So, we followed a Lean Sigma framework,
an approach to our problem and we spoke to various stakeholders.
We went to the gemba,
we went to the front line,
to the wards, and we observed the admissions process and we walked through the process,
spoke to pediatric residents, nurses, pharmacists,
and patients and families, individual discussions,
group discussions, and even surveyed for their input.
So one of the key aspects of trying to define
the problem is to obtain the voices of the customers.
So in discussions with the residents nurses and,
pharmacists, and patients, and families,
we were able to better understand the process and we developed
a Swim Lane diagram to help us define
the process and the individual roles in the process.
We also created an Ishikawa Cause and Effect diagram or a Fishbone bone diagram,
to help us identify the root causes contributing to the problem.
In this diagram, we can see that
the various contributing factors are organized by categories.
Such as; people, including physicians, nurses,
and pharmacists, where patients and families,
also categories of task related factors,
technology related factors, and policy related factors.
Through this Fishbone diagram coupled with the discussions with our various stakeholders,
we identified two areas needing improvement and where we could target our interventions.
These included; the lack of knowledge about the RxWriter system and how to use it,
and the lack of accountability for not performing
medication reconciliation in the RxWriter system,
and for clearing and ensuring home medications.
So, how do we define the measurement for medication reconciliation?
We spent quite a bit of time thinking about this.
So it was a bit of an iterative process.
And we had different ideas for measurements and then had to think
about whether or not our definitions were feasible to collect.
Did we want to go with a count?
Will the constant relief really portray a real picture of what was going on?
If we had 10 patients who did not have medication reconciliation performed,
what we didn't know was how many patients did we look at.
Was that 10 out of 10 patients that were admitted?
Then, that would be everybody.
Or was it 10 out of 100 patients?
So, it gives a different picture if we're dealing with percentages or ratios.
We thought that a rate or a percentage would be a better measure.
We had the opportunity to work with IT folks in figuring out what was
feasible to obtain from
our electronic health record data and to provide automated data reports.
So we needed to check to make sure that the reports were accurate,
which took a bit of back and forth before launching.
So the key measure that we finally decided upon was to look at our numerator,
the number of patients with old prescriptions from
prior discharges remaining in RxWriter for more than 24 hours after admission.
And this metric was a reflection of
medication reconciliation not being performed appropriately on admission.
Our denominator included the number of patients who were admitted for more than 24 hours.
So, once we had defined our metric,
the next step was to collect baseline data.
And one of the questions that we had to ask ourselves is,
does the baseline data reflect a true problem?
Because if not, it's indicating that everything is okay.
We either need to move on or if we really know there's a problem,
then we need to select a different metric to demonstrate improvement.
So, in our case,
we found that 32 percent of
the patients did not have appropriate medication reconciliation performed on admission.
So they still had old prescriptions from
prior discharges remaining in the RxWriter system.
So there's definite room for improvement,
and we selected our goal to reduce this by 50 percent.
Taking a closer look at the data,
it would have been better if we had had more data points in our baseline period.
But what we do see is that there are
not wide variations of the data that we had collected.
And the reality was that at this point in time,
we were pressed to improve
this system to prevent adverse events from occurring in our patients,
and we had taken time to ensure
the accuracy of the data that was being pulled from our electronic health records.
So there's a constant push and pull in
this improvement process of the pressures of wanting to move forward to try to
improve the process with taking the time to
try to collect as much baseline data as possible to demonstrate improvement.