Welcome, everyone, and thank you for joining this Quality Summit Hot Topic Session focused on the appropriate use criteria for coronary revascularization with percutaneous coronary interventions, or PCI. My name is Connie Anderson, and as the product manager for the Cath PCI Registry and the Chest Pain MI Registry, I'll moderate our discussion today. I'm joined by two of my Cath PCI Registry team members, John Gerrout and Kim Lavin. John is a team leader on the Clinical Quality Advisor, or CQA, team, and I'm confident many of you have spoken with him directly and understand his commitment to supporting all of you in your Cath PCI Registry experience. Kim is the lead science team liaison to the Cath PCI Registry and is a tireless advocate for the data. She is critical to the development, refinement, and the many enhancements you see implemented in all aspects of metric reporting. I hope you take a few minutes to become better acquainted with both Kim and John through their bios. Now, let's get started. Kim, I'm going to turn this over to you. Thank you, Connie, for that introduction, and welcome, everyone, to Troubleshooting Your Way to Accurate Appropriate Use Criteria Indications. Our objectives today include discussing the purpose of the Revascularization Appropriate Use Criteria, which going forward we will refer to as AUC, identify version 5 data elements critical to AUC mapping, discuss how to recognize gaps in documentation, demonstrate dashboard functionality, and recognize the value of the AUC big picture. The Appropriate Use Criteria provides us a framework to evaluate clinical practice patterns. The AUC are specifically focused on patient populations, case mix over time, and quality improvement while also informing but not dictating care for individual patients. The Appropriate Use Criteria also guides us with patient selection to minimize risk and maximize patient benefit. They promote informed risk benefit decisions and shared decision making. They provide a measurement of utilization patterns over time, as well as provide a practical standard to assess and understand variability and self-regulation. I think it's important to note that the AUC are intended as guiding documents with the final decisions remaining at the bedside to include risk benefit and shared decision making. A great resource that goes over the methodology for AUC criteria is actually noted here below, and its most recent update was in 2018. The scope of the Stable Ischemic Heart Disease AUC indications include clinical presentation, utilization of anti-anginal medications, results of non-invasive evaluation, presence of confounding factors and comorbidities, extent of anatomical disease, prior coronary artery bypass surgery, and invasive testing such as FFR and IFR. And I think, you know, we use the word indications because that is what is noted within the document, but we really should be thinking of those as clinical scenarios. For instance, a patient with one vessel disease with asymptomatic on no anti-anginal therapy and no non-invasive testing, that is the clinical scenario and that is what is noted as an indication. Also, we need to pay particular attention to the assumptions and the definitions within the document really to understand what is meant within the indication tables. I also want to note here that there are limitations of the AUC in that really just relying on types of characteristics or actually only at several types of characteristics in order to categorize individual patients might not consider the subtle findings that is driving the decision being made at the bedside. So again, you know, just keep all that in mind when looking at that. The appropriate use criteria has three categorizations, appropriate, maybe appropriate, and rarely appropriate care. Each of these clinical scenarios are rated against the statement, a coronary revascularization is appropriate care when the potential benefit in terms of survival and health outcomes exceed the potential negative consequences of the treatment strategy. The three categories are an effort to define populations of patients that may or may not benefit from the procedure. They are not absolute. And I think it's also important here that we state as all our documents state, it is never expected that any physician or facility would have a zero rate of rarely appropriate procedures because there may be unique clinical and patient specific reasons to justify that procedure. Not saying they shouldn't be reviewed, but just saying, again, that that is not the expectation. The AUC metrics provide a method for assessment of care decisions in aggregated patient populations. More specifically, the revascularization AUC breaks up the populations into stable ischemic heart disease and acute coronary syndromes. The AUC metrics also provide a method for assessment of overall patterns of care, both at the facility level and at the registry wide level. The AUC metrics also provide a way of delivering feedback to providers regarding how their care decisions compare to those caring for the same populations. Remember the assessment for overall patterns of care is not an arbitrator of individual cases. Remember that the metrics are reporting after their FACTS, so they're a retrospective look at what is going on at a facility and within a population. The AUC assignment is not intended to be an absolute of whether a PCI should or should not have been performed. And so this is due to the fact that we are unable to capture nuances of risk benefit and shared decision making discussions that are occurring at the provider patient level. Now I'll turn it over to John and he will get into more of the details. So thank you, Connie, for the introductions in the beginning and thank you, Kim. So I'm going to first talk about the anatomy of an AUC indication or score for the stable ischemic heart disease. There are 34 variables that are abstracted in the data set that assist the AUC in determining an AUC indication and score that a classifiable procedure receives for patients with stable ischemic heart disease. Now it's how each of these data elements is coded that impacts whether the procedure is categorized as stable ischemic heart disease or ACS, as well as which table indication and score is achieved. So the eReports dashboard is your tool for assessing your facility's overall performance as well as each eligible procedure's AUC table indication and score. So with this portion of the presentation, we turn our attention towards the eReports dashboard and identifying variables that are critical to ensuring that procedures receive the correct rating according to the documentation in the medical record and accurate abstraction. While Kim has already identified the value of analyzing the metric detail, to continue to accomplish our objectives, we now need to shift gears and focus on the patient details. So as we move through this discussion of AUC metric patient detail, it's important to know there will be references to and screenshots of tables and indications that are all located in the AUC reference documents. So when you return to your office or desk and you find yourself reviewing your facility's AUC metric results, we strongly advise having these resources available as well as the AUC companion guide at your disposal. And all of these resources are available by visiting the CAHPS PCI Registry and then Resources and Documents. Our objective with AUC classification is to ensure that each and every procedure receives an accurate assessment and is placed in the correct AUC modality table indication and score. Now, this requires precise interpretation and coding of the variables mentioned based on the documentation in the medical record. Pro documentation in conjunction with accurate abstraction presents your single greatest opportunity to ensure correct classification, resulting in meaningful feedback and hopefully targeted quality improvement. So we're going to first talk about metric 30 and then later on in the discussion we'll go and talk about the stable ischemic heart disease metrics 34, 35, and 36. It's possible the greatest opportunity to make an immediate impact on your overall AUC performance rests with metric 30, which is the proportion of PCI procedures not classifiable for AUC reporting. The vast majority of non-classifiable procedures that would have been assigned to the stable ischemic heart disease AUC if there was enough information provided to the algorithm to deliver an indication and score. And much of this can be remedied simply through improved documentation. So let's examine metric 30 a little bit closer before moving on to those other metrics, 34, 35, and 36, and naturally this closer examination begins with the patient detail. So two very important pieces of information are provided when viewing metric 30 patient detail. The first is the PCI indication. The four PCI indications that correlate with the patients identified with stable ischemic heart disease are new onset angina less than or equal to two months, stable angina, CAD without ischemic symptoms, and other. The second informative piece of information is the reason that the patient was not classifiable. And for stable patients, that is identified as no stress test, no stress test result, triple vessel disease, or left main with no syntax, and in some instances, other unmappable reason. So when a stress test is performed within six months of the procedure, per sequence 5204, which is the most recent stress date, and we have documentation of the test type in sequence 5201, it is vital that we have sufficient provider documentation indicating the test result and the risk or extent of ischemia, or at a minimum, documentation that can support coding according to the supporting definition in the data dictionary. Missing test results and or missing risk or extent of ischemia will assign the procedure as not classifiable. The best opportunity to move this patient out of metric 30 and into the appropriate classifiable AUC metric is thorough provider documentation of the stress test type, date, test results, and the risk and extent of ischemia. So an important point here, we find that often provider documentation reflects an overall abnormal stress test result per the medical record. Now in the absence of documentation indicating an overall positive or negative stress test result, we would advise clarifying with the physician with respect to the result of the stress test. The registry position throughout has been that abnormal and normal are broad and nondescript terms and they can't really be extrapolated to mean a stress test was viewed as positive or negative for coding purposes. So instead, a definition is provided in the data dictionary for each of these coding options that when the stress test results meet the definition provided for positive, it can be coded. In the event that the documentation does not support coding positive or negative and there is no provider documentation indicating as such, then we advise clarifying. Otherwise, indeterminate must be abstracted. So documentation of a syntax score for patients with left main and or triple vessel disease burden may represent another big opportunity to move patients out of metric 30 and into their rightful place in the stable ischemic heart disease metrics 34, 35, and 36. So the syntax score, what is it? The syntax score is a tool that was developed with the syntax trial. That's a meta-analysis of studies and trials that show the presence of multiple complex features that are associated with more favorable outcomes with CABG as the treatment option. And we can appreciate just how critical a role the documentation of a syntax score plays through closer examination of three stable ischemic heart disease tables. That's table 1.3, which is three vessel disease, table 1.4, which is left main coronary artery stenosis, and table 3.1. When patients are identified as having three vessel disease or left main disease, in most scenarios, a syntax score is required to enable the AUC to provide an indication and score. In the absence of a syntax score, the procedure may be consigned to non-classifiable in metric 30. Now it's important to discuss a few points about the syntax score. The syntax score only applies to the stable ischemic heart disease patient population. And that's those procedures in which the patient did not present with STEMI or non-STEMI ACS. The score can be mapped to the appropriate selection, either low, medium, or high, using the data dictionary definitions for those selections. But also a physician or licensed independent practitioner estimate of the syntax score will also support coding. Now syntax score is not a requirement in the registry. And there is also an option to code unknown when the syntax score is not documented. And this is because there simply is not value in capturing syntax score for all stable patients. Again, to reiterate, the AUC doesn't consider syntax score in one vessel disease and two vessel diseases. And that's reflected in tables 1.1 and tables 1.2. Rather, the syntax score becomes a factor in arriving at indications for three vessel disease in table 1.3, left main disease, table 1.4, and patients with left main or triple vessel disease who ultimately will be undergoing a renal transplant or percutaneous valve replacement. And that's in table 3.1. Shown here are resources available at the syntax score website. And they include a calculator and a tutorial. The providers, as a FYI, are to use the syntax score 1. There's two different options. For our purposes, we use syntax score 1 for purposes of collecting the syntax score. And I think ultimately we really encourage you all to foster provider engagement with the syntax score, its purpose, its relevance to AUC, and the resources that are available to support them. And this may also involve discussing with your providers and physicians the patient scenarios and disease complexity and coronary vessel involvement that are most applicable to collecting the score and those in which it's irrelevant. All right. So, we discussed metric 30 in the first part of this part of the presentation. And now we're going to look at the stable ischemic heart disease metrics 36, 35, and 34. So, patient-level detail, just as with metric 30, for these metrics also provides the opportunity to review and ensure that procedures receive the correct indication and score, as well as plenty of potential to move the procedures from rarely appropriate to maybe appropriate or from maybe appropriate to appropriate. And again, through the encouragement of thorough provider documentation as well as accurate abstraction according to the correct interpretation of coding instructions and target values. So, turning our focus now to metrics 34, 35, and 36, once again begins with a closer examination of the dashboard patient detail. Patient-level detail for stable ischemic heart disease AUC metrics has been expanded. We have additional columns that provide insight and help with fostering further understanding of the AUC appropriateness rating. Shown here are the columns that you will find populated in the patient-level detail for those procedures that are assigned to either metrics 34, 35, or 36. Some elements assist with determining the disease burden and the table that the procedure will be assigned. And those would include prior CABG, concomitant valvular procedure, and renal transplant, and others impact indication. These would include diabetes mellitus, stress test performed, coronary calcium score, adjunctive measurements obtained, and the syntax score. And then finally, we have the data elements that determine the procedure score. And those are chest pain symptom assessment and anti-anginal therapy. The very first algorithmic determination that the AUC makes, and one of course of great consequence with respect to AUC classification, involves the PCI indication selected in sequence 7825. An indication of new onset angina less than or equal to two months, stable angina, CAD without ischemic symptoms, or other, will direct the procedure to the stable ischemic heart disease classification table and the associated metrics 34, 35, and 36. Any of the six applicable STEMI indications as well as NSTEMI ACS will classify the procedure in the acute coronary syndrome tent, which can be appreciated in metrics 31, 32, and 33. So we will use sample patient data a couple times in the rest for the remaining part of this presentation. So shown here, we have a patient detail of a sample patient. On closer examination of the patient level detail metric 36, we can appreciate that for this particular patient, suspected CAD was selected as a cath lab indication, which in turn correlates with coding other as the PCI indication, effectively placing this procedure in the stable ischemic heart disease AUC. Now we've already discussed the importance of thorough stress test documentation as a critical piece to arriving at an AUC classification. We should note that stress test risk of ischemia is a key driver of identifying the procedure indication and contributing to a determination of overall appropriateness in metrics 34, 35, and 36. Now another indication level non-invasive free procedure tests that is worth discussing is the coronary artery calcium score, which is also known as the Gadsden score. The AUC stable ischemic heart disease algorithms now recognize a coronary calcium score as a non-invasive finding if the score was obtained within 182 days or six months of the procedure start date. For purposes of coding and how that correlates in the algorithm, a CAC score greater than 400 Gadsden units is high risk, 100 to 399 units is intermediate risk, and less than 100 units is correlated to low risk. One more very consequential variable reviewed by the AUC algorithm is the use of adjunctive measurements. Now the use of adjunctive measurements such as FFR, IFR, and other coronary physiology modalities are utilized by the stable ischemic heart disease algorithm as they, like pre-procedure non-invasive testing, can also demonstrate evidence of ischemia. Adjunctive measurements are utilized by the AUC algorithm at the indication level when no non-invasive testing, either stress test or calcium scoring, has been performed or the results were indeterminate. Valid results obtained during the episode of care and inform the reason for PCI support are captured in the dataset. Now, an announcement was posted in 2020 on the CAF-PCI site regarding the decision of the CAF-PCI registry to begin collecting CT-FFR results as well as non-hyperemic flow reserve ratios that inform the need for qualifying a CAF-LAB procedure. IFR ratios are coded as usual according to coding instructions. However, these other values may also be coded in the IFR field when they are utilized. Zero is coded to indicate ischemia was identified as with a positive result that meets the threshold of the applicable device, and then one is coded to indicate ischemia was not identified. So looking back over our patient-level detailed data, let's now turn our attention toward two variables that play a major role at the score level in every stable ischemic heart disease AUC table, and that would be anti-anginal therapy and chest pain symptom assessment. So by looking again at sample patient table 1.1, indication one, we can appreciate that the combination of these two variables can ultimately result in a range of rarely appropriate, which would be like a score of two, to appropriate with a score of seven. Now, when we're talking about anti-anginal medication use in the appropriate use criteria, the AUC is specifically looking at pre-procedure medications administered, which is captured in sequence 6991. There are four pre-procedure anti-anginal medications that are utilized, and those are beta blockers, calcium channel blockers, long-acting nitrates, and ranolazine. Now, there's some important considerations with respect to abstraction of pre-procedure medications. It's sequence 6991 that captures the medications that were prescribed or administered to the patient within the two-week period prior to and up to the start of their current procedure. All medications the patient was prescribed prior to arrival, regardless of the patient's compliance, as well as medications the patient received at your facility prior to the cath lab procedure should be captured. Anti-anginal medications prescribed or administered in both of these settings and coded appropriately essentially contribute to AUC credit at the scoring level and can make all the difference between a final score of rarely appropriate, maybe appropriate, and appropriate. So let's visit another sample patient whose disease burden assigned the procedure to table 1.1 and stress test findings to indication number one. So their symptomatic status with regard to chest pain and having had no anti-anginal therapy prescribed prior to arrival or administered after arrival assigns a score of three, rarely appropriate. Now had any one anti-anginal medication been initiated and documented to meet the target value and correctly abstracted, the score moves from a score of three, which should land you in metric 36, rarely appropriate, to four, metric 35, maybe appropriate. Two or more anti-anginal medications equate to a score of seven and an appropriate classification in metric 34. Chest pain symptom assessment identifies the quality of the patient's chest pain symptoms that prompted their procedure. And as I previously stated, carries great significance in determining a patient's final AUC score and metric classification. So let's visit another sample patient whose disease burden assigned the procedure to table 1.2, two vessel disease, and low risk stress test findings to indication number seven. Now their symptomatic, excuse me, their asymptomatic status with regard to chest pain and having had no anti-anginal therapy described prior to arrival or administered after arrival assigns a score of three, rarely appropriate. Now had this patient presented with either atypical or typical angina, the algorithm would have assigned the procedure a score of four, maybe appropriate. Having one anti-anginal medication on board in conjunction with ischemic symptoms provides a score of five and two or more documented anti-anginal medications along with ischemic symptoms moves the procedure from metric 35, maybe appropriate, to metric 34, appropriate. So let's ensure that we are correctly capturing the patient's chest pain symptom assessment in sequence 7405 according to the documentation in the patient's medical record. The coding instructions here you can see in the screenshot provide a very robust definition of documented characteristics of chest pain that are sufficient to capture typical and atypical angina, the two selections that qualify as ischemic symptoms with respect to the appropriate use criteria. But in the event that the documentation provided does not specifically indicate typical or atypical angina and there is not sufficient information provided to meet either definition, there are plenty of other tools in your arsenal. So there are FAQs 24812, 24829, and 24830, which expand on the acceptable documentation that can support coding. And they have been consolidated and paired up with the selection definitions to provide a really comprehensive resource shown here. And this is available on page five of the Version 5 Data Dictionary Supplement with pending data element updates. Included is additional documentation that supports capturing either typical or atypical angina, such as the Canadian Cardiovascular Society Grading of Angina or CCC class, a diagnosis of unstable angina, as well as documentation of symptoms that are represented of an angina equivalent. So can't underscore enough that you really want to make sure that you're utilizing all the available resources. And just a couple of last points about chest pain symptom assessment that I think are really relevant. The target value is value on current procedure and the intent, excuse me, the intent of this data element and target value is to identify the presence of chest pain symptoms that have prompted the patient to present for their CAT Lab procedure. Now, there's no stipulation with respect to coding that the patient must exhibit the symptoms on the day of or on presentation to their procedure. Rather, abstraction of chest pain symptom assessment should reflect the presence or absence of chest pain symptoms that informed the need for the cardiac CAT procedure. Another important point, additionally, just as the CAT Lab indication carries over from a diagnostic-only procedure to a PCI, so does the patient's chest pain symptom assessment. So when a patient has a diagnostic-only coronary angiogram followed by a PCI in a separate CAT Lab visit during the same episode of care and that patient's clinical status is unchanged, the chest pain symptom assessment prompting the patient's diagnostic angiogram will also be relevant for the PCI and captured as such. So just want to wrap things up here with some key highlights and takeaways. The opportunity really abounds with respect to ensuring every procedure is correctly classified by the AUC, as well as moving procedures out of metric 30 and improving performance among the stable ischemic heart disease AUC metrics. It all begins with thorough documentation and accurate abstraction, begins with recognizing and responding to non-classifiable reasons, such as a missing stress test information and a syntax score for those patients with triple vessel and left main disease, and then moving patients up from rarely appropriate and maybe appropriate in metrics 35 and 36 requires a keen focus on documenting and abstracting all relevant details with respect to non-invasive testing, including stress tests and a Gadsden calcium scores, adjunctive measurements, including non-hyperemic flow reserve ratios, the prescription and administration of pre-procedure anti-anginals and an accurate chest pain symptom assessment. After all, really what the AUC is essentially surveying in the interest of providing appropriate care to patients who undergo PCI is to show me the ischemia. And finally, your number one resource for close analysis and evaluation of all the various moving parts that make up the AUC is the e-reports dashboard. Again, please utilize this resource to the fullest extent as covered in this presentation. Thank you, John. The focus on our talk so far has been on the four metrics seen on the screen. There are several areas within our dashboard that help you assess overall patterns of care as it relates to the appropriate use criteria. As you can see here on the screen, we're looking at the metric performance in the metric detail area, and we can see your hospital or facility compared to the benchmarks. We also have comparison level groups, which you can see here. And this will allow you to assess your performance against the registry as a whole and within your PCI volume group. Thank you, John and Kim, for such a thorough discussion about the AUC. All right, so let's get into a few questions. Firstly, Kim, we've talked extensively about how participants can ensure their PCI procedures receive the most accurate rating. Unfortunately, we keep hearing about clinical scenarios where the AUC indication doesn't quite fit. Can you explain why this happens? I think the most significant limitation of the appropriate use criteria is that they rely on sometimes three to four characteristics in order to categorize an individual patient or scenario. And a good example is looking at an asymptomatic patient, lower stress tests on no anti-anginal therapy. Just looking at those items, we're not aware of any other information that may be informing the provider patient decision-making. Good points. And John, in your experience, what is the one thing, okay, maybe many things, that participants can do to help troubleshoot why PCI indications didn't receive the AUC indication they thought it would? Great. Yeah, thanks for the question. So procedures that didn't receive an AUC indication or were non-classifiable, to use our terminology, it's essentially due to the fact that through the abstraction process, the AUC algorithm just wasn't provided sufficient information to effectively place the procedure in one of the buckets. Sometimes I refer to them, that being the appropriate bucket, the maybe appropriate bucket, and the rarely appropriate bucket. And so what you have is an opportunity to troubleshoot individual procedures as well as identify trends, sorry, trends in provider documentation that are contributing to the procedures being labeled as non-classifiable. So number one, review the dashboard to identify the missing data. It can be helpful sometimes to re-examine the patient record to ensure that the documentation isn't already there to support the collection of the data. Also communicate with your providers. I think this is really important. Educate them on any common trends that you see. The lion's share of non-classifiable procedures are the result of incomplete documentation that doesn't correspond to things like non-invasive testing that doesn't support a date, type, positive, negative result or risk. And in our internal review, excuse me, in our internal review of the data at NCDR, we see a large disconnect in the volume of procedures in which the patient has a heavy disease burden, talking left main disease, triple vessel disease, but no syntax were provided. Higher engagement and increased comfort level with that tool and utilizing it is really pivotal to arriving at an AUC classification and can go a really long way. Yeah, and John, I doubt that folks will remember all the many valuable things that you shared when they're sitting down all by their lonesome in front of their dashboard. So can you just quickly review some of the resources that are available for them? Yeah, sure. I mean, and so, you're evaluating and analyzing this data on the dashboard. So it all starts right there. So the dashboard user guide is such a good resource for dashboard functionality, demonstrations, good screenshots, how to use the patient drill down, definitely for folks that don't have a whole lot of experience. And there's also a lot of information there for people that do. So in addition to that, the AUC companion guide, that is wealth of knowledge, yet it's still relatively succinct. Identifies assumptions that the AUC makes and which procedures or patients are excluded. There is an AUC recording that Kim and Connie both prepared and recorded. That is available on the Learning Center, excuse me. Yes, Learning Center portion of the registry website under the heading registry focused training non-CE. So that's really good. And then really the 2016 and 2017 AUC manuscripts, that's the basis behind all of this. So I thoroughly, highly, and always recommend when I'm talking to participants that they go ahead and read through that. Because that's gonna give you a lot of the rationale. Right, and those manuscripts are a little intimidating at first, but you broke it down nicely on your slides. And if folks spend a little time with those, it does start to make sense. I think they're actually a good read. I don't wanna sound like a total nerd or anything, but I think they're actually a pretty good read. Oh, you're kind of a little bit of a nerd, but it's okay, we like it. Okay, so Kim, when you say there is more to the AUC than giving a PCI procedure an indication and that participants should focus on the population, can you explain what you mean by that? Because I think we're all a little focused on the indication. Sure, and I think that before I answer that question, I think it's a good reminder that our revascularization AUC is really looking at whether patients would be better served by a CABG versus a PCI, right? We only report the PCI. And so I think that's an important thing to keep in mind. And as John said, read the documents. I think the document that really sort of drove home the idea of population is the most recent AUC methodology publication that was released in 2018. And they really focused on, this is a population. And so at first glance, when you look at an indication, I think you just focus on that in isolation, but step back and begin to look at the structure of how the AUC is made really, the tables. Okay, we're looking at SIHD patients within those populations. We're looking at the vessel disease, one versus two versus three versus et cetera. And then it's saying of that disease, did they undergo non-invasive testing? Have they been treated with anti-anginal therapy? And that begins not just one patient, that's in this population of patients with this common criteria, clinical scenarios, this is what we consider appropriate, rarely appropriate, maybe appropriate. And so I know it's easy when you're looking at data at the hospital level to say, oh, this patient ended up in this particular class and why, but take a step back first and just keep this in mind when reviewing your metrics, looking at your performance against the benchmarks, your volume group and the provider performance. Good, I think that's, I think it's helpful when a participant steps back and sort of assumes the role of the AUC. It becomes more objective, right? If you say to yourself, well, what is the AUC trying to understand about my patients, my population, then it's less personal, if you will. And you understand that the role of the AUC is to drive a shared decision-making approach between the clinical team and the patient. So just before we go, one last question, you ended on this slide, Kim, and you stress the need to review the aggregated metric data. And that's kind of a bit of a broader scope again, not looking at the individual rating, but looking at that metric rating. And can you explain again why this is so important and how someone needs to do that? Again, where is it located? And then again, what do they do with that information? Yeah, and I think getting back to the idea of population, you know, compare yourself in each category, appropriate, maybe appropriate, rarely appropriate, to the overall registry population, to the physicians within your organization first. You know, in my experience, I always think it's a great start to look at the high level, right? So look at the high level. And for instance, the last slide I shared was a professional level dashboard, which showed all the physicians on that facility. And you see within that dashboard, there are some that were performing much lower than compared to their peers. What's the story there, right? Is that my opportunity for a discussion at least, as opposed to, oh, trying to review every patient's rarely appropriate and determine why. Start high level and then get down to the patient level indication. And again, it supports that idea of population instead of a singular decision. Well, thank you both again very much. I just want to reiterate that the opportunity for our participants is not to just go and update their coding to affect indications, but to figure out how to improve the available information they have in the medical record and to encourage documentation that supports coding. That's actually all a very much harder ask than to just turn around and change how something is coded. Obviously, our audit process is all about validating that folks are coding exactly what's in the medical record. We're just completing last year's audit cycle and the preliminary results are pretty incredible. So I'm not worried that our participants are not coding appropriately, but I just don't want the message to get lost that the opportunity or what our role is and what the role of the abstractors are in the clinical team is to make sure all the patient's information is a part of the medical record and that there's documentation that supports accurate abstraction. And I want to thank you again, everyone, for joining us. If you have any questions, of course, please email us at ncdr.acc.org and you may direct your questions specifically to Kim, John, or myself, or you can reach out to the general and you will get a response soon. Thank you again for joining us. Thank you. Thank you.

appropriate use criteria

coronary revascularization

percutaneous coronary interventions

dashboard functionality

patient selection

thorough documentation

aggregated metric data

provider engagement