Hello and welcome to the introductory course on Healthcare Data. This is the first course in the Healthcare Data Literacy specialization offered by UC Davis. My name is Brian Paciotti. And I have over 15 years of experience working as a data scientist, within the healthcare industry. I started my career with an interdisciplinary PhD in Human Ecology from the University of California, Davis. In my dissertation, I used ethnography and games from the field of behavioral economics to understand cooperative behavior among ethnic groups in Tanzania, East Africa. After being accused of being a CIA spy by the Tanzanian government, I left the country and analyzed homicide data. After graduate school, I was interested in more applied aspects of research, thus I earned a Healthcare Informatics masters degree from UC Davis in 2010. In my thesis, I used data mining techniques to understand the quality of administrative hospital data. With an Informatics degree, and a strong background in research, especially in the area of organizational behavior, I've enjoyed a variety of employment adventures. First, I worked for the State of California to use administrative data and vital statistics to create hospital-level outcome reports. Second, in 2010, I joined the bioinformatics group at UC Davis to provide informatics and statistical services to autism researchers. Third, I joined the UC Davis Institute for Population Health Improvement where I created analytical reports to understand health disparities and high-cost patients among California's Medicaid population. Lastly, continuing with Medicaid research and data science, I worked for health analytics company called Optum where I continue to provide analytical consulting services to California's Medi-Cal population. Okay, enough about me and onto the course. Over the next four weeks, I will help lay the foundation of your healthcare data journey, and provide you with knowledge and skills necessary to work in the healthcare data industry, as a data scientist. Healthcare is unique because it is associated with continually evolving and complex processes associated with health management and medical care. For example, hospitals and clinics offer a huge amount of diagnostic and treatment options to patients and these behaviors lead to diverse data outputs. Our first module begins with a crash course in the US healthcare system. There are many facets to consider in healthcare and at the end of this module, you will be able to determine the value and the growing need for data analysis in healthcare. You will also be able to describe the Triple Aim and other data enabled healthcare drivers. In the second module, we will cover different concepts and categories of healthcare data. You will be able to describe how ontologies and related terms such as taxonomy and terminology organize concepts and facilitate computation. You will be able to identify the common clinical representations of data in healthcare systems including ICD-10, SNOMED, LOINC and drug vocabulary such as RxNorm as well as clinical data standards. For the third module, we'll discuss the various types of healthcare data and you will begin to see the complexity that occurs as you work with pulling in all the different types of data to aid in decisions. You will be able to analyze the various types and sources of healthcare data, including clinical, operational claims, and patient generated data. You will also be able to differentiate unstructured, semi-structured, and structured data within healthcare data contexts. In the final module, we take a closer look at the inner workings of data and conceptual harmony. We will start with a discussion about how data conflicts between sources of data can make data integration challenging. I will then offer some solutions to the data integration problem, and define some important concepts, methods and applications that are important to this domain. For example, I will define terms such as data mapping. Finally, I will address the domain of entity resolution and record linkage. We have a lot to cover. So, let's dive right in.