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.
Ce cours fait partie de la Spécialisation Health Information Literacy for Data Analytics
Healthcare Data Literacy
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Programme du cours : ce que vous apprendrez dans ce cours
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.
Meilleurs avis pour Healthcare Data Literacy
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.
Enseignant
Brian Paciotti
Healthcare Data ScientistÀ propos de Université de Californie à Davis
À propos du Spécialisation Health Information Literacy for Data Analytics
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