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Internet of Things (IoT)
component of good data analysis. In my experience, more than 50% of acted-upon
analyses resulted from stumbling upon something in the discovery process.
• Interpretation: Most people think of the interpretation step when they think about
analyzing data, but it’s actually the smallest sub-step in a long process in terms of
the time analysts spend on it.
• Presentation: Presentation is critical. After all the work for getting data to this
point, the analyst needs to tell a story with the data in a consumable, simple way
that caters to the audience. Presentation principles need to be considered.
These three stages of insightful data analysis will drive improvements. Each step alone,
however, is not enough to create sustainable and meaningful health care analytics. It’s
equally important to empower data analysts to focus on analyzing data, not just capturing
and provisioning data.
8.8 Common Health-Care Analytics Solutions and Their Pitfalls
Many health systems opt to implement one of the following three types of analytics solutions. Although these solutions may initially show promise, they inevitably fall short of
expectations.
8.8.1 Point Solutions
When developing an analytics platform, some health systems deploy one or more bestof-breed or point solutions. These applications focus on a single goal and a single slicing of the data. For example, the solution might focus solely on reducing surgical site
infections or central-line-associated blood infections. One problem with this approach
is something called sub-optimization. While the organization may be able to optimize
the specific area of focus, these point solutions can have negative impacts both upstream
and downstream. They also don’t offer much in the way of insight outside of the specific
area of focus.
Another problem is what is called the “technology spaghetti bowl.” When a hospital
or group practice has only a few point solutions in its dish, a small IT shop can provide
adequate support. But with additional point solutions (consider them noodles, if you will),
an IT department finds it all but impossible to unravel all the disparate noodles in the
spaghetti bowl. Imagine there are 10 different point solutions, and it’s necessary to update
coding standards from multiple source systems in each of these point solutions. You’ll end
up with sauce on your face by the time you’re done.
Eventually, one or two of the senior IT employees may own this spaghetti bowl mess
with a huge dependency placed on these individuals, creating an unstable house built out
of playing cards. While this situation works well initially, if either of the individuals leaves
the organization, then the house of cards will crumble, leaving a mess for someone else to
clean up.
In addition, point solutions typically result in multiple contracts, multi-cost dependencies,
and multiple interfaces. These, in turn, lead to complexity, confusion, and organizational
chaos that impede improvement and continued success.
Internet of Things (IoT)
component of good data analysis. In my experience, more than 50% of acted-upon
analyses resulted from stumbling upon something in the discovery process.
• Interpretation: Most people think of the interpretation step when they think about
analyzing data, but it’s actually the smallest sub-step in a long process in terms of
the time analysts spend on it.
• Presentation: Presentation is critical. After all the work for getting data to this
point, the analyst needs to tell a story with the data in a consumable, simple way
that caters to the audience. Presentation principles need to be considered.
These three stages of insightful data analysis will drive improvements. Each step alone,
however, is not enough to create sustainable and meaningful health care analytics. It’s
equally important to empower data analysts to focus on analyzing data, not just capturing
and provisioning data.
8.8 Common Health-Care Analytics Solutions and Their Pitfalls
Many health systems opt to implement one of the following three types of analytics solutions. Although these solutions may initially show promise, they inevitably fall short of
expectations.
8.8.1 Point Solutions
When developing an analytics platform, some health systems deploy one or more bestof-breed or point solutions. These applications focus on a single goal and a single slicing of the data. For example, the solution might focus solely on reducing surgical site
infections or central-line-associated blood infections. One problem with this approach
is something called sub-optimization. While the organization may be able to optimize
the specific area of focus, these point solutions can have negative impacts both upstream
and downstream. They also don’t offer much in the way of insight outside of the specific
area of focus.
Another problem is what is called the “technology spaghetti bowl.” When a hospital
or group practice has only a few point solutions in its dish, a small IT shop can provide
adequate support. But with additional point solutions (consider them noodles, if you will),
an IT department finds it all but impossible to unravel all the disparate noodles in the
spaghetti bowl. Imagine there are 10 different point solutions, and it’s necessary to update
coding standards from multiple source systems in each of these point solutions. You’ll end
up with sauce on your face by the time you’re done.
Eventually, one or two of the senior IT employees may own this spaghetti bowl mess
with a huge dependency placed on these individuals, creating an unstable house built out
of playing cards. While this situation works well initially, if either of the individuals leaves
the organization, then the house of cards will crumble, leaving a mess for someone else to
clean up.
In addition, point solutions typically result in multiple contracts, multi-cost dependencies,
and multiple interfaces. These, in turn, lead to complexity, confusion, and organizational
chaos that impede improvement and continued success.