Welcome to Lesson 3 of this learning activity titled Metric 1, PCI in-hospital risk-adjusted mortality all patients. The content in this lesson was developed by myself, Kate Malish, and I will also be narrating this lesson. The objectives for the learning activity are to understand the parameters of Metric 1, discuss detail line labels and explanations, interpret facility data in your dashboard and outcomes report. Now that we have covered the details of Metric 1, let's discuss interpreting your facility's data presented on the dashboard and in your outcome report. Correct interpretation of data will allow your facility to complete performance improvement when needed. Risk-adjusted metrics behave slightly differently than regular metrics with respect to how they are reported on the dashboard. In risk-adjusted metrics, there are no numerators or denominators reflected in the patient-level detail report. Rather, all patients who were reviewed by the metric are listed, then patients deemed risk-eligible, those meeting the metric conclusion criteria, are identified. You will see this when you go into patient detail in the dashboard for Metric 1. If the patient had the outcome being assessed, which in this metric is mortality, it will be identified in the observed row. Again, reviewing the detail lines for risk-adjusted metrics will help you understand opportunities for improvement in these measures. The patient-level drill-down functionality provides detailed information on individual patients, such as their eligibility for inclusion in the model, their observed outcome, and their expected probability of that outcome. The drill-down also provides a profile for each individual risk factor in the model. When first entering your facility's dashboard, you have the option to view the executive summary of all metrics, or to only view the detail lines for each metric. When viewing the executive summary for the ending timeframe of choice, Metric 1 is the first metric shown on the dashboard. You have the option of viewing the metric detail or patient detail. Let's start by clicking on metric detail. Although this screen does not contain any actual metric information, this is the outline of information you will see when clicking on metric detail. You will see your facility's metric performance, along with a comparison of your facility to the U.S. Registry 50th percentile. Finally, you will see a graph of your facility's quarterly trends for Metric 1. This will allow you to pinpoint certain quarters in which your facility had more or less mortalities. Going back to our executive summary, let's click on patient detail for Metric 1 and see what that shows us. This is the patient-level detail report for the ACC, which, as you can see, has no actual data. When you pull up your facility's detail report, you will have a metric summary of mortality for all four quarters based on the selected ending timeframe, along with your hospital rolling four quarters data. Going back to the dashboard main page, if you only want to view a specific metric and look at which patients were included, set the view to detail lines and choose the ending timeframe you would like to view. Note, only quarters which have been published will have data to examine. Finally, in the category column, select risk-adjusted mortality, all patients. Remembering our detail line labels and explanations already covered, we will now see how they are displayed in the dashboard. This is an example of the detail lines for Metric 1. It shows you data from each quarter based on the four quarters prior to the ending timeframe selected on the previous slide. Then you have the My Hospital R4Q for the ending timeframe selected, which will be the sum of all four quarters shown. Finally, on the far right-hand column is the U.S. Registry R4Q for the same four quarters. This is the total number of each detail line for the U.S. Registry. Detail line 4739 is your facility's risk-adjusted mortality rate. This is calculated by your facility's O to E, or observed to expected ratio, multiplied by the registry aggregate observed mortality rate. The rows on this slide show every variable in Metric 1. By clicking on patient detail, either from the previous screen or on the eReports dashboard home page for Metric 1, you will see every patient entered and submitted and the corresponding values for each variable. At the patient detail level, the participant can view the patient variables or patient characteristics reviewed by Metric 1. The patient level detail also informs us of the patient eligibility and when the outcome mortality occurred. To test your ability to interpret the dashboard, let's look at a few last practice scenarios and questions. When reviewing the R4Q performance for Metric 1, can you determine whether your facility is performing above or below the U.S. Registry benchmark? The answer is your facility is performing above the U.S. Registry benchmark. You can determine this by looking at detail line 4739. The number for my hospital R4Q is 1.47, whereas the U.S. Registry benchmark is 1.8. Our last practice question, when reviewing your R4Q performance, is your facility performing better, worse, or the same as expected? The answer is your facility is performing worse than expected. You can determine this by looking at detail line 4748. The O to E ratio is 1.11, which is greater than 1. This means your facility's observed risk-adjusted mortality rate is higher than the expected mortality rate. What if, after viewing your dashboard for Metric 1, your mortality rates are higher than you or your physicians expected? Well, here are some tips in interpreting this data, which may help to put things in perspective. Don't immediately assume there is a problem with the quality of your program or with a specific operator. Although we recommend all data abstractors in the same facility communicate with each other, read the resources available on ncr.com, and stay current on educational offerings and announcements posted by the Registry. We understand there may not always be complete continuity in coding within your facility. Secondly, look at the big picture rather than only one quarter of data. Mortality rates may drastically increase one quarter with just a few deaths. This often does not reflect your institution's overall mortality rate. Look at several quarters to gain a better picture of your facility's mortality rates. Items to examine include data quality reports, which will show you whether you met coding thresholds for all data elements, along with any outliers or errors made during data abstraction. This is an important step since coding certain data elements incorrectly can dramatically increase or decrease the patient's expected risk of mortality. We have already discussed viewing the patient level drill down on the dashboard, which shows you every patient included in the risk model, along with each detail line included in the metric. Finally, we must emphasize the importance of accurate data collection and data entry. When documentation is not in the medical record, you cannot accurately capture the patient or the episode of care. This is often an area facilities take on for performance improvement, as it often directly affects your metric performance. Finally, good communication between your team of abstractors is paramount to ensure consistent coding. To locate your data quality report, click on Data, then DQR. This is an example of what you will see after each data submission. You will either pass or fail the data assessment, and either pass or fail the completeness assessment. Finally, you will see the benchmark inclusion status, which will be red, yellow, or green. Remember, you must have green status to receive metric results. Clicking on the hyperlink will allow you to see which data entered is causing you to fail the assessment. This is an easy way to make corrections and ensure you achieve a green status. We recommend submitting data early and often so that you may have ample time to correct any errors prior to the data deadline. We will round out this presentation on metric 1, PCI in-hospital risk-adjusted mortality, by discussing what you as an abstractor can do to improve your facility's mortality rates. First, we cannot stress enough the importance of solid data collection practices. Prior to submitting your data to the DQR, perform a self-audit to make sure you have coded every data element applicable. Running a quality check before submitting often catches small errors, which can easily be fixed, such as keystroke errors or missing a data element. The quality check will also show you any outliers. These are data elements which were coded with a value outside the normal range or a coded value that does not match another coded data element. The more outliers you have, the more chance there is for inaccuracy in your data collection. Think about your team of abstractors. Is it a small or large group? Do the abstractors meet on occasion to discuss coding of data elements? Are all abstractors clear as to data definitions and submitting data? It is very important that your team of abstractors communicate and have the same understanding of data capture to ensure accuracy and consistency. Utilize educational resources provided by NCDR. Once you become comfortable navigating the registry website, you can hopefully begin to review educational materials provided to assist with coding practices, interpretation of data, and important updates made in the registry. Education such as monthly case scenarios, registry webinar calls, and the QII Learning Center exist solely for your benefit. With that, the FAQ database consists of commonly asked questions along with the correct answer. We recommend reviewing these FAQs periodically as they offer an instant answer to many of your questions. This concludes Lesson 3 of Metric 1, PCI and Hospital Risk-Adjusted Mortality, All Patients. Thank you for your participation. For more information, visit www.ncdr.nlm.nih.gov

Lesson 3

Metric 1

PCI in-hospital risk-adjusted mortality all patients

dashboard

outcome report