University of California, Davis

Healthcare Data Literacy

Brian Paciotti

Instructor: Brian Paciotti

18,062 already enrolled

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Gain insight into a topic and learn the fundamentals.
4.4

(119 reviews)

Intermediate level
Some related experience required
13 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.4

(119 reviews)

Intermediate level
Some related experience required
13 hours to complete
3 weeks at 4 hours a week
Flexible schedule
Learn at your own pace

Details to know

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Assessments

4 assignments

Taught in English

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This course is part of the Health Information Literacy for Data Analytics Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

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.

What's included

7 videos1 reading1 assignment3 discussion prompts

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.

What's included

6 videos1 assignment4 discussion prompts

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.

What's included

6 videos1 assignment2 discussion prompts

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.

What's included

7 videos1 reading1 assignment1 peer review2 discussion prompts

Instructor

Instructor ratings
4.6 (35 ratings)
Brian Paciotti
University of California, Davis
2 Courses19,818 learners

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4.4

119 reviews

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