Wait TimeReduction ProjectBy Andrew PhillipsJuly 16, 2012
Situation              Three (3) new Medical Oncologists (Doctors) joined the practice six weeks ago,               incre...
Problem     The patients have complained about the increase wait time.     This backlog increases throughout the day whi...
Measurements     Using Lean Six Sigma tool set of statistics analysis see how to      better manage the patient flow and ...
8 :0                                                                           0                                          ...
Current Data Analyses             Average (Mean) patients per time slot is 5.27.             Standard deviation is 2.4. ...
Data Analyses     Other tools which we need to use:                   Cause and Effect Diagram.                   Desig...
Improve     Continue collecting data to see if patient wait time has improve.     Example of how we might improve the pa...
Control     Document new process and new procedures for reduced wait      time.     Communicate this to customers as wel...
Process MapCopyright © 2010 US Oncology, Inc. All rights reserved.
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Wait time reduction

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Wait time reduction

  1. 1. Wait TimeReduction ProjectBy Andrew PhillipsJuly 16, 2012
  2. 2. Situation  Three (3) new Medical Oncologists (Doctors) joined the practice six weeks ago, increasing the number of Medical Oncologists at the Practice from 5 to 8. Since the addition of the 3 new oncologists, patient wait times for Exam visits have risen sharply from an average of 13 minutes to 31 minutes. This is a 140% increase.  Reduce average wait time to x number of minutes (see what the customer would like to see as a minimum, an average, and maximum wait time.)  The patients are scheduled in 15 minute intervals for each physician.  There was increase from 140 to 224 opportunities for patients. This represents a 60% increase in patient load.  Assumption: Percentage of the number of patients per physician was constant.  The laboratory employs 3 full time phlebotomists who draw blood, and each draw takes 7.5 minutes on average.  Assumption: The Laboratory will need another 10 minutes to deliver the results to the physician. Copyright © 2010 US Oncology, Inc. All rights reserved. 2
  3. 3. Problem  The patients have complained about the increase wait time.  This backlog increases throughout the day which causes overtime and working through lunch and breaks.  Patients may leave, causing decrease in revenue.  Workers are overworked.Copyright © 2010 US Oncology, Inc. All rights reserved.
  4. 4. Measurements  Using Lean Six Sigma tool set of statistics analysis see how to better manage the patient flow and find solutions  Map the process with Swim lanes.  Develop a CTQ tree to see what is important to the customers  Measure Lab process times and the time it takes to get the data to the physician.  Individual Moving Range (IMR) chart – showing the variance in the process and how we can reduce the variance. This is using variable data and sample size of 1.  Measure the wait times more closely and see if there is a correlation between physician and number of patients scheduled.Copyright © 2010 US Oncology, Inc. All rights reserved.
  5. 5. 8 :0 0 1 2 3 4 5 6 7 8 9 0A 8 :1 M 5A 8 :3 M 0A 8 :4 M LCL=2.4188 UCL=8.1329 CEN=5.2759 5A 9 :0 MCopyright © 2010 US Oncology, Inc. All rights reserved. 0A 9 :1 M 5A 9 :3 M 0A 9 :4 M 5A 10 M :00 10 AM :15 10 AM :30 10 AM :45 11 AM :00 11 AM :15 11 AM :30 11 AM :45 12 AM :00 12 PM :15 Individuals Chart 12 PM :30 12 PM :45 P 1 :0 M 0P 1 :1 M 5P 1 :3 M 0P 1 :4 M 5P 2 :0 M 0P 2 :1 M 5P 2 :3 M 0P 2 :4 M 5P 3 :0 M 0P 3 :1 M 5P 3 :3 M 0P 3 :4 M 5P 4 :0 M Individual Moving Range Chart 0P M
  6. 6. Current Data Analyses  Average (Mean) patients per time slot is 5.27.  Standard deviation is 2.4.  Total number of patients for the day is 153.  Total number of time slots is 224.  Usage 68.3%.  Mode 7.  Median 6.  Average wait time 31 minutes.  Data needed:  Wait time per lab process.  Wait time per drawing blood.  Wait time per patient/per physician.  Wait time per patient load.Copyright © 2010 US Oncology, Inc. All rights reserved.
  7. 7. Data Analyses  Other tools which we need to use:  Cause and Effect Diagram.  Design of Experiment (DOE) to see what impacts the wait time the most.  Scatter Diagram to show relationship between wait time, physician, patient load, and time of day.  Hypothesis testing to prove or disprove any hypothesis.Copyright © 2010 US Oncology, Inc. All rights reserved.
  8. 8. Improve  Continue collecting data to see if patient wait time has improve.  Example of how we might improve the patient wait time.  Increase Average (Mean) patients per time slot is 5.27 to 6  Hire 1-2 phlebotomists.  Increase total number of patients 194 74% usage instead of 58.8% (48% increase)  Reduce - Standard deviation from 2.4 to 1.Copyright © 2010 US Oncology, Inc. All rights reserved.
  9. 9. Control  Document new process and new procedures for reduced wait time.  Communicate this to customers as well as physicians and workers.  Communicate improve and savings to everyone.  Share success.Copyright © 2010 US Oncology, Inc. All rights reserved.
  10. 10. Process MapCopyright © 2010 US Oncology, Inc. All rights reserved.
  11. 11. 8 :0 0 1 2 3 4 5 6 7 8 9 0A 8 :1 M 5A 8 :3 M 0A 8 :4 M LCL=2.4188 UCL=8.1329 CEN=5.2759 5A 9 :0 MCopyright © 2010 US Oncology, Inc. All rights reserved. 0A 9 :1 M 5A 9 :3 M 0A 9 :4 M 5A 10 M :00 10 AM :15 10 AM :30 10 AM :45 11 AM :00 11 AM :15 11 AM :30 11 AM :45 12 AM :00 12 PM :15 Individuals Chart 12 PM :30 12 PM :45 P 1 :0 M 0P 1 :1 M 5P 1 :3 M 0P 1 :4 M 5P 2 :0 M Individual Moving Range 0P 2 :1 M 5P 2 :3 M 0P 2 :4 M 5P 3 :0 M 0P 3 :1 M 5P 3 :3 M 0P 3 :4 M 5P 4 :0 M 0P M

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