About this Course
3,191

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 6시간 필요

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

영어

자막: 영어

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

탄력적인 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

완료하는 데 약 6시간 필요

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

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 2시간 필요

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
6개의 동영상
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
2개의 읽기 자료
A Note From UC Davis10m
Data quality assessment for comparative effectiveness research in distributed data networks30m
1개 연습문제
Module 1 Quiz30m
2
완료하는 데 4시간 필요

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
7개의 동영상
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
1개의 읽기 자료
Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research45m
1개 연습문제
Module 2 Quiz30m
3
완료하는 데 2시간 필요

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
5개의 동영상
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
2개의 읽기 자료
Managing Chaos Part 1: Putting a Change Control Process in Place15m
Managing Chaos Part 2: Change Control Decision Making15m
1개 연습문제
Module 3 Quiz30m
4
완료하는 데 5시간 필요

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
6개의 동영상
Defining Data Governance in Healthcare5m
Why Data Governance Matters in Healthcare8m
Data Governance Committees in Healthcare6m
Data Governance Systems in Healthcare5m
Course Summary58
3개의 읽기 자료
Big Data, Bigger Outcomes30m
Welcome to Peer Review Assignments!10m
Why Doctors Hate Their Computers50m
1개 연습문제
Module 4 Quiz30m

강사

Avatar

Doug Berman

Director, Data Acquisition and Architecture
UC Davis Health System

캘리포니아 대학교 데이비스 캠퍼스 정보

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....

Health Information Literacy for Data Analytics 전문 분야 정보

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

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.