I’m working on a very
interesting project in a healthcare environment this week. It isn’t my usual type project where we are
interested in improving the throughput of patients through a process like an
Emergency Department, but it’s clear that improved throughput will happen as a
result of this team’s efforts. This
project involves the billing for immunizations at a healthcare organization’s 6
different satellite operations. It seems
as though there are numerous billing errors that translate into lost revenue
for the hospital, so they called me in to help them reduce these errors. The team make-up was important because it
included Medical Assistants (MA’s), front desk personnel, and an expert in
Clinical Informatics plus two Green Belts.
The first thing I had the
team do was create a Pareto Chart of the six satellite operations to determine
the distribution of billing errors between the operations. As you can see, there is a wide disparity
between each of the offices in terms of number of billing errors. for data collected over a three month period
(Oct-Dec, 2012). On the surface one
might conclude from this analysis that we should review the process in Location
# 2 and have all sites use their process.
This would be a good approach if all things were equal, but digging
deeper into the data indicated that there is a wide disparity between locations
in terms of the number of immunizations delivered (i.e. number of patients
immunized).
The next thing I had the
team do was to develop a causal chain to determine potential root causes for
billing errors across the different satellite organizations. This effort proved to be very valuable from a
problem solving perspective, but even more so it was very enlightening for the
Medical Analysts who comprised the majority of the team. It helped them crystallize their thoughts
about what was happening in their own site location.
For those of you who may not
have used a causal chain before, we started by developing a very succinct
problem statement which in our project was Billing Revenue Lost. The object (Billing Revenue) is listed on top
of the line and the state that it’s in listed directly beneath it (Lost). We then asked the question why? To determine potential reasons why
revenue was being lost.
The answer to this first “why?”
was actually five potential reasons as follows:
1. Order
(Object) – Not documented in TW (State).
TW is the name of the database being used.
2. Order
(Object) – Not Bubbled (State)
3. Billing
Company (Object) – Commits Errors (State
4. Charges
(Object) – Incorrect (State)
5. Standardized
Work (Object) – Not followed (State)
We continued asking why
until we arrived at an actionable item we could work on for each of the five
potential root causes. The team’s most
enlightening potential root cause listed on the chain had to do with the
standardized work which had been developed for each of the sites. When the team asked, “why is the standardized
work not being folled?” there were three reasons:
1. The
standardized work was not communicated, meaning that several of the MA’s didn’t
even know it existed.
2. The
standardized work was not “owned” by the MA’s
3. The
standardized work was not enforced by the leadership.
For reasons 1 and 2, the
team asked “why?” again and concluded that the MA’s weren’t involved in the
development of the standardized work so they didn’t own it and didn’t know it
was even available to follow. As I’ve
written about many times in this blog, if the subject matter experts (in this
case the MA’s) aren’t involved in creating the process, it won’t be credible to
them. And because it isn’t credible, it
won’t be followed and used. It really
matters not what the industry is, in every case what I typically see is
leadership creating the process with very little, if any, input from the SME’s
so it’s not owned, believed or used.
The team then reviewed each
potential root cause to determine one of three different “ratings.” If they believed the entity was “solvable” it
was color-coded as green; if they believed the entity could be influenced, it
was color coded as yellow; if they did not believe they could solve the entity,
it was color-coded as red. In all cases
it was determined that all entities were either “solvable” or “influenceable.”
The team then decided to develop
process maps for each different locations and then perform a value analysis on
each one. What the team found was very
interesting in that, not surprisingly, each of the locations had a completely
different process. Although I’m not at
liberty to visually display each of the different process maps here, suffice it
to say that some of the differences were quite significant as were the number
of billing errors.
In addition to the five
current state maps, the team then developed two separate future state maps, one
for scheduled patients and one for walk-in patients as demonstrated in the
figures above. This was an important
step because in order for the team to improve the process (i.e. build their
future state), it had to first understand their current reality.
In the table below we see
the results of this team’s actions in terms of the before and after for total
number of steps, value-added, non-value-added, and non-value-added but
necessary and as can be seen, the differences were quite dramatic. Of particular interest was the reduction in
the number of yellow and red steps.
The team is very confident
that if the two, new future states are implemented, the number of billing
errors will be significantly reduced (i.e. >75% or more), but there is
another benefit that is much larger. The
team was able to identify a key policy constraint that is delaying the
administration of immunizations. The
team reasoned that if the physicians will simply enter the order into the data
base, instead of waiting to verbally explain to the MA’s what the order is when
they exit the examining room, the MA’s can prepare all of the necessary forms,
supplies, medication, etc. needed to administer the vaccine in advance, thus significantly
reducing the wait time for the patients.
The team believes that breaking this policy constraint will have several
very important effects:
1.
By decreasing the patient wait times, patient
satisfaction scores will increase which could have a positive impact on
reimbursement rates.
2.
Since the overall immunization cycle time
will decrease, the number of patients that can be seen each day by each
location should increase, thus increasing the overall revenue base.
3.
Because the MA will no longer be getting
verbal orders from the physicians, the number of mistakes due to interpretation
of the verbal orders, errors will decrease significantly.
These effects are clearly
very important, but the team believes that a significantly larger financial impact will be seen if their new
process is expanded to other processes such as EKG’s, Point of Care Testing,
etc. These items are much higher revenue
generators than the revenue gained through immunizations, so the impact on the
organization’s bottom line could be substantial.
This team learned several important
things during this event, not the least of which was the value of bringing
together the true subject matter experts, the people actually doing the work,
to develop the “best” way to perform different functions. Comments like, “I finally have a voice!” were
not uncommon.
One of the things I always
do in an event like this is to measure the team’s pulse before and after the
event. The purpose of this action is to
get a feel of the team’s belief in two distinct areas:
1.
Does the team believe that what they came
together to do would be difficult or easy?
2.
Does the team feel that it will be empowered
or authorized to make their recommended changes.
The figure below is the tool
I use to measure the pulse of the team by having them place red and green dots
in one of four quadrants:
·
Quadrant 1:
Hard to do and not authorized to make changes
·
Quadrant 2: Easy to do, but not authorized to
make changes
·
Quadrant 3: Hard to do, but empowered to make
changes
·
Quadrant 4:
Easy to do and empowered to make changes
At the beginning of the
event the team is instructed to place a red dot in one of the four quadrants
and then a green dot at the conclusion of the event. At the beginning of the event, for the most
part this team felt empowered, but that it would be difficult to make
changes. At the end of the event, the
team believed that they actually were both empowered and that the changes would
be easy to make.
Bob Sproull
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