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CA
SE
ST
UD
YC
AS
E S
TU
DY
CA
SE
ST
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YC
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TU
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Bernadette O’Brien, RNVice President, Operations
Increased Efficiency in Cardiac Cath Lab
In Scope:Includes cycle time between scheduled start time and actual start time of first case starts.
Out of Scope: Cycle time in holding area, in room prep, patient ready to MD arrives, case time, post procedure, room clean, time between patients.
Defect: Scheduled First Case that Starts Late (USL = 15 min)Unit: Scheduled First Cases
Opportunity: The first case of the day in every lab
Current DPMO: 523,810*
Current ZST: 1.44*
Business Case: This project will focus on improving the first case on-time starts in the Cath Labs at CHONY. Improving this will improve departmental productivity which can be measured in labor savings. This project could generate $20,427 labor savings in 2004.
Problem Statement:
Data has shown that first case starts are delayed 62% of the time over a data collection period during the month of September 2003.
Goal Statement:Improve on time first case starts in the Cardiac Cath Lab by implementing improve mechanisms by March/April 2004. Data will be collected in May to ensure process control and that the goal of 80% on-time first case starts is reached.
Start / Stop Points for Project: Project Kickoff January, 2004Analyze February, 2004Improve March/April 2004Control May, 2004Project Transfer August, 2004
Operating Mechanisms with NYPH:PI Steering Committee & Meetings
Project Definitions:- “On-Time” refers to patient on table
- On-Time Start Times are defined as: Room #1: M,T,W,F: 8:00AM, Th: 9:00 AM, Room #2: M,T,W,F: 8:30 AM, Th: 9:30 AM
- A case is considered late if it starts later than 15 minutes of the scheduled start time
CCL First Case Start Project Charter
Process Map
Compliance
Brainstorming/Prioritizing Critical Xs
80%On -Time
First Case
Start
Registration
Transport
Logistics
Anesthesia
Patient
Equipment
Ass
essm
ent
Phys
icia
n
Ava
ilabi
lity
Patie
nt P
rep
–IV
Star
t
Pre
-M
ed
80%On -Time
First Case
Start
Registration
Transport
Logistics
Anesthesia
Patient
Equipment
Ass
essm
ent
Phys
icia
n
Ava
ilabi
lity
Patie
nt P
rep
–IV
Star
t
Pre
-M
ed
Critical X ’s:
Anesthesia
Pre -Med
Assessment
Fish Bone Diagram
Statistical AnalysisA
nest
hesi
olo
gis
t
95% Bonferroni Confidence Intervals for StDevs
9
8
7
6
5
4
3
2
1
0
8007006005004003002001000
Bartlett's Test
0.556
Test Statistic 9.65P-Value 0.047
Levene's Test
Test Statistic 0.77P-Value
Test for Equal Variances for Difference
Ora
l P
re-M
ed Y
es/
No
95% Bonferroni Confidence Intervals for StDevs
Yes
No
5040302010
Ora
l P
re-M
ed Y
es/
No
Difference
Yes
No
100806040200-20-40
F-Test
0.981
Test Statistic 2.10P-Value 0.156
Levene's Test
Test Statistic 0.00P-Value
Test for Equal Variances for Difference
Late
st A
sses.
Tim
e
95% Bonferroni Confidence Intervals for StDevs
Nurse
Neither
Card
Anesthesia
3500300025002000150010005000
Bartlett's Test
0.132
Test Statistic 16.19P-Value 0.000
Levene's Test
Test Statistic 2.18P-Value
Test for Equal Variances for Difference
Nurs
e
95% Bonferroni Confidence Intervals for StDevs
3
2
1
250200150100500
Nurs
e
Difference
3
2
1
100806040200-20-40
F-Test
0.725
Test Statistic 0.76P-Value 0.566
Levene's Test
Test Statistic 0.13P-Value
Test for Equal Variances for Difference
Anest
hesi
a Y
es/
No
95% Bonferroni Confidence Intervals for StDevs
Yes
No
5045403530252015
Anest
hesi
a Y
es/
No
Difference
Yes
No
100806040200-20-40
F-Test
0.318
Test Statistic 1.34P-Value 0.552
Levene's Test
Test Statistic 1.03P-Value
Test for Equal Variances for Difference
X Test ResultsStatistically Significant?
Nurse Test for Equal Variances p=.725 NoNurse Moods Median p=.583 NoNurse Regression p=.762 No
Latest Assessment Time Moods Median p=.432 No
Latest Assessment Time Test for Equal Variances p=.132 No
Latest Assessment Time Regression p=.177 No
Anesthesia Yes/No Moods Median p=.710 NoAnesthesia Yes/No Test for Equal Variances p=.318 No
Oral Pre-Med Yes/No Test for Equal Variances p=.981 NoOral Pre-Med Yes/No Moods Median p=.288 No
Anesthesiologist Moods Median p=.389 NoAnesthesiologist Test for Equal Variances p=.013 YesAnesthesiologist Regression p=.625 No
Patient Arrival Test for Equal Variances p=.909 NoPatient Arrival Moods Median p=.615 No
Difference versus Card Assessment Regression p=.042 Yes
Time Patient on Table versus Card Assessment Regression p=0.00 Yes
Difference versus Anesthesia Yes/No Regression p=.531 NoDifference versus
Nursing Assessment Regression p=.658 No
Data Analysis Results
• There is a statistical difference between the time the Cardiology Assessment is completed and the time Patient is on Table
• There is a difference in variation in anesthesiologist and Time Patient on Table
• Cases not involving anesthesia were most likely to go late
• There is a variation in lateness among anesthesiologists
-20 20 60 100 140 180 220
95% Confidence Interval for Mu
0 10 20 30 40 50 60 70
95% Confidence Interval for Median
Variable: Current and
A-Squared:P-Value:
MeanStDevVarianceSkewnessKurtosisN
Minimum1st QuartileMedian3rd QuartileMaximum
12.922
42.550
0.000
1.8530.000
38.238155.61653093.192.259566.37742
21
0.000 0.000 13.000 65.000230.000
63.554
80.314
60.000
Group: Baseline
Anderson-Darling Normality Test
95% Confidence Interval for Mu
95% Confidence Interval for Sigma
95% Confidence Interval for Median
Descriptive Statistics
-20 20 60 100 140 180 220
95% Confidence Interval for Mu
-5 0 5 10 15
95% Confidence Interval for Median
Variable: Current and
A-Squared:P-Value:
MeanStDevVarianceSkewnessKurtosisN
Minimum1st QuartileMedian3rd QuartileMaximum
-3.1126
17.3861
-5.8662
1.7720.000
6.333322.3698500.4061.825863.26045
24
-20.0000-10.0000 0.0000 14.2500 70.0000
15.7793
31.3794
10.3465
Group: Current
Anderson-Darling Normality Test
95% Confidence Interval for Mu
95% Confidence Interval for Sigma
95% Confidence Interval for Median
Descriptive Statistics
BASELINE IMPROVE
Improve/Control Data Results:83% On-Time First Case Start
Baseline z = 2.47Median = 0 minutes
Mean = 6.33 minutes St Dev = 22.4 minutes
Baseline Data Results:38% On-Time First Case Start
Baseline z = 1.44Median = 13 minutes
Mean = 38.24 minutes St Dev = 55.62 minutes
Results
Dashboard
Data will be collected daily on paper indicating whether the first case started on time as well as any reasons for delay. This paper will then be given to Ellen Moquete who will input the results into the “Data Collection” Excel sheet to the left. The “On Time Trend Dashboard” (below) will be compiled weekly and sent to Margaret Millar, Manager. If there is a trend downward for more than two weeks revealing less than 80% of first cases are starting on time, the team has agreed to review the Reason for Delay in the manual data collection in order to understand the change as well as collect data to make sure that the critical x is in control.
Plan
Weekly Goal: 80% of First Cases Start on Time
Control Plan
Implement Process Control
Control of the Y and Critical X:
MetricTarget Values
Measurement Definition
Measurement Method
Upper/Lower Spec Limits
Control Method
Frequency-How often will you measure
Responsibility (Who Will Measure)
Alert Flags Action
"On-Time" First Case
Start
80% of Cases Start "On-Time" Patient On Table
Manual -Data Collection Sheet USL = 15 minutes Dashboard Weekly
Cath Lab Manager
Trend downward for more than
two weeks revealing less
than 80% of first cases are
starting on time
Alet Director. Review reasons for delay. Collect data on critical X
Cardiology Assessment
Completed by 7:30 AM
Assessment completed and documented in
chart by physicianManual Chart
Review
100% of assessments
completed on time DashboardTBD by Project
Y Alert Flag Cath Lab Manager
One or more "late" cases
associated with cardiology
assessment not being completed
on timeAlert
Director.
CH
ON
Y
Control Plan
Financial Impact
CUMC WCMC CHONY
40 hours/month 110 hours/month162 hours/month
Improved First Case Start Time
Reduce Preprocedure Time < 15 min
33 hours/month
17 hours/month 42 hours/month 19 hours/month
48 hours/month
Total
Improved Room Turn Around Time 23 hours/month 20 hours/month
Total Procedure Hours Gained = 312 hours/month
Lessons Learned
Key stakeholders involved at the grassroots level
Communicate, communicate, communicate!
Six Sigma allows us to move from anecdotal assumptions to rigorous data driven decisions
Because of the rigorous statistical analysis utilized, there is buy-in from everyone