Steps for Maintaining Data Integrity
MANAGING AN ORGANIZATION’S data and those who enter
it is an ongoing challenge requiring active administration and
oversight. The following best practices help organizations
maintain their data dictionaries and data integrity:
x Know the data
x Map the data across all systems
x Develop a data quality management process that
includes ongoing maintenance and review of the data
x Comply with regulations and standards
x Ensure accuracy of data collection and reporting
x Establish change management policies and proce-
x Develop active and ongoing user education and train-
Organizations should consider transitioning to a core data
service model where the key or common data are centralized
and can be accessed by many, thus reducing the incidence of
potential introduction of error in the collection process.
Organizations should understand the data’s importance before making changes. They should define and document a data
dictionary for each system and understand what data are currently collected, why they are collected, and how they are used.
They should research what impact a change to the system would
have on the data.
Map the data across all systems. Exchanging information
among systems within an organization and with outside organizations is vital to conducting the business of medicine. Data
exchange has gained new importance in light of the meaningful
Organizations should ensure all data uses are consistent by
mapping each element across each system and facility and resolving any discrepancies.† An organization should not assume
that other organizations do what it does with a data element of
the same name.
Organizations should also identify what data are required for
HIE participation and any local requirements for coding and
reimbursement. Stages 1 and 2 of the meaningful use program
define what data will be shared (data capture and data sharing).
Many of the meaningful use requirements are built around
production of aggregate data from multiple systems. This makes
it imperative that organizations ensure consistency of data
The key to achieving meaningful use success is effective data
management and mapping; understanding and effective implementation of vocabulary standards; and alignment with terminologies and classifications. 4
Develop a data quality management process that includes
ongoing maintenance and review of the data dictionary. 5 To ensure data consistency and accuracy across an organization, the
process must be under the direction of an enterprise data qual-
ity steering committee. It should include four key components:†
x the purpose for which the data are collected;
x the processes by which data are collected and changes
x the processes and systems used to archive data and data
x the process of translating data into information utilized
for an application.
Examples of data quality management activities include:
x Frequently reviewing and validating data dictionary con-
tent by checking the data quality of clinician entries to en-
sure proper application and use of data
x Reviewing documentation for errors based on poor tech-
niques such as pulling information forward in the EHR
through copy and paste that was not verified or validated
by the clinician
x Ensuring overall record integrity among enterprise sys-
tems as well as across organizations through periodic re-
view and audit of actual practices
Comply with regulations and standards. Standards are critical because they are the basis for data exchange and interoperability. To ensure compliance, it is essential that all data collected
be compared against current state and federal regulations and
accreditation agencies (e.g., the Joint Commission) when developing new data fields or performing routine updates.
Ensure accuracy of data collection and reporting. Data reports must be validated to ensure the accuracy of the information produced.† Examples of questions to ask when reviewing
data include: Are outliers based on accurate data or are they the
result of end user error? Are errors related to a single end user or
are they systemic?
If the data reveal apparent inaccuracies, the organization may
need to review its data collection process to ensure it is correct
and being followed correctly.
Establish change management policies and procedures.
Organizations should develop a formal change management
process through which all changes to data dictionaries are coor-
dinated. Change management policies and procedures will help
organizations prevent disruption of other systems that interact
with that particular application.†
Implementing a process for changes, modifications, or dele-
tions to the data dictionary will also ensure consistency in in-
terpretation and version control, if multiple iterations exist or in
the event of staff turnover.
Develop active and ongoing user education and training.
Organizations must institute an active and ongoing education
and training program for all staff who collect, use, analyze, or
interact with data on any level. Ongoing education is critical to
maintaining a high-quality data dictionary.† Staff turnover and
changing data requirements and demands necessitate continuous training.