19 Burst results for "One Two Three Four Five Weeks"

"one two three four five weeks" Discussed on Scientific Sense

Scientific Sense

44:57 min | 5 months ago

"one two three four five weeks" Discussed on Scientific Sense

"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

"one two three four five weeks" Discussed on Scientific Sense

Scientific Sense

44:57 min | 5 months ago

"one two three four five weeks" Discussed on Scientific Sense

"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

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

Scientific Sense

44:57 min | 5 months 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

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 | 5 months 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

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
"one two three four five weeks" Discussed on WGR 550 Sports Radio

WGR 550 Sports Radio

08:21 min | 1 year ago

"one two three four five weeks" Discussed on WGR 550 Sports Radio

"Here know what you park, happy to have you here with this kind of a quiet day around the bills rookies are they got their rookie development week going on that all in soon veterans have been gone since like last Thursday Friday when, when minicamp ended and of course, we are I think training camp, what we say five weeks from tomorrow, I was five I believe so thirty days. Thirty six days, six days, public math. But so it's coming. It's coming one two three four five weeks away. I brackets five weeks away and those two practices with the Carolina Panthers in South Carolina on August thirteenth and August fourteenth. We've talked about it, they'd been having joint practices for decades, you know and, and there's been a story out now and they're getting a little bit of pushback from their fans in Philadelphia. Because Doug Peterson has said listen, we're going to open up one practice to the fans this year, and they're going to charge admission. Now, the profits from that are going to go to charity, but they're selling them on stub, hub, and that kind of thing and some of the secondary markets and those, those entities take their fee off the top, of course. But the rest of it's going to go to charity fine and dandy, but they're getting some real pushback because their fans are getting to see one practice of training camp. That's it. That's, that's unusual. I think I do I think I agree. I don't know what it's about either. I mean look, the leaks changing it used to be training camp. I think was viewed by the NFL forty years ago as a way to promote interest in the upcoming season. Let's get out there have opened public practices and get people excited. And we'll sell more tickets and blah, blah, blah that that is gone. Right. P teams know how many tickets are going to sell tickets sales, or less of a percentage of what teams revenue is they know pretty much what their ticket basis and who's gonna come and why they're gonna come. And so the promotional aspect of training camp is over the, you know, the hassle remains an and you get a team and you can see it here when a team has such a good setup here with the brand new weight room in the brand new conditioning and player performance center. They have you cannot replicate that anywhere else going. Yeah. What exactly now coaches and bills coach McDermott makes us point too. There is a bonding aspect. I think training Kip talk to us about that. Steven you guys, you know, the bills extremely close team extremely talented team. And you would spend four plus weeks for every years, much longer than any training camps nowadays. Did you get bonding out of that aside from the football benefits where they're bonding was? Absolutely. I mean there's a lot of free time. Mean we're, we're notorious we joke about it now about how much time we spend downtown Dunkirk Fredonia would and or know, of course. Yeah whatever doing the stuff away from the field. But there's no doubt that training camp from what it was in the sixties. And seventies was much different than when I came in the league, they had a times guys that I played with the older guys that I came into the league with back when they were young, there were like one hundred fifteen guys in camp. They just bring they'd roll guys in the hundred and twenty guys in training camp. And it was six weeks long and there were like six preseason games and they used it to get in shape. Trey it was training camp. You had to train to get back into football shape. Nobody was making enough money to be a professional athlete. They were just getting paid to play football. There's a difference now these guys are pro athletes, do it twelve months year. They trained twelve months year. They do it on their own dime for the most part, and it has evolved from that. Now training camp is teaching. It's learning the offense. It's a learning taking reps. It's, it's honing technique. It's a much different atmosphere than it was. You know, certainly fifty years ago, but even twenty five years ago. It's, it's a different kind of atmosphere. No hitting, there's no today's or maybe one, two a day, every three days, I can't even remember what it is. There's no dude. That's there used to be teams that go out. Have a walk a practice. Go back in have a walk-through go back in and then come back out and have another practice. I mean it was like three days they were on the field all the time. That's all gone away. And it's interesting to think about where it's headed. I mean you got a facility like one bills drive. Now why you leave? Why did you leave? There's got to be something else. And one reason is that this franchise has a plan to, to have a big regional footprint. Right. And Rochester's an enormous part of that, and they embrace that, and they they've actively chase the Rochester market. And they send the team over there. And there's good reason for it. I think it's a lot of bills fans are from there. So there's a reason for that here. I don't know if New York and LA I don't know if they have the same reasons for doing it, you know, the eagles have really strong committed loyal fan base, and it's pretty widespread to at least all over eastern Pennsylvania, and into New Jersey as well. I just think things are changing now in an economic sense in the league where as much as they value. I'm sure the eagles value all those relationships, although sponsors, other season ticket holders and yet, they, they might look at training camp is not an important part of that. You know, like oh, yeah. You come out and watch practice. We'll make it available. This one. I assume that one practices at at the stadium right Lincoln, Bill idea. No, but gets sixty five thousand people in there to watch one training, camp practice serve the same purpose of charitable donation. Fifteen days of two thousand people three thousand of practice. I get I get it. But it is evolving. I, I don't know words headed but I think let's take it from when this first start happening when a guy like Dan Schneider, and I and I remember this vaguely in my past I may get this wrong. But here's what I remember he bought the Redskins and there was, there's such remember the Redskins. They sold out one hundred ninety thousand games in a row and in old RFK stadium. Remember and you couldn't get it ticket and and everybody in Washington wanted to see the Redskins and they were great. So he was going to take advantage that he bought the team, and he started charging mission prices and open up every preseason practice. Right now, he started charging admission so. What did the eagles and the Dallas Cowboys and the giants do fine? They sit there. They sent scouts down there and watched every practice and filmed up about a ticket. They bought a ticket and filmed them and they lost competitive advantage. I mean these teams were filming their practices. How are you going to get you gonna keep anything secret from your division rivals, when they can come in and film, you, you know, it's crazy. So that now it's kind of swinging back the other way say, listen. If they're doing we're doing it. And now it's you know it's common practice. They got an open practice. You've got somebody there watching, you know it's just the way it works. So now the football, guys and the and the competitive nature of the game, taken back over this way we, let's, let's have some competitive secrets here if we can even through training camp and fans are nice, even when you give away free tickets. But you know we got to we've got to have some things that are off limits as well. Yeah. The value of training camp is I'm sure something that every team thinks about what do we get out of this? Why are we making this effort and they think hard about it and you know there was discussion last week on the bills training camp? Schedule came out, and what are they got eight total eight open practices in Rochester this year for? six eight and people say well what's the point why there is a value there and i i think it's you know in sean mcdermott talk about it with us on the show last week what they get out of it what they're looking to get out of it and yet they're you know as mcdermott said last week i think is quote was we're constantly wayne we're constantly looking at the value of being away being familiar surroundings for a couple of weeks and this year it's what about a week and almost two weeks versus being here the work you get done you get it done and the benefit that comes to marketing and fan support and and season ticket sales i do think that's item it is around the league not just in buffalo around the league that's gone down right teams no longer need to be six weeks at niagara university in july.

football eagles sean mcdermott Rochester Redskins Doug Peterson Carolina Panthers Philadelphia South Carolina NFL RFK stadium Kip Steven Washington Dan Schneider Lincoln New York Pennsylvania niagara university Dallas Cowboys
"one two three four five weeks" Discussed on Dentistry Uncensored with Howard Farran

Dentistry Uncensored with Howard Farran

04:24 min | 2 years ago

"one two three four five weeks" Discussed on Dentistry Uncensored with Howard Farran

"Greater variable than water, Florida. I didn't know that you know, the the. No. No clue. But one thing I love about Japan is if you're talking about working access to care of disabled or medically complex patients, nurses and dental professions are all together in the same room you when I go to London you'd lecture in the same room, you go to Tel Aviv. The professions are all together. And I don't know why we're not doing that in the United States. Why do I have to go out? The country. Why do I have to go outside if you're gonna talk oral systemic? It's so much better of you have something you could talk to about it all goes back to university of Baltimore. It all comes back to the deal where the physicians needed to lay down bed and the dentist needed sit up chair, and that chaired a bed is why we separated and the west the didn't in the Soviet Union. So you same for years undergrad. And then then you decide like you decide that you're going to be a dermatologist BUSTER wants to be near nose throat, and I go into odontologist. Yeah. So we're all the same. And what I'd like to that is dentistry is all surgery. You know, a lot of physicians. Don't do any surgery, right? Just like most the far majority of lawyers never do trial work. Right. And so we've you lose an eye or you get the disability. And you're a communist dentist, you can just say, well, you know, what I'm gonna go to another rotation and being opthamologist anti so. Stay move around. I liked that part of another country's much nicer to have different professions in London, speech and language therapist. Do oral care in hospitals. Not Donald people, not nurses speech language there. So it's so nice to have a whole group of professions. You could sit down and talk together. But in the US, I'm not going to be to speech language conference with dental people together. And it's it's, you know, a lot of people think we do it right here in the US. But in some ways, we don't at all. And I think is the food chain in were beneath the MD's because in my trinity one years, and anytime I invited a physician over to my house for dinner out for dinner, whatever it's like camp in thirty one years, maybe three or four but a hundred percent of the chiropractors. Which have been in my house. Damn you're all the pharmacist natural pass is kind of like the MD's. They wanna come. And what do you wanna talk about sleep apnea team j you know, just any anything? So there. It's it's it's a they're really suffered. So you're speaking townie meeting, which would dating on I'm Friday afternoon Friday afternoon. How many so that's about thirty five about a month away? Okay. I'll be there. And so what day is so you're probably afternoon on Friday afternoon. So is that the what day does that that's the March twenty so your March twenty second? Okay. I'll do one two three four five weeks. What are you going to be talking about March twenty second? And will your course have beer. Trey I don't drink beer entering PR. What are you doing so water little south and you on blown? I could do a little soften young plunk down during peer. I'm so Kansas. I can't even see that word somebody. I'm blown. Sounds for. Yes. Actually, generally, nude from New Zealand softened. Yes. Low sweet little great Purdy little sweet. You know, what I was most fries. When I lived in New Zealand is how every restaurant you went to like America, they'll have steak, right? They offer sheep and the ones were pumpkin PUM homes left Gump gun, ULA, pumpkin. I've never had it in my whole life. Always told my boys said, you know, what forget college, forget everything. A lot.

United States London MD Tel Aviv Florida New Zealand Japan university of Baltimore Soviet Union BUSTER Purdy Donald Trey America Kansas twenty second one two three four five weeks thirty one years
"one two three four five weeks" Discussed on WFAN Sports Radio_FM

WFAN Sports Radio_FM

05:46 min | 2 years ago

"one two three four five weeks" Discussed on WFAN Sports Radio_FM

"The fact I'm eat it makes me feel good. So those facts need to be out there because. Analyze these two games. And just take those bets throw it away. It's almost impossible. I want the ravens to win. I want the bears to win. Now. Do I understand the fears of what the chargers can do in this game? They certainly have the experience at quarterback and what scares me in this game. And honestly if they didn't play three weeks ago if the chargers didn't see how the Baltimore Ravens run the football consistently. If they hadn't seen this quarterback who is six and one in the seven starts he's made if they had seen him if the ravens hadn't gone to LA slash sandiego slash soccer stadium. I probably would feel better about this game. The fact is games in Baltimore means absolutely nothing to me. Joe lay. This how yesterday the chargers have played sixteen road games this year. And on the chargers side, you've got a quarterback that may know deep down because Philip rivers is Eli Manning's aid. We spent a lot of time talking about and his age and looking for replacement and all that Philip rivers. Eli Manning's age except one of the differences between Philippine Eli and saw Phillips fault. Is that Philip has a big fat zero next two Super Bowls. And he knows that this may be it. That this may be the last opportunity. What concerns me about the chargers is their health right now. Melvin Gordon is not one hundred percent their best run stuff. Brandon me bane. The former Seahawk. He's not gonna play in this game. They've had injuries to their linebacking core all season long. They have got to stop the run. That's the key. Now, the ravens don't have the best defense in the NFL. The Chicago Bears do and here's why because the Chicago Bears force turnovers. The ravens haven't been able to do it. If the ravens are gonna win this game tomorrow, they've got to force one wants to conservative two turnovers in this game. And obviously Lamar Jackson and he's done this very well. Lamar Jackson has to avoid the bad turnover. Not talking about fumbles, but as far as interceptions and granted he's not throwing the ball a lot. I understand that hasn't pick and five weeks in his first postseason game Ken Lamar Jackson make a few throws and avoid that bad turnover. And the eagle bear game is all about the magic of Nick foles in the magic of this eagle team in the eagles are incredibly talented team, especially on the defensive side of things they can stop the run which is a big concern. Because if the eagles are stopping the rod can a young quarterback like Mitchell Trubisky make enough plays. But one thing this bear team has done all year long, and I just touched on it. And that's why this is the best defense in the NFL. They force turnovers. They have forced thirty six turnovers. This year. Amongst playoff teams. The LA Rams have forced thirty there. The next highest. That's a big difference. After that you're talking about the Texans at twenty nine. The patriots are Twenty-eight even the chiefs at twenty seven but the bears force a huge amount of turnovers. And what's crazy is how the ravens known of all the teams in the postseason. The Baltimore Ravens have forced the fewest turnovers. In fact, they've lost the turnover battle this year. So of the eagles, but the ravens have. And that's why as great as their defense has been as physical as they are. It's tough to say they have the best defense amongst the playoff teams. It's the Chicago Bears, but hey, it's going to be a fun weekend. And unless you did what I did which is put a couple of betting chips in there and New Jersey before the season started, you're going to get to put your feet up and you get to relax and enjoy some wild card weekend football. And Joe has said this every single year. It's one constant Joe says right before wild card weekend. And it's so true. Joe's taught me a lot about life. And it's something. My parents always say to time flies time moves quicker when you get older. Her. And these playoff games that we have cherish them. Because it's not going to be too long before they're all gone, and we're sitting here on a Saturday in the middle of February talking about ping pong balls. You know, what I'm talking about ping pong balls about the Mets center field job about Troy to Lewinsky cherish or and let's not forget, whoever the new coaches of the jets, which I'm sure we'll discuss for the next three hours today is m day. It's Mike McCarthy interview day, it's a national holiday at the beningo. Household. He was worried they were going to interview him and who the giants are going to pick it six and if they should replace Eli manning so for the next one two three four five weeks. Well, really, yeah. Five weeks? We'll throw in that that off weekend as well. Here's what you do cherish every moment. Because before you know, what the football is going to be gone, and we're gonna have a very very very long offseason. Because remember the NFL. L has the longest off season by far of all the major sports. We'll get your calls next. For the seven people, maybe twelve now who tuned in saying, hey, Evan. I wanna hear you giddy about the net. I'll give you good forty five seconds on the nets later because I was giddy last night. I was blasting walking in Memphis by what's his name? Mark cohen. I was woke up my wife house blasting it because I was so giddy about paying them some revenge last night and good job by the next getting a victory. So we'll make sure all the other stuff the baseball as well. The jet coaching search the giant off season everything over the next three hours toll free number is eight seven seven three three seven sixty six sixty six the great Bob Glover will join me at eleven twenty when we come back your telephone calls..

ravens chargers Baltimore Ravens Eli Manning Chicago Bears NFL Joe football bears eagles Philip rivers Lamar Jackson giants Ken Lamar Jackson Baltimore Philippine Eli LA Rams Nick foles LA Memphis
"one two three four five weeks" Discussed on ESPN Chicago 1000 - WMVP

ESPN Chicago 1000 - WMVP

12:59 min | 2 years ago

"one two three four five weeks" Discussed on ESPN Chicago 1000 - WMVP

"Yeah. Yeah. Can. Pretty good. Maybe two times all year. What for me when I did the players that you haven't had to do it as much with. Yeah. Yeah. Because they get it. And that was earlier in the year. It hasn't happened in a while. Because they know they they didn't know me. They didn't know who I was how I coach how I teach our react to certain situations. And they're learning. They were learning the process they didn't know where to go stand on a Saturday morning for Wachter. So you stand over here you come out here with the ratios you got the wrong shoes on you're going to get the right shoes on and so they didn't know those things. And so, and then they do it for three weeks in any test me, and they come out, and they said forgot will. No, you didn't forget go back and get shoes on now. They don't do that, you know. So they get and and and so I don't have to do it anymore. I coach of the year. I believe. A. Have you ever heard of head coach talk? Like, I know it must happen. All the time. Some coaches are able to succeed, and you know. Here's how we're doing your shoes you stand over there. But does that also imply that a lot of other coaches, let it slide? Oh, sure. Sure it does. And this guy even though the players from all we can tell they love them. Well, the winds help, of course, if they're losing they take all of his shoe stuff. And they tell them stick your shoes and everything else we don't care about it. But they're winning. So they figure they've got a guy there that one cares about them. And to help is helping them for you know, in the long run they win. They all get rings. They make more money all kinds of good things. So if they find a guy that can help them with that. Yeah. They're gonna they're gonna listen to things he says, let's play chicken or the egg. Wins comfort. But yes, of course, however, but does the discipline help. You coach them to win. In other words, when they realize here's where we stand. Here's shoes. Who were? Here's what time we get. Here. We listen to this. We do that. I think players like that number one. They wanna have an organ is organized day. They want to have the ritual you want to know what to do, but doesn't that also perhaps lead them to play better to listen more to try to cross every T dot every I is opposed it. Well, you know, what I'll just half listen because I can get away with everything else. I don't know. I mean, I've never been in the locker. Yeah. It could it can't hurt. No. It can't hurt. You know, once once you get one guy, you gotta get the main guy to buy in. So once Khalil Mack got here, which was actually too late into the process by if there were any people questioning, and when Khalil Mack out here, they saw him as a guy that came to practice and busted his tail and didn't show that hey, I'm the biggest I'm the highest paid defensive player in the league. I don't have to do this this this and this then everybody else fell in line. I'm not sure if it was like that before the season started, right? So now more when it listening Martin Martin Nagy now he's talking and I didn't hear any of these cuts is week because there was so much flying around. But he let in a few more little things about the progression. And the learning curve of niche Trubisky. Remember, Fred, Dan, Felix couple weeks ago, one of our Twitter polls, and we talked to a few of the experts out there that we have on like a mock podcast joins us in a little while today from the sometimes, and we speculate how much of the overall playbook that eventually Matt Nagy is gonna put in how much of that playbook is in now. And I believe I'd Twitter poll was, you know, twenty percent forty percent sixty eighty and then we had a few experts and one I remember as a twenty five percent about one fourth, and we were in an speculating less than that. If I recall, I thought there was forty percents. Okay. I was more at twenty. Okay. But regardless of what that number is here was Nagy. Now, the national press still hasn't figured out the bears. We'll talk about that. Which is fine. I like it and the national president might have been hasn't figured out Trubisky at the local bears. A experts haven't figured out. I'm getting a little tired. Fred of quote him in talk radio sports, talk radio referred me status for twenty five years. Now, we have the responsibility to risk being robbed. Right. That's what we do. That's why they're that's why they station says we might check because we went your opinions and some. Sometimes hey, melons one hundred percent years ago. I remember working with Dave vomited caller called up and says, Dave could you please stop giving your opinion? He laughed and he said, well, hey, stop giving my opinion that we don't reason for the radio to be here. Shan't champ grades hope. You're right there. But you know, what there's a guy here in Chicago. Doesn't matter who he is. And he's on the bears insider type thing with and in the beginning of the year. Until the bears. This was after the Packer. Lots until the bears win three in a row. I'm not saying nothing. Then they then they won game two. I don't know. I can make a decision till they win three in a row, then they win their second in a row game three, right? No. No. No. No. No. No. No. No. I'm then they went three in a row, right? Right. Eight teams good. Anyone could do that? So trubisky. Let's eavesdrop here and Nagy. Now, I don't know if it's twenty percent of the playbook is fifty percent one hundred percent. But according to some of these little nuggets that he drops, you know, we don't we're we're sort of geared up. Not to listen to press. I am at least for Qaeda. From the past. Like, we talked about not even listen to his press conferences, you don't turn it off. I come back in five minutes. What's he gonna say? Well, here's what he says is talk a little bit more about Trubisky. And are they putting you know, what plays now is able to absorb for him being able to all of us together. Finally, figure out conceptually, you know, some some things within this offense without getting into detail. I think he's starting to really understand who we are becoming as an identity within this office or whether it's first and second down whether it's third down. Are there plays that we're field that he's feel comfortable in running? And it doesn't matter. What coverage you bring out feel comfortable calling knowing that he's comfortable, and you mesh that altogether. It takes time. It doesn't happen in week one week to week three you build it up into this part right now. And now what we need to do as a staff is pullback say, okay? What do we do best? How do we use it versus this upcoming defense? And and try to keep his own stop the tape. There's a lot right there. He's basically admitting that in the first two three four five weeks. You know, we didn't know what he could do. He didn't know what we could call comfortable with him. He's gotta be comfortable with us. And then it says plays that basically the pair play. Plays that weren't in week one week to week things are couldn't do week one-two-three. There might be stuff read that they can't do in year one. Oh, sure. Sure. They won't even try stuff. Probably. It's it's so it's so potentially infinite with what he's doing what the offense that. I I don't think any of us are able to grasp it unless you're actually an NFL inside offensive coordinator or a scheme guy or a quality control breaking down tape. I mean, we try to watch it. We're just fans even the experts. They can't make an opinion till they win three in a row. This guy. Nagy is an uncharted territory. I believe as far as what we are used to seeing what certainly what we're used to sing. But maybe he might be a trendsetter with at the head of the NFL. Now, he's he's in the same of group is a Andy Reid and Peterson and things like that. You guys that have very very creative offenses guys that are not. Not has it. They won't hesitate to be aggressive. If they are quarterback, and do it if he can't they're they're not, you know, they're prepared to pull back you you've seen Trubisky throw nearly as many balls downfield because they realized that he's really good with the short intermediate passing game. Like they did a couple of weeks ago, the Dink and dunk stuff that works. They may a shot or two downfield. But maybe not as many as they did in the past. And that seems to be an adjustment that they've made in a nine minute drive. The other day sixteen plays seventy five yards nine minutes. That's that's pretty darn amazing. We we talked earlier this year about overtime, which is ten minutes. And if you took a drive the took all day long. They just had a drive. There was nine minutes and five seconds sixteen plays marching downfield. So that's what you try to keep the ball and eventually score. And that's exactly what they did. So I think the Nagy is learning a game by game. Astra Bisky is what's going to work, and what's going to be the best? That's why over the last four games. You've seen them last five games. You've seen them do things. And it's all worked. They haven't trail very often. They've lead in the second half of every game this year the only team in the league stunned death. So I think that they're they're both learning both at the hip of each other. They're learning you see Matt Nagy he's learning more each and every game. And so as Trubisky. How's he go into this? Hopefully, they've got a game plan that they both thing is going to work. Well, speaking of that one of Mark potash little nuggets, which will have Mark with stuff. I intend the other day was unbelievable. But he had one he said this is the first season. The bears have not lost a game by more than seven since the nineteen sixty three judge Alice Cooper Super Bowl champs. It was called the world championship bad debt haven't lost again gonna last four by by more than southern now. You're talking about in the last few games and shorted pass the Dinkins Dunkin. We all know everyone's gonna low Howard's run. How come Howard's running note or giving them all a Howard? Well, I don't know much. But from the little things when you read, and you gotta read you gotta read because you get this get this there, and it's pretty evident that the forty Niner game they the forty Niners when from play man to man the entire season. Some of the reports they went to almost totally. Zoned out, and they call it soft deep zone type thing because they're going to cut off those twenty yard slant patterns to Burton and those things were chunks of yard. Right. So what is Nagy? Do. He starts running the ball. Because now they only in seven in the box Nolan's got eight in a box anymore. So now you run the ball. And everyone's saying, oh, boy, you know, he's running the ball more born as really adjust. Well, no, he's adjusting. He's adjusting to what the opposition is doing necessary said he's all year long. That's what he said. He was going to do when people kept saying are you going to run the ball more? He's a well. Run the ball based on our game plan based on who were playing defense. They're plan. I mean Howard the last five games. He's got three hundred ninety nine yards four touchdowns and four point five yard average, which is pretty darn good. Because there's only seven in the box because teams are now playing deeper zone, and they're not able to roll the strong safety up. Right. I mean, I'm I mean. Neither teams did you know Minnesota was rolling up everything last week and Howard still ran for a lot of yards. So, you know, and then you saw place like a blitz and all of a sudden he fights he finds Kevin white on a great pass to the right side. And that was a tremendous play that didn't get a lot of talk. Well, it got a little bit Asakusa cavalry caught the ball. But people gotta give Trubisky credit because that was a blitz right in his face. And he found the guy who was supposed to find nail the pass got the first down, and we actually got to see Kevin Whitelaw, again, just write to our next cut here. Now, this is coach Nagy earlier in the week. And he says something that I've never heard before he's talking about trubisky's progression, not progressing through the zone and looking who's open. I man second man from. Sure now might be the better word, and he says something it's a short little about fifteen seconds. And he's talking about well here when they break the huddle Trubisky. Here's what he now is looking at as opposed to what he had been looking at. Mitch..

Matt Nagy Trubisky Howard Martin Martin Nagy NFL Wachter Khalil Mack Packer Fred Dave president Twitter Kevin Whitelaw Minnesota Alice Cooper Kevin white Chicago Andy Reid Mitch
"one two three four five weeks" Discussed on Talk 650 KSTE

Talk 650 KSTE

12:44 min | 2 years ago

"one two three four five weeks" Discussed on Talk 650 KSTE

"You can follow me on Twitter at jimbotalks. And thank you for joining us tonight as we are getting closer and closer to the big election, November the sixth to be precise. And that is one two three four five weeks five weeks from today. Polls of late are not kind to the Republican party. And in case, you are among those who believed that the polls are right, anyway, keep in mind that they do have a pretty good batting average for the most part. We're gonna be talking a bit about a new poll suggesting a hardline immigration strategy for the GOP, and we'll do so in the company of Niles Stanage is White House columnist with the hill newspaper. And am I butcher in your name? I'm I'm terribly. Sorry. I know we've had you on before. But unfortunately, pronounced there's not a prime factor in my existence. I'm afraid to the contrary, Jim. That's perfect. Will I'm very glad to hear that. All right now. Then just to look at precisely what we're talking about here. This is a poll from the the Bannon group, and I'm assuming that that's Steve Bannon, it is absolutely. And therefore this poll suggests that a a full bore attack from the conservative side of things is the appropriate path to follow here. And I'm wondering your thoughts about this. I mean, certainly there are those who say that it is not only good for Republicans to be a democrat light. If you will your thoughts. Yes. Well, there are schools of thought really a by the Republicans face. A difficult environment difficult landscape in the midterms one is that Republicans should run campaigns pretty much based on the concerns of their district or their state very localized not necessarily tied in themselves overly closely to President Trump. But instead of putting a greater emphasis on for example, the strong economy, the pull that we're referring to Jim which was commissioned by Steve Bannon scrape is pointing at a very different direction, it's suggesting the Republicans should nationalize this election should make it to some extent a referendum on President Trump a referendum on whether Donald Trump should be impeached or not, and as you alluded to in your introduction fought the country should take on a hot button issues such as illegal immigration. I don't know that we're necessarily talking about an automatic either. Or here, I think that nationalizing the election on the part of the Republicans is. Tremendous idea look at what again going back twenty four years to the contract with America. And Newt Gingrich. I think nationalizing this election is a splendid idea, of course, midterms do tend to be a collection, of course of local and state wide races. But having said that why not nationalize it on? The fact that the economy is perking along perfectly. I mean, whatever happened to James Carville famous dictum, it's the economy stupid that is a great question. And I think that there are many people particularly in the center right of the Republican party who would endorse that belief. There is an argument in some quarters, and I'm not putting a thumb on the scales either way, but there's an argument in some quarters that when the economy is going, well that is not always a motivating factor for people to actually get autumn vote. The the argument essentially is the dissatisfaction is a more powerful motivating factor than satisfaction. And that therefore you have to raise the stakes in some fashion to get Republican voters who are more reluctant to competitor or less energized to get to the polls. I don't think anyone in either party Jim disputes the factor contests. The fact that Democrats are fired up and ready to go in President Obama's old phrase that they are very much ama- mated by opposition to President Trump and very intense in their desire to deliver a rebuke to him at these maternal election. I have been arguing myself that that it depends on how you see President Trump on the ballot. And that Democrats see President Trump everywhere on the ballot. They they don't see his name, of course. But anywhere, they see the letter R that that close enough, it may not be Donald Trump, and I could vote against this Republican that Republican Matt Republican over there and whereas Republicans look at the ballot, and they don't see da. Donald trump. They they see a lot of people who in many cases, they may consider to be rhinos Republicans in name only or otherwise unworthy of their vote. And that is coupled with the fact that there was recently a poll taken this was by the Republican National Committee, no less which said that fifty seven percent of Republicans felt that it was impossible for Democrats to obtain control of either house of congress. And boy that's about ninety light years away from the truth. It's very possible. In fact, maybe even likely at this stage of the game. Yes. Absolutely. I agree. One hundred percent. This is something that has come up in my conversations with Republicans sources a concern that Republican voters are to some degree complacent. They are so supportive of President Trump in many cases that they don't think that really there is a plurality of people who are Trump or who wish. To vote against the Republican party. That isn't what the experts think. In other words, the experts think there is definitely enough enthusiasm and intensity on the democratic side. Put at least the house of representatives in peril from a Republican perspective and just to your point about rhinos briefly Jim conversations that I've had with Steve Bannon. He has mentioned that one of the challenges is to get the Trump base to vote in some cases for candidates. They would consider rhinos. I know that seems perhaps in congress pointer in congress viewpoint for Steve Bannon to take given that he show identified with the populist nationalist movement. But he does believe that when it comes to these midterm elections. Well, there certainly is one word that ought to provide a basis for Republican unity in that word. Of course was the I word impeachment. I don't doubt for one minute. Democrats are hardly unified on a lot of fronts. Just look at some of the fractious primaries they've had but I think if Nancy Pelosi gets a two eighteen to two seventeen margin in the house one vote, I think that they'll vote articles of impeachment against President Trump before April arrives. Now granted impeachment is one thing and conviction is another and there's no way on this earth that they'll ever get sixty seven votes in the Senate to convict and remove him from office. But nonetheless, it casts a pall over a presidency, and I would think that that alone might be enough to get Republicans to turn out to vote. Yes. And that's Bannon's view view that is shared. I think by many people on the on the right in the broadest definition of that term the idea that President Trump could be impeached. In fact, is likely to be impeached. If Democrats take the hice could possibly be a significant motivating factor for Republican voters in other words loyalty to the president might be. Bring Republican voters insufficient number to balance or to neutralize this expected surge all democratic enthusiasm and voter intensive course, we often see surges of enthusiasm. You look back to the Barack Obama elections of await and two thousand twelve and in both cases was able to get people who ordinarily don't turn out to vote very much to go out and actually vote the young people since eighteen to twenty one year olds got the right to vote. They have consistently year after year after year after year have been the group least likely to vote, and he could do it Obama could do it when his name was on the ballot. Oh, eight then in two thousand ten he said, well, you know, my name is not on the ballot. But please vote for these Democrats, and they didn't and the tea party revolution resulted and the Republicans got control of the house twenty twelve Obama's names back on the ballot. They turn out for him. And he wins reelection. And then in two thousand fourteen Obama says, hey, we could really use your vote here and his name was not on the ballot. This time the Republicans keep the house, and they take the Senate, and then of course, again in in two thousand sixteen Obama's name was not on the ballot. And the baggage that Hillary Clinton brought to the table, plus the appeal of Donald Trump to an awful lot of voters who felt that they had not been taken seriously led to the Trump victory. And of course, the Democrats were not able to take either the house or the Senate in two thousand sixteen. So again that proves Obama didn't have coat tails. I'm curious to see if Trump has coattails. Yeah. I think it's a great question. And it's a very well made. Point Jim, there are presidents on other prominent politicians have personal magnetism and President Obama clearly had dot for his supporters, President Trump Tilly hazard for his supporters and the issue of whether that can be transferred. I think is a fascinating one. You've described the Obama phenomenon very well. We'll see how that works with Trump. One eight six six five zero JIMBO and Nile Stanage? Our guest White House columnist for the hill dot com. Newspaper of Capitol Hill and of issues inside the beltway. And we have Joe calling in from cast Bill, Missouri. Hello, joe. Good show. I like hearing both of you tonight. Thank you. Taty? I think the attack is against Barack Obama. I think that if you look at the midterm elections as they're happening. They will be smart to go back and look at what happened the election, and there was a a billing, but so many of it had about the eight years that we were living that we would have voted for anybody who wasn't connected for him. And they're Republican states up for grabs and quitting the Senate. That should be the quote and one of them that you'll see a loss. I truly believe Claire mccaskill will do she will not make it now that the Kabanov thing has happened. But I do believe if I was running. I would have Brock Obama's record and picture and everything on everything I would attack. I think that's what they need to attack. And I think that would be very successful for anyone. Interesting thought Joe, and I would added a postscript, by the way that the attorney general of Missouri. Josh Holly who is running against Claire mccaskill Senate reelection bid has shall we say not performed quite as vigorously or as well as many thought that he would and that has become a real dog five. But again, it's such a a completely red state in many regards Claire, I guess remains the only statewide elected democrat if I'm not mistaken. So Nile your thoughts about nationalizing the race. If you will with the pictures of Obama man who has not been on a ballot now for six years. Well, I appreciate use question and him calling in. He would not in fact, get much support for his viewpoint in this poll that we're talking about one of the things that I think gives this pool credibility is that it does produce some results that wouldn't be certainly aligned with Steve balance own personal views. One of those is that attests the popularity of a number of Democrats and former President Obama. Is by some distance the most popular of them at Nancy Pelosi, for example, vastly less popular, Hillary Clinton, vastly less popular. So I'm sure Joe clearly has strong feelings on the Obama presidency. But this particular pull that we're talking about would not vindicate his view that place in President Obama front and center would aid Republicans in a search for victory. White House columnist with the hill newspaper the hill dot com. Great reading, by the way, if you're into politics, and we'll come back, and we'll take a look at some more about this new poll from the Bannon group and other thoughts as we get very close to what is unquestionably one of the most important midterm elections in recent memory back in.

President Trump President Obama Steve Bannon Republican party Barack Obama president Senate Jim Trump White House President Trump Tilly Bannon group Republican National Committee Nancy Pelosi Hillary Clinton Matt Republican Niles Stanage congress Joe
"one two three four five weeks" Discussed on KTAR 92.3FM

KTAR 92.3FM

12:25 min | 2 years ago

"one two three four five weeks" Discussed on KTAR 92.3FM

"At jimbotalks. And thank you for joining us tonight as we are getting closer and closer to the big election, November the sixth to be precise. And that is one two three four five weeks five weeks from today. Polls of late are not kind to the Republican party. And in case, you are among those who believe that. The polls aren't right anyway. Keep in mind that they do have a pretty good batting average for the most part. We're gonna be talking a bit about a new poll suggesting a hardline immigration strategy for the GOP, and we'll do so in the company of Niles Stanage is White House columnist with the hill newspaper. And. I am I butchered your name, I'm I'm terribly. Sorry. I know we've had you on before. But unfortunately, pronounced there's not a prime factor in my existence. I'm afraid to the contrary, Jim. That's perfect Nile Stanage? Perfect while I'm very glad to hear that all right now. Then just to look at precisely what we're talking about here. This is a poll from the the Bannon group, and I'm assuming that that's Steve Bannon, it is absolutely. And therefore this poll suggests that a a full bore attack from the conservative side of things is the appropriate path to follow here. And I'm wondering your thoughts about this. I mean, certainly there are those who say that it is not only good for Republicans to be a democrat light. If you will your thoughts. Yes. Well, there are two schools of thought really by the Republicans facing a difficult environment. Difficult landscape in the midterms one is that Republicans should run campaigns very much based on the concerns of their district or their state, very localized not necessarily tied in themselves overly closely to repress Trump. But instead putting a great deal of emphasis on for example, the strong economy the pull that we're referring to GM which was commissioned by Steve Bannon scrip is pointing in a very different direction. It's suggesting the Republicans should nationalize this election should make it to some extent a referendum on President Trump a referendum on whether Donald Trump should be impeached or not and as you alluded to in your introduction. What course the country should take on hot button issues such as a legal immigration. I don't know that we're necessarily talking about an automatic either. Or here, I think the nationalizing the election on the part of the Republicans is a tremendous idea look at what? Again, going back twenty four years to the contract with America. And Newt Gingrich. I think nationalizing this election is a splendid idea, of course, midterms do tend to be a collection, of course of local and state wide races. But having said that why not nationalize it on? The fact that the economy is perking along perfectly. I mean, whatever happened to James Carville famous dictum, it's the economy stupid that is a great question. And I think that there are many people particularly in the center right of the Republican party who would endorse dot belief. There is an argument in some quarters, and I'm not put in some on the scales either way, but there's an argument in some quarters that when the economy is going, well that is not always a motivating factor for people to actually get out and vote. The the argument essentially is that disatisfaction is more powerful motivating factor than satisfaction. And that therefore you have to raise the stakes in some fashion to get Republican voters who are more reluctant to competitor or less energized to get to the polls. I don't think anyone in either party Jim disputes the factor contests. The fact that Democrats are fired up and ready to go and President Obama's old phrase that they are very much animated by opposition to President Trump and very intense in their desire to deliver a rebuke to him at these maternal, I have been arguing myself that that it depends on how you see President Trump on the ballot and the Democrats see President, Trump everywhere. On the ballot. They don't see his name, of course. But anywhere, they see the letter are that close enough, it may not be Donald Trump, but I could vote against this Republican and that Republican and that Republican over there and whereas Republicans look at the ballot and they don't see Donald Trump. They see a lot of people who in many cases, there may consider to be rhinos Republicans in name only or otherwise unworthy of their vote. And that is coupled with the fact that there was recently a poll taken this was by the Republican National Committee, no less which said that fifty seven percent of Republicans felt that it was impossible for Democrats to obtain control of either house of congress. And boy that's about ninety light years away from the truth. It's very possible. In fact, maybe even likely at this stage of the game. Yes. Absolutely. I agree. One hundred percent. This is something that has come up in my conversations with Republicans sources a concern that. That Republican voters are to some degree complacent. They are so supportive of President Trump in many cases that they don't think that really there is a plurality of people who are anti Trump or who wished to vote against the Republican party. That isn't what the experts think. In other words, the experts think there is definitely enough enthusiasm and intensity on the democratic side trip. Put at least the house of representatives in peril from a Republican perspective and just to your point about rhinos pretty flea. Jim conversations that I've had with Steve Bannon. He has mentioned that one of the challenges is to get the Trump base to vote in some cases for candidates. They would consider rhinos. I know that seems perhaps an in congress pointer in congress viewpoint for Steve Bannon to take given that he show identified with the populist, nationalist move. But he does believe that when it comes to these maternal elections. Well, there's really is one word that ought to provide a basis for Republican unity in that word. Of course was the I word impeachment. I don't doubt for one minute. Democrats are hardly unified on a lot of fronts. Just look at some of the fractious primaries they've had but I think if Nancy Pelosi gets a two eighteen to two seventeen margin in the house one vote, I think the fail vote articles of impeachment against President Trump before April arrives. Now granted impeachment is one thing and conviction is another and there's no way on this earth that they'll ever get sixty seven votes in the Senate to convict and removing from office. But nonetheless, it casts a pall eighteen over a presidency, and I would think that alone might be enough to get Republicans to turn out to vote. Yes. And that's Bannon's view and of you that is shared. I think by many people on the on the right in the broadest definition of that term the. Idea that President Trump could be impeached. In fact, is likely to be impeached. If Democrats take the hice could possibly be a significant motivating factor for Republican voters in other words loyalty to the president might bring Republican voters insufficient number to balance or to neutralize this expected surge of democratic enthusiasm underwater. Intensive course, we often see surges of enthusiasm. You look back to the Barack Obama elections of oh eight and two thousand twelve and in both cases Obama was able to get people who ordinarily don't turn out to vote very much to go out and actually vote the the young people since eighteen to twenty one year olds got the right to vote. They have consistently year after year after year after year had been the group least likely to vote, and he could do it Obama could do it when his name was on the. The ballot got him there. And then in two thousand ten he said, well, you know, my name is not on the ballot. But please vote for these Democrats, and they didn't and the tea party revolution resulted and the Republicans got control of the house twenty twelve Obama's name is back on the ballot. They turn out for him. And he wins reelection. And then in two thousand fourteen Bama says, hey, we could really use your vote here and his name was not on the ballot. This time the Republicans keep the house, and they take the Senate, and then of course, again in in two thousand sixteen Obama's name was not on the ballot. And the baggage that Hillary Clinton brought to the table, plus the appeal of Donald Trump to an awful lot of voters who felt that they had not been taken seriously led to the Trump victory. And of course, the Democrats were not able to take either the house or the Senate in two thousand sixteen. So again that proves Obama didn't have coattails I'm curious to see if Trump has coat-tails. Yeah. I think it's a great question. And it's. Very well-made point. Jim there are presidents on other prominent politicians have personal magnetism and President Obama clearly had that for his supporters, President Trump Tilly hazard for his supporters and the issue of whether that can be transferred. I think is a fascinating one. You've described the Obama phenomenon very well. We'll see how that works with Trump. One eight six six five zero JIMBO and Nile Stanage? Our guest White House columnist for the hill dot com, definitive newspaper of Capitol Hill and of issues inside the beltway. And we have Joe calling in from cast Bill, Missouri. Hello, joe. Hi, good show. I like hearing both of you. Thank you. I want to mention something I think the attack is against Barack Obama. I think that if you look at the midterm elections as they're happening. They would be smart to go back and look at what happened with last election. And there was a sick feeling that so many other pad about the eight years that we were living that we would have voted for anybody who wasn't connected for him. And there are some Republican states up for grabs quitting in the fed at that should be the quote and one of them that you'll see a loss. I truly believe it Claire mccaskill will lose. She will not make it now that this Cavanaugh things happen. But I do believe if I was running or anything, I would have Rocco bomb us record and picture and everything on everything I would attack. I think that's what they need to attack. And I think that would be very successful for anyone. Interesting thought Joe. And I would edit a post gripped, by the way that the attorney general of Missouri. Josh Holly who is running against Claire mccaskill Senate reelection bid has shall we say not perform quite as vigorously or as well as many thought that he would and that has become a real dog five. But again, it's such a a completely red state in many regards and Claire I guess remains the only statewide elected democrat if I'm not mistaken. So Nile your thoughts about nationalizing the race. If you will with the pictures of Obama a man who has not been on a ballot now for six years. Well, I appreciate juice question on and him calling in. He would not in fact, get much support for his viewpoint in this pull that we're talking about one of the things that I think gives this poll credit. Ability is that it does produce some results that wouldn't be certainly aligned with Steve balance own personal views, one of those that attests the popularity of a number of Democrats and former President Obama is by some distance the most popular of them at Nancy Pelosi, for example, vastly less popular, Hillary Clinton, vastly less popular. So I'm sure Joe clearly has strong feelings on the Obama presidency. But this particular pool that we're talking about would not vindicate his view that placing President Obama front and center would aid Republicans in a search for victory NIO standards house columnist.

President Trump President Obama President Trump Steve Bannon Republican party Barack Obama Jim Senate President Trump Tilly Republican National Committee White House Niles Stanage Hillary Clinton congress Joe Nancy Pelosi Missouri
"one two three four five weeks" Discussed on WTMJ 620

WTMJ 620

01:42 min | 2 years ago

"one two three four five weeks" Discussed on WTMJ 620

"You can follow me on Twitter at jimbotalks. And thank you for joining us tonight, as we are getting, of course, closer and closer to the big election, November the sixth to be precise. And that is one two three four five weeks five weeks from today. Polls of late are not kind to the Republican party. And in case, you are among those who believe that the polls are right, anyway, keep in mind that they do have a pretty good batting average for the most part. We're gonna be talking a bit about a new poll suggesting a hardline immigration strategy for the GOP, and we'll do so in the company of Nile Stanage is White House columnist with the hill newspaper and. I am I butcher in your name. I'm terribly sorry. I know we've had you on before. But unfortunately, pronounced you're not a prime factor in my existence. I'm afraid to the contrary, Jim. That's perfect Nile Stanage? Will I'm very glad to hear that. All right now. Then. Just to look at precisely what we're talking about here. This is a poll from the the Bannon group, and I'm assuming that Steve Bannon, it is absolutely. And therefore this poll suggests that a a full bore attack from the conservative side of things is the appropriate path to follow here. And I'm wondering your thoughts about this. I mean, certainly there are those who say that. It is. Not only good for Republicans to be democrat light. If you will your thoughts. Yes. Well, there are two schools of thought really are both Republicans..

Nile Stanage Republican party Steve Bannon Bannon group Twitter White House Jim one two three four five weeks five weeks
"one two three four five weeks" Discussed on BizTalk Radio

BizTalk Radio

03:15 min | 3 years ago

"one two three four five weeks" Discussed on BizTalk Radio

"End of the world but the way to the evidence a lot of these other areas starting to you know the dow finished below the fifty day moving average also by smidge nasdaq finished below the fifty day moving average bias mitch and by the way they were above the fifty day by a smidge and the same for the nasdaq one hundred the transports which had a strong two out of three days this week more like a normal little pullback so far we'll let you know as it moves forward the financials kinda comatose you look at the xl left one two three four five weeks sitting under the fifty day but above the two hundred day they were definitely stronger earlier today until the market got kicked in the teeth but the how's that yeah so we remain in the soup okay we remain in the soup and the soup can last a while you do know that right you do know that the snp kinda sorta topped out in december fourteenth and really didn't break out a november of sixteen right with a couple of ugly drops in between in september fifteen and first quarter of sixteen you do know these things could happen i'm not saying we're going to get the same thing eighteen to twenty four months of nothing this question member we have a strong move from the an out of that sometimes you get long consolidations and it's a pain in the rear so far that's what we're still in my thoughts have not changed ping pong but underneath the ping pong we will outline view all the areas and man they were less than style with the last couple of days that said massive amount of earnings to come out in the next two three weeks and we'll get a little bit more detail to take the negative side i'm just giving the f if at any time we break the two hundred day moving average when we're not close right now well three percent two percent three percent and after that break the lows that we saw in february and early april i am telling you under no uncertain terms i think hell will break loose i say that because we have not had a darn good one in a long time and i'm pretty sure that'll be how do i explain it best that would be the institutional crowd the big money crowd the hedge funds the mutual funds the.

mitch fifty day two hundred day three percent one two three four five weeks twenty four months two three weeks two percent three days
"one two three four five weeks" Discussed on BizTalk Radio

BizTalk Radio

03:09 min | 3 years ago

"one two three four five weeks" Discussed on BizTalk Radio

"The snp the snp did finish below the fifty day moving average today just by a smidge it doesn't mean the end of the world but the weight of the evidence a lot of these other areas starting to the dow finished below the fifty day moving average also mitch nasdaq finished below the fifty day moving average bias mitch by the way they were above the fifty day by switch and the same for the nasdaq one hundred the transports which had a strong to two out of three days this week more like a normal little pullback so far will let you know as it moves forward the financials kinda comatose he look at the xl left one two three four five weeks sitting under the fifty day but above the two hundred day they were definitely stronger earlier today until the market got kicked in the teeth but the how's that so we remain in the soup okay we remain in the soup and the soup can last a while you do know that right you do know that the snp kinda sorta topped out in december fourteenth really didn't break out till november of sixteen right with a couple of ugly drops in between in september fifteen and first quarter of sixteen you do know these things could happen i'm not saying we're going to get the same thing eighteen to twenty four months of nothing not question remember we had a strong moved from the an out of that sometimes you get get long consolidations and it's a pain in the rear so four that's what we're still in my thoughts have not changed ping pong but underneath the ping pong we will outline view all the areas and man they were less than style the last couple of days that said massive amount of earnings to come out in the next two three weeks and we'll get a little bit more detail to take the negative side i'm just giving the earth if at any time we break the two hundred day moving average when we're not close right now well three percent two percent three percents and after that break the lows that we saw in february and early april i am telling you wonder no uncertain terms i think hell will break loose i say that because we have not had a darn good one in a.

fifty day two hundred day one two three four five weeks twenty four months two three weeks three percent two percent three days
"one two three four five weeks" Discussed on BizTalk Radio

BizTalk Radio

03:15 min | 3 years ago

"one two three four five weeks" Discussed on BizTalk Radio

"Financials kinda comatose you look at the exile left one two three four five weeks sitting onto the fifty day but above the two hundred day they were definitely stronger earlier today till the market got kicked in the teeth but the how's that so we remain in the soup okay we remain in the soup and the soup can last the while you do know that right you do know that the snp kinda sorta topped out in december fourteenth in really didn't break out till november of sixteen right with a couple of ugly drops in between in september fifteen and firstquarter sixteen you do these things could happen i'm not saying we're going to get the same thing eighteen to twenty four months of nothing this question remember we had a strong move from the an out of that sometimes you get get long consolidations and it's a pain in the rear so for that's what we're still in my thoughts have not changed ping pong but underneath the ping pong we will find view all the areas and man they were less than style with the last couple of days that said massive amount of earnings to come out in the next two three weeks and we'll get a little bit more detail to take the negative side of just giving the earth if at any time we break the two hundred day moving average when we're not close right now well three percent two percent three percent and after that break the lows that we saw in february and early april i am telling you under no uncertain terms i think hell will break loose i say that because we have not had a darn good one in a long time and i'm pretty sure that'll be how do i explain it best that would be the institutional crowd the big money crowd the hedge funds the mutual funds the funds of funds and all that crap that'll be them giving way which will lead to believe intense selling now i am not saying it's going to happen but i just have to put it out there based on the fact that we're way overdue and everybody keeps telling me about the twenty percent growth but we're not there and we held pretty good so far but the last couple of days and look yesterday i thought something was up with those semis then we come in.

two hundred day three percent one two three four five weeks twenty four months two three weeks twenty percent two percent fifty day
"one two three four five weeks" Discussed on BizTalk Radio

BizTalk Radio

03:10 min | 3 years ago

"one two three four five weeks" Discussed on BizTalk Radio

"Same for the nasdaq one hundred the transports which had a strong two two out of three days this week more like a normal little pullback so far we'll let you know as it moves forward the financials coma toasts you look at the xl left one two three four five weeks sitting on the fifty day but above the two hundred day they were definitely stronger earlier today until the market got kicked in the teeth but the how's that the so we remain in the soup okay we remain in the soup and the soup can last the while you know that right you do know that the snp kinda sorta topped out in december fourteenth and really didn't break out until november of sixteen right with a couple of ugly drops in between in september fifteen and firstquarter sixteen you do know these things could happen i'm not saying we're going to get the same thing eighteen to twenty four months of nothing this question number we had a strong move from the an out of that sometimes you get long consolidations and it's a pain in the rear so far that's what we're still at my thoughts have not changed ping pong but underneath the ping pong we will outline you all the areas and man they were less than stellar the last couple of days that said massive amount of earnings come out in the next two three weeks and we'll get get a little bit more detail to take the negative side i'm just giving the f if at any time we break the two hundred day moving average when we're not close right now well three percent two percent three percent and after that break the lows that we saw in february and early april i am telling you wonder no uncertain terms i think hell will break loose i say that because we have not had a darn good one long time and i'm pretty sure that'll be how do i explain it best that would be the institutional crowd the big money crowd the hedge funds the mutual funds the funds of funds and all that crap that'll be them giving way which will lead to believe in ten selling now i am not saying it's going to happen but i just have to put it out there based on the fact that we're way overdue.

two hundred day three percent one two three four five weeks twenty four months two three weeks two percent three days fifty day
"one two three four five weeks" Discussed on KBNP AM 1410

KBNP AM 1410

02:10 min | 3 years ago

"one two three four five weeks" Discussed on KBNP AM 1410

"And now all the analysts are coming out and lowering the guidance today earnings sales the works apple down now apple reports may i and i don't think they're worried about what they're gonna report i think there were gonna worry about what's the come that's the story on apple and definitively a huge influence and the apple suppliers are getting mauled semiconductors not so you know as i mentioned if you look at the semiconductor group sox socks that's back at the april lows of come straight down your now tracing out the rocky mountains i am all my nickname so things when you start seeing the rocky mountains on a chart that's not good you know you get a mountain and it's not as big and then you've got bigger mountain comes down and then you get these jagged moves up and down but it makes mountains it's usually not good and that's what the semiconductor index looks like and again it's very important to our work and that is not good news simple as that you look at the snp the snp did finish below the fifty day moving average today just by a smidge it doesn't mean the end of the world but the weight of the evidence a lot of these other areas starting to you know the dow finished below the fifty day moving average also by smidge nasdaq finished below the fifty day moving average bias mitch and by the way they were above the fifty day by a smidge and the same for the nasdaq one hundred the transports which had a strong two two out of three days this week more like a normal little pullback so far we'll let you know as it moves forward the finance kind of kobe toast you look at the xl left one two three four five weeks sitting under the fifty day but above the two hundred day they were definitely stronger earlier today until the market got kicked.

apple mitch fifty day one two three four five weeks two hundred day three days
"one two three four five weeks" Discussed on BizTalk Radio

BizTalk Radio

02:08 min | 3 years ago

"one two three four five weeks" Discussed on BizTalk Radio

"Exegen i believe i mentioned this two weeks ago and three weeks ago why didn't i buy it on that friday that a big break out it was the it was triple witching friday eighty five dollar breakout ninety five bucks one two three four five weeks nice pretty door nice on other areas where the market you don't just oils that stole that much going on there the oil refiners bestof after that just really eight happening a will let you know if that changes it's definitely off the lows but still no leadership they are accept the view those names mentioned are green across the board in the retail not big green but green and it's been pretty much dead in the water stuff as i mentioned transports bet it they norfolk southern jb heart that ex delta dollar gainers the more of big losers today i will tell you is the biotech cell gene doubt fourteen i think that was on earnings that a drug thank you at weakness and some of the food drug beverage tobacco household product the consumer staples a little weakness than the restaurants but not much at all and gold and that's the anti market if the market start setting down you by the goal that so that's basically is going on at next bear market we ever have won again by gold simple as that moves that day i'm sure we can find the few we'll start with a downside 'cause i'm sure that short us sell gene dealt thirteen 122 that's a big break to the downside regenerate that eleven biogen down almost seven spanek the well boston beer fourth test left four uh we'll see when tesla reports their losses how the market takes three a more equal next here's wanted the dow proctor and gamble down three thirty eighty eight big break down today on that autozone a rally on a boat of both threes so wished children place again is we've told you'd be careful.

tesla autozone boston dow one two three four five weeks eighty five dollar three weeks two weeks
"one two three four five weeks" Discussed on BizTalk Radio

BizTalk Radio

03:49 min | 3 years ago

"one two three four five weeks" Discussed on BizTalk Radio

"Cold commending you don't feel better vo and once again to invest edge thanks for being with us today all right other areas in the market today us semiconductors with down finished up nicely nvidia some announcement up three mike rob witschge just reported earnings recently up another dollar today so another decent day for the semis and to be blunt semis and financials semis and financials continue to watch them as leadership of the market they up not wavered the financials got in trouble held major supports and soon as yields on the long and started rallying yields up the financial started moving up on top of that recently the transports that were relatively weak have had a good one two three four five weeks some attributed the hurricanes who knows just let you know the small caps which are week as all heck on a relative basis still overall but they even gone to new highs as a lot of small banks are involved in the small cap indices the industrials that's the xl ally and whatever names are underneath broke out at sixty nine and a half seventy one eighty so they're getting good recently i told you look like the airlines with turning up united airlines made some comments today up three bucks other airlines up decently today so it is spreading out a little bit this still forty percent of the market that's really on the crappy side forty percent of the market still on the crappy side so she's stick with the sixty maybe a little bit better than sixty right now we've been telling you the sector's now we head into earning season bunch of financials are going to be reporting this week a ton next week and the week after i am very interested in sink with overbought little frothy too much bullish conditions used the word extend that also how things react to numbers every day we will be reporting to you names moves reactions numbers guidance especially on the leaders the dow thirty transports and any of the leading group that is out there your job do what we do yes it you'll lie in the weeds you hang out you wait for the.

mike rob witschge dow nvidia forty percent one two three four five weeks
"one two three four five weeks" Discussed on BizTalk Radio

BizTalk Radio

04:07 min | 3 years ago

"one two three four five weeks" Discussed on BizTalk Radio

"Mm yeah investors highly recommended you don't feel better you thought what what is get through was edge thanks for being with us today all right other areas the market today us semiconductors with down finished up nicely nvidia some announcement up three mike rob which just reported earnings recently are up another dollar today so another decent day for the semis and to be blunt semis and financials semis and financials continue to watch them as leadership of the market they have not wavered the financials got in trouble held major supports and soon as yields on the long and started rallying yields up the financial started moving up on top of that recently the transports that were relatively weak had that a good one two three four five weeks some attributed the hurricanes who knows just let you know the small caps which are a week as all on a relative basis still overall but they even gone to new highs as a lotta small banks are involved in the smallcap indices the industrials that's the xl ally and whatever names were underneath broke out at sixty nine a half seventy one eighty so vera getting good recently i told you look like the airlines were turning up united airlines made some comments today up three bucks other airlines up decently today so it is spreading out a little bit this still forty percent of the market that's really on the crappy side forty percent of the market still on the crappy side so stick with the sixty maybe a little bit better than sixty right now we've been telling you the sector's now we head into earning season bunch of financials are going to be reporting this week a ton next week and the week after i am very interested in sink with overbought little frothy too much bullish conditions use the word extend that also how things reacts to numbers every day we will be reporting to you names moves reactions numbers guidance especially on the leaders the dow thirty the transports and any of the leading group that is out there your job do what we do is it you'll lie in the weeds you hang out you wait for the reports seen the.

mike rob dow nvidia forty percent one two three four five weeks