Ways to Present Statistical Information
Analytics is the driving mechanism in many decisions made in
healthcare today. Without a basic understanding of statistics and being able to
interpret models of data it can lead organizations down an erroneous path. The
complexities of data can leave decision makers distraught and put them in a
position of uncertainty. If organizations want to prosper in analytics they
need to develop a strong analytics team. These members of the organization
understand the data and with clear goals can translate data into consumable
format for clinical and business leadership. Analytical teams must understand
the context of the data and who the recipient is to provide meaningful
information (Strome, 2013).
Today’s organizations are swimming in data. With the vast
amount of data in the healthcare system it is easy to misinterpret information.
The first step in improving transparency of data is to have a strong analytics
team. Analysts can extract necessary data to translate it to consumable
information. Moreover, analysts can develop dashboards that gives the reader a
simple representation of current indicators along with other information that can
be understood by different members of leadership (Strome, 2013).
Organizations should also implement different analytical
tools to reduce transparency of data. Implementing data mining tools such as
text mining will allow organizations to transform unstructured data into
structured data that can be reviewed and analyzed. Lorant (2016) proposes a
four step process to improve data transparency.
1.
Get data in one place.
2.
Index and catalog.
3.
Automate data sets against federal safe harbor
guidelines.
4.
Control access at the data level.
This process is focused beginning to end in cleaning and
structuring data so the it can be easily accessed and reviewed for future
projects. This is a crucial step after developing a team. In healthcare there
are many variances of data that can make it difficult to review and analyze
data appropriate for specific projects. Moreover, data mining becomes a
cumbersome task when there is no structure to the storage of data.
Using the steps from creating a team of analyst,
implementing new tools, and having a structured storage plan will make data easily
accessible. Teams will be able to create understandable metrics that can be
used for current and future business or patient care initiatives.
References:
Lorant, A. (2016). HIT Think Four steps to make your data more consumable. Retrieved from: https://www.healthdatamanagement.com/opinion/four-steps-to-make-your-data-more-consumable
Strome, T. (2013) Basic statistical methods and control
chart principles. Healthcare Analytics for Quality and Performance
Improvement. New Jersey: John Wiley & Sons, Inc.
