Influence of Data Quality
How does data quality influence/impact an organization's
strategies, revenue and financial objectives?
Data quality is fundamental in creating information that
administrative, clinical, and operational staff within a healthcare
organization (HCO) can use to streamline processes, develop polices
etcetera. All levels of an HCO use data
for different purposes and rely on accuracy to complete specific tasks.
Operations for example, relies on accurate documentation from coding to
complete billing. If inaccurate diagnosis codes are used or invalid procedure
codes entered, then it can cause claims to get denied. Similarly,
administrative personnel may rely on accurate demographic data entered at
intake to review the geographic information of current patients. This type of
data could help determine their current market and see if there are other target
markets they can tap into to increase revenue. The integrity of data quality is
an important aspect of healthcare and poor data can lead to many issues
including liability risks, quality of care, and increased costs and
inefficiencies (Wager, Wickham Lee, Glaser, 2013, p52).
There are many challenges for data quality including lack of
universal data standards. Unfortunately, the issue with healthcare as in other
sectors is that data driven information differs greatly. For example, marketing
may need general diagnosis metrics within the organization to compare care
outcomes between organization A and organization B. Whereas clinical staff may
need specific diagnosis information to develop care plans. The issue with data
driven by situations is that it does not allow data to be standardized thus
creating issues of how data should be entered and stored (Wager, Wickham Lee,
Glaser, 2013, p55). According to the Markle foundation, seventy-nine percent of
errors come from data entry. Random errors and systemic errors are the two
terms used when discussing data errors (Wager, Wickham Lee, Glaser, 2013, p
52:59). The combination of lack of data standards and data errors have the
potential to impact organizational strategies, revenue and financial objectives
in a negative way.
Organizations such as the Medical Records Institute (MRI)
and American Health Information Management Association (AHIMA) have helped
create guides for organizations to develop data management tools to ensure
quality data. Through implementation of these guidelines to develop quality
data sets organizations can set policy on how and what information is entered.
This information can later be shared across multiple groups within the
organization to develop information that can be used in various situations
(Wager, Wickham Lee, Glaser, 2013, p55). By organizations implementing these
steps to create a standard order of how data should be entered it will not only
help develop and streamline new processes, but also make it easier to share data
across organizations. McEvoy (2009), argues that data integrity is challenged
when sharing data across organizations. If organization A shares inaccurate
information with organization B then it can negatively affect the clinical
team, the patient and so on. Moreover, having policy on how long data is stored
is also an integral part of patient care. Sarbanex- Oxley act based on three
rules destruction of records, retention of records, and type of records stored helps
healthcare organizations manage data that can be used for future use or current
use. The ability store and share accurate data benefits the patient and
organization. According to AHIMA (2008), Healthcare data collected during the
course of care can improve performance measurements, patient safety
initiatives, and population health reporting.
Moreover, the quality of data has the potential to impact
organizations in many settings. Accuracy and guidelines on how data should be
entered and used is a key for organizations who plan on using data sets to
create information. The storage of data is also very important because it
allows organization to perform many functions such as quality performance
measurements. The healthcare industry will benefit greatly if all organizations
develop plans based on MRI and AHIMA guides to store, created and access date.
References:
AHIMA. (2008) Statement on data stewardship. Retrieved from:
http://library.ahima.org/doc?oid=100307#.WCFiJqOZNBw
McEvoy, C. (2009) Data issues in hie. Retrieved from: http://health-information.advanceweb.com/Article/Data-Issues-in-HIE.aspx
Wager,K., Wickham Lee, F., & Glaser, J. (2013).
Healthcare information systems a practical approach for healthcare management
(3rd ed.) San Francisco, CA: Jossey-Bass
