Welcome to Module 6, Session 2 of the Cardiovascular Program Coordinator course. This module is Data Analysis with content provided by Yara Guck and Christy Chambers. My name is Bobby Buddy. This Data Analysis session will cover the last two agenda items, Part 3, Interpreting Data for Process Improvement, and Part 4, Presenting Data for Stakeholders. Part 3, Interpreting Data for Process Improvement. The vast amount of data available to you can be overwhelming. If you recall, the definition of interpreting data is the process of assigning meaning, determining the significance, implications, and conclusions of data that has been collected. This information can help you describe what is happening, identify relationships between variables, identify whether improvement has occurred or not, monitor improvements over time to assess for maintenance of an improved process, determine the significance of your results, and communicate your conclusions effectively to stakeholders. Here we will dig a little deeper into the two types of data, quantitative and qualitative. Quantitative data analyzes numerical values, such as counts, sums, ratios, rates, and percentages. Measures of central tendency are also quantitative. The mean is the average of a group of numbers, the median is the middle number of a group of numbers, and the mode is the most common number appearing in a group of numbers. Range, standard deviation, and interpersonal measure identify reliability and spread between a group of numbers or values. An example of when quantitative data would be used is to track and trend. How long it takes to do something, such as patient arrival to reap refusion time. How long it takes to administer a medication, or how often does a desired behavior or outcome occur. Quantitative data is used to examine, understand, and describe a phenomenon. It is also used to describe how people experience an event, and it allows study of the human side of an issue. Qualitative data can help us get to the why behind an issue. There are many types of qualitative data and examples of how qualitative data can be presented. This slide identifies the most common. It is beyond the scope of this presentation to go into detail about each type. Please refer to the references at the end of this module to explore this subject in more detail. Data can be much easier to interpret when visually prepared in colorful charts and graphs. These catch the eye and communicate findings in a much more powerful way than simple text. Technology and software applications make it easy to create graphs and charts. Be cautious not to overwhelm your audience with too much information. Keep it simple for the best results. This slide provides the best type of chart or graph to display what you desire to demonstrate. Another caution is to make sure you are using a sufficient amount of data to draw conclusions or make possible inferences. Fifty or more data points is much more significant than findings from two data points. There is much to learn regarding statistical significance of data results and is beyond the scope of this presentation. The following slides show how a group of metrics or measures can be presented together. We will demonstrate a balanced scorecard, dashboard, and benchmarking. A balanced scorecard typically presents a group of measures compared to target measure or goal. You can see here that multiple different types of graphs and charts are being used. This is an example of an NCDR dashboard. You can see here that the quantitative data is represented in bar graph form. Bar graphs will have a horizontal x-axis, which, in this case, is the time frame quarter in which the data has been assessed, and a vertical y-axis, which, in this case, shows the proportion or percentage of time an event takes place, such as percentage of STEMI patients that receive primary PCI within 90 minutes. The broken horizontal line is the benchmark for the data set. As you can see, the graphs are easy to interpret with very little text required. This is an example from NCDR Executive Summary Metrics, showing the benchmark for this hospital in each metric and whether the metric is above or below the 50th percentile of reporting U.S. hospitals. Part 4, Presenting Data for Stakeholders. In this part of the module, we are going to discuss how and when to present data findings to the stakeholders. To understand the role of data in quality improvement more clearly, it is useful to consider the four phases of the Plan, Do, Study, Act cycle. This is essential to every quality improvement effort and is used to test, propose changes, and improvement in the real health care setting. Data plays an important role in each of the phases, as you can see from the diagram below. Let's consider each one of them separately. In the planning phase of the PDSA cycle, data is used to plan a quality improvement change to be implemented, perform data collection, as well as locate relevant data sources and reports. In this phase, it is important to define a data collection strategy and the data analysis techniques to be applied later. The Do phase focuses mainly on implementing changes on a small scale to test their effectiveness and impact on an organization's performance or quality of care. This phase involves real-time analysis of the outcomes while changes are being tested. In the Study phase of the cycle, most of the data and report analysis occurs. In this phase, data is used to conduct analysis of the implemented changes, evaluate the results, and draw conclusions. Last, but not least, the Act phase focuses on implementing changes on a broader scale, refining the changes, and deciding on further actions. For these purposes, data is used for ongoing analysis of the results or implemented changes in order to identify if a tested change is worth implementing on a larger scale. Based on the cycle of quality improvement implementation, findings of performed data analysis can and should be shared with the team at every stage in order to consider the impact on the normal workflow. This should be done in order to integrate quality improvement effort with existing processes when possible to ensure that everyone is knowledgeable about the requirements and reasons for data collection and the resultant benefits. Insights should also be shared in order to seek feedback at all steps in your data management and quality improvement cycles. However, there are two phases in the PDSA cycle, during which it is crucial for the success of the quality improvement project to share data analysis results with key stakeholders. These phases are Planning phase and the Study phase. In the Planning phase, it is important to share collected reports, results of conducted preliminary data analysis, and research with relevant stakeholders to provide the value of suggested quality improvement initiative, its visibility, and projected effectiveness. Buy-in of the key stakeholders is essential for ensuring allocation of sufficient resources to support the quality improvement effort, as well as its best possible alignment with the organization's goals and objectives. Overall, in the Study phase, it is important to provide outcomes of the local tested changes when advocating for or against their implementation on a broader scale. At this stage, sufficient data collected during the Do phase should be available to prove effectiveness of the proposed changes and share evidence-based results with key stakeholders. If findings revealed during the Plan and Study phases are relevant to the goals of the QI initiative, they should be communicated to all involved parties, including all involved stakeholders, subject matter experts who can assist with implementing identified QI changes on a broad scale, and managers of relevant divisions who will be involved in and overseeing the QI change implementation. Personal commitment of the above-mentioned stakeholders to the quality and performance improvement is integral to create an effective frontline engagement in the transformation of quality and safety. This drives organizational energy for the change and provides an ongoing commitment to the transformation. It also creates increased demand on their organization for supporting the change. Therefore, when possible, early engagement of the chief executive officer and board members in the quality improvement project can provide a strong support system and facilitate successful completion. Moreover, the involvement of subject matter experts who are knowledgeable about the issue can assist with implementing identified quality improvement changes in the most efficient and effective way possible. Always seek feedback from your peers and senior leadership to better understand how to achieve your ultimate objective. When implementing a QI initiative, cross-departmental involvement is also essential for the success of the project. Therefore, it is recommended to ensure that managers of relevant divisions who will be involved in and overseeing a QI change implementation are on board with it early on. Ensure their support and cooperation by sharing your findings and data analysis results. When presenting your findings, it is crucial for the outcome of the quality improvement initiative to clearly communicate them. There are various techniques for organizing and presenting data. They are helpful in guiding your analysis and presenting your results. Your presentation should outline clearly the original objectives of the QI effort. Ensure that no deviation from the original objective has occurred during data collection, change, implementation, and analysis. Your final findings should speak to the original goal of the QI effort and present a solution for the issue at hand. Data collection methods and data sources. Data analysis strategy and methods used throughout the QI project should be explained clearly. And finally, visual representations of your findings make it easy to identify patterns or trends. They are also useful for communicating your findings to others in a way that is easily understood. Based on what we have learned thus far in this part of the module, let's consider hypothetical data findings of the quality improvement initiative, which targeted lowering the risk standardized readmission rate at a specific facility by providing two-week telephone follow-up call and care plan reinforcement to discharge patients with acute myocardial infarction. The bar chart you see below shows side-by-side comparison of the 30-day risk standardized readmission rates for four quarters of the facility for discharge patients with and without follow-up. As you can see, rates of readmission when no follow-up was provided are consistently higher, peaking at 13% in quarter two. Following these steps will help you make an impactful presentation of your research findings. State the objective of the research. Improve the facility's risk standardized readmission rate by introducing two-week telephone follow-up for discharged AMI patients. Involve hospital administrators who are interested in lowering CMS' hospital readmission reduction program penalty based on its improved risk standardized readmission rate. This potential benefit of implementing proposed change on a broader scale will help obtain stakeholders' support and generate return on investment made in implementing the change. Provide evidence-based proof of effectiveness of your suggested approach. And lastly, include visualizations into your presentation. So you have collected and analyzed your data. Now what? How do you come to conclusions about what is going on in your program, and how do you link these conclusions to actions? Depending on your project, this can be a complex process. Therefore, it is important to involve your stakeholders and seek input regarding the meaning of your data in the context of your organization. Particularly, data findings should be disclosed during the plan and study phases of the PDSA cycle so that your next steps are well-informed and agreed upon. Make sure that your findings clearly support the objective of the Quality Improvement Initiative and identify the right group of stakeholders to share the findings with. The CEO and board are usually the best advocates of quality and patient safety. Provide a concise and clear presentation of the research findings with supporting data and visuals. Draw conclusions and suggest further actions. And finally, justify your suggestions with data analysis results and real-world evidence. Additional references for this module are listed here. This concludes Module 6, Session 2 of the Cardiovascular Program Coordinator Course. For more information, visit www.ncbi.nlm.nih.gov

Cardiovascular Program Coordinator

Data Analysis

Process Improvement

Interpreting Data

Stakeholders