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.
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加州大学戴维斯分校
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 101
In this module, you will be able to identify how biological and social systems are features of human well-being and health. You'll be able to describe important organizations in the US healthcare system and be able to discuss specific examples that document high cost and possible waste in the US healthcare system. You'll be able to identify and discuss the knowing-doing gap and be able to describe evidence-based efforts to transform fragmented care processes into coordinated patient-centered activities.
Concepts and Categories
In this module, you will be able to compare forms of communication and describe why people us ontologies to describe the world. You'll be able to describe the evolution of standardized railroads in the US and recognize why the evolution of railroad tracks also applies to medical terminologies. You'll be able to analyze a dataset with disease codes and also be able to select which codes refer to specific diseases. You'll be able to match different terminologies with different descriptive domains as well as be able to contrast the different ways of organizing information into hierarchies or other categories.
Healthcare Data
In this module, you will be able to identify different types of medical processes and be able to explain why specific data formats emerged from these varied processes. You'll be able to list numerous data types that are found within EHRs and link specific clinical processes that created these outputs. You'll be able to trace why various types of administrative data are collected and describe the value of this data for analytics. You'll be able to identify the common ways that gene sequences are stored in computer readable files and be able to describe how big data formats are different than common relational database technologies that require a lot of data modeling and planning.
Data and Conceptual Harmony
In this module, you will be able to tell leaders and coworkers why they should invest time in creating data dictionaries and other meta-data. You'll be able to describe why one burn registry had data fragmentation issues, and how a variety of standardization and centralization processes helped to achieve data harmony. You'll be able to answer why it is necessary to integrate data, even though the data is coming from disparate sources. You'll be able to perform data mapping as well as communicate the technical terms used to describe and perform record linkages.
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来自HEALTHCARE DATA LITERACY的热门评论
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.
关于 Health Information Literacy for Data Analytics 专项课程
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.
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