2. 2
Three years ago, Thomas Carlough, a
former lead pharmacist and pharmacy
informaticist who is now the Coordinator of
Pharmacy Data Services at Atlantic Health
System, began working on data analysis
for a study on the safe and effective use
of insulin. The study, led by the Joint
Commission Center for Transforming
Health Care, explored a basic question:
Why do some patients who successfully
managed their blood glucose level at
home become hyperglycemic when
they are a hospital patient? In Six Sigma
terminology, the Joint Commission probed
for hospital processes that could contribute
to a patient’s defect status by identifying
where data differed between groups.
The variety of data points and data sources
was complex, taking into account not
only glucose levels but also the timing
of meals, size of meals, which parts of
the meal were eaten, timing of insulin,
amount of insulin, and other variables
that might impact the measurement of
glucose. The tools that were available
for Carlough to process this data were
simple. An executive report writer and
SQL queries from flat files could produce
a printed sheet of numbers and simple
aggregations, but there was no easy way
to attach different data sources to each
other and find value in them.
“I was pulling huge data sets, but
creating a cohesive picture from all
that information required a lot of
manual labor.”
The first stage of the project was a
success—reflecting a 30 percent drop in
the defect rate—but processing the data
was cumbersome and time-consuming.
When Atlantic Health System began a
new and wider-reaching medication safety
initiative, a more powerful data tool was
needed.
Introduction
Atlantic Health System
Morristown, New Jersey
• Five hospitals providing care in the New Jersey,
Pennsylvania and the greater New York metropolitan
area, with more than 600 community-based affiliated
health care providers
• Named Hospitals and Health Networks magazine’s Most
Wired health organization six years in a row, 2010-2015
• Named one of Fortune magazine’s 100 Best Companies
to Work For seven years in a row, 2009-2015
Critical Issues
• Complex medication safety initiatives required a level
of data analysis not available in previously used report
writing systems
• Accessing and validating data could be cumbersome and
slow
• Flat reports did not include the reasons behind the
numbers, preventing a full understanding by key users
Solution Spotlight
• McKesson Analytics Explorer™
• McKesson Performance Analytics™
Results
• Combining multiple streams of data into one accessible
source empowered users to make new connections
among disparate elements of patient records
• Data visualization drove discovery of root causes for gaps
between performance goals and actual outcomes
• In-house control of the data increased both accuracy and
confidence in data quality
• Provided data transparency and easy access via
customized dashboards to directly improve quality
3. 3
Transitioning to McKesson Analytics Explorer™
Atlantic Health System has a tradition of
quality and employs several adverse drug
event (ADE) reporting tools and surveillance
technologies to safeguard its patients. As
Atlantic Health System looked to expand its
surveillance from reported ADEs to event
indicators, it was clear that conventional
tools would prove inadequate. Atlantic Health
System began using McKesson Analytics
Explorer, McKesson’s data visualization
solution, to build medication safety
dashboards that would track six types of
event indicators and instances of a patient
going into defect status, according to different
types of medication:
• Opiate rescue
• Anticoagulant bleed
• Insulin hyperglycemia
• Oral therapy hyperglycemia
• Anticoagulant Heparin dosing
• Anticoagulant Warfarin dosing
To make the most of available data
streams, Atlantic Health System leveraged
McKesson Analytics Explorer to combine
clinical, administrative and financial data.
Creation of the med safety dashboards
drew information from multiple sources:
• Encounters
• Lab results
• Diagnoses
• Medication administration
Carlough joined the core team to build
the dashboards. He quickly saw the
difference a comprehensive analytics and
visualization solution could make. Atlantic
Health System used specialized software
for each individual department or service
line. As a result, the data didn’t yet exist in
one common data warehouse.
# IndicatorID ADEAlertEvent Denominator
1 OpiateRescue
Naloxonew/in72hrsofnarcoticadmin
Countedeachtimeonefollowstheother.
MonthlyPatientswith
NarcoticsAdmin
2 ACBleed
Anticoagulantadminwithnon-POAhemorrhage
relatedDx.EventshowninMonthofDischarge.
MonthlyPatientswith
AnticoagulantAdmin
3 InsulinHyperglyc
Glucose>300postSCInsulinAdmin.Counteach
highGlucoseResult3hrsafterprevious.
MonthlyPatientswithSC
InsulinAdmin(ExcludePOADKA)
4 OralTXHyperglyc
Glucose>300postSCInsulinAdmin.Counteach
highGlucoseResultpreceededbyAdminand3hrs
afterprevious.
MonthlyPatientswithoral
HypoglycemicAdmin
5 ACHeparinDosing CountEachPPT>100postIVHeparinIV MonthlyPatientsIVwith
HeparinDays
6 ACWarfarinDosing CountEachINR>4; 48hrspostWarfarinAdmin
MonthlyWarfarinDays48hrs
post1stAdmin
“One exciting part of this
project was the complexity
of the data and the number
of data sources used to pull
information. McKesson was
able to add clinical data to the
administrative and financial
data already in use.”
4. 4
Transitioning to McKesson Analytics Explorer™
Combining into one source: In the
Inpatient Glycemic Control project, the
report writer created simple totals and
averages. McKesson Analytics Explorer
allowed a wide range of possibilities,
combining data from different sources
and connecting it in different ways.
“I no longer had 16 different flat
files. I could load them all into
McKesson Analytics Explorer and
choose the connections I wanted to
make between them.”
Reducing manual labor: With real-
time connections to data sources and
business insight subsets, users could
spend less time on data input and more
time on finding value in the data.
Visualizing to cue further actions:
Visual representation of the data
made it easier to find opportunities
for intervention. For example, the new
medication safety dashboard not only
showed the gap between the target rate
for ADEs and the actual rate, but also
allowed users to explore the reasons
behind the gap. Atlantic Health System
found that distinguishing between the
patient rate and the event rate – the
number of glucose measurements over
180, and not simply the number of
patients who exceeded a glucose level
of 180 – allowed them to identify an
important quality measure for patient
care. A bar graph next to the tabular view
of the data made it easier to understand
the size of the performance gap, compare
performance among facilities or, by using
red or green color cues, highlight the
severity of the gap.
5. 5
During the Inpatient Glycemic Control
project, Carlough’s primary means
of sharing information was to create
individual reports for each nursing unit,
including them in a document and
emailing them to each manager one at a
time. With the new dashboards, not only
can reports be more easily automated,
but users can also access the information
themselves at any time.
“McKesson Analytics Explorer
helped get the data where I
needed it to be and helped us do
more significant analysis. The
greatest fruit that it is going to
bear, however, is giving managers
access to that information at their
fingertips.”
With medication safety dashboards
drawing from data within McKesson
Performance Analytics, McKesson’s
healthcare analytics solution, managers
can determine at a glance how they are
doing on each of the ADE measures, by
weeks or months. By simply switching
to different views, users can make
comparisons by facility, department,
service line, and diagnosis-related group
(DRG). Units can assess how they are
performing comparatively and see
whether they are improving over time.
Making Information Usable
6. 6
Using color-coded tree maps, Carlough
also created visualizations that show
the different locations in a hospital at a
glance. Users can see how well they are
doing compared to each other, to the
hospital average, or to a stated goal on a
given measure. When nursing managers
log into the dashboard, they see a
series of measures with visual cues. A
measure in red shows an area needing
improvement, while green bars indicate
the unit is performing well. The dashboard
also gives users the ability to drill down
and identify the factors driving the
performance measure. For example, the
data may show that a unit is holding too
much basal insulin. From the dashboard,
a user can drill down to identify individual
practitioners who aren’t performing
according to benchmark practices and
work with them to improve.
“The number is less important
than the impact it has. We are able
to get to the reasons behind the
data and take appropriate action.
Accessible data can also point toward
opportunities for further exploration. In the
case of anticoagulant bleed, the collected
data showed that the difference in the
length of stay between a patient who
bled and a patient who did not was 11
days — a significant divergence. Additional
analysis of those cases may clarify the
relationship between an anticoagulant
bleed and length of stay. That in turn
could dramatically affect both the quality
of patient care and the eventual cost of
individual hospital stays.
Making Information Usable
7. 7
Assuring Data Quality
As Atlantic Health System integrated a
new system with the data streams already
in place, transparency, quality assurance
and data confidence were top priorities.
Although some debate was inevitable,
ultimately working through the process of
building new reports and dashboards led
to data that was considered trustworthy
and usable.
Data definitions
A key factor in building data confidence
was standardizing the definitions behind
it—and understanding when those
definitions needed to be customized.
Often an element of data will have
different definitions depending on the
context. When disparate data streams are
combined, conflicts can arise. Through
discussions with multiple stakeholders,
Atlantic Health System built consensus on
data definitions that ensured the accuracy
of the results.
Visualizing problems
Being able to visualize the data also
ensures that the numbers add up. Drilling
into the data through the detail screen
allowed Atlantic Health System to ensure
categorization remained consistent,
by including data that may have been
previously overlooked.
“Not only is McKesson Analytics
Explorer great for creating
visualizations for the end users,
but it also makes developing
analysis simpler. Because of the
detailed drill downs, you can do
your own quality assurance as you
build it out. The software really
made it as easy as possible.”
Confirming accuracy
The immediate availability of data
provided by using McKesson Analytics
Explorer made locating conflicts in the
data simpler and faster.
8. 8
Improving Patient Care
Data visualization isn’t just the latest
tech buzzword. When put into practice, it
means seeing old problems in new ways
that lead to new solutions.
For Atlantic Health System, that meant
moving into the next phase of its
medication safety projects with a more
robust system for collecting, processing
and utilizing data. Beyond measuring
improvements in outcomes, they were
able to measure process statistics, such
as the number of times basal insulin was
held, or the timing of an insulin dose with
respect to a meal. Atlantic Health System
is creating visualizations that highlight
any gaps between individual practitioners’
insulin management and the current
benchmark practices in their discipline.
The capacity to keep adding new
data sources continues to expand
possibilities. Data from hospitalist groups
has recently been added to the mix
to allow comparisons among hospital
units. Seeing the difference in patient
outcomes drives discovery of root causes.
In the case of the Inpatient Glycemic
Control project, such root causes could
be outdated methods of using insulin
that are no longer the health system’s
standard for care. Giving this information
to the hospitalist groups helps them to
push for concrete changes in physician
practices.
Adopting McKesson Analytics Explorer
also led to combining elements of data
that had not been connected previously.
For example, while ICD-9 codes were
present in the billing systems and
encounter data, they were not included
in the clinical database for the Inpatient
Glycemic Control project. Connecting
these data sources allowed Atlantic
Health System to distinguish between
patients based on diagnosis. Looking at
the blood glucose level of diabetic and
non-diabetic patients separately, the
analysis verified that both patient groups
showed a decline in blood glucose day
over day while in the hospital, into the
normal range. As raw numbers, the blood
glucose measures look very different
—165 for diabetic patients vs. 110 for
non-diabetic — but when put into context
they tell a consistent story about the
effectiveness of Atlantic Health System’
insulin management
9. 9
“The medication safety initiative
was a nice project to show the value
of the solution, but the sky is the
limit.”
To capitalize on the enhanced availability
of their data resources, Atlantic Health
System created a new pharmacy position
to perform data analysis. Soon, Pharmacy
Services and other Atlantic Health System
service lines and departments hope to roll
out more dashboards to influencers in the
organization—hospitalist groups, nursing
management, certain PCUs—who can take
action based on visualized information.
According to Carlough, “We would like
to get it in the hands of those who can
affect change. Then we can advance more
quality improvements and watch these
metrics move.”
Next Steps for
Atlantic Health
Atlantic Health System has
planned and initiated new
projects to take advantage of
their upgraded analytic and
visualization capabilities:
• A dashboard allowing users to follow
the path of medication around the
hospital, from the pharmacy to the
floor
• Coordination of data in the financial
and clinical systems, using data
visualization to find points of conflict
between data sets
• An audit of ADEs
