The goal is to achieve consistently defined and standardized
A simple dictionary can be managed in a spreadsheet or table;
a complex dictionary may require a data management program
application. AHIMA’s “Health Data Analysis Toolkit,” available
in the AHIMA Body of Knowledge at www.ahima.org, includes
an example of a data dictionary.
Data dictionaries should be accessible to data analysts and
those authorized users in the organization or enterprise who
manage data, use data to manage their work, contribute data to
other internal or external systems, and external audit organiza-
tions conducting assessments of information system capabili-
ties. The ability to edit the dictionaries, however, should be lim-
ited to system administrators.†
A data dictionary can be consulted to understand a data ele-
ment’s meaning and provenance. It is a dynamic document that
must be updated as data collection requirements change. The
dictionary acts as a resource when reviewing results of reports
generated from a data system. It serves as an important tool
during data sharing, exchange, or integration purposes.
While data dictionaries are useful for the consistent collection
of data, it is imperative that the data are managed and validated
for accuracy in reporting.
Why Data Standards Matter
Data standards are an integral component to an organization’s
data dictionary. Organizations should align the entries in their
data dictionary with current data standards to ensure they are
Standards play an important role in healthcare. Without standards, the steps toward interoperable HIE might never have
been taken. Standards also have enhanced organizational leaders’ ability to interpret their data for patient care, business, research, and comparative performance improvement reporting
The American National Standards Institute (ANSI) governs all
standards development organizations in the United States. It
uses a consensus process to ensure all interested parties associated with standards are involved in their development.
The federal government has established an initial set of standards to support HIE for meaningful use, but adoption of national standards has not become widespread. 2 As HIE continues
to increase, healthcare organizations will need to properly identify their data elements for appropriate transmission.
Best Practices for Maintaining Data Integrity
Decisions are only as good as the data on which they are based.
The data dictionary is the foundational document for maintaining the integrity of an organization’s data. A detailed and exacting process is required to create a data dictionary.
A data dictionary is a dynamic document that is evaluated as
data needs change or grow. Managing an organization’s data
and those who enter it is an ongoing challenge requiring active
administration and oversight.
The Benefits of a Data Dictionary
A DATA DICTIONARY promotes data integrity by supporting
the adoption and use of consistent data elements and terminology within health IT systems. By adopting a data dictionary, organizations can improve the reliability, dependability,
and trustworthiness of data use.
An established data dictionary can provide organizations
and enterprises many benefits, including:
A data dictionary promotes clearer understanding of data
elements; helps users find information; promotes more effi-
cient use and reuse of information; and promotes better data
1. Department of Health and Human Services. “Health
Information Technology: Initial Set of Standards,
Implementation Specifications, and Certification Criteria for Electronic Health Record Technology.”
Federal Register, July 28, 2010.ht tp://federalregister.
The following best practices help organizations maintain their
data dictionaries and data integrity.
Know the data. Organizations should define the metadata
required of their health information systems and identify implications on technology decisions. The “Data Quality Attributes
Grid,” in the online version of the September 2007 practice brief
“HIM Principles in Health Information Exchange,” provides a
guide for defining data and their attributes.
When possible, organizations should design the data collection system well in advance of system implementation. This will
allow for thoughtful design to identify data elements needed to
achieve the purpose of the collection.
Organizations should not collect data simply because they
can. Irrelevant data become distractions during the analysis
and decision-making processes. Irrelevant or unnecessary data
add hidden, unnecessary costs throughout their life cycle.
Organizations should use the “collect once, use many” rule
for data collection. “Using and reusing health data for multiple
purposes can maximize efficiency [and] minimize discrepancies and errors caused by multiple data entry processes...” 3