practice guidelines for managing health information
WHILE THE IMPORTANCE of data quality in providing
high-quality clinical care in today’s healthcare setting is
typically well understood, the quality of data for report
building and validation activities is often not well articulated—and potential data quality issues that impact the accuracy of reports are a frequent, unwanted outcome. Quality data for reporting and validation is critical to ensure that
business decisions based on data have positive outcomes. As
a result, data quality must be fully understood and continually managed to avoid possible false conclusions or, even
worse, negative outcomes.
This Practice Brief outlines best practices regarding data
quality characteristics. Application of these characteristics
can be applied to healthcare data to ensure success when
building reports, validating data, planning methodologies,
and analyzing data for both clinical and operational business needs.
Data Quality Defined
To understand how to improve the quality of data reporting,
one first must understand what is meant by the term data
quality. Data quality simply means that the data that is being reported is meaningful and serves its intended purpose.
The Centers for Disease Control and Prevention (CDC) has
defined the six core data quality dimensions as: 1
Completeness. The data is comprehensive and complete.
All data values are recorded.
Uniqueness. Data is unique and one-of-a-kind. Duplicates are avoided.
Timeliness. Data represents reality at the point in time in
which it is collected.
Validity. Data measures what it is intended to measure.
Accuracy. Data is reflective of real-world values.
Consistency. Data values are consistent across data sets.
Data can be matched. There is no conflicting information.
These six dimensions can be managed through data quality
management. Data quality management refers to “the business processes that ensure the integrity of an organization’s
data during collection, application (including aggregation),
warehousing, and analysis.” 2 Both data quality and data quality management are essential to the success of report building
and data validation.
Data Collection and Report Building
Building a report starts with the data collection. This is especially important and pertinent for current health information
management (HIM) practices with increased information
technology and rapidly growing mountains of information.
The first step is understanding the purpose of the data collection, different types and sources of data, and key factors that
relate to building a report.
Purpose of Data Collection
Healthcare organizations collect healthcare data for different
The ability to compare hospitals’ performance with a peer
group, especially with an organization of excellence, is
beneficial in today’s competitive environment. Benchmarking has become a common tool used even at the departmental level.
Clinical decision support provides expert knowledge to
healthcare providers to assist them in making the best
decisions regarding patient treatment and care.
The Medicare.gov website Physician Compare maintains information on hospitals, doctors, nursing homes,
home health agencies, dialysis facilities, and drug and
health plans for the Medicare beneficiary. All of this
information is available to the public. One can compare information about the quality of care and services
these providers and plans offer and obtain helpful tips
on what to look for when comparing and choosing a
provider or plan.
Any healthcare organization or vendor collects and uses
information to understand a population and make operational decisions with the purpose of improvement.
It can be for quality, payment, productivity, accuracy,
financial, resource management, or trending. Software
is designed around data elements necessary to capture
the information necessary for use. More health information and informatics management professionals are
needed in the development of health information technology because HIM professionals have knowledge of
the information they are trying to capture. Excluding
HIM in decisions causes other problems instead of providing a solution, which ends up costing organizations
Best Practices for Data Analytics Reporting
Lifecycles: Quality in Report Building and