Data Quality and Accuracy in a BI solution
Considering the various data sources a Business Intelligence
(BI) solution may utilize--why is data quality and accuracy an issue and how
can data quality/accuracy be achieved in a BI environment?
With the various sources of business intelligence (BI) being
utilized. Quality and accuracy are key in dictating success of a BI project. IT
can be leveraged to increase value and improve patient satisfaction as well as
improve many other areas of an organization. According to Amarasingham,
Plantinga, Diener-West, Gaskin, & Powe, “higher levels of automation of
notes and patient records were associated with a fifteen percent decrease in
the adjusted odds of a fatal hospitalization” (as cited in Wager, Wickham Lee,
& Glasser, 2013, p. 558). Moreover, BI strategies drive organizations to
develop processes to improve in all areas of the enterprise. Aside from
healthcare organizations (HCO’s) and patients benefiting. Payers also share
process improvements by implementing BI strategies to improve data quality and
accuracy.
Poor data quality can lead to many issues from system
abandonment to poor reporting models. Pant (2009), suggests data quality
approach should be holistic and include enterprise perspective. Moreover,
monitoring of data from beginning to end will be key in ensuring issues are
addressed appropriately. Key performance indicators (KPI’s) will also be
helpful in determining the state of the business, but can also be used to
measure data quality. A major issue in revenue cycle programs is data quality
and reporting accuracy. Using KPI’s to benchmark denials for example and
separating the denials by reasons such as coding, authorization, or medical
necessity denials will help leadership teams determine whether BI is being used
appropriately or if the issue is not directly related to the BI systems.
Although KPI’s and continuous monitoring are key to BI data
quality improvement. Other strategies such as appointing a management position
or leadership role to someone who is familiar with analytics and can help
monitor data integrity will also assist in data improvement. Having a
structured BI strategy will help find areas of improvement and will improve
patient outcomes as well as reduce leapfrogging into inappropriate solution
purchases. With the meaningful use
mandate on data quality to improve care outcomes. Organizations jumped into new
and appealing systems. Although many of the features of these new systems may
be irrelevant to process improvements HCOs felt the need to purchase these
systems under the impression they would help improve data quality. According to
Strausmann “spending more on IT has no guarantee that the organization will be
better off” (as cited in Wager, Wickham Lee, & Glasser, 2013, p. 587).
There are many steps that can be taken to improve data
quality and accuracy. My suggestion would be to develop KPI’s to validate
outliers and find what areas still have room for improvement and what areas are
doing well. Also to appoint a management position to someone to monitor data
accuracy and establish a post-implementation auditing process.
References:
Pant, P. (2009). Essential components of a successful bi
strategy. Retrieved from: http://www.information-management.com/specialreports/2009_155/business_intelligence_bi-10015846-1.html
Wager, K., Wickham Lee, F., & Glasser, G. (2013). Assessing
and achieving value in healthcare information systems. (3rd ed). Healthcare Information Systems A Practical
Approach For Healthcare Management (pp. 558 and 587). San Francisco, CA:
Jossey Bass
