About this Course

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Flexible deadlines

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Intermediate Level

Approx. 6 hours to complete

Suggested: 4 weeks of study, 2-5 hours/week...

English

Subtitles: English

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 6 hours to complete

Suggested: 4 weeks of study, 2-5 hours/week...

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
2 hours to complete

Why Data Quality Matters

In this module, you will be able to define data quality and what drives it. You'll be able to recall and describe four key aspects of data quality. You'll be able to explain why data quality is important for operations, for patient care, and for the finances of healthcare providers. You'll be able to discuss how data may change over time, and how finding those changes allows us to recognize and work with the issues the changes cause. You will be able to explain why requirements for data quality depend on how we intend to use that data and understand four levels of quality that may be applied for different kinds of analysis. You will also be able to discuss how all of this supports our ability to do our best work in the best ways possible....
6 videos (Total 34 min), 2 readings, 1 quiz
6 videos
Module 1 Introduction2m
Why Data is Collected and Defining Quality3m
Why Data Quality Matters, Part 17m
Why Data Quality Matters, Part 29m
How Data Quality Assessment Varies in Different Data Uses7m
2 readings
A Note From UC Davis10m
Data quality assessment for comparative effectiveness research in distributed data networks30m
1 practice exercise
Module 1 Quiz30m
Week
2
4 hours to complete

Measuring Data Quality

This module focuses on measuring data quality. After this module, you will be able to describe metadata, list what metadata may include, give some examples of metadata and recall some of its uses as it relates to measuring data quality. We will describe data provenance to explains how knowing the origin of a data set can help data analysts determine if a data set is suitable for a particular use. We’ll also describe 5 components of data quality you can recall and use when evaluating data. You will also learn to be able to distinguish between data verification and validation, recalling 4 applicable data validation methods and 3 concepts useful to validate data. In addition to your video lessons, you will read and discuss a scholarly article on Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research. We wrap up the module with a framework abbreviated as S-B-A-R that is often used in healthcare team situations to communicate about issues that must be solved....
7 videos (Total 34 min), 1 reading, 1 quiz
7 videos
Describing Metadata in Healthcare4m
Data Provenance in Healthcare4m
Components of Data Quality4m
Data Validation Methods5m
A Framework for Validating and Verifying Data6m
The SBAR Methodology7m
1 reading
Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research45m
1 practice exercise
Module 2 Quiz30m
Week
3
2 hours to complete

Monitoring, Managing and Improving Data Quality

In this module, we focus on monitoring, managing, and improving data quality. You will be able to explain how to monitor data on a day-to-day basis to see that it remains consistent. You will explain how measures can help us monitor the patient health and the quality of care they receive over time. Also, you will be able to discuss establishing the culture of quality throughout the data lifecycle and improving data quality from the baseline by posing questions to determine a baseline of data quality. You will be able to manage data quality through expected and unexpected changes, along with tracking monitoring strategies along the data pipeline. After this module, you will be able to identify and fix common deficiencies in the data and implement change control systems as a monitoring tool. You’ll also recall several best practices you can apply on the job to monitor data quality in the healthcare field. ...
5 videos (Total 27 min), 2 readings, 1 quiz
5 videos
Establishing the Culture of Quality throughout the Data Lifecycle4m
Improving Data Quality from the Baseline7m
Managing Data Quality: Expected and Unexpected Changes5m
Monitoring Strategies Along the Data Pipeline8m
2 readings
Managing Chaos Part 1: Putting a Change Control Process in Place15m
Managing Chaos Part 2: Change Control Decision Making15m
1 practice exercise
Module 3 Quiz30m
Week
4
5 hours to complete

Sustaining Quality through Data Governance

IIn this module, we focus on sustaining quality through data governance. We will define data governance and consider why it matters in healthcare. You will discuss who makes up data governance committees, how these committees function relative to data analysts and describe how stakeholders work together to ensure data quality. You’ll be able to describe how high-quality data is a valuable asset for any business. You will also define data governance systems. You will recall several ways data can be repurposed and explain how data governance maintains data quality as it is repurposed for a use other than that for which it was originally gathered. In addition to your video lessons, you will read and discuss the article, Big Data, Bigger Outcomes and practice applying some of these important concepts....
6 videos (Total 28 min), 3 readings, 2 quizzes
6 videos
Defining Data Governance in Healthcare5m
Why Data Governance Matters in Healthcare8m
Data Governance Committees in Healthcare6m
Data Governance Systems in Healthcare5m
Course Summary58s
3 readings
Big Data, Bigger Outcomes30m
Welcome to Peer Review Assignments!10m
Why Doctors Hate Their Computers50m
1 practice exercise
Module 4 Quiz30m

Instructor

Avatar

Doug Berman

Director, Data Acquisition and Architecture
UC Davis Health System

About University of California, Davis

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....

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....
Health Information Literacy for Data Analytics

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • 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.

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