A Case Study – The Children’s Hospital of New York-Presbyterian
CASE STUDY Bernadette O’Brien, RN Vice 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 Z ST : 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, 2004 Analyze February, 2004 Improve March/April 2004 Control May, 2004 Project Transfer August, 2004 Operating Mechanisms with NYPH: PI Steering Committee & Meetings <ul><li>Project Definitions: </li></ul><ul><li>“ On-Time” refers to patient on table </li></ul><ul><li>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 </li></ul><ul><li>A case is considered late if it starts later than 15 minutes of the scheduled start time </li></ul>CCL First Case Start Project Charter
Compliance Brainstorming/Prioritizing Critical Xs Fish Bone Diagram 80% On - Time First Case Start Registration Transport Logistics Anesthesia Patient Equipment Assessment Physician Availability Patient Prep – IV Start Pre - Med 80% On - Time First Case Start Registration Transport Logistics Anesthesia Patient Equipment Assessment Physician Availability Patient Prep – IV Start Pre - Med Critical X ’ s: Anesthesia Pre - Med Assessment
Data Analysis Results <ul><li>There is a statistical difference between the time the Cardiology Assessment is completed and the time Patient is on Table </li></ul><ul><li>There is a difference in variation in anesthesiologist and Time Patient on Table </li></ul><ul><li>Cases not involving anesthesia were most likely to go late </li></ul><ul><li>There is a variation in lateness among anesthesiologists </li></ul>
BASELINE IMPROVE Improve/Control Data Results: 83% On-Time First Case Start Baseline z = 2.47 Median = 0 minutes Mean = 6.33 minutes St Dev = 22.4 minutes Baseline Data Results: 38% On-Time First Case Start Baseline z = 1.44 Median = 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: Control Plan
Financial Impact Total Procedure Hours Gained = 312 hours/month
Lessons Learned <ul><li>Key stakeholders involved at the grassroots level </li></ul><ul><li>Communicate, communicate, communicate! </li></ul><ul><li>Six Sigma allows us to move from anecdotal assumptions to rigorous data driven decisions </li></ul><ul><li>Because of the rigorous statistical analysis utilized, there is buy-in from everyone </li></ul>
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