Career prospects are bright for those qualified to work with healthcare data or as Health Information Management (HIM) professionals. 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. This serves care providers, patients, doctors, clinicians, and those who carry out the business of improving health outcomes.
"Big Data" makes headlines, but that data must be managed to maintain quality. High-quality data is one of the most valuable assets gathered and used by any business. This holds greater significance in healthcare where the maintenance and governance of data quality directly impact people’s lives. This course will explain how data quality is improved and maintained. You’ll learn why data quality matters, then see how healthcare professionals monitor, manage and improve data quality. You’ll see how human and computerized systems interact to sustain data quality through data governance. You’ll discover how to measure data quality with metadata, tracking data provenance, validating and verifying data, along with a communication framework commonly used in healthcare settings.
This knowledge matters because high-quality data will be transformed into valuable insights that can save lives, reduce costs, to improve healthcare and make it more accessible and affordable. You will make yourself more of an asset in the healthcare field by what you gain from this course.
Overview
Syllabus
- 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.
- 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.
- 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.
- 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.
Taught by
Doug Berman
