This course will help lay the foundation of your healthcare data journey and provide you with knowledge and skills necessary to work in the healthcare 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. We'll learn about the many facets to consider in healthcare and determine the value and growing need for data analysts in healthcare. We'll learn about the Triple Aim and other data-enabled healthcare drivers. We'll cover different concepts and categories of healthcare data and describe how ontologies and related terms such as taxonomy and terminology organize concepts and facilitate computation. We'll discuss the common clinical representations of data in healthcare systems, including ICD-10, SNOMED, LOINC, drug vocabularies (e.g., RxNorm), and clinical data standards. We’ll discuss the various types of healthcare data and assess the complexity that occurs as you work with pulling in all the different types of data to aid in decisions. We will analyze various types and sources of healthcare data, including clinical, operational claims, and patient generated data as well as differentiate unstructured, semi-structured and structured data within health data contexts. We'll examine the inner workings of data and conceptual harmony offer some solutions to the data integration problem by defining some important concepts, methods, and applications that are important to this domain.
カリフォルニア大学デービス校（University of California, Davis）
UC Davis, one of the nation’s top-ranked research universities, is a global leader in agriculture, veterinary medicine, sustainability, environmental and biological sciences, and technology. With four colleges and six professional schools, UC Davis and its students and alumni are known for their academic excellence, meaningful public service and profound international impact.
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HEALTHCARE DATA LITERACY からの人気レビュー
This course is well loaded with information that is useful for data scientists (or intending data scientists) in healthcare. The instructor did a wonderful job.
Overall I liked the course. I am giving 4 star due to issue with lack of forum responses. Did not look any TA was assigned to the program.
It was an informative learning experience filled with practical & useful insights in healthcare data systems.
Excellent introduction to the context of USA medical care state of business from politeconomy point of view.
Health Information Literacy for Data Analytics専門講座について
This Specialization is intended for data and technology professionals with no previous healthcare experience who are seeking an industry change to work with healthcare data. Through four courses, you will identify the types, sources, and challenges of healthcare data along with methods for selecting and preparing data for analysis. You will examine the range of healthcare data sources and compare terminology, including administrative, clinical, insurance claims, patient-reported and external data. You will complete a series of hands-on assignments to model data and to evaluate questions of efficiency and effectiveness in healthcare. This Specialization will prepare you to be able to transform raw healthcare data into actionable information.