Welcome to our first lesson in this module on monitoring, managing, and improving data quality. In this lesson, we look at establishing the culture of quality throughout the data lifecycle. After this lesson, you'll be able to describe the stages of the data pipeline, and discuss how to establish a culture of quality by making sure data quality is maintained through each stage data moves through in the data life cycle. You will be equipped to discuss how we collect, communicate, and use data over the course of time. Additionally, you'll be prepared to discuss how to maintain quality at every step of the data lifecycle by monitoring and checking data quality along the way. Let's get started. I hope you recall in earlier class, we spoke about the information lifecycle. That's where data is collected or captured in clinical operations where patients are receiving care or a clinician is doing their work. The data moves from these operational systems to a data warehouse where it's stored, then it goes through a series of aggregations and transformations to become something we can use in our data analysis and for reporting. Again, we collect data as a result of healthcare operations, documenting care including examinations, procedures, all types of orders, labs, medications, x-rays et cetera. We create builds we process claims for payers. We manage care teams. Once the data's collected, it's further used in operational systems to communicate among caregivers to other parts of the hospital to help us manage our operations, and to determine what kinds of inventories of materials we need to keep doing our business. We use data to track patients health over time. Ultimately, we extract the data out of our operational systems into analytic systems, and to be stored in data warehouses where we can work with them. Our overall goal is to move from day-to-day work that we're doing, into a place where we can use the data that comes out of our day to day work to turn it into aggregate information, gain insights from it, and use those insights to produce knowledge about what we're doing, and how to do it better. That happens in the reporting part of the lifecycle. In order to do this, we have to have certainty that our data is of high-quality. Remember, the way I define quality here is that it's able to be used for its intended purposes or to do some specific intended purpose. As we can see, working through the data lifecycle every point along the data lifecycle is important to making sure that data is useful in the way we need it to be useful. At each point, we need to ensure that data is managed by each person who touches it in a way that ensures that the quality stays high. Maintaining this consistent culture of data quality, ensures that we're able to use the data the way that we intend. Now, as we go through the lifecycle, we monitor data quality along the way. That begins with doing checks and edits, and ensuring that we're inputting the data properly at the beginning. We need to make sure that we have clinical workflows that support data collection. Check that the data is transmitted accurately from stage to stage. We need systems in place to support that care, to make sure that everyone who's involved in working with those systems are all working together to bring data together in the highest quality possible. This applies whether we're the clinicians entering the data, the patients working with the clinicians, or someone working in the office to help the patients through their day. We talked a little bit about reminding ourselves how data is collected through an information lifecycle, and how that lifecycle is touched by nearly everyone involved in the care of a patient, and the patient's themselves, and how that in order to ensure quality at the end of the process. To make sure our data is useful, we need to ensure quality at every step of the process. In the next lesson, we'll talk about measuring data quality at a baseline, and how to use that baseline to look at quality over time.