Healthcare Six Sigma Project

ED Wait Times and Service Quality

Michael J Floriani

12/2/2010

Project Overview

A recent report from the Centers for Disease Control and Prevention indicates that over the past decade, trips to the emergency department (ED) increased twenty percent, while the number of available emergency centers fell by fifteen percent. Another study from the American Hospital Association (AHA) indicated that sixty-two percent of hospitals feel they are at, or over operating capacity. That number jumps to ninety percent when considering Level 1 Trauma Centers and larger (300+ beds) hospitals.

These statistics are frighteningly familiar to many hospitals and patients. The pressures are mounting and a faltering economy has swelled the ranks of the uninsured – those who often rely on the local ED for primary care. Countless emergency departments are literally on life support as they try to cope with the following:

Capacity issues Workforce shortages

Preparing for, or responding to emergency threats such as bioterrorism and SARS, only increases the strain on the system. In hospitals across the U.S., emergency departments face a similar story of delays and dissatisfaction from both patients and clinicians.

Some hospitals, however, are finding new ways to overcome the challenges and are creating safer and more efficient environments. Through a combination of Six Sigma and Lean, hospitals are targeting critical aspects of patient flow, patient access, service-cycle time, and admission/discharge processes. A growing number of hospitals are taking steps to identify and remove bottlenecks or inefficiencies in the system. As a result, they are seeing a positive impact on patients, staff, and the bottom line.

By using the principles in the Villanova Six Sigma Black Belt course, the objectives of the project are:

Decrease door to doctor time Decrease the patient’s total length of stay (LOS) Decrease the number of patients who leave the ED without being seen as a result of being tired

of waiting.

Last year, the hypothetical hospital received 43,800 patients into its emergency department with 6.3% leaving without treatment – essentially because of their dissatisfaction with the wait time.

The nation’s emergency care network must be strong – not only to maintain its ability to serve basic community needs, but also to ensure it will have the necessary capacity and processes in place to respond quickly during a crisis.

Project Charter (Define)

The project charter of this Six Sigma/Lean healthcare project establishes the first phase of the DMAIC process by defining the problem and other key elements to motivate the team and ensure the project meets the stakeholders’ needs. Additionally, it establishes “buy-in” of the project.

The project charter is composed of various elements; however, the key elements include:

Business Caseo The sponsor must know what the project is about and how it impacts the strategic

objectives of the organization. This business case statement should be limited to one to two sentences.

Problem Statemento The sponsor must be sold on why we need to do this project and needs a short, to-the-

point compelling reason why we need to do this. It is in the problem statement, we 'sell' the need for the project with specific and measurable data.

Goal Statemento This includes the target improvement for this project and target date.o Six Sigma projects should target the project for an initial 50% improvement as a best

practice. Project Scope

o The project scope identifies the boundaries of the project to include what is and is not included as part of the project. Assumptions and constraints may also be included in the scope statement which affect the budget or project team.

The following project charter deliverable has been established for this healthcare case:

Business Caseo Paoli Hospital’s emergency department is facing increased patient volumes, constrained

capacity and employee shortages as it moves towards a Level 1 Trauma Center. o Excessive delays and length of stays negatively impact patient outcomes and satisfaction

requiring us to initiate this project to improve key ED metrics. Problem Statement

o Since 2009, patients who left the emergency room without waiting due to delays, accounted for 6.3% of a total 43,800 ED visits. This 50% higher than desired increase resulted in 2,759 “balked visits”, lost hospital revenue, negative hospital reputation and poor emergency room preparedness.

Goal Statemento The project will commence June 1, 2010 and meet all objectives six months prior to the

hospital becoming a Level 1 Trauma Center, currently planned for June 2011 and result in increased patient satisfaction and improved financial performance.

o Goals for the project include 1) Improving “door to doctor time” by 50%, 2) Decreasing total LOS by 20% and 3) Reduce “unseen” patients by 75%.

o A project plan will be provided to management by June 15 outlining tasks involved, risk plan and communication plan. Weekly status reports will be distributed and a mid-phase and final phase implementation plan will be presented to executive management.

Project Scopeo Registration process, ED flow process, lab process and discharge process will be included

in this project.o Included in this scope is the time from patient entry to the ED, either by foot or by

emergency transport, and ends when the patient is officially discharged by the physician.

o Outside of the scope is the admission process for patients admitted due to severity of illness.

o Not included are delays attributed to patients, patient families or other members outside of hospital personnel.

Baseline Sigma (Measure)

The baseline sigma establishes the “original state” sigma before any process improvement initiative is implemented.

Because the defect, the number of potential patients leaving the hospital’s ED, is attribute data (only one possible defect per opportunity – a two state condition in which the patient either stays or leaves ), the opportunity to calculate uses the value “1” for opportunity; hence:

Units: Hospital ED visits which, according to the case, are 43,800 visits per year.Defects: 6.3% or 2,759 people leaving the hospital ED without being seen by a doctor.DPMO or DPMU result in same because of the 1 opportunity and therefore the formula and result is

2759/(1*43,800) = .062991 or 63,000 DPMO. This equates to 3.03 Sigma with 1.5 shift.

SIPOC (Define)

The following SIPOC represents a high-level identification of the “current state” process to observe the major process elements. This includes the 1) Suppliers 2) Inputs 3) Processes 4) Outputs and 5) Customers associated within this project.

The SIPOC begins by identifying the key steps within the process by listing five to seven process elements. Once identified, other areas are listed associated with the project’s SIPOC.

Further breakdown of sub-processes can be achieved later within this project; however, the purpose of the SIPOC. The SIPOC diagram helps to identify the process outputs and the customers of those outputs so that the voice of the customer can be captured.

Emergency Room SIPOCSuppliers Inputs Process Outputs Customer

Patient Medical Records Patient Arrival to ED

Discharge Documents

Patient

Triage Nurse Patient Symptoms Triage patient Prescriptions ED Doctor/Hospitalist

Registration Clerk Rx Information Register Patient Physician Notes ED ManagerNurse Insurance Data Assign Patient to

RoomED Activity Log Orderly/Nurse/Aid

ED Doctor/Hospitalist

ED Activity Log Assign Physician Empty ED Room Lab Personnel

Room Data Physician Examines Patient

Physician Orders Tests

Physician Treats Patient

Physician Discharges Patient

Pareto Diagram (Analyze)

The principle is based on the unequal distribution of things in the universe. It is the law of the "significant few versus the trivial many." The significant few things will generally make up 80% of the whole, while the trivial many will make up about 20%.

The purpose of a Pareto diagram is to separate the significant aspects of a problem from the trivial ones. By graphically separating the aspects of a problem, a team will know where to direct its improvement efforts. Reducing the largest bars identified in the diagram will do more for overall improvement than reducing the smaller ones.

Based on information provided by those who entered the hospital, the following reasons were stratified for leaving:

Got tired: 6Not necessary: 4People Waiting: 4Doctor Treatment: 3Staff Treatment: 2Environment: 2Went Elsewhere: 1Ignored Me: 1Too Expensive: 1Had to Leave: 1

From the above data, the project team should focus, at most, on the first six reasons while accepting others as the “useful many”. This information is substantiated from the Pareto Chart below.

Expected Variation (Analyze)

During the past month, the patient wait times were logged and are noted within this document. All figures are in minutes with a wait time operational definition of the patient entering the ED facility until brought into an ED room. All values are rounded to the nearest minute.

24 27 18 11 22 27 17 23 17 517 28 24 17 8 21 26 23 17 3118 27 22 27 17 40 22 18 17 1828

From the thirty-one observations, the following results are provided:

Average wait time: 21.1935484 minutes Standard Deviation: 6.9013011 Range of Expected Variation

o Lowest Point: 0.4896451o High Point: 41.8974517

Histogram to determine Normal Distribution or Assignable-cause variation is normally distributed as shown on the following histogram by the bell curve.

5 12 19 26 33 More0

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Patient Wait Times

Minutes

Freq

uenc

y

Stem and Leaf Diagram (Analyze)

The Stem and Leaf diagram preserve the actual data values compared to the histogram which categorizes values into bins. To get a visualization of the variation in wait times over the past seventy days, it can be determined by this diagram, if assignable cause variation exists.

The values over the past seventy days are indicated within this document as shown below:

16 21 11 1616 17 6 48 47 20

16 18 47 2644 22 49 47 20 64

17 75 38 1748 10 48 20 50 16

37 15 17 6545 18 47 71 35 44

47 17 20 1550 51 48 47 21 82

32 13 49 1749 14 52 50 46 51

48 47 19 4863 80 46 95 48 58

9:::

5                                            

8:: 0 2                                          

:7:::

1 5                                          

6:::

3 4 5                                        

5:::

0 0 0 1 1 2 8                                

4:::

4 4 5 6 6 7 7 7 7 7 7 7 8 8 8 8 8 8 8 9 9 9  

3:::

2 5 7 8                                      

2:::

0 0 0 0 1 1 2 6                              

1:::

0 1 3 4 5 5 6 6 6 6 6 7 7 7 7 7 7 8 8 9      

0:::

6                                            

The above Stem and Leaf diagram shows that this data is not normally distributed. As a recommendation, the project team should focus on wait times of forty minutes and more. The number of wait times is fairly evenly distributed at the point of >= 40 minutes and <40 minutes.

Design of Experiment (Improve)

The Design of Experiments (DOE) approach has been recommended to hopefully realize an improvement considering variables that impact wait times. The project team brainstormed five possible reasons for the delay to include the following:

1. Staff size2. Order of treatment3. Treatment method4. Tracking software5. Waiting room temperature

Using a statistical software package for Fractional Factorial Designs, the effects of the five factors can be achieved in as little as eight experiments with no interactions of these factors.

The factors or each experiment were:

Level (-) Level (+)A Staff Size 8 16B Order of Treatment FIFO By PriorityC Treatment Method Iterative All at OnceD Tracking Software Product A Product BE Waiting Room Temp 68 Degrees 75 Degrees

The breakdown of the experiments is as follows:

Trial # Staff Order Method Software Temp (Y) Wait Time1 1 1 -1 -1 -1 92 -1 -1 -1 1 1 73 1 1 1 1 -1 254 -1 -1 1 1 -1 285 -1 1 1 -1 1 266 1 -1 -1 1 1 87 1 -1 1 -1 -1 288 -1 1 -1 -1 1 6

To determine the correlation, average wait times must be taken for various conditions. For the first experiment for staff size, the average wait time for a staff size of 8 and 16 was as follows:

Staff Size of 8: (7+28+26+6)/4 = 16.75Staff Size of 16: (9+25+8+28)/4 = 17.50

Charting these results in the following:

05

1015202530

Staff Size

05

1015202530

Treatment

05

1015202530

Method

05

1015202530

Software

05

1015202530

Temperature

Since the objective is “Less is Better” relative to wait times, the following should be achieved:

1. Immediately make an adjustment to room temperature from the 68 degree setting to achieve a more comfortable setting. This has the most significant impact, based on the DOE data, for a reduction in wait times.

2. Begin prioritizing patients as this has reduced the average wait time.3. While the average wait time has decreased slightly using product B, this should be used as the

other software may contribute to not effectively tracking patients; thus, increasing wait times. This software should be evaluated to ensure it prioritizes patients.

Scatter Diagram (Improve)

The following data and Scatter Diagram are used to determine the correlation between the volume of patients and the impact on the number of patients that leave without treatment (LWT). This was believed by the project team, and as such, the data identifies the correlation.

Number in for treatment per specific day

Leave without treatment incidents

172 4

132 6130 2

206 4199 6223 4201 8

169 7135 5

200 3189 7110 8203 6189 5224 8197 4188 8125 2

199 6194 8207 7

100 120 140 160 180 200 220 2400

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Series2Linear (Series2)

Correlations of 1.0 to -0.7 indicate a strong negative association while a correlation of -0.7 to -0.3 indicates a weak negative association. The correlation of -0.3 to +0.3 indicates little or no association and +0.3 to +0.7 weak positive association. A +0.7 to +1.0 indicates strong positive association.

The Scatter Diagram does not indicate any correlation with a correlation coefficient of .2256432 (very weak) and is positive.

XmR Chart (Control)

The earlier Stem and Leaf diagram indicated a bi-modal condition; therefore the project team identified the source of this condition and eliminated one of the sources and optimized accordingly. The two sources were 1) some patients were first-time visitors and 2)some of the patients were return visitors.

Order Order

1 10 16 322 17 17 333 29 18 114 39 19 205 55 20 136 64 21 97 28 22 148 6 23 129 5 24 30

10 3 25 5611 39 26 62

12 46 27 7313 35 28 5414 30 29 1015 6 30 9

The project team changed the process so that first-time visitors were processed ahead of time; thus, reducing their wait time in the waiting room. This dramatically decreased the overall average wait time. Part of the control strategy is to employ the use of an on-going capability study; however, one must first determine the process is in statistical control. To do this, an XmR Chart of new wait times has been established in order of occurrence. This data is shown above.

The Upper Control Limit (UCL) and Lower Control Limit (LCL) are indicated which shows that continuous improvement is necessary.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 300

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Series1MeanUCLLCL