Welcome to Module 6, Session 1 of the Cardiovascular Program Coordinator course. This module is Data Analysis with content provided by Yara Gook and Christy Chambers. My name is Bobby Bunney. The Data Analysis module will cover three major learning objectives. Using available tools, locate and interpret available reports to improve the process. Analyze reports and apply analysis to performance improvement process. Identifying when and who to present reports. The Data Analysis module will be broken into four parts. The first session will cover Part 1, What Data to Use, and Part 2, How and When to Use the Data. Session 2 will cover Part 3, Interpreting Data for Process Improvement, and Part 4, Presenting Data for Stakeholders. Part 1, What Data to Use. One of the biggest challenges for any quality professional is knowing where to begin or where to focus their efforts. Common terms can be confusing and easily misunderstood. For example, the terms of analyzing data and interpreting data could be used interchangeably, but these terms are actually quite different. A quality professional analyzes data to determine if the metrics are meeting an expected goal or benchmark. The same person may interpret the same data to dig deeper, to assign meaning, determine significance, which can vary depending on the volume of data, and to identify gaps to plan for future improvement activities or initiatives. A critical step in data analysis is to take a step backward and make sure that the data you're assessing is clean. You have probably heard the saying, garbage in, garbage out. If there are wrong numbers or data fields are not accurate, the data is useless and can create distrust of presented data to key stakeholders in the future. Ensure that data abstractors are well-trained and familiar with the data dictionaries and operational definitions of fields, which typically include inclusion and exclusion criteria. Data that is reliable, valid, and unbiased will provide valuable information in order to make sound decisions for improvement processes. You need to make sure the data is accurate, complete, and timely. One thing is for certain, healthcare organizations and quality professionals have access to a vast amount of information and data. The challenge is to identify what is relevant, meaningful, and important to plan a course of action. In other words, how do you get the biggest bang for your buck? Data analysis and performance improvement initiatives come at a cost, so you want to make sure that you provide your institution with a good return on their investment. The Pareto Principle can be of help in this situation. The Pareto Principle, also known as the 80-20 rule, can identify 20% of your care process that are being consumed with 80% of your resources, where you want to focus your time and energy. This graph illustrates this principle. In a perfect world, data would be distributed evenly, as demonstrated by the red line. We don't live in a perfect world, and rarely is data distributed evenly. Enter your data and recognize your Pareto factor in your institution. This will help make the best decision for change, based on allocation of resources, time, and effort. Once you have done this, get buy-in from your team on the data that has been identified as optimal for improvement activities. Part 2. How and when to use data. In this part of the data analysis model, we are going to discuss how and when to use data in the healthcare setting. Data is an essential tool which can be leveraged in order to achieve the following. First of all, data analysis can help identify problems and opportunities for process improvements, performance metrics, and quality of care. A good example here would be comparing your facility's readmission rates with those at other facilities to understand if there is room for improvement. Data can also assist with prioritizing quality initiatives. For instance, if there are multiple quality improvement initiatives to address, it is important to understand which one is more crucial to your organization's overall performance and quality of care. Data can also be used to conduct analysis of your organization's key performance indicators, such as treatment cost per age group or average length of stay. Last but not least, data can be leveraged to define post-quality improvement implementation analysis on whether a desired change actually occurred. For instance, if quality improvement effort targeting lower readmission rates, data in the post-quality improvement phase will show the rates actually dropped. So now, let's discuss in detail each of the above-mentioned users of data in the healthcare setting. As was noted previously, all data can be used to identify problems and opportunities for improvement which can be referred to as reactive quality improvement and proactive quality improvement, respectively. Identifying problems with performance and or quality of care and trying to address them is called reactive quality improvement because an issue has already occurred and has or keeps affecting your organization in some shape or form. On the other hand, proactive quality improvement effort refers to an attempt in trying to identify areas of performance which doesn't necessarily suffer from any problems now, however, can benefit from some type of improvement. Examples of reactive quality improvement would be analyzing performance measures with a score lower than the national average or identifying population with high risk of revision due to abandonment of prescribed treatment plan. A good example of proactive quality improvement, on the other hand, would be a case when data analysis is performed in order to predict daily patient admission rates to improve staff and eliminate unnecessary labor costs or improve patient outcomes and prevent further emergency room visits. The second user case for the data analysis mentioned earlier was quality improvement prioritization. When pursuing multiple quality improvement initiatives to address various performance or quality concerns, relevant data can help identify which of them will add most value and therefore be undertaken first. In order to address your particular problem and fit your organization's needs and goals fast, data can help answer these questions. What are the opportunities for improvement and which of them to pursue? Which QI initiative is most feasible while adding the most value to your organization? And what QI implementation strategy will help you meet your final objective? In order to understand how your healthcare organization is performing, we need to look at its key performance indicators, or KPIs. Healthcare KPI is a well-defined performance measurement used to monitor, analyze, and optimize all relevant processes to increase patient satisfaction and quality of care provided. Many of the metrics used to assess hospitals' performance are specific to healthcare only. Some of them are hospital readmission rates, which track how many patients readmitted after discharge, wait times for a procedure to be performed, and treatment costs per patient or procedure. Hospitals' KPI can be assessed through quantitative and qualitative data analysis. Quantitative analysis utilizes mathematical and statistical modeling measurement and research and represents reality in terms of numerical values. Some examples of quantitative analysis include calculating mean, median, frequency performance analysis. Qualitative data analysis works a little differently since it focuses mainly on words, observations, images, and is mostly used for explored research. A good example here would be content analysis of a survey filled out by patients after they are discharged. When assessing KPIs using one of the above methods, data helps us to demonstrate problems and achievements within an organization, compare your performance to others, or evaluate patient satisfaction with the quality of care provided. Besides being helpful before and during the quality improvement implementation, data analysis can also be utilized in the post-quality improvement phases, such as impact assessment and improvement sustaining phases. Data analysis should be applied continuously to assess the impact of the implemented change on your organization's performance or quality of care. If the desired impact was achieved, then applied data analysis can be a useful tool in sustaining achieved improvement. During the impact phase and improvement sustaining phase, data can be used in the following ways. It can be used to measure and record the effect of the change. It can be used to perform ongoing monitoring and planning for future improvement. And finally, data can be used after an improvement has been achieved in order to sustain it. In this phase, data analysis is also used to determine that the objective of the quality improvement initiative has been met by achieving the desired outcomes. This is used for reporting purporting purposes to inform involved stakeholders or share evidence-based results of your initiative with other hospitals. Now, based on everything we have learned thus far, let's consider an example of data analysis utilization in a real-world scenario. Newly published outcomes report for Hospital X shows relatively high rate of post-operative pneumonia complication after cabbage surgery when compared with other inpatient facilities. Hospital management decides to launch a QI initiative to address the issue. The chart below shows the post-cabbage pneumonia complication rates for each quarter of 2018 for Hospital X and the national average. As you can tell from the chart, the hospital's rates are higher by at least half a percent in each quarter. In order to tackle this issue, thorough planning should be done first. This would include research and analysis of existing data, studies, and release publications. These can help immensely in preparing for this quality improvement effort. Let's consider the following uses of data in this case study. We can start off the planning phase by performing a comparative analysis of this facility's rates with regional facilities. This can help better understand whether post-operative pneumonia complication is affecting other regional hospitals as well, or is it isolated to this specific facility, in which case a deeper dive is necessary. Locating and analyzing published studies can be useful since they can point to the potential root cause of the issue under investigation. Data analysis of the affected population by risk stratification can then be performed. For instance, grouping patients by comorbidities will reveal if patients with existing chronic obstructive pulmonary disease are affected disproportionately. Analysis can also be performed of the resources used associated with receiving cabbage surgery at this facility compared with other facilities. For instance, low amounts of disinfectants used per day per bed in the ICU might be contributing to the spread of the infection. As you can see from what we have discussed thus far, data analysis could and should be an integral part of each phase of the quality improvement initiative in order to achieve the most value from the efforts applied to achieve the quality improvement objective. Now let's summarize what we have discussed in this part of the data analysis module. First and foremost, it is important to remember that data collection and analysis are central to the function of quality improvement in any healthcare system. It should be used to identify problems and opportunities for improvement at your organization, optimize performance and reduce penalties under various CMS programs, understand overall trends in the industry and perform comparative analysis, and last but not least, sustain achieved improvements and plan future ones. The table below shows a breakdown of data use cases based on the quality improvement stage. In diagnostic and planning phase, data can be used to identify problems as well as opportunities for improvement. Examples of such problems in need of being addressed are long patient arrival to ECG waiting times, high readmission rates, or other benchmarking against different healthcare providers. In the intervention phase, data analysis can be applied to monitor progression of change implementation and analysis of real-time results. For instance, continuous review and analysis of readmission rate increase or decrease while the change is being implemented. During the impact analysis phase, data can be used to assess outcome of the change. For example, correlation analysis between observation stage and readmission rates will reveal that readmission rates go down. In the monitoring and sustaining phase, data should be used exactly for that purpose to continuously monitor outcomes of implemented change and sustain achieved results. For instance, weekly analysis of average waiting times in order to identify outlier cases to be reviewed and addressed accordingly. Additional references for this module are listed here. This concludes Module 6, Session 1 of the Cardiovascular Program Coordinator course.

Module 6

Cardiovascular Program Coordinator

Data Analysis

Learning Objectives

Clean and accurate data