How will you know if your patient safety and quality project is meeting its objectives? Peter Drucker once said “What gets measured, gets managed.” In this course, students will learn why measurement is critical to quality improvement work. Equally important, they will learn which data sources provide the most meaningful information and tools for how and where to locate them. Finally, students will learn how to interpret data from their patient safety and quality projects to guide and modify them during implementation to maximize their chances of making a difference for patients.
Case Study: Improving Medication Reconciliation on Hospital Admission
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來自 Johns Hopkins University 的課程
Measuring the Success of a Patient Safety or Quality Improvement Project (Patient Safety VI)
11 個評分
從本節課中
From Data to Action and Iteration
In this module, learners will be exposed to how mixed-methods research, which includes integrating qualitative and quantitative data together, can be used successfully in quality improvement projects.Learners will also become familiar with how data and measures are used to evaluate the success of a quality improvement project. Two case studies will be shared to demonstrate how these concepts have been used in real-life quality improvement projects.
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Matt AustinAssistant Professor
Anesthesiology and Critical Care Medicine
In this video, we'll be reviewing a case study
about Improving Medication Reconciliation on Hospital Admission.
It will start with definitions,
what is Medication Reconciliation?
Medication Reconciliation is the process of reviewing medications to create
the most accurate lists possible of all medications
that a patient is taking to identify any changes,
and then correctly prescribe medications on hospital admission, transfer, and discharge.
Steps include; obtaining, verifying,
and documenting current home meds.
To consider home meds when ordering
medications in comparing home meds to the ordered med list.
It also includes verifying
the home med list and discussing discrepancies with the patient or family.
And providing an updated medication list and discussing medications on discharge.
So, Medication Reconciliation is an important part of caring for patients and families.
And so, if Medication Reconciliation is not done correctly or not done at all,
then it can result in harm to
the patient and family and result in unintended consequences.
To provide context around this improvement project case study,
several policies on Medication Reconciliation were in place at our institution.
The joint commission is an organization that
accredits hospitals and healthcare organizations in the US.
And their policy states that hospitals need to maintain and communicate
accurate patient medication information and this policy was in place.
Meaningful Use refers to the use of certified electronic health record technology,
to improve quality safety patient engagement care coordination
and the privacy of patient health information.
The Centers for Medicare and Medicaid Services or CMS had set
Meaningful Use objectives that hospitals needed to
achieve to qualify for CMS incentive programs,
to improve clinical outcomes.
This required for a coded list of
home medications and discharged
medications that were captured in the electronic health records.
And so, since our electronic health record at that time did not have
the ability to codify or capture a coded medication list,
our institution selected the RxWriter System which
integrated with our electronic health records to fulfill this meaningful use requirement.
So the problem that was addressed through
this quality improvement project is
that inaccurate documentation of a home medication list,
can lead to incorrect orders and medications being given resulting in potential harm to
the patient and that
medication lists are not always verified and updated on hospital admission.
Especially in this context where there was an integration of two systems with
our electronic health records and
the RxWriter system to help us to capture and codify medication list.
So our project goal was to improve
the process of medication reconciliation on hospital admission.
And then, we wanted to narrow the scope during the process of our project and we wanted
to reduce the number of patients that
did not have medication reconciliation performed on admission,
as indicated through the RxWriter system.
And then, we wanted to become a little bit more specific
and we wanted to reduce the percentage of patients with
old prescriptions from prior discharges remaining in our RxWriter after 24 hours of
admission by 50 percent from the baseline of 32 percent to 16 percent in three months.
We had to think about the scope of our project and so we
selected the Medication Reconciliation process on admission.
We also selected certain pediatric units of our children's center,
certain medical units in our children's center.
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.
So, our improvement plan that was informed by our discussions with
stakeholders and our Ishikawa cause and effect diagram included two aspects,
an educational campaign to inform folks about
the RxWriter process followed by
an automated email that was sent to the admitting pediatric resident,
to notify them that prescriptions still remained in
the RxWriter system after 24 hours of admission.
And this automated email was signed by our department chair.
So we had leadership support that was demonstrated through our intervention.
Here we can see the control chart of our improvement process,
and it shows the proportion of admitted patients
without documentation of medication reconciliation.
And you can see the upper and lower control limits indicating three sigma from the mean.
You can also see time metric is along the x horizontal axis,
and our key metric is along the y vertical axis.
Imagine extending the baseline mean across the control chart.
On doing that, you can then identify four special causes.
In our control chart,
we had a number of shifts in the data and at which point then,
we adjusted the mean for the new phase.
You can also see that we had indicated the time points where our interventions began.
The educational intervention occurred in the beginning,
at which point we saw a shift in the data towards improvement.
And then, you can see when the automated email reminders occurred.
You can start to see a trend and then eventually,
there was a trend and a shift in the data towards improvement.
So, through this control chart,
we can see that there was an improvement from a baseline of
32 percent down towards 15 percent.
And once we had achieved improvement,
we then moved to the control phase of
our improvement process where we wanted to sustain our improvements.
So we continued with our daily automated emails.
We incorporated the resident responses to emails in their feedback.
We educated new incoming residents of this process,
and we continued to review the data,
striving for elimination or mistake proofing of our process.
So, hopefully, this case study provides a better understanding of
how to use data to measure quality improvement.
And here are some references related to
our quality improvement project as well as the topic of medication reconciliation.
