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Health Information Literacy for Data Analytics

Doug Berman and Brian Paciotti

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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What's inside

Four courses

Healthcare Data Literacy

(0 hours)

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.

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Healthcare Data Models

(0 hours)

Career prospects are bright for those qualified to work in healthcare data analytics. This course gives you a glimpse into why this work matters, what you’d be doing in this role, and what takes place on the Path to Value where data is gathered, prepared for analysis, and transformed into valuable insights that can save lives, reduce costs, and improve healthcare.

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Healthcare Data Quality and Governance

(0 hours)

Career prospects are bright for those qualified to work with healthcare data. Perhaps you work in data analytics but are considering a move into healthcare, or you work in healthcare but are considering a transition into a new role. In either case, Healthcare Data Quality and Governance will provide insight into how valuable data assets are protected to maintain data quality.

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Analytical Solutions to Common Healthcare Problems

(0 hours)

In this course, we will explore analytical solutions to common healthcare problems. We will review business problems, build data structures, group data, categorize medical codes, extract, transform, and load data, and harmonize data from multiple sources. Finally, we will create a data dictionary to communicate the source and value of data.

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Learning objectives

Analyze the various types and sources of healthcare data, including clinical, operational, claims, and patient generated data.
Compare and contrast common data models used in healthcare data systems.

Assess the quality of healthcare data and make appropriate interpretations of meaning according to data sources and intended uses.
Create a data dictionary to communicate the source and value of data.

Analyze the various types and sources of healthcare data, including clinical, operational, claims, and patient generated data.
Compare and contrast common data models used in healthcare data systems.
Assess the quality of healthcare data and make appropriate interpretations of meaning according to data sources and intended uses.
Create a data dictionary to communicate the source and value of data.

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Time

0 hours

Level

Intermediate

Via

Coursera

Institution

University of California, Davis

Instructors

Doug Berman

Brian Paciotti

Level

Intermediate

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