech_Male> hopefully <Speech_Male> the listeners of learn <Speech_Male> more about it. <Speech_Male> How <Speech_Male> your data's <Speech_Male> being collected being <Speech_Male> used being aggregated <Speech_Male> and you're going to <Speech_Male> give your it guy <Speech_Male> a big high five <Speech_Male> the next time you see him. Because <Speech_Music_Male> it's way more complicated <Speech_Male> than i thought. <Speech_Male> <Advertisement> So <Speech_Music_Male> <Advertisement> thank you so much for joining <Speech_Music_Male> david <SpeakerChange> <Speech_Music_Male> <Advertisement> my pleasure. Thanks for having <Speech_Music_Male> <Advertisement> me until next <Speech_Music_Male> time be saved <Speech_Music_Male> be good. <SpeakerChange> Have a great <Speech_Music_Male> day and <Speech_Music_Male> by.

Jon rahm us
"mehta" Discussed on #hottakeoftheday

#hottakeoftheday

04:53 min | 1 year ago

"mehta" Discussed on #hottakeoftheday

"That understands what you'll be feed sane too close to critical equipment. Do we know who that person is can identify. Somebody wearing a green raincoat on platform number nine at paddington station between tuesday and thursday of last year april. Those kinds of insights. That can be run on that for security purposes for asset performance and optimizing your how machines work understanding that data understanding how it's performing next to spirits when was it last maintained if i don't maintain it right now but run it at half power extended where i do have scheduled maintenance. Is it better for you to let it fail or should i wait for run it at half hour so i don't have to send out a new truck roll to maintain it so so let let's stop staring this sharing this slide. 'cause i wanna see your face for this one and in sort of our last. It's our last question in. It's it's it's embiid it's big one and it's it's a bit of a you know if you think to the next five years to the next ten years the technology evolution but then everything you just described ninety percent of the data's being collected in the last two years that we can identify someone who was wearing a green jacket on paddington station with anytime within the last year the the amount of computing power and storage and data and memory and processing in networking required to do. That is so immense. Where do we go in terms of like artificial intelligence robots taking over the world and humans. Just not being relevant like in order for a business to perform at its highest level when all that. Data's out there you're crazy not to use it to compete but it makes us who process slower kind of obsolete. So where do you fall in that whole balance of of robots and am machine learning in the future. Next five to ten years. I think it will make us smarter. Here's what i mean. Before homo sapiens came along neanderthal. All out of their ancestors. They all had far larger brains. That'd be do bigger skulls and bigger brains than we do. The human brain is a lot smaller now and shrinking. Further head sites is not going up. They're going down right and is our that's happening is because breeds are getting so much smarter. That'd be don't need as much area for all that information anymore. So what used to be. Large amounts of brain cells resort for understanding whether this animal is somebody that lie with a spear or. I better run a lead. This one alone is gonna kill me instead. I don't need that anymore. Because i go to supermarket and buy the piece of meat anymore now so brings get smaller and they get more efficient because now look at what we are capable of compared to what our prehistoric ancestors capable of similarly. We're going to start outsourcing some of our functionality of the brain two things that we would never imagined today for example never needed to know directions east west north south of never knowing where to go. How will i have to take to go from here to new york city. I would never need to know that we need to remember name face again. Because they'll be augmented reality in my reading glasses or contact lenses that automatically pull up the name of the person and how i know that i in my field of vision and would never need to remember who i met in my life.

paddington station new york city
"mehta" Discussed on #hottakeoftheday

#hottakeoftheday

04:48 min | 1 year ago

"mehta" Discussed on #hottakeoftheday

"We then do is tell you what's available in terms of different products so this not a big corporate plug here. I did not want appear that kind of marketing person but we have to called labata. Data integration lamont edge. Intelligence one is a catalogue as i explained to understand what's happening at the edge or not and those and where they play are listed out over here and then my second slide is to just give like a market. Sure what that means right whether you were prosecuted at the edge or taking it to the cloud multiple clouds. You're doing this data management function. And what does that really look like. What does that mean. And so this again goes from left to right as well. You are the left at your the sources then you can go onto. What happening at the edge. Then you process the data and then one but the left you have your it. Sources and you're ot source. Can you love technology. Yeah could you explain the difference terms. That was something that was new to me was the difference between the source and the ot source right so it sources are going back to. Are you the example. They may be starting customer data at by credit card information and everything in their article database or as sap implementation or something like that would be called. Erp enterprise resource planning right. That's where all your called business. Data recites and is usually text. Data are some photographs and others but is usually text based data you're eighty or ot sources operational sources operational technology sources are. You're not and that's where you're kings are sending that could be from a process like robot or it could be from a thermometer it could be from a from agricultural moisture sensor detecting homeless moisture is in the roots and telling your sprinkler system to turn on. It could be an an meter measures of wind. Speed wind turbine to detect when wind patterns getting too fast and you may need to shut down the turbine or a on the ocean. That measures wave height based on what how budgets bouncing and how frequently do alert abode grew nuts to come out because of hazardous conditions. Right so those kind of those are the things that are sending these data over all of that inside the iot platform that we call amara is then processed said the edge and determined whether it needs to be understood and analyzed right at the edge or sent over to the cloud or other deeper portions of the software.

amara
"mehta" Discussed on #hottakeoftheday

#hottakeoftheday

04:52 min | 1 year ago

"mehta" Discussed on #hottakeoftheday

"I live in california. My local utility has been serving the state eighty percent of the state for the past. Seventy years think about how much customer data they have my own house here. They have data on this house. That goes back fifty five years to four different owners in all the what the meter data is while the energy usage data is what my neighbors are using around me while the transformers at the end of the street. What it service. What the energy pat tub in this state in this house or the seventy years. And i'm just one guy and they've been doing it for thirty million customers over seventy years. Take all the data that they have now. Imagine isn't a knitting data that can be useful to them absolutely. Let's say how many people in this block of cupertino is likely to buy electric car in the next five years game today to tell him that absolutely can they do it. They're not data scientists. Their utility right should they even bother no and they're not going to what they can do is they can run. They higher data scientist or some other under the firm. Maybe when someone like us to come and look at the data and find them a trend from it but before that third party company can do and find any trend analysis on it runs benefit data science model or machine learning model. They have to do everything. all photos. Arrows the black gray. The orange agreed arrow has to be followed through that takes eighty percent of the time getting data ready into a format into a database into a storage unit into the schema that you need before i can use. It is eighty percent of the effort and data ops data operations. Everything all the other buzzword that you heard about everybody is trying to make that cycle as short and easy as possible. you've done all that you just have to visualize the data and created and present. it informs. That people are used to like a dashboard.

cupertino california
"mehta" Discussed on #hottakeoftheday

#hottakeoftheday

04:44 min | 1 year ago

"mehta" Discussed on #hottakeoftheday

"They. What's available to you. What's available to you is important because not everything is available to you or should be available to you. Either now is in fair to say that. Like i thought about data in the past and again working on gas and thinking about. We're collecting production data. We have seismic data. We have drilling data. We have all this data. And generally i knew they were in disparate systems legacy systems new systems. Each engineer had their own spreadsheets. There are multiple copies of the same system. And when i thought about that death sorta where i stopped to be honest. There was sort of like where is the data. How do i put it into the thing that i need. And then once. I figured that out. I never really thought about it again and again one of the reasons i really wanted to have this conversation live and for the podcasts was because like this is where my knowledge base stopped and there was like seventeen more eros and a whole bunch of other things that happen which is fascinated because i never thought about. It even just the complexity of what it is trying to do in an organization. I never thought about it as deeply so so pleased carry right. So let's say you go to your. You oughta shoe designer. Any go to your it sorcerer of beta and you say i want this kind of data right He can help you create this catalogue. How she can help you create this catalogue. More importantly he also or she understands. What data do you have access to assure you have access to forget. Forget all this data from your third party seller and understand why your shoes have been sold at. Who's been buying it well from that source database. You may have columns in there which includes a user's credit card number or personal street address or social security number. Whatever else right. The so called pp. But personally. i haven't be i or an information which is not personally identifiable but you have no business knowing it right if that is the case. Then that saw those kalo data or that those brands have to be skated. And you do not need it..

"mehta" Discussed on #hottakeoftheday

#hottakeoftheday

06:07 min | 1 year ago

"mehta" Discussed on #hottakeoftheday

"Aws right so but by partnering with someone like us we can bring that value and still keep it on the aws cloud and provide one unified angle. Would face the customer a common customer and so in a retail construct would be thinking about it in the same way that that app developers are two apple. What perhaps some of these amazon partners are to aws in terms of you know that sometimes apple develops it but mostly they're relying on external the external music external books and the apple framework. The app store has sort of integrated everything and so each doing the thing. They're best at is that a fair described is there that is fair and that's very correct. There will always be specific nations of expertise required even at aws or microsoft or google that they would not have and they can't go out and become an expert. Everything the clothes whether or not. That's not exactly how things work all the time. And that's not what they want. The best thing to do there would be to find the community of very smart people very driven people with bell test products. Integrate them on the back end. Make sure that it's working. Go through an extensive technical checklist the business checklists to understand that what the upper providing on the network call the customer or to an end customer not even seen by application provider is going to stand the test of time be of high quality and performed service. It's supposed to after they have done this rigorous. Listen done this before with aws and others. It's not easy to get on departure network because there's a lot of vetting enrolled same thing for an app developer. Who wants to get something on the apple store you I store you had to have to go through several processes to get that thing certified and only after you've done that. Can you get exposure to the outside world. It's a partnership model that continues to exist. It will always exist. A new one company is gonna own the domain of everything from game creation to stelling sticker. Backs that's not the forte for apple rather have a sticker pack creator who can sell the products. They apple app store. So yeah it's a competition bottle that's all mixed. So this convinces..

apple app store amazon microsoft google
"mehta" Discussed on #hottakeoftheday

#hottakeoftheday

04:45 min | 1 year ago

"mehta" Discussed on #hottakeoftheday

"I'd love for you to tell people about the birth of aws why it was the first really to cloud. And how they've transitioned their business model. It's allowing other companies to use their expertise and offload a lot of this day. Dealing be data storage company data out story now. I wanna be willing gas company industrial company. Whatever great natural evolution in aws abuses case. They built this business of selling books online and then it all kinds of products online to customers right and the whole philosophy was the more customers knows the more products. I have and better service now. How fast. I could get that product to a customer means i can attract more sellers. The more and more shops i have means more products. I have that means. I have bigger marketplace more choice for the that attracts more customers. So all of this continues to scale a in a positive cycle the more customers more variety more sellers lower prices more customers. More settlers lower prices faster delivery. So you can imagine that the infrastructure they would need like the example. I gave you about your your Us open a website. Where he wanted to sell some seats now think about the complexity. When you have something like this you have hundreds of countries. You're thousands of cities you have data that you need to deliver in real time and it has to be the same. So the same product information with the same image and the description of what's available and how soon it will ship bays upon whether the customer is a prime member not and depending on who the sellers. The sellers ratings are all of this information inc during the actual transaction. That needs to happen. Once you save by now or now or at takhar all the transaction that need to happen has happened. Within seconds microseconds nanoseconds anywhere in the world to complexity that is enrolled to create that on invested a lot in it to create that and then they were done. They realized that this is something that they could offer to. Other people as a service eight during the competition if it needed to but because it had already built out the infrastructure for commerce on an unprecedented scale they were able to offer the same cloud competing basics that i just told you about compute storage networking in many different places and extremely reliable ninety nine.

takhar
"mehta" Discussed on #hottakeoftheday

#hottakeoftheday

05:23 min | 1 year ago

"mehta" Discussed on #hottakeoftheday

"Well how do you do that. You're not going to be able to run one laptop or one computer do it. So you need a giant rack of compute and storage and networking abilities. Something like a giant eighty center. Rack as they call it. You may have seen them in a data center that looked like a like a refrigerator on steroids. With all the fancy lights and cabling and everything going in right as you had more of those you can handle more users. You can compute power. You put all these machines together. The ball down court wiring everything else and put them inside a building nicely condition. You have a data center. That's physically where a larger amounts of computing is going to be the same. So what will you talk being implicated wilson and those people. All they're doing is they're collecting and have invested a lot in his data center technology. Not only for their own business. Which is how this started off it but also to rent it out to other people so that smaller businesses like your site. You don't have to rely on managing all of those processor Off air and the security and all the headaches that you may otherwise have to on your own. You'll get outsource the whole thing. And that was what i was gonna say because it's interesting when you think about how simple this is in the conventional thinking would have been network means all the computers connected but from my own personal standpoint as i thought about the cloud one of the things so my computer recently crashed and i was so concerned i was going to lose all my data because i don't do a very good job of backing it up. So then i think about these mass commercial places where you back it up but our expectation boy that better be reliable and i better be able to rely on them to do it and then the second piece on the same token is i recently and this is the worst everyone knows i dropped my phone and so now we have all the cracking and now the battery won't charge and say no..

wilson headaches
"mehta" Discussed on #hottakeoftheday

#hottakeoftheday

05:57 min | 1 year ago

"mehta" Discussed on #hottakeoftheday

"Of the hot tag of the day. Podcast and today's guest. I'm really excited about not only because he's a brand new co worker mine. Although this is not a podcast. This is not an advertisement. This is because shamikh meta. And i had a conversation last week about data and i have to tell you i thought i understood. Data or data depending. I didn't even know how to say the word. If we're being honest but i thought i knew about data and systems in architecture and integration and everything that's going on in the world as we move from on premises storage in legacy systems to cloud in aws microsoft and google and instead of me trying to explain it. I felt listeners would really benefit from our conversation. So that is what we're doing. And i wanna welcome to. The show should be. How how're you doing today. I am well look at that are heatwaves winding down. It's in the low eighties today. Going to beat high seventies. Tomorrow is perfect. I i love it and did you do anything exciting for father's day. We are recording this on monday. January twenty first so it will come out in the next week or so Father's day what was it like. Well as usual was a good father. Got a ton of attention. I got a free breakfast out of it. And then to celebrate. I went out and took a couple of friends on a bike. Ride up on route. Nine you're behind. my house. rode my bicycle up. Two thousand two hundred feet and back again. It a top speed of thirty miles per hour and stayed on the bike and i'm happy to be alive so i'm glad to be here today. I love it. I love what. I did a post yesterday. Unle didn't talking about fathers and sons and the learnings and it was. It was interesting. I didn't think we would end up there. But i love the us open. It's my favorite. It's my favorite tournament. Probably other than the masters but you know and other than playoff hockey but i love. It always ends on father's day. There was a great win. For jon rahm but just talking about the relationship fathers and sons and what you learn all good and all the bad and tiger and earl so it was a beautiful day. There fist. fist-pumping jon rahm took down the trophy and and it was great. So so why had you on as you know is so you're went to touchy ventura. Why don't you tell people a little bit. About what a touchy tara is. People probably familiar with the attached to name but not so much the ventura and then we'll your role with the company as before we kickoff. Let's talk all things. Data data absolutely all things data. Well as she already know my nate the pronounce it perfectly. So that's great. I am a director industry solutions marketing. So i market offi touchy materials products which should go all the way from the data center to it modernization and app modernisation data ship on the digital modernisation. Stuff everything that you heard about cloud computing and everything. I the internet of things a dusty internet of things and market those additions to our customers in industries such as energy.

jon rahm Unle microsoft google ventura hockey earl tara us
"mehta" Discussed on RiYL

RiYL

04:44 min | 1 year ago

"mehta" Discussed on RiYL

"And then when i find it i don't identify it right away but i find that. I'm kind of obsessed with the song and then and then i'll i'll mehta click as to why and it'll give me a deeper understanding of my own son. And so they are. I think you said inexorably linked. That's a really good way to put it. They just they just share the same space to me. Do you find that if you that when you were not competent in your playing you were unable to right. I wasn't able to write up written a little bit now. Since i was when i was not able to write or play. I'm not sure. Which thing was stifling. The other on. I'm really not sure 'cause i can play now. But i'm so focused on the how to approach the instrument right now that i i guess i find myself maybe satiated if that makes sense From exploring like just even if it's just the mechanics of thing. I'm playing right now. It's usually like got elusive moment. Happens in between learning and mastery. It's like almost mastery. When i when i went of nearing the point of mastering whatever the technique is or whatever. Then i'll use the be creative without moment without tool. But i'm really learning it. It's hard to be creative moment. But i'm also not sure to be quite. Frankly do you like i think. Maybe i'm just not there yet. I think that. I've had you have to have a certain reservoir of energy to drawn to be creative in a pretty spent on the bright side right before we left for the twenty th anniversary tour. I recorded a new record so i have at least a buffer of a. It's not to say that. I have a buffer of time that would have been incorrect to say but i'm always eager to share music with people and a half some to share so i don't have that feeling of same feeling saint same sense of urgency yet and maybe you know what maybe.

mehta
Snobbery, Like Most Things In Life, Is Relative

Trent365

01:53 min | 1 year ago

Snobbery, Like Most Things In Life, Is Relative

"I was having a discussion recently about how much i've enjoyed mine. Espresso coffee capsules during these various lockdowns and that comment boorda reaction from a mehta mine. Who said i'd become a coffee. Snob no longer was instant coffee. Good enough for trent. Trent now needs his espressos. He's become a coffee snob and at the same time. Another made the comment that i was a total neanderthal. When it comes to coffee how could. I possibly consider myself to be a real coffee drinker. When i drink espresso a real coffee connoisseur would never stoop so low as to drink and espresso but that to me. Pretty much sums up this whole concept of snobbery of being a snob of considering your taste a little bit superior to it's totally relative. I don't consider myself to be a coffee connoisseur for what it's worth. I just like coffee. That tastes to me a little bit better than what. I was drinking out of instant coffee but i. It wasn't so important to me that was willing to put in the extra to create a superior coffee to the spray. So i i worked just fine for me in some people's eyes that will make me a coffee snowed in other people's is it makes me in the end of a when it comes to something like coffee and over the years i've had similar conversations about whiskeys and wines and cheeses all sorts of things and i think the reality is for most of us. We think about how that reacts how that relates to other people we think about how other people view us are. They going to consume snow before. Drink this particular wine or drink it this way or taste this way. Well the reality is some will. Someone doesn't really matter. Be comfortable with what you know what you like. Just be self aware. Be aware that others will think you're snob. Some will think you're not doesn't really matter because like most things in life is relative

Boorda Trent
Interview With Mayank  Mehta

IT Visionaries

02:46 min | 1 year ago

Interview With Mayank Mehta

"Welcome everyone to another episode of it visionaries and today we have a special guest. The ceo of pulse qa. Mehta mayak. Welcome to the show. Thanks so much for having me appreciate it all right so right out the gate. We always allow all of our guests. Tell our audience. What the product that you bill does. What is pulse. Qa the best way to describe pulses What google is to search. Pulse is to research people have gotten really used to being able to do a search and come up with fast accurate and free results and use that to sort of informed their decisions and research on the other hand is extremely hard. Where you've to set up an account become a master and how to do surveys figure out how to contact the right people especially on the bbc this is even more challenging and wait a few weeks if not months pay tens of thousands of dollars for as incentives and then come back with poor quality data. And if you just juxtaposed the search experience with the research experience just a massive gap to be had our mission at pulse is to close that gap so ideally make pulse to a point where research becomes is easy as search where you can pick up your phone hoste question to the right audience get back results in real time and use that to better inform your decision so tell. Our audience. Wind is materially different. From let's say relying on a forrester or gartner to produce a report data on if i have like b. two b. application questions. Yeah a great question so we got started As you mentioned in the b. two b. world and specifically within that with technology decision makers reason being no technology decision makers spend hundreds of billions of dollars on technology every year some cases a trillion plus have been also cited. And there's lots of research that goes into making these decisions. There are three things that are happening. that are changing the traditional world of research in the landscape and why we think pulse is a good fit within this chain landscape number one. Is it used to be forty companies. That ruled the world. And now they're forty thousand. Would hundreds more coming out every your with accelerator wycombe. Or another almost just chugging out and you know it used to be okay for a centralized authority to cover. All of this is now increasingly difficult to cover those forty thousand companies but the power of the crowd and the platform that his pulse can actually enable that because on pulse it's not analysts that you're learning from. It's actually your appears that have been verified and brought into the network so you when you ask question and when you want to know detail about something that actually goes out to the right people they come in and the answer you get data back in real time.

Mehta Mayak BBC Gartner Google
AI driven Privacy tool developed to protect COVID-19 tracing data

Cyber Security Weekly Podcast

05:48 min | 1 year ago

AI driven Privacy tool developed to protect COVID-19 tracing data

"Welcome to carry tv and ad tech insect weekly chris coverage on the editor with more security media. And this is al. Friday morning episode. Normally stream live on tuesday afternoons and on fridays and today's episode where with dr did hornets strategic advocacy manager would stand into strategy and up the sushmita rush at senior research scientists with data. Sixty one gonna be looking at data. Sixty one's recent ion driven promising tool. Personally my shin factor. I think it's go piff and with with don't this mehta rush a senior research scientist with data sixty one. Thank you very much for joining us. Law soc me that look thank you. So much We covered off. And we're gonna be talking about It's great to have data sixty one on obviously as well but this caught mile. I released a new data privacy tool for a anonymous covid. Nineteen tracing data and keeping that secure. And it's cold personal information factor or this. You're the senior research saunas on the project. Might be the adult. Talk us through It's a big topic. Accident had to stop. Whether we start with the i ought to the covid. Non tain tracing data Update class. I can just a little bit about fifth avenue. Exactly the information and Just to be fit. That need talk about why we are doing this. And one of the use cases of course the covid nineteen of data about this is a much more universal kind of a tool which which actually helps to share data a to protect the privacy of individuals whose data is there in the assets and its stock. V personal information factor is essential information content in the data affect and. Just imagine that if i were to the custodian was to release the state asset. Then it looks reveal definitely information individuals so freshman is that went to release and when not to release and this personal information factor is a measure of that information content in that deep affect the identified data and what the tool does is that not only. Does it publishes the data in an in an in some kind of transformed fashion. But it's also evaluates. The risk of free identification. Is very very important. Like when i when i want to share my data. The first thing that i ask is that what is happening to my data. What are the risks associated rely. We get out in this whole list of data that has been you know released. No you can. I ask you. Is it reverse engineering. The fact it's released. And we reverse that. Can i identify that will happen. Writer the tool essentially doctor that you know it. I evaluate what happens. What are the risks. And if it feel that you know the risk is low then it's released the data at the high than it suggests very thoughts of transformations Aggregations techniques so as to make the data more suitable to be released that in it's not reeducation is not possible so the two of you know a lot lot more. To protect the privacy of individuals this is donald sixty runs on another saying albumin. Doctor men whose new south wales chief scientists it shifts on and We've he's also hit it up. We'll previously headed up. The new south wales at data analytics as well is that this is all great working together on this because we've heard from duct tape and previously about the work that they're doing with a lot of this data across the south wales in sydney in particular. Very interesting work. But yeah it's that that The day anonymous anonymously information and they identified. Information is a challenge. Because if you join. The dots suddenly can start to identify. They so yeah. This is actually a on oprah man. The project actually started with the initiative of yet overman dr yang obama and what we are essentially trying to do at the data was also involved from the very beginning. But what we are trying to do. Is that enhance that tool so we want to enhance such way that weekend. Mitigates against various attacks so we are trying to identify what other attacks. What are the attack vectors. That are possible that might breach the privacy of individuals and beth. Israel comes into picture. We are essentially studying what a- what the attacks are and can be do it in a more sophisticated sway to learn from the attack and suggest suggests techniques to protect the privacy of individuals of be it aggregation be. It's probably secure Secure approval private algorithms for like Differential privacy or there would be other solutions that can help to the data at make it fit to be

Mehta Rush AL Chris South Wales Dr Yang Obama New South Wales Donald Trump Sydney Oprah Beth Israel
Serie A Race Tightens

ESPN FC

03:04 min | 2 years ago

Serie A Race Tightens

"We saw in italy where a c milan are windsor. Champions at the halfway stage in syria they are top of the table. Two points clear of into despite the fines. They lost three now at home against atalanta. Insecurity manage a nil nil draw. Meanwhile away against 'euthanazi and romo a seven goal thriller against four three moving them up to third in the table. It's welcoming shall we shoot robson show robot. Let's start with milan against atalanta talk about impressive. The visitors are brilliant. That difficult i ten or fifteen minutes but once they start planning football and they get illegit you vote and he kept on coming in off of that right hand side onto his left. The patter led the line brilliantly. Froylan back in midfield ghosts down one side patty board down the other three centre backs defended really well as well as i've seen the defend for quite some well they dominated the game. I mean thrown in midfield kept on making challenges and milan could get into their front plays enough. So he was at the game for long periods the young plas- planning behind him and mehta and on the other side castillejo. They couldn't get involved in the game for once able to dominate midfield so brilliant performance by its atlanta and now the informed seem at the moment and if they depict their best side against tonight as a earlier on earlier this week and last week they would be right up with the front runners of course is at the moment who are the front runners. I it's interesting is this same regressing to the mean slightly robots. Yes they're a good side but not a great side you know when you go abraham of issues who's thirty nine and he's Lead the line. The three young players played in behind on several different occasions came on on. He's going to be quite good enough to take them to the next level. Amac is got saints off last week. And he's quite good enough at the moment guests. The good game in midweek against calgary didn't play whatsoever today. May you make these Food first full appearance didn't really impress. So they're they're aside. That can improve. But let's do what some young players that needs to improve quickly to narnia didn't think dominated the midfield. And that's what kissy had a problem today. Impressive milano after that defeat which not forget. It was their first domestic defeat against events as they bounced by. Didn't they they got the win. Against torino gonna win against cavalry as well so after this defeat robbo you would be hoping for the same sort of mentality going forward. You think they can get themselves together. They can regroup because it's against the small teams in which they've picked up those crucial points this season. I need some of that players. Back the nia challinor who need right. But you wanted to substitute. He'll you him back. The got manzu kitchen now to help out ibrahimovic if he if he needs to be swapped any point so i think they've got the mentality pill has done a really good job mentally with them. He's a good coach as well. They got the you know exactly what they're trying to do but he's also got them on that even kill so when they lose games they don't panic too much. They come back again. So i think they'll they'll certainly putting performances but i still don't think they're quite good enough to win syria

Milan Euthanazi Castillejo Atalanta Romo Robson Windsor Syria Mehta Italy Football Atlanta Abraham Nia Challinor Calgary Milano Robbo
"mehta" Discussed on MyTalk 107.1

MyTalk 107.1

01:31 min | 2 years ago

"mehta" Discussed on MyTalk 107.1

"Wrinkles, improve skin firmness, smooth skin texture, increased glow, lower taxes and cure cancer. It is the last two, I added for emphasis. So it goes on to talk about crap. This is like she is that you guys She is a snake oil sales person. First of all, what is broke, Uchi all and do you wanna put by choking up on your face? Can I read to you a little bit about the coochie all because it is important. It's very important. Oh, I bet that's what y'all is A mirror Trippin. It's in the class of Terp ethanol. It was first isolated in 1966 by Mehta at AL from So so really a chorus, little old. You're speaking of foreign language. Don't even know what I'm saying. Yeah, it's based on the Sanskrit name makuuchi of the plant. Okay, So there's a plant called makuuchi. I'm not rubbing that on myself, much less my face. Oh, man, but she just loved makuuchi all Like that. She is so like up her own book, Hoochie hoochie, But she doesn't realize how like who's the woman that's like, Oh, my God! This because she uses all the buzz words right high performance results driven? Yes, nourishing. It's a miracle isn't a miracle. For the miracle. I mean, it does. I don't know. Like you put it on.

Mehta
Podcast Taxonomy Consortium gives us clearer job descriptions

podnews

03:29 min | 2 years ago

Podcast Taxonomy Consortium gives us clearer job descriptions

"The podcast taxonomy consortium has posted. Its first white paper proposing standard for job descriptions in podcasting the product of five months of work it enables all of us to know the difference between a technical director an audio engineer for example. Where is one. Apparently they also welcome further. Participation charter has launched twenty twenty one podcast privacy. Report the free. Pdf explains how online privacy rules in tech will change next year. Red circle has launched programmatic advertising today for its community of podcasters advertisers. The company says it's the only podcast host offering a complete set of monetization options including donations subscriptions dynamic host ads and programmatic ads. Today is the last day to enter the ambiance. The new podcast awards from the podcast economy. Be quick podcast. Advertising barely slowed down this year. According to again in a i which has released the state of podcast advertising in two thousand and twenty a full analysis of the podcast at market portable is a new service that aims to connect businesses with podcasters. It also offers a search. Api the stitcher sub reddit. Pretty sight. Following the podcast. Apps redesigned thread titles. Include this update has altered the meaning of the word premium. And how can you make an update worse than the old version pouch. we're also on reddit slash. Our sash pod us. Nothing's going wrong there. The pew research center a us-based fact tank has released news consumption in a digital era. A paper that finds that more americans using podcasts to get the latest news but also highlights the danger of a survey. Several of those surveyed forgot that they listens to music podcasts until they specifically asked about them. Union members spotify gimblett podcast. The wringer stopped work for two hours on friday due to the slow pace of agreeing contract with the company. Edison research is planning a final webinar for the ten for twenty will showcase the company's ten most notable findings from two thousand and twenty. Like how rubbish here. It was and things that other podcast. Companies don't have to worry about launching a satellite the s x seven satellite for sirius. Xm ways seven point seven tons and work until twenty. Thirty six my is. I can't fix the country road or even the town. But i can bring my own advice from the story. Core podcast from npr. It's for new season with new hosts camilla shaney founded in two thousand and three story call has brought more than six hundred thousand americans together to record conversations about their lives. It's a wonderful lie. Is the first time audio chunk of mehta comedy. Podcast is hosted by ashley flowers and is a twelve part. Short form comedy show hosting every day until december twenty fourth and from now is a new science fiction. Podcast from q code. The podcast publisher. Who snags steve. Wilson from apple podcasts earlier this month as supplied that press release links to apple podcasts. Three times including an embed

Reddit Red Circle Spotify Gimblett Edison Research Pew Research Center Camilla Shaney Ashley Flowers NPR United States Apple Wilson Steve
The Dark Divide: Sasquatch

Camp Monsters

05:48 min | 2 years ago

The Dark Divide: Sasquatch

"Long ago the women and the children had spent most of the day picking the sweet tiny lack berries as a son was falling to the west the women started gathering their baskets of berries and head. Back to the vill- each when they heard this rustling in the brash as a rustling in the brush came closer the women motion with just there is to the children who could run quickly enough and fast enough to run back to the longhouse now for the children who were too young. They quickly picked them up. Held them underneath. Their arm took their head and held close to the mother's heart. When the small children heard the fast beating the mother's heart they knew they had to be very quite the women cup their hands and brought it behind their ear in order to hear as well as a deer ask a rustling in the brush came closer. They knew that it wasn't dear. Because fear has dumping sound through the brush. They knew that it wasn't fair. All bear lump those little blackberries as much as we did but as long. This bear had no cub. Bare would ran away but as a wrestling fresh came even closer all of a sudden there was this horrible horrible stench out of the brash. Came this huge monster. His legs were as big as tree trunks. His skin was covered with hair and his eyes at a hypnotic. Red glow to him. This monster started chasing the women all through the berry batch and as he was chasing them he took his huge big feet and he started kicking over every basket of berries. Wasting them on the ground. Now the women managed to escape and they made it back to their longhouse. The men decided. Maybe we should go check that berry patch when they got to the berry patch. They look for footprints found. They look for maybe hair that mighty came up but there was none to be found that night when everyone was sound asleep all of a sudden the guard dog stood up on all fours and they just froze now in the past. Those guard dogs would have ran out and chased. What ever they was. That was coming but in this case they didn't make a noise. The hair on the of the dogs came straight up and for the first and only time the dogs mehta sound. that went like this Ooh now some of the people escape from the secret tunnels we have in our longhouses. Others just froze. That monster came and started throwing pieces of driftwood on the roof screaming and hollering through the entire night. Just before the sun came up he disappeared now. Not having any sleep. What so ever. The salmon fishman went down to the river and they started to pull up their traps and as each of them pulled up their traps. Lo and behold there had not been one salmon caught. It was then. The salmon fishermen looked up the river and standing where no man would be able to stand in this scoop gums or the white rapids of the river stood this monster. He picked up his smelly stinky feet and he started laughing at salmon fisherman. It was then they realized that as long as this monster was a stand in the river with his dirty stinky feet that the salmon people who live at the bottom of the ocean will never travel up the river up again.

Wrestling Salmon Fishman Salmon
Storm Eta expected to make landfall along U.S. coast after destruction in Honduras

The Splendid Table

00:56 sec | 2 years ago

Storm Eta expected to make landfall along U.S. coast after destruction in Honduras

"Tropical Storm Mehta has made landfall in Cuba. The National Hurricane Center in Miami says they came ashore this morning along the south central coast. Data has its sights set on South Florida's Central American countries still relate, Maria Martin reports on the situation in Honduras. Analysts agreed the devastation caused by ETA is making a bad situation even worse in Honduras. The health and economic crisis due to the pandemic is taking up scares government resource is millions of endurance have attempted to flee violence and poverty by going north to the U. S. While the latest hit this week Ah, powerful storm that dumped 40 inches of rain in some parts of the country. Has destroyed homes, infrastructure and taken lives over 400,000 have been displaced just in the solar valley. While economists estimate the monetary loss from ETA could be more than 20% of Honduras is gross national product.

Tropical Storm Mehta South Central Coast Maria Martin Honduras National Hurricane Center Cuba U. S. ETA South Florida Miami Solar Valley
Storm Eta expected to make landfall along U.S. coast

Weekend Edition Saturday

00:16 sec | 2 years ago

Storm Eta expected to make landfall along U.S. coast

"And flooding from Mehta this weekend. Forecasters expect the storm to strengthen back into a tropical storm today before making landfall on Cuba's southern coast. The storm is expected to report South Florida late tomorrow. Rock and Roll Hall of Fame is annual

Mehta Cuba South Florida
Tropical Depression Eta forecast to restrengthen today

South Florida's First News with Jimmy Cefalo

00:23 sec | 2 years ago

Tropical Depression Eta forecast to restrengthen today

"Will start to feel the effects of Tropical Story Mehta this weekend. The National Hurricane Center says it's a tropical depression right now, but it will re strengthen possibly into a tropical storm as it passes over Cuba Sunday. National Hurricane Center says ETA will be approaching the Florida Keys a Monday before turning northwest over the Gulf and heavy rain and gusty winds are expected throughout south Florida, Tampa Bay and central Florida.

National Hurricane Center Mehta Depression Cuba ETA Florida South Florida Tampa Bay
Dr. Mark Hoffman, Research Associate Professor at the University of Missouri, Kansas City - burst 01

Scientific Sense

44:57 min | 2 years ago

Dr. Mark Hoffman, Research Associate Professor at the University of Missouri, Kansas City - burst 01

"Welcome to the site of accents podcast. Where we explore emerging ideas from signs, policy economics, and technology. My name is Gill eappen. We talk with woods leading academics and experts about the recent research or generally of topical interest. Scientific senses at unstructured conversation with no agenda or preparation. Be Color a wide variety of domains red new discoveries are made. and New Technologies are developed on a daily basis. The most interested in how new Ideas Affect Society? And, help educate the world how to pursue rewarding and enjoyable life rooted in signs logic at inflammation. V seek knowledge without boundaries or constraints and provide unaided content of conversations bit researchers and leaders who low what they do. A companion blog to this podcast can be found at scientific sense dot com. And displayed guest is available on over a dozen platforms and directly at scientific sense. Dot? Net. If you have suggestions for topics, guests at other ideas. Please send up to info at scientific sense dot com. And I can be reached at Gil at eappen Dot Info. Mike yesterday's Dr Mark Hoffman, who is a research associate professor in the University of Minnesota Against City. He is also chief research inflammation officer in the children's Mussa hospital in Kansas City. Kiss research interests include health data delayed indication sharing initialisation Boca Mark. Thank you for inviting me. Absolutely. So I start with one of your papers Kato you need the use by our system implementation in defy date data resource from hundred known athlete off my seasons. So Michio inflicted. Data aggregated for marketable sources provide an important resource for my medical research including digital feel typing. On. Like. Todd beat to from a single organization. Guitar data introduces a number of analysis challengers. So. So you've worked with some augmentation log and in almost all cases be used. Data coming from that single macy's listen primary care behavioral. Or specialty hospitals and I always wondered you know wouldn't be nice. Get a data set. That sort of abrogates data from the radio on-ice. Asians but a lot of different challenges around that. So you wanted to talk a bit about that. I'd be happy to the resource that we've worked with. Is primarily a called health fax data resource. It's been in operation for almost twenty years. And the the the model is that organizations who are. Using these Turner Electronic. Health. Record. Enter into an agreement was turner they agreed to provide data rights to sern are. The identifies the date of affords aggregated into this resource. And certner provides data mapping, which is really critical to this type of work. It also the aggregate the data. And for the past probably six years. Then, they provide the full data set to especially academic contributors who want to do research with that resource. And I've been on both sides of that equation Lead that group during my career there, and then now I have the opportunity to really focus research on that type of data. So before we get into the details smog so e Itar Systems. So this is. Essentially patient records. So he gets dated like demographics out family history, surgical history hats, medications, lab solves it could have physician nodes no snow. So it's it's a combination of a variety of different types of data, right? A couple of things on the examples you gave it includes demographics. Discreet Laboratory results Medication orders. Many vitals so If access the blood pressure and pulse data. It does not include text notes because those can't be. Automatically identified consistently. So. We don't have access currently to TEX notes. Out of an abundance of caution. That his Hobby Stephen, physician writes something down they could use names they could use inflammation that could then point back to their. Patients Makita Perspective been the data's aggregated, the primary issue shoe that date has completely the identified, right? Correct. So. So yeah. So the data that we receive there's eighteen identifiers. Hip requires be removed from data. And those include obvious things like name address email addresses are another example One of the. Things. That is also part of the benefit of working with this particular resource. The. Dates of clinical service are not allowed to be provided under hip. White is done with this resource that allows us to still have a longitudinal view is. For any given patient in the data set the dates are shifted by A. Consistent. Pattern that for any given patient it can be. One two three four five weeks forward or one, two, three, four or five weeks backward. But that preserves things like day of the week effect. So for example, you see -nificant increase in emergency department encounters over weekends and you don't WanNa lose. Visibility to that. but it also allows us to receive. Very, granular early time stamped events in so. We can gain visibility into the time that a blood specimen was collected, and then the time that the result was reported back. And so we're able to do very detailed analyses with this type of resource. Right right and I don't know the audience our market is fragmented. Tau himself e Amorebieta providers out there. and so two issues. One is sort of. Standardization as to how these databases are designed and structured and others even that standardization that the actual collection of the data. In itself is not standardized played. So vk CAV vk potentially lot inability coming from different systems. Correct and that's part of what the paper that you mentioned Evaluates so. Often, night you out in the field in conferences you hear. Comparisons kind of lumping all organizations using one. Vendor lumping all using another together but as you get closer to it, you quickly learn that. It's not even clear. It's within those. Vendor markets. There's variation from organization to organization in how they use the e Hr and so. Because the identities of the. Contributing organizations are blinded to those of us who work with the data. We have to be creative about how we. Infer those implementation details, and so with this paper, we describe a couple of methods that We think move things forward towards that goal. Yes. So I'm not really familiar with that. So you mentioned a couple of things here. One is the the merge network. So this initiative including electric medical records and genomics network and pc off net the national patient, centered clinical research network support. Decentralized analyses that goes disparate systems by distributing standardized quotas to site. So this is a situation where you have multiple systems sort of. Communicating with each other and this net folks at allowing to sort of quickly them In some standardized fashion. So In this type of technology, there's janitorial core models. One is the. Federated or distributed model, the other is a centralized data aggregation. So there are examples including those that are mentioned in the paper where. Queries are pushed to the organization and. They need to do significant work upfront to ensure that there are standardizing their terminologies the same way. And once they do that upfront work than they're able to perform the types of queries that are distributed through those. Federated Networks. With. Okay. So that just one click on so that the police have standardized. So all on the at Josh site, then they have like some sort of a plan slater from from Stan Day squatty do all the data structure. And in many cases, they work through an intermediate technology. that would be. In general, consider it like a data warehouse. And so the queries are running against the production electric. Health record. That has all kinds of implications on patient care where you don't want to slow down performance. By using these intermediaries They can receive queries and then Follow that mapping has occurred. Than, they're able to to run those distributed queries. Okay. And the other model is You know. You say the g through the medical quality, improvement consortium and sooner to the health facts initiative. So this says in Sodas case, for example, in swags. This is essentially picking up data from the right deals, clients and Dan standardizing and centralizing data in a single database is that that is correct. One benefit of that model is that Organizations who for example, may not be academic and don't have the. Resources to do that data mapping themselves by handing out over that task over to the vendor you get a broader diversity of the types of organizations so you can have. A safety net hospitals you can have. Critical access rural hospitals, and other venues of care that are probably under represented in some of those. More academically driven models. And clearly the focus on healthcare about I would imagine applications in pharmaceutical out indeed to right I. Don't know if it s use and bad direction there has been some were performed with these data resources to. Characterize different aspects of medications, and so it does have utility in value. In a variety of. Analytical contexts. I was thinking about you know a lot of randomized clinical trials going on into Kuwait context and One of the issues of dispatch seem development toils that are going on that one could argue the population there are not really well to percents. it may be number by Auditees, men, people that deputy existing conditions. and. So he will serve at my come out of facedly trial. granted might work for the population. Tried it minority have sufficient? more largely. So I wanted this type of well I guess we don't really have an ID there right. So clearly, you don't know who these people are but they could be some clustering type analysis that might be interesting weight from It's very useful for Health Services Research and for outcomes research for you know what I characterize digital phenotype being. they can then guide. More, more formal research. you know you can use this type of resource to. Make sure. You're asking a useful question and make sure that there's likely to be. Enough patients who qualify for given study. Maybe you're working on a clinical trial in your casting your net to narrow you can. Determine that with this type of data resource. And is the eight tiff date who has access to it typically. So for this data resource on, it's through the vendor so. You need to have some level of footprint with them. which is the case with our organization. They're definitely a broadening their strategies. So they're. Gaining access into health systems that aren't exclusively using their electronic health records so. It's exciting to be a part of that that process. and to again work with them to. Analyze the data. I think. To the example you gave a formal randomized trials. In key part of what were growing our research to focus on is because this is real world data. You learn what's happening in practice whether or not it's well aligned with guidelines or formal protocols. And doing that there's many opportunities for near-term interventions that can improve health outcomes simply by. Identifying where providers may be deviating more from. Best Practices in than taking steps through training and education to kind of get them back towards those best practices. This data is a fresh on a daily basis. It's not. It's because it's so large and bulky? Typically we've received it on a quarterly basis in since it's retrospective analysis that's not been a major barrier. But. mechanistically, on onto soon aside is data getting sort of picked up from this system that it's harvested every day and then it's aggregated bundled and distributed on A. On a different timescale. Okay okay. So. From again, going to the, it's our system designed issue and implementation You say many HR systems comprised of more news at specific clinical processes or unit such as Pharmacy Laboratory or surgery talked about that. But then then people implement them this of fashion right they they implement modules by that can be a factor or sometimes they may want. One vendor for their primary electronic health record, but another vendor for their laboratory system. and so that's where you don't see a hundred percent usage of every module and every organization. And detailed number of different you know sort of noise creating issues in data one. This is icy speech over from ICT denied ten. and I don't know history of this but this was supposed to be speech with sometime in twenty fifteen. That's correct. So there is A. You know. There's a date in October of Twenty fifteen where most organizations were expected to have completed that transition. When I see with researchers who aren't as familiar with the you know the whole policy landscape around `electronic health records that? you can imagine researchers who assumed that all data before that date in October is is nine and all data after that date would be icy the ten. While we demonstrate in this paper, is that that transition was not Nearly, that clean and it was a much more, you know there are some organizations who just It the bullet and completed in twenty fourteen, and there are other organizations that were still lagging. In. Two Thousand Sixteen. Potentially because they weren't as exposed to those incentives in other things that you know stipulated the transition so. Part of why were demonstrating with that particular part of that work was that. you know these transitions aren't always abrupt. Yeah and and and so that is one issue and then you know a lot of consistency inconsistency issues fade. So we see that in in single systems and one of the items note here as you know if you think about the disposition code for death. you could have a right your race supercenter, right? It's a death expire expedite at home hospice, and so on. if this is a problem for a single system, but then many think about aggregating data from multiple sources this this problem sort of increased exponentially. Absolutely. So one of the challenges with documenting and and finding where you know if a patient has A deceased that. There's just multiple places to put that documentation in the clinical record. The Location in the record that. We have found to be the most consistent is what's called discharge disposition. By as we show in that analysis, that field is not always used document that and so if you're doing outcomes research and one of your key. Outcome metrics is death. And there are organizations that. Aren't documenting death in a place that successful. You should filter those out of your analysis before moving forward. And so part of what we wanted to promote is the realization that. That's the type of consideration that needs to be made The four. Publishing. Your data about an outcome metrics like death that. You're not. If you're never gonNA see that outcome it doesn't mean that people are. Dying in that particular facility, it just means it's not documented in the place that successful. Right. Yeah. So you know you on your expedience. Unique Position Mark because you you look at it from the from the vendor's perspective you're in an academic setting you're also in practice in a hospital. What's your sense of these things improving the on a track of getting getting this more standardize or it's camping in the other direction I think in general there is improvement I think The. Over the past eleven years through various federal mandates, including meaningful use and so forth. Those of all incentive organizations to utilize. Standard terminologies more consistently than was the case beforehand. I think there's still plenty of room for improvement and You know it's it's a journey, not a destination, but I think things have improved substantially. I was wondering there could be some applications of artificial intelligence here to In a clearly TATECO systems and you'd like the most them pity human resource intensive Yvonne to get it completely right. So one question would be you know, could be actually used a Dick needs to get it maybe ninety nine percent white. And that the human deal with exceptions I definitely think that that's an exciting direction that You want those a algorithms to be trained with good data, and that's a big part of what's motivated us to. Put this focus on data quality and Understanding these strange nuances that are underpinning that date has so that. As we move towards a in machine learning and so forth. We have a high level of confidence in the data that's training those algorithms. Right. Yeah. I think that a huge opportunity here because it's not quite as broad as NFL, not natural language processing it is somewhat constrained. that is a good part of it. The back part of it is that is highly technical. and so. you know some of the techniques you know you can have a fault tolerance in certain dimensions such as you know, misspellings lack of gambling and things like that. But as you have Heidi technical data, you cannot apply those principles because he could have misspelling the system may not be able to. Get, sometimes, and that's where you know I think. It's totally feasible to use. Resources to you know when you're dealing with. Tens of millions of patients and billions of detailed records. Using a I'd even identify those patterns of either. Inconsistent data or missing data it's also very powerful just to. kind of flag in identified. Areas that need to be focused on to lead to a better analysis. Greg Wait Be Hefty. Use that information somehow did is a belt of information that you know and so it just filtering into decision processes that the are really losing it. So hopefully getting improving in that dimension I've jumping to another paper bittersweet interesting. So it's entitled rates and predictors of using opioids in the Emergency Department Katrina Treat Mike Dean in Young Otto's and so so this is sort of a machine learning exercise you have gone through to locate you know coup is getting prescribed. OPIOIDS water the conditions for the Democrat not Nestle demographics but different different maybe age and things like that gender. and and then ask the question desert has some effect on addiction. In the long term rights. So that project To great example of team science though. We. Assembled a team of subject matter experts in neurology pain management. And Data Science and. The neurologist and pain management experts. Identified an intriguing question that we decided to pursue with data. In their question was. Based on anecdotal observation and so we thought it'd be interesting to see how well the data supported that. Observation is that. for youth and young adults Treated or admitted into the emergency. Department. With a migraine headache that. All too often they were treated with an opioid. And so we Use the same day to resource that we were discussing earlier. To explore that. Question. And using data from a hundred and eighty distinct emergency departments. We found that on average twenty, three percent of those youth and young adults were treated with. An opioid medication while they were in the emergency department. In general, it should be almost zero percent in general. There's really Better medications to us, four people presenting with a migraine. and. So this fits into obviously the OPIOID crisis it. it demonstrates the. Scenario describing that. You know using real world data. You can identify patterns of clinical behavior that. Don't match guideline. And the good news is that the? correctable and so through. Training and communication there's great opportunity to. To, manage this. Really. Striking. So fifteen thousand or so inevitably the encounters. And nearly a quarter of this encounters you say involved inoculate. and these are not just Misha and Congress right. It is not filtered down to migraine encounters. Okay. Okay. So these fifteen thousand just might in encounters might vein being repeating disease So once you. If you make a statement and. This or not Easter conditioning issue here. So you get your pain, you go to an emergency department and you get treated with an opioid you get quick tactical relief. From pain. auditing condition expect that in the next episode. So you can say we didn't pursue that particular question, but that is Definitely key part of. Managing the OPIOID crisis is that drug seeking behavior and so Part of our goal was to quantify that and use this as an opportunity to educate providers that. You really shouldn't be treating migraines with an opioid in there are better alternatives and. So we we felt that this was an important contribution to that national dialogue, but we didn't specifically pursue the question of whether the patients we analyzed. Within. Encounter show up Subsequently. With the same symptoms. Right right. Yeah you it develop into period when problematic patterns of drug use comedy. FEST MERGE THE PREVALENCE RATE OF OPIOID misuse estimated to be two to four percent and debts in each goofy just young adult drew from overdoses are rising. and. You say that literally prescribe IOS has been slumping loose future opioid misuse by thirty three percent. Betas Mehta say really huge number. I think just validates the importance of this of this work. Interesting mark. I don't know you exploded on data. Last the question if you look at the aggregate data, it'd be flying opioid. Misuse. what percentage of the total number. Actually started from. You know some sort of medical encounter has mike or some sort of. related encounter that could be completed otherwise was three a bit opioid. in that encounter documented resulted in that misuse. So what so If you look at the active misuse problem that we have today. do you have a sense of what percentage of that goal is actually started I? Think the exciting thing about this type of research is for everyone questioned that you pursue you have. You have ten new that you can pursue. We haven't. Delved into that specific area, but it's It's very ripe for further analysis and A considerable part of where I end my colleagues and our time as. We do this type of work to get an initial analysis published. And then You know in my leadership role I just WANNA. support people like my colleagues on this paper Mark Connelly Jennifer Bickel. in in using data to. Support their research into identify those follow. I mean, he tests policy implications. So it's sweet important work. and. If you find it direct relationship here than you have to ask you know from from a medical perspective what is right intervention? maybe is not just added of care just best practice but clearly should be the bay You know things should be looked at you say you're American Academy of Neurology has included avoidance of using opioid to treat gain one of stop top flight choosing wisely recommendations. For high-value duck in this gives Really evidence to to support that. The other thing that's really intriguing is this level of variation from site to site in. Some Sun facilities are very much aligned with the guidelines. Others are at the you know well, above twenty three percent. And that gives an opportunity for a really precision. conversations about you know, where does our organization stand on that spectrum? Yeah that's a that's an interesting avenue to right. So you know one could ask he says some sort of push sliced Intervention if we can fly goal of patients who who had gone an opioid sexually don't have an addiction problem. that as you know Anna, the kofoed does. if you can fly those type of patterns than you can think about. A customized within electronic health record systems. There's. The ability to provide decisions poor. There's certainly phenomena called pop up fatigue were physicians. You know they don't like having so many pop up windows but at the same time. It's Within the capability of an e e Hr to do that if then logic if patient has. migraine medication order equals opioid. encourage the provider to pause and reconsider that. Right, right and so this is supervised machine learning type analysis where so you have. you have number features that comes directly from each else. So each sex race ethnicity. insurance type. Encounter prostate suggest duration. time of the year and so on. and you have labeled data in this case I guess you have able tater because you would know if op- inscribed on trade. Okay and so are the two questions here. One is to ask the question given a new patient and those features. you could assign a probability that that patient will be prescribed will. Definitely. Impress the data from that predictive Minds. Right and then can you so that data definitely tell you if the patient is going to progress into some sort of an addiction issue. So. Earn Predicting Substance Abuse. So. Yeah. Yeah. Yeah. There's additional diagnosis codes that document. whether a patient has a history of substance abuse disorder. and. So it would be feasible to. Identify the with those diagnosis codes in than really look at their prior history. Of What other conditions were they treated for? What medications were they give in? to develop that model. One of the things in this case that helped with this study is that just in general, it's not advised get. So there are other things that are much more of a gray area. Or whether opioid is as useful, but in this case. The really not. Considered. To be helpful for migraines compared to other options and so that help us have a fairly clear cut scenario to do this work. Yeah. This this won't be the data like you say once you do something like this, you have been other things you could. You could stop asking. So unquestioned that that been to my mind as you know, how did they hugged the actually prescribing opioids? Is it the patient asking for it all so? Off that was another scoping thing with this project is focused on what happens within the emergency. Room. So it's it's. Really, medication order in administration that happens. In that emergency room setting. Whether or not the patient. was. Requesting that you know if they came in and said, this has worked for me before. Can I have it again? we don't have visibility to that. Right. Right. And so from a practical perspective So the the analysis that you did slightly ended up with the Family Clyde power we think it is. Compelling. Pretty compelling. So as as a new patient gets into e D either high. and what I mean by that probably is if there is a history of substance abuse property. the physician has really think twice about. The use of may be the well, and in this case, even without that history. Just because it's not considered to be an effective treatment. You know encouraging them to pause in that decision making. In this particular case is as effective as wall. Right. So looking forward. In if you think about both of these issues, one is the data quality data aggregation data standardized recent problem in the the right of Utah Systems have did that the talked about? And then if we can get to a level that we can look at cross a large data set. Beacon, ask. More. US specific questions, treatment. Optimum treatment type questions. subpoenaed. US The mark big think B be hunting. Certainly, the volume and variety of data that we're able to work with will be even greater I, think the. Opportunity To. Look, holistically at how upstream data capture. Effects Downstream data. Analysis. example I frequently give is if we have a Aggregate Data said we identify. Ten patients whose way in that data such shows up as being. Something that's completely infeasible. let's say they're documented is being. Fifty year old person who weighs two pounds. Clearly air. What's important is? Creating the process to communicate that back upstream. Because that clinical decision. Support. Many drug dosing things are evaluated using weight based logic and so. That same logic that's Evaluating the appropriateness of dosage. It's going to be running against an incorrect value in that may or may not always be visible. So I really am intrigued with that holistic opportunity. In it I am I remain just we have three or four additional papers coming out. About other examples where Provider behaviors not aligned with Best Practices and I'm just excited about you know when you compare that to how long it takes to develop a new drug or how long it takes to. To a really long term research. This research has the opportunity for a pretty quick turnaround on an effective intervention. A really that. Other so much that right. Providers. been taught in a no, but they're. Not always using that in practice and so to help them. Identify, those topics in just modifying behaviors is. In the scheme of things, it's a very straightforward way to improve. So. You know the entire spectrum from essentially getting the data. Right or cleaner like you know Missa mischaracterized or miss input data like wait or something like that. To to get. Better diagnosis better treatment modalities. policies there and from a femme perspective clearly inflammation therefore clinical trials. I was even thinking about drug interaction type. Inflammation. I haven't been involved in the former de for awhile but. Typically, this type of data doesn't get back into automatic processes that fast but I think that is all I know there's strong interest in Pharma in. Working with this type of data there a again looking at real world behavior. This is an excellent resource for off label medication use at. you know where Pharma's Always interested in repurposing existing medications the. Regulatory Processes, much more straightforward for that because the safety is already been. Evaluated and so. The. Significant Opportunity With this, there's also just exciting. Patterns of you know. What are those unrecognised correlations? That's where the machine learning opportunities are really exciting where. You know we're not always asking the right question. And the data can show us what we should be. Yeah exactly. So if the machine a sort of red flags something or create hypotheses. that Cubans have missed sometimes, those types of things are extremely powerful. because maybe that sometimes it's countering tutor. and so we all look at data with an Incan bias. The beauty of machines that at least on the surface began deploy Michigan. This volume of data. Techniques like machine deep learning can recognize those subtle but consistent associations. Wait quite. Excellent. Idea this has been great mark Thanks so much time with me. I enjoyed it very much. Thank you. But

Policy Technology Economics Science Gill Eappen Mike Yesterday Dr Mark Hoffman Children's Mussa Hospital Turner Electronic Certner Migraine Inflammation Federated Networks Stan Day Squatty Michio Kato University Of Minnesota Makita GIL Federated Kansas City
Dr. Mark Hoffman, Research Associate Professor at the University of Missouri, Kansas City - burst 01

Scientific Sense

44:57 min | 2 years ago

Dr. Mark Hoffman, Research Associate Professor at the University of Missouri, Kansas City - burst 01

"Welcome to the site of accents podcast. Where we explore emerging ideas from signs, policy economics, and technology. My name is Gill eappen. We talk with woods leading academics and experts about the recent research or generally of topical interest. Scientific senses at unstructured conversation with no agenda or preparation. Be Color a wide variety of domains red new discoveries are made. and New Technologies are developed on a daily basis. The most interested in how new Ideas Affect Society? And, help educate the world how to pursue rewarding and enjoyable life rooted in signs logic at inflammation. V seek knowledge without boundaries or constraints and provide unaided content of conversations bit researchers and leaders who low what they do. A companion blog to this podcast can be found at scientific sense dot com. And displayed guest is available on over a dozen platforms and directly at scientific sense. Dot? Net. If you have suggestions for topics, guests at other ideas. Please send up to info at scientific sense dot com. And I can be reached at Gil at eappen Dot Info. Mike yesterday's Dr Mark Hoffman, who is a research associate professor in the University of Minnesota Against City. He is also chief research inflammation officer in the children's Mussa hospital in Kansas City. Kiss research interests include health data delayed indication sharing initialisation Boca Mark. Thank you for inviting me. Absolutely. So I start with one of your papers Kato you need the use by our system implementation in defy date data resource from hundred known athlete off my seasons. So Michio inflicted. Data aggregated for marketable sources provide an important resource for my medical research including digital feel typing. On. Like. Todd beat to from a single organization. Guitar data introduces a number of analysis challengers. So. So you've worked with some augmentation log and in almost all cases be used. Data coming from that single macy's listen primary care behavioral. Or specialty hospitals and I always wondered you know wouldn't be nice. Get a data set. That sort of abrogates data from the radio on-ice. Asians but a lot of different challenges around that. So you wanted to talk a bit about that. I'd be happy to the resource that we've worked with. Is primarily a called health fax data resource. It's been in operation for almost twenty years. And the the the model is that organizations who are. Using these Turner Electronic. Health. Record. Enter into an agreement was turner they agreed to provide data rights to sern are. The identifies the date of affords aggregated into this resource. And certner provides data mapping, which is really critical to this type of work. It also the aggregate the data. And for the past probably six years. Then, they provide the full data set to especially academic contributors who want to do research with that resource. And I've been on both sides of that equation Lead that group during my career there, and then now I have the opportunity to really focus research on that type of data. So before we get into the details smog so e Itar Systems. So this is. Essentially patient records. So he gets dated like demographics out family history, surgical history hats, medications, lab solves it could have physician nodes no snow. So it's it's a combination of a variety of different types of data, right? A couple of things on the examples you gave it includes demographics. Discreet Laboratory results Medication orders. Many vitals so If access the blood pressure and pulse data. It does not include text notes because those can't be. Automatically identified consistently. So. We don't have access currently to TEX notes. Out of an abundance of caution. That his Hobby Stephen, physician writes something down they could use names they could use inflammation that could then point back to their. Patients Makita Perspective been the data's aggregated, the primary issue shoe that date has completely the identified, right? Correct. So. So yeah. So the data that we receive there's eighteen identifiers. Hip requires be removed from data. And those include obvious things like name address email addresses are another example One of the. Things. That is also part of the benefit of working with this particular resource. The. Dates of clinical service are not allowed to be provided under hip. White is done with this resource that allows us to still have a longitudinal view is. For any given patient in the data set the dates are shifted by A. Consistent. Pattern that for any given patient it can be. One two three four five weeks forward or one, two, three, four or five weeks backward. But that preserves things like day of the week effect. So for example, you see -nificant increase in emergency department encounters over weekends and you don't WanNa lose. Visibility to that. but it also allows us to receive. Very, granular early time stamped events in so. We can gain visibility into the time that a blood specimen was collected, and then the time that the result was reported back. And so we're able to do very detailed analyses with this type of resource. Right right and I don't know the audience our market is fragmented. Tau himself e Amorebieta providers out there. and so two issues. One is sort of. Standardization as to how these databases are designed and structured and others even that standardization that the actual collection of the data. In itself is not standardized played. So vk CAV vk potentially lot inability coming from different systems. Correct and that's part of what the paper that you mentioned Evaluates so. Often, night you out in the field in conferences you hear. Comparisons kind of lumping all organizations using one. Vendor lumping all using another together but as you get closer to it, you quickly learn that. It's not even clear. It's within those. Vendor markets. There's variation from organization to organization in how they use the e Hr and so. Because the identities of the. Contributing organizations are blinded to those of us who work with the data. We have to be creative about how we. Infer those implementation details, and so with this paper, we describe a couple of methods that We think move things forward towards that goal. Yes. So I'm not really familiar with that. So you mentioned a couple of things here. One is the the merge network. So this initiative including electric medical records and genomics network and pc off net the national patient, centered clinical research network support. Decentralized analyses that goes disparate systems by distributing standardized quotas to site. So this is a situation where you have multiple systems sort of. Communicating with each other and this net folks at allowing to sort of quickly them In some standardized fashion. So In this type of technology, there's janitorial core models. One is the. Federated or distributed model, the other is a centralized data aggregation. So there are examples including those that are mentioned in the paper where. Queries are pushed to the organization and. They need to do significant work upfront to ensure that there are standardizing their terminologies the same way. And once they do that upfront work than they're able to perform the types of queries that are distributed through those. Federated Networks. With. Okay. So that just one click on so that the police have standardized. So all on the at Josh site, then they have like some sort of a plan slater from from Stan Day squatty do all the data structure. And in many cases, they work through an intermediate technology. that would be. In general, consider it like a data warehouse. And so the queries are running against the production electric. Health record. That has all kinds of implications on patient care where you don't want to slow down performance. By using these intermediaries They can receive queries and then Follow that mapping has occurred. Than, they're able to to run those distributed queries. Okay. And the other model is You know. You say the g through the medical quality, improvement consortium and sooner to the health facts initiative. So this says in Sodas case, for example, in swags. This is essentially picking up data from the right deals, clients and Dan standardizing and centralizing data in a single database is that that is correct. One benefit of that model is that Organizations who for example, may not be academic and don't have the. Resources to do that data mapping themselves by handing out over that task over to the vendor you get a broader diversity of the types of organizations so you can have. A safety net hospitals you can have. Critical access rural hospitals, and other venues of care that are probably under represented in some of those. More academically driven models. And clearly the focus on healthcare about I would imagine applications in pharmaceutical out indeed to right I. Don't know if it s use and bad direction there has been some were performed with these data resources to. Characterize different aspects of medications, and so it does have utility in value. In a variety of. Analytical contexts. I was thinking about you know a lot of randomized clinical trials going on into Kuwait context and One of the issues of dispatch seem development toils that are going on that one could argue the population there are not really well to percents. it may be number by Auditees, men, people that deputy existing conditions. and. So he will serve at my come out of facedly trial. granted might work for the population. Tried it minority have sufficient? more largely. So I wanted this type of well I guess we don't really have an ID there right. So clearly, you don't know who these people are but they could be some clustering type analysis that might be interesting weight from It's very useful for Health Services Research and for outcomes research for you know what I characterize digital phenotype being. they can then guide. More, more formal research. you know you can use this type of resource to. Make sure. You're asking a useful question and make sure that there's likely to be. Enough patients who qualify for given study. Maybe you're working on a clinical trial in your casting your net to narrow you can. Determine that with this type of data resource. And is the eight tiff date who has access to it typically. So for this data resource on, it's through the vendor so. You need to have some level of footprint with them. which is the case with our organization. They're definitely a broadening their strategies. So they're. Gaining access into health systems that aren't exclusively using their electronic health records so. It's exciting to be a part of that that process. and to again work with them to. Analyze the data. I think. To the example you gave a formal randomized trials. In key part of what were growing our research to focus on is because this is real world data. You learn what's happening in practice whether or not it's well aligned with guidelines or formal protocols. And doing that there's many opportunities for near-term interventions that can improve health outcomes simply by. Identifying where providers may be deviating more from. Best Practices in than taking steps through training and education to kind of get them back towards those best practices. This data is a fresh on a daily basis. It's not. It's because it's so large and bulky? Typically we've received it on a quarterly basis in since it's retrospective analysis that's not been a major barrier. But. mechanistically, on onto soon aside is data getting sort of picked up from this system that it's harvested every day and then it's aggregated bundled and distributed on A. On a different timescale. Okay okay. So. From again, going to the, it's our system designed issue and implementation You say many HR systems comprised of more news at specific clinical processes or unit such as Pharmacy Laboratory or surgery talked about that. But then then people implement them this of fashion right they they implement modules by that can be a factor or sometimes they may want. One vendor for their primary electronic health record, but another vendor for their laboratory system. and so that's where you don't see a hundred percent usage of every module and every organization. And detailed number of different you know sort of noise creating issues in data one. This is icy speech over from ICT denied ten. and I don't know history of this but this was supposed to be speech with sometime in twenty fifteen. That's correct. So there is A. You know. There's a date in October of Twenty fifteen where most organizations were expected to have completed that transition. When I see with researchers who aren't as familiar with the you know the whole policy landscape around `electronic health records that? you can imagine researchers who assumed that all data before that date in October is is nine and all data after that date would be icy the ten. While we demonstrate in this paper, is that that transition was not Nearly, that clean and it was a much more, you know there are some organizations who just It the bullet and completed in twenty fourteen, and there are other organizations that were still lagging. In. Two Thousand Sixteen. Potentially because they weren't as exposed to those incentives in other things that you know stipulated the transition so. Part of why were demonstrating with that particular part of that work was that. you know these transitions aren't always abrupt. Yeah and and and so that is one issue and then you know a lot of consistency inconsistency issues fade. So we see that in in single systems and one of the items note here as you know if you think about the disposition code for death. you could have a right your race supercenter, right? It's a death expire expedite at home hospice, and so on. if this is a problem for a single system, but then many think about aggregating data from multiple sources this this problem sort of increased exponentially. Absolutely. So one of the challenges with documenting and and finding where you know if a patient has A deceased that. There's just multiple places to put that documentation in the clinical record. The Location in the record that. We have found to be the most consistent is what's called discharge disposition. By as we show in that analysis, that field is not always used document that and so if you're doing outcomes research and one of your key. Outcome metrics is death. And there are organizations that. Aren't documenting death in a place that successful. You should filter those out of your analysis before moving forward. And so part of what we wanted to promote is the realization that. That's the type of consideration that needs to be made The four. Publishing. Your data about an outcome metrics like death that. You're not. If you're never gonNA see that outcome it doesn't mean that people are. Dying in that particular facility, it just means it's not documented in the place that successful. Right. Yeah. So you know you on your expedience. Unique Position Mark because you you look at it from the from the vendor's perspective you're in an academic setting you're also in practice in a hospital. What's your sense of these things improving the on a track of getting getting this more standardize or it's camping in the other direction I think in general there is improvement I think The. Over the past eleven years through various federal mandates, including meaningful use and so forth. Those of all incentive organizations to utilize. Standard terminologies more consistently than was the case beforehand. I think there's still plenty of room for improvement and You know it's it's a journey, not a destination, but I think things have improved substantially. I was wondering there could be some applications of artificial intelligence here to In a clearly TATECO systems and you'd like the most them pity human resource intensive Yvonne to get it completely right. So one question would be you know, could be actually used a Dick needs to get it maybe ninety nine percent white. And that the human deal with exceptions I definitely think that that's an exciting direction that You want those a algorithms to be trained with good data, and that's a big part of what's motivated us to. Put this focus on data quality and Understanding these strange nuances that are underpinning that date has so that. As we move towards a in machine learning and so forth. We have a high level of confidence in the data that's training those algorithms. Right. Yeah. I think that a huge opportunity here because it's not quite as broad as NFL, not natural language processing it is somewhat constrained. that is a good part of it. The back part of it is that is highly technical. and so. you know some of the techniques you know you can have a fault tolerance in certain dimensions such as you know, misspellings lack of gambling and things like that. But as you have Heidi technical data, you cannot apply those principles because he could have misspelling the system may not be able to. Get, sometimes, and that's where you know I think. It's totally feasible to use. Resources to you know when you're dealing with. Tens of millions of patients and billions of detailed records. Using a I'd even identify those patterns of either. Inconsistent data or missing data it's also very powerful just to. kind of flag in identified. Areas that need to be focused on to lead to a better analysis. Greg Wait Be Hefty. Use that information somehow did is a belt of information that you know and so it just filtering into decision processes that the are really losing it. So hopefully getting improving in that dimension I've jumping to another paper bittersweet interesting. So it's entitled rates and predictors of using opioids in the Emergency Department Katrina Treat Mike Dean in Young Otto's and so so this is sort of a machine learning exercise you have gone through to locate you know coup is getting prescribed. OPIOIDS water the conditions for the Democrat not Nestle demographics but different different maybe age and things like that gender. and and then ask the question desert has some effect on addiction. In the long term rights. So that project To great example of team science though. We. Assembled a team of subject matter experts in neurology pain management. And Data Science and. The neurologist and pain management experts. Identified an intriguing question that we decided to pursue with data. In their question was. Based on anecdotal observation and so we thought it'd be interesting to see how well the data supported that. Observation is that. for youth and young adults Treated or admitted into the emergency. Department. With a migraine headache that. All too often they were treated with an opioid. And so we Use the same day to resource that we were discussing earlier. To explore that. Question. And using data from a hundred and eighty distinct emergency departments. We found that on average twenty, three percent of those youth and young adults were treated with. An opioid medication while they were in the emergency department. In general, it should be almost zero percent in general. There's really Better medications to us, four people presenting with a migraine. and. So this fits into obviously the OPIOID crisis it. it demonstrates the. Scenario describing that. You know using real world data. You can identify patterns of clinical behavior that. Don't match guideline. And the good news is that the? correctable and so through. Training and communication there's great opportunity to. To, manage this. Really. Striking. So fifteen thousand or so inevitably the encounters. And nearly a quarter of this encounters you say involved inoculate. and these are not just Misha and Congress right. It is not filtered down to migraine encounters. Okay. Okay. So these fifteen thousand just might in encounters might vein being repeating disease So once you. If you make a statement and. This or not Easter conditioning issue here. So you get your pain, you go to an emergency department and you get treated with an opioid you get quick tactical relief. From pain. auditing condition expect that in the next episode. So you can say we didn't pursue that particular question, but that is Definitely key part of. Managing the OPIOID crisis is that drug seeking behavior and so Part of our goal was to quantify that and use this as an opportunity to educate providers that. You really shouldn't be treating migraines with an opioid in there are better alternatives and. So we we felt that this was an important contribution to that national dialogue, but we didn't specifically pursue the question of whether the patients we analyzed. Within. Encounter show up Subsequently. With the same symptoms. Right right. Yeah you it develop into period when problematic patterns of drug use comedy. FEST MERGE THE PREVALENCE RATE OF OPIOID misuse estimated to be two to four percent and debts in each goofy just young adult drew from overdoses are rising. and. You say that literally prescribe IOS has been slumping loose future opioid misuse by thirty three percent. Betas Mehta say really huge number. I think just validates the importance of this of this work. Interesting mark. I don't know you exploded on data. Last the question if you look at the aggregate data, it'd be flying opioid. Misuse. what percentage of the total number. Actually started from. You know some sort of medical encounter has mike or some sort of. related encounter that could be completed otherwise was three a bit opioid. in that encounter documented resulted in that misuse. So what so If you look at the active misuse problem that we have today. do you have a sense of what percentage of that goal is actually started I? Think the exciting thing about this type of research is for everyone questioned that you pursue you have. You have ten new that you can pursue. We haven't. Delved into that specific area, but it's It's very ripe for further analysis and A considerable part of where I end my colleagues and our time as. We do this type of work to get an initial analysis published. And then You know in my leadership role I just WANNA. support people like my colleagues on this paper Mark Connelly Jennifer Bickel. in in using data to. Support their research into identify those follow. I mean, he tests policy implications. So it's sweet important work. and. If you find it direct relationship here than you have to ask you know from from a medical perspective what is right intervention? maybe is not just added of care just best practice but clearly should be the bay You know things should be looked at you say you're American Academy of Neurology has included avoidance of using opioid to treat gain one of stop top flight choosing wisely recommendations. For high-value duck in this gives Really evidence to to support that. The other thing that's really intriguing is this level of variation from site to site in. Some Sun facilities are very much aligned with the guidelines. Others are at the you know well, above twenty three percent. And that gives an opportunity for a really precision. conversations about you know, where does our organization stand on that spectrum? Yeah that's a that's an interesting avenue to right. So you know one could ask he says some sort of push sliced Intervention if we can fly goal of patients who who had gone an opioid sexually don't have an addiction problem. that as you know Anna, the kofoed does. if you can fly those type of patterns than you can think about. A customized within electronic health record systems. There's. The ability to provide decisions poor. There's certainly phenomena called pop up fatigue were physicians. You know they don't like having so many pop up windows but at the same time. It's Within the capability of an e e Hr to do that if then logic if patient has. migraine medication order equals opioid. encourage the provider to pause and reconsider that. Right, right and so this is supervised machine learning type analysis where so you have. you have number features that comes directly from each else. So each sex race ethnicity. insurance type. Encounter prostate suggest duration. time of the year and so on. and you have labeled data in this case I guess you have able tater because you would know if op- inscribed on trade. Okay and so are the two questions here. One is to ask the question given a new patient and those features. you could assign a probability that that patient will be prescribed will. Definitely. Impress the data from that predictive Minds. Right and then can you so that data definitely tell you if the patient is going to progress into some sort of an addiction issue. So. Earn Predicting Substance Abuse. So. Yeah. Yeah. Yeah. There's additional diagnosis codes that document. whether a patient has a history of substance abuse disorder. and. So it would be feasible to. Identify the with those diagnosis codes in than really look at their prior history. Of What other conditions were they treated for? What medications were they give in? to develop that model. One of the things in this case that helped with this study is that just in general, it's not advised get. So there are other things that are much more of a gray area. Or whether opioid is as useful, but in this case. The really not. Considered. To be helpful for migraines compared to other options and so that help us have a fairly clear cut scenario to do this work. Yeah. This this won't be the data like you say once you do something like this, you have been other things you could. You could stop asking. So unquestioned that that been to my mind as you know, how did they hugged the actually prescribing opioids? Is it the patient asking for it all so? Off that was another scoping thing with this project is focused on what happens within the emergency. Room. So it's it's. Really, medication order in administration that happens. In that emergency room setting. Whether or not the patient. was. Requesting that you know if they came in and said, this has worked for me before. Can I have it again? we don't have visibility to that. Right. Right. And so from a practical perspective So the the analysis that you did slightly ended up with the Family Clyde power we think it is. Compelling. Pretty compelling. So as as a new patient gets into e D either high. and what I mean by that probably is if there is a history of substance abuse property. the physician has really think twice about. The use of may be the well, and in this case, even without that history. Just because it's not considered to be an effective treatment. You know encouraging them to pause in that decision making. In this particular case is as effective as wall. Right. So looking forward. In if you think about both of these issues, one is the data quality data aggregation data standardized recent problem in the the right of Utah Systems have did that the talked about? And then if we can get to a level that we can look at cross a large data set. Beacon, ask. More. US specific questions, treatment. Optimum treatment type questions. subpoenaed. US The mark big think B be hunting. Certainly, the volume and variety of data that we're able to work with will be even greater I, think the. Opportunity To. Look, holistically at how upstream data capture. Effects Downstream data. Analysis. example I frequently give is if we have a Aggregate Data said we identify. Ten patients whose way in that data such shows up as being. Something that's completely infeasible. let's say they're documented is being. Fifty year old person who weighs two pounds. Clearly air. What's important is? Creating the process to communicate that back upstream. Because that clinical decision. Support. Many drug dosing things are evaluated using weight based logic and so. That same logic that's Evaluating the appropriateness of dosage. It's going to be running against an incorrect value in that may or may not always be visible. So I really am intrigued with that holistic opportunity. In it I am I remain just we have three or four additional papers coming out. About other examples where Provider behaviors not aligned with Best Practices and I'm just excited about you know when you compare that to how long it takes to develop a new drug or how long it takes to. To a really long term research. This research has the opportunity for a pretty quick turnaround on an effective intervention. A really that. Other so much that right. Providers. been taught in a no, but they're. Not always using that in practice and so to help them. Identify, those topics in just modifying behaviors is. In the scheme of things, it's a very straightforward way to improve. So. You know the entire spectrum from essentially getting the data. Right or cleaner like you know Missa mischaracterized or miss input data like wait or something like that. To to get. Better diagnosis better treatment modalities. policies there and from a femme perspective clearly inflammation therefore clinical trials. I was even thinking about drug interaction type. Inflammation. I haven't been involved in the former de for awhile but. Typically, this type of data doesn't get back into automatic processes that fast but I think that is all I know there's strong interest in Pharma in. Working with this type of data there a again looking at real world behavior. This is an excellent resource for off label medication use at. you know where Pharma's Always interested in repurposing existing medications the. Regulatory Processes, much more straightforward for that because the safety is already been. Evaluated and so. The. Significant Opportunity With this, there's also just exciting. Patterns of you know. What are those unrecognised correlations? That's where the machine learning opportunities are really exciting where. You know we're not always asking the right question. And the data can show us what we should be. Yeah exactly. So if the machine a sort of red flags something or create hypotheses. that Cubans have missed sometimes, those types of things are extremely powerful. because maybe that sometimes it's countering tutor. and so we all look at data with an Incan bias. The beauty of machines that at least on the surface began deploy Michigan. This volume of data. Techniques like machine deep learning can recognize those subtle but consistent associations. Wait quite. Excellent. Idea this has been great mark Thanks so much time with me. I enjoyed it very much. Thank you. But

Policy Technology Economics Science Gill Eappen Mike Yesterday Dr Mark Hoffman Children's Mussa Hospital Turner Electronic Certner Migraine Inflammation Federated Networks Stan Day Squatty Michio Kato University Of Minnesota Makita GIL Federated Kansas City
UNC Student Journalists Share What It's Like To Cover Campus Reopening

All Things Considered

04:07 min | 2 years ago

UNC Student Journalists Share What It's Like To Cover Campus Reopening

"Journalists have had a front row seat to the Corona virus on campus, covering the parties and the outbreaks as they try to hold their schools accountable. Editors at the University of North Carolina Chapel Hill's student paper were thrust into the spotlight. After using an expletive to describe what was happening on their campus. NPR's Elissa Nad Warney stopped by the offices of the daily Tar Heel to see what it's like to live and cover college re openings. Hello, Thea Newsroom is tucked behind Franklin Street, the main drag once in such positive? Meanwhile, earlier this month, thousands of students, including Mehta made their way to campus. There's nowhere he could have stopped this university from opening, but I like hope that I did as best of a job I could trying, Tio But as much information out there and show that there was a large amount of backlash this plan before it even took off. Really. As the national headlines about UNC recede, the journalists at the Tar Heels still have a big story to cover you. Just you all wanna

University Of North Carolina C Tar Heel Elissa Nad Warney Thea Newsroom Mehta NPR
Jos Ralat, Taco Editor

Latino USA

03:58 min | 2 years ago

Jos Ralat, Taco Editor

"Roulette. Welcome to let you know USA. Thanks for having me to be like the one and only standing official dot co editor of the United States of America. That's a big deal. Congratulations. Thank you. It's an honor fed I don't. Take lightly because I. I have the responsibility of. Not just. For reading about the food but. priding about the people and I think that's really the most. Critical part of the job but before we continue. People might be saying, wait what's going on and so you're very open about the fact that you stutter that is something that happens and so we might as well just say, Hey, it happens in your cool with. Saying Yeah and and moving on, right? Yes. I am thank you. Yes. It's part of my life and it's never stopped me from doing things like. Live TV or radio segments I. Love that. I. GotTa Say I really do I completely loved that. So. What you may not know is that I've been Taco fanatic since probably before you were even born, I'm Mexican I grew up with this stuff. You know I mean, my mom made dot goes by our leader. Mehta goes you're Puerto Rican you were growing up with this stuff. So what's the story as to why this Puerto Rican dude ends up falling in? Love with TACO's growing up in the states I knew about duck was generally speaking at A. Fast Food Product but. As a Mexican food item. was in. Brooklyn from. A. China and I don't know. Who I fell in love. With I. The woman. or The food. So before we get to talking about that goes which again we talk about forever. One of the things that stood out to us is from the beginning of your book. And this is where you refer to something you call the alita principal. And having just mastered Jose, you'll be proud of me finally having just mastered my I will lead us the you like I finally figured it out. I'm just like Oh my God I can't believe it. I unlocked it. What is this thing about the alita principle when it applies to Dacas? So, whenever people? Talk about Mexican food eventually the conversation. Pros around to. Well. My Willett I made the best Mexican. Food she made best diesels. Hurling. was, the best or her? Malia was the best. and. For them. That's as far as Mexican food goes. Nothing else. Counts as Mexican. which is unfortunate because. Mesko is a large country with micro regions. And Different, cuisines. It's not that. Simple. We shouldn't box it in. Boxing it in. His misguided at. Best and racist that worst Could also. Be. Maybe so and so's grandmother wasn't

United States Jose Mesko Malia Boxing Taco America Mehta Brooklyn Official Principal Editor A. China Hurling.
Is Bitcoin Heading To $10,000?

The Trader Cobb Crypto Podcast

05:14 min | 2 years ago

Is Bitcoin Heading To $10,000?

"With back to you know we still get a higher loan bitcoin. Looks Good Pulled back ten thousand would be splendid. Love to see it. It will just. Translate to higher lot way bravely breathing. that. You know the question that what someone got very well said to me. When I sit on a remember what the exact question though I also Irrelevant but the answer was will debry of breath out. And of course, the answer is you do both right and that's what markets do they breathing they breathe out. Now. This is what we're saying here. They breed in up up up up the breath out they breathe in the pop-up they breathe out. That's what they do. So. Is there a concern none? What are we got up pullback? Am I saying great trading conditions? Not a great deal night. If I do say them I think tomorrow if we get a small bullish candle. To Morrow think that's going to be. Nothing we're going to see lot of opportunity at the it's the it's a twelve hour and the daily with my focus is at. Bitcoins at eleven thousand, seven, hundred, and thirty I down two percent. Theorem six, six once bounced four hundred is down full percent. Except pays down four and a half percent twenty, eight point nine struggling with that thirty cents once again bitcoin cat below three hundred to ninety two, fifty, five deaths three point six percent lot twenty, sixty, one dollars ninety one cents down a half. Base vague to a six down five cal Donna twelve point nine down five and a half botanist onto two point nine at twenty two dollars thirty, six cents. A US down six point six, three dollars study foreign link flat sixteen dollars eighteen very sure little talk the about the top ten. Why will there's not much more detail to give you? Am I looking for shorts? No. I'm looking for longs. No. What am I doing I'm whitening. Okay. The market right now is in a pullback phase is this pool by going to be a lodge pullback by on a weekly? We're GONNA about the ten thousand. We're GONNA get. Where are we going to? I don't know I do not know. What I can say Is. It. This is all very much part of the plan. If we GONNA pull back ten thousand and that weekly comes into that Area. I, say that it's extraordinarily bullish because it's like starting your engines. You know it comes back you know. It it's like I used to be summarized to compete in swimming. Now when you go to a swimming event. It's it's grueling. You know you tend to go. Well, what's My pet events MoD pet event was a fifty in hotter Mehta sprints, freestyle, and butterfly. Yet Butterfly worst start ever was my best struck ever wanted absolute shock. That was body. Would you do that to me? Anyway. You if you'll forced if you'll mate, you've got and I try and spice it and things at once for certain. Types of. But if I was the guy from say. Doing the Honda made about a fly get out of the pool at a ten minute break, and then do the Honda made fray. Then the hunter made a freeze probably going to suffer I'm not going give it the same amount of energy exertion the I gave the Honda bottle. Or by strikes are extraordinarily physical. Specially Butterfly. So that's what I mean. By the further we go up, it can become less good. To have that break in the middle to have that NAS claimed pull the very predictable. Okay. Great. Yet, this trend is looking fantastic and then to sit push on, is GonNa have that follow through of course, that's a repulsive for me. Now I'm not suggesting that we're GONNA get there at this stage. I haven't seen any confirmation of anything other than as a trend with a bit of divergence on many of these jobs especially bitcoin. So it's about why saying what we get over the next two tomorrow is GonNa be well, the next twenty, four hours from closing his right now is going to be interesting. Cannell could make. You know sorry could create many opportunities It's certainly getting there. We'll have to wait and

Honda United States Morrow Cannell Bitcoins Donna Mehta
As Grocery Deliveries Soar, Startup Raises $10 Million to Unseat Giant Instacart

Business Wars Daily

04:32 min | 2 years ago

As Grocery Deliveries Soar, Startup Raises $10 Million to Unseat Giant Instacart

"As a result of COVID, nineteen instinct cart has become the Goliath of grocery delivery businesses as virus fears. Keep us out of grocery stores. We've been relying on food delivery mainly, INSTA- carts GIG workers called shoppers to keep a stocked up. This April INSTA- cart sales were five times greater than a year ago the Financial Times reported that I've popping growth hardly escaped. Investors Notice INSTA-. Cart raised another two hundred twenty five million dollars earlier this month that evaluation, approaching fourteen billion dollars. That's nearly double its valuation only eighteen months ago, perhaps needless to say instant carts thirty-three-year-old. A poor va mater is now a billionaire. But less you jump to the conclusion that Mehta can sit back and relax safe in his seat at the top of this rapidly growing industry. Thank again. A small startup called Ling is coming rich share of the grocery delivery business, though tiny compared to Insta- card investors are also taking notice last week dumpling raise six point five million dollars for a total of ten million dollars in funding so far founded in two thousand seventeen dumpling helps people create independent delivery businesses, the difference between shopping for instance card and dumpling is simple, but fundamental dumpling delivery workers own their own businesses Insta- cart workers are subcontractors paid by the shopping trip. Dumplings Co CEO's Joel. Shapiro Innate Danna make no bones about what motivated them to create dumpling. They felt sharing economy companies like Insta- card were treating workers on fairly so they created an APP that helps perspective entrepreneurs go into business for themselves to create a new business users pay a ten dollar fee for access to dumplings services and listing on the company's site where customers can search for shoppers. Bhai Zip Code. Shoppers paid dumpling a five dollar fee for each job or when they get busy enough switched to a monthly payment of thirty nine dollars. Dumplings model is not unlike Amazon's two year old partner delivery service, which also helps people start their own businesses in their case exclusively delivering Amazon packages supporters of the independent model say self employment in the delivery industry offers a number of advantages including higher revenue and insulation from the anti-god worker legislation that's cropping up all over the country so far fueled in part by the pandemic. The models seems to be working dumplings. Founders say customers or ordering twenty times more groceries than they were before in nineteen, and the company says it has helped. Two thousand people start their own delivery companies people. People like Kelly Vilchez in Orange County California. Vilchez operates under the business name shop girl. Oh, see. She told the L. A. Times that she's earning three to five thousand dollars a month. She's clearly a dumpling success story. According to the shop girl OC facebook page villages is featured in dumplings advertising campaign in contrast Insta- card has been plagued by shopper complaints since the onset of the coronavirus in March and April, many walked off the job, demanding better protection against Covid, nineteen higher tips and sick leave the Winston card initially claimed the strikes made no impact. Courts reporter Michelle Chang Rights. That's debatable on June. Fifteenth the Seattle. City Council voted unanimously to require that. GIG were companies like Insta- card and Grub pay their workers an extra two dollars and fifty cents hazard pay per order. That requirement is intended to last through the end of the COVID nineteen civil emergency declared by Seattle's mayor. Insecure has vowed a legal fight. Similar legislation is pending in Philadelphia New York and San Francisco Chang writes. It is these sorts of ills shortages of personal protective equipment, low pay and lack of sickly. The dumpling claimed self employment solves, but entrepreneurship isn't easy dumpling delivery workers have to do their own marketing and find their own customers, and they're on the hook. If grocery buyers failed to pay, which dumpling says is rare, that said any platform that helps the millions of unemployed grow their own wealth. Is appealing to investors, and it's interesting to note that dumplings mission says nothing specifically about the grocery business rather they say. We WanNA. Make dumpling trusted partner that helps anyone launched running grow their own service based business. Sure dumpling starting groceries. Be Questions. Where will they go next?

Insta Dumplings Co Partner Kelly Vilchez Financial Times Amazon Seattle Covid L. A. Times Michelle Chang Mehta Shapiro Insecure CEO Joel Reporter San Francisco Chang City Council