1. Quality Control & Improvement
Chapter 12.
Quality Control & Improvement
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement 1

2. Outline Quality in Healthcare Quality Experts Quality Certification
TQM & CQI
Six-Sigma
Monitoring Quality through Control Charts
Control Charts for Attributes
Control Charts for Variables
Process improvement
Methods for Generating New Ideas
Tools for Investigation
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

3. A Broad Definition. . .
Quality refers to the ability of a product or service to consistently meet or exceed customer expectations
quality in healthcare is evaluated from differing perspectives of providers, recipients and third-party payers.
Most clinicians accept the Institute of Medicine (1990) definition: “Quality is the extent to which health services for individuals and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge.”
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

4. QUALITY? What is quality?
You are a world renowned surgeon that has just completed a radical new surgical technique. There were few complications, largely due to the excellence of the hospital’s staff and technological capabilities.
QUALITY?
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

5. QUALITY? What is Quality?
You are a patient who has just undergone radical new surgery. Although the surgery went without technical difficulties, your were upset at the doctor’s uncaring attitude. Furthermore, the nursing staff often failed to respond to your calls, and twice you were served meat despite the fact that you are a vegetarian. Also, there was a used bedpan that sat next to your bed for three days.
QUALITY?
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

6. A question of perspective
Quality of care depends upon who is making the assessment
Clinician -- technical components, adequate skills, resources, conditions
Patients -- outcomes, interpersonal processes, amenities, overall satisfaction
Health Facility Managers -- appropriate and effective utilization
Community -- availability, access, reputations, general health status of community
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

7. Quality Measurement Clinicians -- cure rates, mortality, morbidity
Patients -- patient satisfaction surveys
Health facility managers -- cure rates, mortality, morbidity, intermediate process measures (patient falls, infection rates, medication errors, appropriate staffing, etc.)
Community -- area service distribution, insurance coverage, incidence and prevalence rates, etc.
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Chapter 12: Quality Control & Improvement

8. Figure 12.1 Quality Measurement
Structure
Process
Outcome
Inputs
Conversion Process
(Throughput)
Outputs
Treated
Patient
Patient,
Provider labor,
Equipment,
Supplies, Etc.
Various hospital
and medical services
transform poor health
to wellness for patients
(diagnosis, procedures,
treatments)
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

9. Quality Measurement
Quality Gaps
Another way to look at the maintenance of quality is how mistakes are to be avoided – design mistake-proof processes across the whole spectrum of the care, to reduce undesired outcomes.
Variance in diagnostic and therapeutic interventions and the associated errors hamper the delivery of safe, effective patient care and add to poor outcomes.
To minimize the variation and the errors – sometimes euphemistically called “quality gaps” – and work toward completely eliminating them are major goals for healthcare systems.
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

10. Quality Measurement Quality Gaps
Chassin (1998) classifies the underlying causes of “quality gaps” into three categories:
1) Over-utilization. When the potential benefit of a therapy is less than its risk, overuse of health services affects the quality of care. Pressures for overuse of services may come from either providers or patients.
2) Under-utilization. A patient’s lack of insurance or insurance that has high co-payments and deductibles can cause under-utilization of necessary health care.
3) Miss-utilization. Avoidable complications, negligent care, mistakes, and mishaps create miss- utilization of services. Healthcare providers who generate such conditions harm the quality of patient care and produce poor outcomes; they also waste the organization’s resources and increase lengths of stay.
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Chapter 12: Quality Control & Improvement

11. Quality Measurement
Healthcare providers do have an arsenal of methods to deal effectively with the problems affecting quality of care.
They include the programs called quality control (QC), total quality management (TQM), continuous quality improvement (CQI), reengineering, and Six-Sigma.
All these programs include data gathering, analysis and statistical monitoring to identify the problem and its cause.
Nevertheless, the crux of the solution to quality problems lies in changing human behavior, changing minds to perform care in new ways.
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

12. Deming -- poor quality caused by the system, not employees; management’s responsibility to correct system;” use 14 points to reduce variation caused by special causes (correctable) and not common (random) causes of variation.
Juran -- 80% of defects are controllable; three elements: quality planning, quality control, and continual quality improvement
Crosby -- zero defects; quality is free
Isikawa -- cause and effect diagrams, quality circles
Quality Experts
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

13. Quality Certification
Organizations can earn awards or achieve certification/accreditation by international organizations or by their own trade organizations; for instance, hospitals are evaluated periodically by the Joint Commission on Accreditation of Healthcare Organizations (JCAHO).
For the medical group practices, the Medical Group Management Association (MGMA) is the principal voice. MGMA leads the profession and assists members through information, education, networking and advocacy” (MGMA, 2004).
Quality is always a major concern in those advocacy and accreditation bodies.
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Chapter 12: Quality Control & Improvement

14. Quality Certification
ISO 9000
Set of international standards on quality management and Quality assurance, critical to international Business
ISO 9000 series standards, briefly, require firms to document their quality- control systems at every step (incoming raw materials, product design, in- process monitoring and so forth) so that they’ll be able to identify those areas that are causing quality problems and correct them.
ISO 9000 requires companies to document everything they do that affects the quality of goods and services.
Hierarchical approach to documentation of the Quality Management System
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Chapter 12: Quality Control & Improvement

15. Total Quality Management
A philosophy that involves everyone in an organization in the quest for quality, with customer satisfaction as the driving force
TQM involves:
finding what customers want
designing services to meet customer needs
designing mistake proof delivery process “pokayoke”
monitoring results and continuous improvement
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

16. TQM, cont. TQM requires: continual improvement
competitive benchmarking
employee empowerment
team approaches
knowledge of tools
Quality at the source -- each worker responsible for his/her own work
Quality function deployment -- involve customers in service design
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

17. Controlling Quality
Quality control focuses on the conversion of inputs into outputs, i.e., the processes
Goal is to reduce the need for inspection of control efforts
Quality assurance efforts occurring during production of services are referred to as statistical process control
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Chapter 12: Quality Control & Improvement

18. Plan Do Study Act Figure 12.2 The Deming Wheel/Shewhart Cycle
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Chapter 12: Quality Control & Improvement

19. Continuous Quality Improvement
A philosophy seeking to make never-ending improvements to the process of converting inputs into outputs
Kaizen -- Japanese term referring to CI
Environment must be conducive to CI
appropriate vision statement, strategies, tactics
management style encouraging trust, openness
adherence to stated philosophy
reward/incentive systems
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Chapter 12: Quality Control & Improvement

20. Continuous Quality Improvement
The CQI is a detailed version of a PDCA cycle that comprises:
selecting a process that needs an improvement
studying and documenting the current process seeking ways to improve it
designing an improved process
implementing the new process
monitoring and evaluation
documenting the process if it worked successfully and publicizing it through the healthcare organization
if it did not achieve its goals, re-starting from step 1.
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

21. Six-Sigma
Six Sigma is one of the latest quality improvement concepts to have emerged during the 1990’s. Its name comes from the measure of variation from the normal distribution (six standard deviations).
Adopting a Six Sigma strategy as a quality goal sets tolerance levels for errors (defectives) to levels that occur only 3.4 times per million observations.
The defect rates in healthcare can be defined in such distinct areas as public health, inpatient care, ambulatory care, and so on.
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

22. Six-Sigma
Healthcare organizations have reduced the deaths caused by anesthesia from per million cases to 5 per million cases since the 1980s through improved monitoring techniques, adaptation of practice guidelines, and other systematic approaches to reduce errors.
This is one area that comes very close to six sigma standards (Chassin, 1998).
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

23. Six-Sigma
Deployment of six-sigma to improve the quality of healthcare and delivery performance can be considered in the following areas:
Clinical excellence
Service delivery
Service costs, and
Patient satisfaction.
The deployment can use either of these methodological sequences:
DMAIC: Define, Measure, Analyze, Improve, and Control
DMADV: Define, Measure, Analyze, Design, and Verify.
DMAIC is generally used to improve existing systems that have fallen the below six- sigma levels,
DMADV is used to design and develop new processes or products at six-sigma levels (Stahl, Shultz, and Pexton, 2003).
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

24. Six Sigma
The essence of Six Sigma methodologies is both improvement of the knowledge and capability of employees, and also changes behavior through training. Thus Six Sigma employs a classification system that identifies education and training for employees, project managers and executives.
Emulating karate honors, certification is granted at Green Belt (GB), Black Belt (BB) and Master Black Belts (MBB) levels.
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

25. Six Sigma
Green Belts (GBs) are the employees who have taken the training courses on implementing the projects.
Black Belts (BBs) are the project leaders, whose training may be more intensive; they may complete several projects a year depending upon their size and scope.
Master Black Belts (MBBs) are generally assigned to an area that needs improvement (for example, human resources), to ensure that objectives are set, targets are identified, plans are made, and resources are secured to implement the projects in their assigned area.
MBBs may oversee many six-sigma projects at a time, working with various BBs.
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

26. Six Sigma
Six Sigma projects require BBs and MBBs to have expertise in basic statistical tools such as Pareto Diagrams, descriptive and higher level statistics including regression, and statistical modeling techniques as well as control processes.
In addition to statistical concepts, they are expected understand project management, finance, leadership, measurement through socio-metric (survey) analysis, reliability and validity.
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

27. Six Sigma
Examples of successful six-sigma deployments in healthcare include:
reduction of emergency room diversions
fewer errors in operating rooms’ cart materials
reduced bloodstream infections in an ICU, and
improved radiology turnaround time (Stahl, Shultz, and Pexton, 2003).
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

28. Quality Measurement and Control Techniques
Process Variability
In the delivery of health care, there are many occasions when an error can happen in the tasks performed by various clinical staff.
Often the same task may not even be performed the same way for all patients, though minor alterations within defined limits can be acceptable.
When provider performance falls beyond acceptable limits, the errors that occur require investigation and correction.
In order to detect noteworthy variations in process, or tendencies that may cause unacceptable levels of errors, healthcare managers must monitor the processes for quality, using various charts.
The intent of the monitoring is to distinguish between random and non- random variation.
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

29. Quality Measurement and Control Techniques
Process Variability
The common variations in process variability that are caused by natural incidences are in general not repetitive, but various minor factors due to chance and are called random variation.
If the cause of variation is systematic, not natural, and the source of the variation is identifiable, the process variation is called non-random variation.
In healthcare, non-random variation may occur by not following procedures, using defective materials, fatigue, carelessness, or not having appropriate training or orientation to the work situation, among many reasons.
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

30. Quality Measurement and Control Techniques
Process Variability
Process variation is the range of natural variability in a process for which healthcare managers use control charts to monitor the measurements. If the natural variability or the presence of random variation exceeds tolerances set by control charts, then the process is not meeting the design specifications.
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

31. Figure 12.3 Process Capability
UCL
Process variability
does not meet
design specifications
Process Variability
Process variability meets
and exceeds specifications
Set design specifications
for process capability
LCL
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Chapter 12: Quality Control & Improvement

32. Figure 12.4 Control Charts Overview
Measurement Metrics
Continuous
Variables
Attributes
c-Chart
p-Chart
Mean Charts
Range Charts
σ Method
Range Method
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

33. Figure 12.5 Control Limits, Random and Nonrandom Sample Observations
α/2
Upper
Control
Limit
(UCL)
+2σ
Process
Mean
95.5%
-2σ
Lower
Control
Limit
(LCL)
α/2 1 2 3 4 5 6 7 8 9 10 11 12
Sample number
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

34. Control Charts for Attributes
When process characteristics can be counted, attribute-based control charts are the appropriate way to display the monitoring process.
If the number of occurrences per unit of measure can be counted, or there can be a count of the number of bad occurrences but not of non-occurrences, then a c-chart is the appropriate tool to display monitoring.
Counting also can occur for a process with only two outcomes, good or bad (defective); in such cases p-chart is the appropriate control chart.
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

35. Control Charts for Attributes: c-Chart
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Chapter 12: Quality Control & Improvement

36. Example 12.1
The number of infections from the Intensive Care Unit (ICU) at the ABC Medical Center over a period of 24 months is obtained. These numbers are the counts of stool assay positive for toxin, segregated by month. The patient population and other external factors such as change in provider have been stable.
Months
Infections in ICU
Year 1
Year 2
January 3 4
February
March 6
April
May
June
July 5
August
September
October
November 7
December
Total 47 49
The nurse manager who serves on the quality team wants to discover whether the infections are in control within 95.5% confidence limits.
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

37. Solution
If we consider each month as a sample of bad quality outcomes, for 24 samples we have a total of 96 quality defects (infections), and the average would be:
= 96/24 = 4.0.
Since the z-value for 95.5% confidence level is equal to 2, using formulas we obtain:
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Chapter 12: Quality Control & Improvement

38. Figure 12.6 ABC Medical Center Infection Control Monitoring
UCL= 8
Infections per month
LCL= 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Sample Number
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

39. Control Charts for Attributes: p-Chart
The proportion of defects in a process can be monitored using a p-chart that has binomial distribution as its theoretical base. The center of the p-chart represents the average for defects and LCL and UCL are calculated as:
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

40. Example 12.2
The indicator Family Satisfaction, which is part of the National Hospice and Palliative Care Organization’s survey, reflects the percentage of respondents who would not recommend the hospice services to others. The following data are from Holistic Care Corporation’s completed surveys from 200 families each month during a year, showing the number of respondents each month who expressed dissatisfaction with the organization’s services.
Months
Dissatisfied
Patient
Families
Percent
January 12
0.060
February 14
0.070
March 16
0.080
April
May 25
0.125
June
July 15
0.075
August
September
October
November 24
0.120
December
Total
192
The manager in charge of quality wishes to construct a control chart for this data within 95.5% confidence intervals.
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

41. Solution:
First, we need to estimate the proportion mean,
Total number of quality infractions
= = = = .08
Total number of observations (200)
Since the z value for the 95.5% confidence level is equal to 2.0, using formulas we obtain:
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

42. Figure 12.7 Holistic Care Corporation’s Quality Monitoring
UCL = 0.128
Proportion of Families Dissatisfied
LCL = 0.042 1 2 3 4 5 6 7 8 9 10 11 12
Sample number
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

43. Figure 12.8 Use of Mean and Range Charts
UCL
Process
Mean
LCL
Stable mean, increasing range process
UCL
LCL
Increasing mean, stable range process
Mean indicator
Range indicator
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Chapter 12: Quality Control & Improvement

44. Control charts for variables
Mean Charts - Standard Deviation Approach
In general the population standard is unknown, and so the average of sample means and the standard deviation of sample distribution σ are used to construct the confidence limits as: .
where σ
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Chapter 12: Quality Control & Improvement

45. Example 12.3 Observation Day-1 Day-2 Day-3 Day-4 Day-5 1 5.1 4.9 5.5
With a time-motion study, the IV startup process has been examined in a medical center nursing unit for five weekdays to determine whether in the future, additional training of nurses is required. Each day 9 new patients’ IV startups were observed and the measurements recorded in minutes, as shown below. Construct 99.7% (z = 3) confidence limits for IV startup times.
Observation
Day-1
Day-2
Day-3
Day-4
Day-5 1
5.1
4.9
5.5
6.1
6.0 2
5.4
5.7
5.6
5.8
5.2 3
6.3
5.3
5.9 4
7.5
5.0 5
6.2 6 7
6.4
4.8 8 9
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Chapter 12: Quality Control & Improvement

46. Solution:
Observation means for each day (sample) are calculated and are shown in the last rows of the following table.
Sample
Day-1
Day-2
Day-3
Day-4
Day-5
5.51
5.88
5.73
5.86
5.46 s
0.6
= ( ) ÷ 5 = 5.69.
with z = 3, n = 9 observations per sample (day), and s = 0.6, we obtain:
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Chapter 12: Quality Control & Improvement

47. Control charts for variables
Mean Charts - Range Approach
Another way to construct a mean chart is to use the average of sample distribution ranges. This approach requires a factor to calculate the dispersion of the control limits. .
Where A2 is a factor from Table 12.1
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Chapter 12: Quality Control & Improvement

48. Factors for Range Chart
Table 12.1 Factors for Determining Control Limits for Mean and Range Charts (for 3-sigma or 99.7% confidence level)
Sample Size n
Factor for Mean Chart, A2
Factors for Range Chart
LCL, D3
UCL, D4 2
1.88
3.27 3
1.02
2.57 4
0.73
2.28 5
0.58
2.11 6
0.48
2.00 7
0.42
0.08
1.92 8
0.37
0.14
1.86 9
0.34
0.18
1.82 10
0.31
0.22
1.78
Source: p. 143, Operations Management by Rusell & Taylor, 1995.
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

49. Example 12.4
During 5 weekdays, each day the number minutes spent for each of 10 patient registration operations were observed in a time study as follows:
Observation
Day-1
Day-2
Day-3
Day-4
Day-5 1
10.2
10.3
8.9
9.5
10.5 2
9.7
10.9 3
11.1 4
9.8 5 6 7
10.0
10.4 8
11.3 9
10.7 10
8.8
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Chapter 12: Quality Control & Improvement

50. Solution:
The overall mean for each sample and range is required to apply the formulas, using the range approach. Here each day is considered as a sample. The range is calculated by taking the difference between the maximum and minimum of each sample (day). The, mean for each day also is calculated and shown as follows:
Sample
Day-1
Day-2
Day-3
Day-4
Day-5
Maximum
11.3
10.5
11.1
Minimum
8.9
8.8
Range
2.4
1.6
2.3
10.12
10.19
9.84
9.85
10.13
= ( ) ÷ 5 =
= ( ) ÷ 5 = 2.06.
UCL = (2.06) =
LCL = – 0.31 (2.06) =
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Chapter 12: Quality Control & Improvement

51. Control charts for variables
Range Charts
Process dispersion is best monitored by range charts. The control limits for range charts are constructed using factors. To calculate LCL, factor score D3 is obtained from a factor chart (Table 12.1) based on the number of observations in the sample distributions. Similarly, to calculate UCL, factor score D4 is required. Control limits for range charts using these factor scores are then constructed as follows: .
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Chapter 12: Quality Control & Improvement

52. UCL = 1.78 (2.06) = 3.67. LCL = .22 (2.06) = 0.45. Example 12.5:
Use the information provided in example 12.4 to construct a range chart.
Solution:
For n = 10, D3 and D4 from Table 12.1 are 0.22 and 1.78, respectively. Using formulas we obtain:
UCL = 1.78 (2.06) = 3.67.
LCL = .22 (2.06) = 0.45.
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

53. Investigation of control chart patterns
Run-Based Pattern Tests
A pattern in a control chart described by a sequence of observations that have similar characteristics is called a “run.” A simple classification of sample observations with respect to the center line that identify consecutive patterns is called an Above/Below run, or A/B run.
Up (U) and down (D) runs is another way to classify and observe patterns. To classify sample observations as U or D, the first observation is used as a reference point. .
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Chapter 12: Quality Control & Improvement

54. * Figure 12.9 Identification of Runs A B B A A A A B B A A B 6 D U U U
UCL CL
LCL 1 2 3 4 5 6 7 8 9 10 11 12
Sample number
Observed runs A B B A A A A B B A A B 6 1 2 3 4 5 6 * D U U U D D D D U D D 5 1 2 3 4 5
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Chapter 12: Quality Control & Improvement

55. Investigation of control chart patterns
Run-Based Pattern Tests
Control chart patterns identified by runs require statistical testing of whether the runs are within expectations and hence the patterns are random, or beyond expectations and hence non-randomness is present. It has been shown that runs are distributed approximately normally (Stevenson, 2002, p.436) and using the z-test the significance of too few or too many observed runs can be determined as follows: .
A z-value within ±2, which provides 95.5% confidence level, would show that the runs are random; however, beyond these values ≤ ±2 ≥, a non-random presence would be shown.
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Chapter 12: Quality Control & Improvement

56. Investigation of control chart patterns
Run-Based Pattern Tests
It is necessary to calculate the expected runs and their standard deviations. The formulas for expected A/B or U/D runs and their standard deviations are as follows: .
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Chapter 12: Quality Control & Improvement

57. conclude that the A/B runs exhibit randomness.
Example 12.6:
Determine the presence/absence of non-randomness for the example presented in Figure 12.8, with 95.5% confidence limits.
Solution:
The example has twelve observations, so N=12. Using the formulas we get:
conclude that the A/B runs exhibit randomness.
conclude that U/D runs exhibit randomness.
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Chapter 12: Quality Control & Improvement

58. Process Improvement Methods for Generating New Ideas:
The 5W2H Approach
Brainstorming
Nominal Group Technique
Interviewing
Focus Groups
Quality Circles “Kaizen Teams”
Benchmarking .
Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

59. Process Improvement
Tools for Investigating the Presence of Quality Problems and Their Causes:
Check Sheet
Histogram
Scatter Diagram
Flow Chart
Cause-and-Effect Diagram
Pareto Chart .
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Chapter 12: Quality Control & Improvement

60. 1 /// ////// 2 //// / 3 4 // ///// 5
Figure A Check Sheet and Corresponding Histogram for Emergency
Room Wait Times
Weeks A
Wait Time to register >10 Minutes B
Registration Time > 5 Minutes C
Wait Time for MD > 15 Minutes 1
///
////// 2
//// / 3 4 //
///// 5
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Chapter 12: Quality Control & Improvement

61. Figure 12.11 Scatter Diagram
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Chapter 12: Quality Control & Improvement

62. Figure 12.12 A Flow Chart for the X-Ray Order Process
in an Emergency Department
E.D. MD
Requests X-ray NO
Hand Write
Patient
Demographic
Information
Computer-
Prepared Form
Available?
Obtain Form
YES
Physician
Completes Form 1
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Chapter 12: Quality Control & Improvement

63. Figure 12.13 Cause and Effect Diagram
Methods/Processes
Structure
Functions
Tests Not Coordinated
Too Many Steps
Lab/Rad./ER
Depts. Report
to Different VPs
Delays in Ordering Tests
Hospital room
not available if
admitted
Test Errors
Patients
Wait Too
Long
Boring Environment
Private MDs
Not on Site
Lack of Feedback
Design is
Not Efficient
Lack of Supplies
Lack of
Transporters
Lack of Incentives
Lack of Automated
System
Lack of ER Beds
People
Rewards
Equipment/Material
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Chapter 12: Quality Control & Improvement

64. Yasar A. Ozcan
Chapter 12: Quality Control & Improvement

65. CHAPTER Supplement: Pareto Diagrams in EXCEL
Instructor may follow steps in supplemental online material to demonstrate to students how to construct a Pareto Diagram in MS Excel.
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Chapter 12: Quality Control & Improvement

66. The End
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Chapter 12: Quality Control & Improvement