Speaker Name - X-CD System Conference Management

Report
Improving MRI Imaging/Protocol Processes Using
Lean and DMAIC Methodology
Ranganath K Iyer
Industrial Engineer
TEAM INFO
Sponsor: Dr. Joseph Steele
Champion: Dr. Eric Paulson
Team Members:
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−
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Habib Tannir
Aziz Benamar
Mary A. Cuellar
Jim Thomas
Ranganath Iyer, Facilitator
Visitors
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Dr. Janio Szklaruk
Nancy Swanston
Victor Arboleda
Steve Venable
Strategic Priority: Enhance the quality and value
of our patient care.
Abstract
MD Anderson's enhanced reputation for quality
imaging studies is helping its MRI volume grow at
an annual rate of 3%. This growth has placed the
scanners close to capacity, and it is difficult to
absorb new patients.
The study describes how MRI imaging protocols
and processes were improved using Lean tools,
thereby reducing overall procedure time. Also,
image quality was optimized, resulting in an
enhanced guide to treating patients.
To improve processes and enhance quality is
aligned not only with MD Anderson’s goal but
also with the Institute of Medicine’s goal of
redesigning care delivery.
Aims
The purpose of the study was to rethink MRI processes
and protocols in a new and dynamic way to reduce
overall procedure time so additional patients can be
added to the schedule. Also, image quality was
optimized, resulting in an enhanced guide to treating
patients.
Methods
The study team used Lean tools to generate and implement 31
new ways to either build or rebuild current MRI processes.
Examples include developing new schedules and pre-screening
patients for implants and anxiety.
In addition, DMAIC provided the framework for carrying out
the study in a systematic manner. Tools included: Process Flow
Diagram, Fishbone Diagram, Voice of Customer, Control
Charts, etc.
Owing to the large volume of abdomen and/or pelvis
procedures , the study team decided to target these
procedures for improvement. Scan time, table time, and
acquisition time were used as the metrics.
Learning Objectives
• Application of Lean tools in improving
imaging/protocol processes.
• Understanding how best practices in the PET area
were used to develop process and quality
improvement solutions in MRI.
• Understanding how “image sequences“ which are
building blocks of a protocol can be standardized.
• What to do (statistically) if bimodal data shows
up in your analysis
• How the “Control” phase in DMAIC
methodology played a role in sustaining the
improvement process.
Background
Some process improvement in performing MRI
studies has been undertaken in the past two years
but has not involved reviewing activities at the
protocol level. The sequences are the core building
blocks of any protocol. Without examining this
buildup, the selection of a single protocol for
abdominal MRI, to require protocol prescription 24
hours in advance, and guidelines for contrast agent
selection, cannot be administered consistently.
The reason, an examination of the sequence has
not been performed previously is that although
both table time and machine time have been
captured in a GE database in real time, this
information has been unavailable for analysis by
end-users owing to proprietary issues and HIPPA
compliancy.
GE, one of the two MRI vendors, was very helpful
and created a customized HIPPA-compliant report
with meaningful information for protocol
analysis.
Application of Lean tools in improving
imaging/protocol processes.
Table A
Literature states eight types • Transportation • Overproduction
• Inventory
• Over-processing
of waste (MUDA), shown in
• Motion
• Defects
Table A.
• Waiting
• Standards
Lacking
A fishbone diagram indicated the following as the
main causes of waste in the MRI department:
• Technicians with different levels of training
• Restricted scheduling of slots
• Customized protocoling for each patient
• Handoffs from technicians to radiologists.
• Standardized work
Cause and Effect Diagram
Improve Process
One quality tool that was used extensively was
the “Voice of Customer” tool to gather customer
requirements at all stages of the study.
Process Flow Diagram
Microsoft Visio was used to process and diagram
the steps needed for lab and protocol-approval
processes. The analysis indicated that the work
process included many delays and calls to
radiologists.
Baseline Metrics
Baseline metrics for scan time, table time, and
acquisition time were established using calendar year
2012 data. The Minitab software package was used to
calculate statistics. The table summarizes January
2012 through December 2012 data for scan time
(RIS), table time (GE), and acquisition time (GE).
These data provided us with targeted goals to achieve
at the end of the study.
Understanding how best practices in the PET area
were used to develop process and quality
improvement solutions in MRI.
MD Anderson is a large institution, and imaging
modalities (PET, MRI, CT, NM, etc.) are housed in
different buildings and locations. Thus, we were
able to use other imaging modalities’ experience
as a benchmark for the study. Some best practices
were borrowed from the PET area, where these
solutions had successfully worked in the past.
The study team brainstormed solutions to
generate 32 new ways to either build or rebuild
current MRI processes for improvement.
Solutions included modifications in the current
practice of radiologists, technologists, and
nurses. Among these, the most effective and farreaching were the selection of a single protocol
for abdominal MRI, a required protocol
prescription 24 hours in advance of imaging, and
guidelines for contrast agent selection.
Some of the other interventions were:
Develop a new schedule to optimize MRI slots
based on time data
Preview patients for all slots
Enforce the anxiety policy and educate patients
on pre-meds
Educate technicians to standardize the RIS entry
tracking process
Pre-screening every patient 1-2 days out for
implant clarifications
Enforce policy and educate patients on “HARD
STICKS”
Understanding how “image sequences," which
are the core building blocks of a protocol can be
standardized.
Analysis of data from February 2012 to
December 2012 indicated that
combined MRI of the abdomen with
or without contrast and pelvis with or without
contrast represented 21% of the total volume of
the top 10 MRI procedures. Also, senior
leadership including the chair of diagnostic
imaging were interested in making
improvements in this area.
Some of the sequences used to build MRI ABD
with or without contrast are shown in the table
on the next slide. Sequences described in the
“Series description” column varied from patient
to patient and from radiologist to radiologist.
The abdominal section radiologists standardized
the exam and published one protocol. Exceptions
or additional sequences were added owing to
patients’ conditions and were noted in the
patients’ charts.
The metrics average exam time, average machine
time, and number of sequences per procedure
were used to monitor improvements and
compliance with the requested changes.
What to do (statistically) if bimodal data shows up in
your analysis
Statistical analysis was done to assess the acquisition
time data for MRI ABD with or without contrast
procedure. The analysis indicated that the data were
bimodal, which indicated that the data contained
two different clusters or protocols.
TIMEPERIOD = Jan-Dec 2012
Anderson-Darling Normality Test
A-Squared
P-Value <
Mean
StDev
Variance
Skewness
Kurtosis
N
0
30
60
90
120
150
Minimum
1st Quartile
Median
3rd Quartile
Maximum
180
70.64
0.005
48.930
23.081
532.743
0.842530
0.729621
2675
1.250
32.020
41.260
66.600
195.260
95% Confidence Interval for Mean
95% Confidence Intervals
48.055
49.805
95% Confidence Interval for Median
Mean
40.203
42.120
95% Confidence Interval for StDev
Median
40
42
44
46
48
50
22.479
23.717
ABD with or without
contrast procedure with
different protocols
embedded in the data
Mean Time ~ 49
minutes
If details are not available, then cluster analysis
could be used to calculate the means. However,
in our example, we did a more detailed analysis
by separating the protocols in the data and then
calculating the means.
Summary for AcquisitionTime
Anderson-Darling Normality Test
A-Squared
P-Value <
0
20
40
60
80
100
13.79
0.005
Mean
StDev
Variance
Skewness
Kurtosis
N
36.876
10.156
103.148
0.22640
4.80707
983
Minimum
1st Quartile
Median
3rd Quartile
Maximum
1.000
32.000
36.000
42.000
109.000
95% Confidence Interval for Mean
36.240
37.512
95% Confidence Interval for Median
36.000
95% Confidence Intervals
9.726
Mean
Median
36.00
36.25
36.50
36.75
37.00
37.000
95% Confidence Interval for StDev
37.25
37.50
10.626
Mean Time ~ 37 minutes
ABD with or without contrast
procedure with ABD protocol
only
How the “Control” phase in DMAIC methodology
played a role in sustaining the improvement
process.
Control charts were used on a monthly basis to
monitor the reduction in scan time, table time,
and acquisition time. If the control charts showed
an upward trend, we reviewed the sequences to
see if the radiologist’s or technician’s practice
pattern changed.
Results
Overall scan time was reduced by 10%.
Conclusions
Findings suggest that the team achieved a 10%
decrease in overall MRI scan time. Lessons learned
include:
-Incremental process changes can result in large
savings.
-Statistical tools are great investigative tools for
large data sets. An imaging procedure could
contain as many as 32 sequences.
-Lean methodology and DMAIC framework yielded
a successful study.
Bio’s
Ranganath K Iyer
Ranganath K Iyer is a certified six sigma black belt and an Industrial Engineer
at MD Anderson Cancer Center. He brings with him over 30 plus years of
Industrial Engineering experience in managing and facilitating cross
functional project teams at major health care institution. His work
experience in healthcare is complimented with a Master's degree in
Industrial Engineering. Also, he has presented at various conferences
including Institute of Industrial Engineers (IIE), American Society for Quality
(ASQ), and Society for Health Systems (SHS). He is a Senior Member for IIE
and ASQ
Habib Tannir
Habib Tannir is the Executive Director of Clinical Operations at MD Anderson
Cancer Center in Houston Texas. He's been in that role for one year. For
seven years before that, he served as Associate Hospital Administrator at
Emory Healthcare and Department Administrator of Emory University School
of Medicine Department of Radiology and Imaging Sciences. He also held
positions at University Community Health in Tampa, General Electric Medical
Systems, and William Beaumont Hospital in Royal Oak Michigan. He is a
biomedical engineer by formal education and has been in the healthcare
industry since 1995.
Bio’s
Dr. Szklaruk Janio
Dr. Szklaruk Janio is a Professor of Diagnostic Imaging at The University
of Texas MD Anderson Cancer Center, where he has been a faculty
member for the last thirteen years. Previously, he was an Assistant
Professor in the Radiology Department at Hahnemann University
Hospital in Philadelphia, and have been a Visiting Professor at several
institutions. His focus is the application of computed tomography and
magnetic resonance imaging in the evaluation of hepatobiliary tumors.
He has over 45 publications, multiple abstracts, multiple book
chapters, and numerous presentations at multiple national and
international meetings. He has trained and mentored residents,
fellows and junior staff at The University of Texas MD Anderson Cancer
Center. He completed a radiology residency in Diagnostic Imaging at
Thomas Jefferson University Hospital.
Bio’s
Steve Venable
Steve Venable has been at M.D. Anderson Cancer Center since 1995. He has
held various administrative positions and is now the Director of Diagnostic
Imaging Strategic Operations. Mr. Venable was the project coordinator for a
Six Sigma Project in Computed Tomography, the first Six Sigma endeavor at
MDACC with a focus on increased capacity. Since that time Mr. Venable has
been a leading proponent in leveraging data sources and information systems
to understand and improve clinical efficiency, safety and project future needs
and has successfully imbedded Industrial Engineers within Diagnostic
Imaging.
Bio’s
Aziz Benamar
In his most recent role, Aziz is a Director of Clinical Operation in the Division
of Diagnostic Imaging at MD Anderson Cancer Center where he is
responsible for the operations of nine imaging modalities including
Magnetic Resonance Imaging, Computer Tomography and Nuclear
Medicine. His work experience of over 21 years includes Houston Northwest
Medical Center and Memorial Hermann Healthcare System , progressing
moving from the position of Radiology Technologist to Imaging Services
Manager and to Director of Imaging Services. He believes strongly in
mentoring and empowering staff and interdisciplinary team members to
provide safe, high quality, cost effective and customer service oriented
patient care. Aziz received his Bachelor of Science in Radiology Technology
from Mid-Western State University and his Master of Business
Administration in 2008.

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