a case study – the children’s hospital of new york-presbyterian

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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


Yes

No

5040302010

Ora

l P

re-M

ed Y

es/

No

Difference

Yes

No

100806040200-20-40

F-Test

0.981


Levene's Test



Late

st A

sses.

Tim

e


Nurse

Neither

Card

Anesthesia

3500300025002000150010005000

Bartlett's Test

0.132


Levene's Test



Nurs

e


3

2

1

250200150100500

Nurs

e

Difference

3

2

1

100806040200-20-40

F-Test

0.725


Levene's Test



Anest

hesi

a Y

es/

No


Yes

No

5045403530252015

Anest

hesi

a Y

es/

No

Difference

Yes

No

100806040200-20-40

F-Test

0.318


Levene's Test



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 Sigma


Descriptive Statistics

-20 20 60 100 140 180 220


-5 0 5 10 15


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 Sigma


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

a case study – the children’s hospital of new york-presbyterian

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