This course is part of the Health Information Literacy for Data Analytics Specialization

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stars
102 ratings

Brian Paciotti

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Health Information Literacy for Data Analytics SpecializationUniversity of California, Davis

About this Course

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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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Course 1 of 4 in the

Health Information Literacy for Data Analytics Specialization

Intermediate Level

Approx. 13 hours to complete

English

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Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 1 of 4 in the

Health Information Literacy for Data Analytics Specialization

Intermediate Level

Approx. 13 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.61/5 (29 Ratings)

Brian Paciotti

Healthcare Data Scientist

Research IT

15,569 Learners

2 Courses

Offered by

University of California, Davis

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Syllabus - What you will learn from this course

Week1

Week 1

4 hours to complete

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.

4 hours to complete

7 videos (Total 52 min), 1 reading, 1 quiz

Week2

Week 2

4 hours to complete

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.

4 hours to complete

6 videos (Total 41 min)

Week3

Week 3

3 hours to complete

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.

3 hours to complete

6 videos (Total 45 min)

Week4

Week 4

3 hours to complete

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.

3 hours to complete

7 videos (Total 44 min), 1 reading, 2 quizzes

Reviews

4.4

30 reviews

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4 stars
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3 stars
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2 stars
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1 star
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TOP REVIEWS FROM HEALTHCARE DATA LITERACY

by JJOct 16, 2022

Great course. Some formatting issues made quizzes hard to read, but overall great intro.

by ASApr 10, 2020

it' s the best experience among all the courses done

by BAMay 18, 2020

Thank you for keeping me on my clothes in this class

by DANov 24, 2019

Great overview to important topics but very conceptual.

View all reviews

About the Health Information Literacy for Data Analytics Specialization

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.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

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Healthcare Data Literacy

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