Healthcare Visualizations: Are You Getting the Entire Story?

Healthcare Visualizations:
Are You Getting the Entire Story
– Justin Gressel
Lisa Lendway
Jack Thompson

© 2014 Health Catalyst
www.healthcatalyst.com
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Data Visualization
The emergence of powerful and
user-friendly data visualization
programs (e.g., Tableau, Qlikview,
Spotfire, Power BI, etc.) has
transformed analytical reporting.
The amount of information
conveyed by all types of graphs,
symbols, sizes, and colors is
staggering, and the ability to “drill
down” on the fly to increasing
levels of granularity allows for all
manner of analyses.

Data Visualization
The power and ease of creating
these visualizations, combined with
the increased emphasis on making
evidence-based decisions, pressures
leaders to request large amounts of
data and graphics in order to make
the most informed decision possible.
The downside of this data hunger is
that it leads to the creation of
simplified, context-free visualizations
which may inadvertently lead to
misinterpretations.

Data Visualization
Cramming as many visualizations as
possible into a report or dashboard
results in dumbed down graphs with
critical information missing.
It’s similar to reading a story where
key details have been left out,
forcing the reader to mentally fill in
the blanks and complete the story.
Without all the relevant details, it’s
impossible to grasp the full story.

Data Visualization
The importance of providing context
for visualizations within the domain
of patient satisfaction is emphasized
in the following slides, although the
lessons can be applied to any area
of data-driven decision making.
Tips are shared to overcome
common pitfalls to ensure that the
entire story is being told.

Include a Comparative Frame of Reference
When a change does occur or a
true trend appears, it may be
difficult to know whether the change
is expected or unplanned.
There may be seasonal (e.g., day of
week, month, quarter) or market-
based (e.g., political/regulatory,
economic, or social/cultural) effects
influencing the metrics.
Having a frame of reference
enables the organization to
accurately, and confidently, evaluate
current performance.

Seasonal effects should be
accounted for via inclusion of
performance over a comparable
time period (e.g., last year).
This frame of reference can be
compared and if a consistent dip
or upturn during a comparable
time period is noticed, it can infer
the possibility of a seasonal effect.
A downward trend may not be of
concern if the rest of the industry
is heading in the same direction.

Figure 1 appears to show an upward
trend in patient satisfaction up
through the midpoint of the year,
followed by a decline.
Before drawing any conclusions, the
performance should be compared to
a similar time frame in the past
and/or relative to peers.
Figure 2 shows the same trend with
the prior year’s data (orange line),
indicating a seasonal effect where
satisfaction peaks midyear.
The increases do not represent
actual improvement based on
historical performance. What would
be worrisome is if the organization
didn’t show increases at all.

Account for Natural Variability
If average patient satisfaction is
higher during the current time
period compared to the previous,
can we say that satisfaction
increased?
Not necessarily. The averages for
any given time period are based
on samples of a patient population.
To definitively say whether a
change has occurred, the natural
variability of the data must be
taken in to account.

The best way to account for natural variability in a graph is
to include upper and lower control limits - horizontal lines
that depicts where the data is expected to be.
The inclusion of control limits effectively turns the graph into
a control chart, a popular tool used within quality control.

Figure 3 shows monthly satisfaction
data rising and falling sharply across
12 months. There’s a lot going on in
that story, except that nothing is
happening.
Keep in mind that the data used here
was simulated around a known
average, so there is no pattern
despite what you think you see.
When looking at the same data in the
format of a control chart in figure 4, it
becomes clear that the ups and
downs are just random variability, or
white noise, because the data fit
comfortably within the control limits.

Conclusion
Data visualization is a form of
story-telling, so it’s important the
visualizations make the story clear
through the graphics and details.
All graphics need to have a proper
frame of reference, whether it be a
properly sized axis, visual cues like
control limits that show the typical
range of data values, data from
comparable time periods, or peer
performance.

Conclusion
The ability to enact meaningful
changes within healthcare is
predicated on the ability to
accurately describe the healthcare
environment and detect trends.
Therefore, much care must be put
into how this information is
presented to and interpreted by
decision-makers within healthcare
organizations.
Otherwise, resources may be
misallocated and opportunities to
conduct change are lost.

More about this topic
How an EDW Enables the Best Healthcare Visualizations
Chris Rains – Data Architect, Michael McCuistion – Technical Director
How Healthcare Visualizations Can Improve Organizational Buy-In
Dan Hopkins – Data Architect
Why Healthcare Requires an EDW, Analytics Applications, and Visualization Tools for
Quality Improvement Initiatives
Chris Rains – Data Architect, Michael McCuistion – Technical Director
3 Keys for Creating Effective and Insightful Executive Dashboards
Russ Staheli – Data Architect
Does your solution support reporting dashboards?
― Health Catalyst
Link to original article for a more in-depth discussion.
Healthcare Visualizations: Are You Getting the Entire Story?

For more information:

Justin Gressel joined Health Catalyst in January of 2015 as a senior data scientist.
Prior to coming to Health Catalyst, he worked both in industry (Disney and Great Wolf
Resorts) and in academia as a marketing professor. Justin has a PhD in Marketing from
Purdue, and an MBA and baccalaureate in Statistics from Brigham Young University.
Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Lisa Lendway joined Health Catalyst in January 2015 as a Senior Stastician. Prior to
coming to Health Catalyst, she worked for Allina Health as a Senior Statistician. Lisa
has a PhD in Statistics from the University of Minnesota.
Jack Thompson joined Health Catalyst in January 2015 as Data Analyst, as a part of
the Health Catalyst and Allina Health partnership. Jack’s primary concentration at
Allina Health has been patient experience analytics, and in the future he is eager to
get involved with other data sets. Jack has a Bachelor of Business Administration in
Health Care Management, and a Bachelor of Arts in Philosophy – Applied Ethics from
the University of Minnesota Duluth.

Healthcare Visualizations: Are You Getting the Entire Story?

Recommended

Recommended

More Related Content

Viewers also liked

Viewers also liked (10)

More from Health Catalyst

More from Health Catalyst (20)

Recently uploaded

Recently uploaded (20)

Healthcare Visualizations: Are You Getting the Entire Story?