Chapter 12: Quantitatve Methods in Health Care Management Yasar A. Ozcan 1 Chapter 12. Quality...

Chapter 12: Quantitatve Chapter 12: Quantitatve Methods in Health Care Methods in Health Care ManagementManagement

Yasar A. OzcanYasar A. Ozcan 11

Chapter 12.Chapter 12.

Quality ControlQuality ControlUCL

LCL

Sample number

1 3 4 5 6 7 8 92 10 11 12

CL x

+1σ

-1σ

-2σ

-3σ

+2σ

+3σ RAxAZone 2

RAxBZone 22

1

RAxCZone 23

1

RAxBZone 22

1

RAxCZone 23

1

RAxAZone 2



OutlineOutline 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



A Broad Definition. . .A Broad Definition. . .

Quality refers to the ability of a product or service Quality refers to the ability of a product or service to consistently meet or exceed customer to consistently meet or exceed customer expectationsexpectations

quality in healthcare is evaluated from differing quality in healthcare is evaluated from differing perspectives of providers, recipients and third-perspectives of providers, recipients and third-party payers. party payers.

Most clinicians accept the Institute of Medicine Most clinicians accept the Institute of Medicine (1990) definition: “(1990) definition: “Quality is the extent to which Quality is the extent to which health services for individuals and populations health services for individuals and populations increase the likelihood of desired health increase the likelihood of desired health outcomes and are consistent with current outcomes and are consistent with current professional knowledgeprofessional knowledge.” .”



What is quality?What is quality?

You are a world renowned surgeon that You are a world renowned surgeon that has just completed a radical new has just completed a radical new surgical technique. There were few surgical technique. There were few complications, largely due to the complications, largely due to the excellence of the hospital’s staff and excellence of the hospital’s staff and technological capabilities.technological capabilities.

QUALITY?QUALITY?



What is Quality?What is Quality?

You are a patient who has just undergone radical You are a patient who has just undergone radical new surgery. Although the surgery went new surgery. Although the surgery went without technical difficulties, your were upset at without technical difficulties, your were upset at the doctor’s uncaring attitude. Furthermore, the the doctor’s uncaring attitude. Furthermore, the nursing staff often failed to respond to your nursing staff often failed to respond to your calls, and twice you were served meat despite calls, and twice you were served meat despite the fact that you are a vegetarian. Also, there the fact that you are a vegetarian. Also, there was a used bedpan that sat next to your bed for was a used bedpan that sat next to your bed for three days.three days.

QUALITY?QUALITY?



A question of perspectiveA question of perspective

Quality of care depends upon who is Quality of care depends upon who is making the assessmentmaking the assessment– Clinician-- Clinician-- technical components, adequate technical components, adequate

skills, resources, conditionsskills, resources, conditions– Patients-- Patients-- outcomes, interpersonal processes, outcomes, interpersonal processes,

amenities, overall satisfactionamenities, overall satisfaction– Health Facility Managers-Health Facility Managers- appropriate and appropriate and

effective utilizationeffective utilization– Community-- Community-- availability, access, reputations, availability, access, reputations,

general health status of communitygeneral health status of community



Quality MeasurementQuality Measurement

Clinicians-- Clinicians-- cure rates, mortality, morbiditycure rates, mortality, morbidity Patients-- Patients-- patient satisfaction surveyspatient satisfaction surveys Health facility managers-- Health facility managers-- cure rates, cure rates,

mortality, morbidity, intermediate process mortality, morbidity, intermediate process measures (patient falls, infection rates, measures (patient falls, infection rates, medication errors, appropriate staffing, etc.)medication errors, appropriate staffing, etc.)

Community-- Community-- area service distribution, area service distribution, insurance coverage, incidence and prevalence insurance coverage, incidence and prevalence rates, etc.rates, etc.



Figure 12.1 Quality Measurement

Structure Process Outcome

InputsConversion Process

(Throughput) Outputs

Patient,provider labor

equipmentsupplies, etc.

Various hospitaland medical servicestransform poor health

to wellness for patients(diagnosis, procedures,

treatments)

Treated Patient




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.

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.

Quality Gaps




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.



Quality ExpertsQuality Experts

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



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.

Quality CertificationQuality Certification



Quality CertificationQuality 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



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

“pakayoke”– monitoring results and continuous

improvement



TQM, cont.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



Controlling QualityControlling 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



Plan

DoAct

Check

Figure 12.2 The Deming Wheel/Shewhart Cycle



Continuous Quality ImprovementContinuous 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



The CQI is a detailed version of a PDSA cycle that comprises:

1) selecting a process that needs an improvement 2) studying and documenting the current process seeking

ways to improve it3) designing an improved process 4) implementing the new process5) monitoring and evaluation6) documenting the process if it worked successfully and

publicizing it through the healthcare organization 7) if it did not achieve its goals, re-starting from step 1.

Continuous Quality ImprovementContinuous Quality Improvement



Six-SigmaSix-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.



Six-SigmaSix-Sigma

Healthcare organizations have reduced the deaths caused by anesthesia from 25-50 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).



Six-SigmaSix-SigmaDeployment of six-sigma to improve the quality ofhealthcare and delivery performance can beconsidered in the following areas:

a) Clinical excellenceb) Service deliveryc) Service costs, and d) 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 havefallen the below six-sigma levels,

DMADV is used to design and develop new processes or productsat six-sigma levels (Stahl, Shultz, and Pexton, 2003).



Six-SigmaSix-SigmaThe 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.



Six-SigmaSix-SigmaGreen 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.



Six-SigmaSix-SigmaSix-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.



Six-SigmaSix-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).



Quality Measurement and Control TechniquesQuality Measurement and Control TechniquesProcess 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 randomrandom and non-randomnon-random variation.




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 randomrandom variation. If the cause of variation is systematic, not natural, and thesource of the variation is identifiable, the process variationis called non-randomnon-random variation.

In healthcare, non-random variation may occur by notfollowing procedures, using defective materials, fatigue,carelessness, or not having appropriate training ororientation to the work situation, among many reasons.




Process variation is the range of natural variabilityin a process for which healthcare managers usecontrol charts to monitor the measurements. If thenatural variability or the presence of randomvariation exceeds tolerances set by control charts,then the process is not meeting the designspecifications.



Set design specificationsfor process capability

Pro

cess

Va

riab

ility

UCL

LCL

Process variability meetsand exceeds specifications

Process variabilitydoes not meetdesign specifications

Figure 12.3 Process Capability



Figure 12.4 Control Limits, Random and Nonrandom Sample Observations

UpperControlLimit(UCL)

LowerControlLimit(LCL)

ProcessMean

Sample number

1 3 4 5 6 7 8 92 10 11 12

Non-random

α/2

α/2

95

.5%

+2σ

-2σ



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 ac-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.



Control Charts for Attributes: c-Chart

czcUCL

czcLCL




Example 12.1The 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 4 3

March 3 6

April 4 3

May 3 4

June 4 3

July 5 5

August 3 6

September 4 3

October 3 3

November 7 6

December 4 3

Total 47 49The nurse manager who serves on the quality team wants to discover whether the infections are in control within 95.5% confidence limits.




SolutionIf 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:

c = 96/24 = 4.0.

Since the z-value for 95.5% confidence level is equal to 2, using formulas we obtain:

.82*24424 czcUCL

.02*24424 czcLCL



Figure 12.5 ABC Medical Center Infection Control Monitoring

UCL=8

LCL=0

Sample number

1 3 4 5 6 7 8 92 10 11 12 13 14 15 16 17 18 19 20 2221 23 24

Infe

ctio

ns

pe

r m

on

th

4c



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:

nppzp )1( UCL

nppzp )1( LCL



Example 12.2The 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

PercentDissatisfied

January 12 0.060

February 14 0.070

March 16 0.080

April 14 0.070

May 25 0.125

June 14 0.070

July 15 0.075

August 16 0.080

September 14 0.070

October 14 0.070

November 24 0.120

December 14 0.070

Total 192 0.080

The manager in charge of quality wishes to construct a control chart for this data within 95.5% confidence intervals.



Solution:First, we need to estimate the proportion mean,

Total number of quality infractions 192 192 = -------------------------------------------- = ----------- = ------- = .08 Total number of observations 12 (200) 2400

Since the z value for the 95.5% confidence level is equal to 2.0, using formulas we obtain:


p

.118.0208. 200)08.1(08.

UCL

.042.0208. 200)08.1(08.

LCL



Figure 12.6 Holistic Care Corporation’s Quality Monitoring

UCL=.118

LCL=0.042

Sample number

1 3 4 5 6 7 8 92 10 11 12

Pro

po

rtio

n o

f F

am

ilie

s D

issa

tisfie

d

08.p



Figure 12.7 Use of Mean and Range Charts

UCL

LCL

ProcessMean

Range indicatorMean indicator

UCL

LCL

Stable mean, increasing range process

Increasing mean, stable range process



Mean Charts - Standard Deviation Approach.

Control Charts for Variables

zxUCL

In general the population standard is unknown, and so the average of sample means )(x

and the standard deviation of sample distribution σx

are used to construct the confidence limits as:

x

zxLCL x

where σ x ns /

.



Example 12.3With 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.

Control Charts for Variables: Mean Chart, σ Method

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 5.5 6.3 5.3 5.9 6.3

4 5.8 7.5 4.9 6.0 5.0

5 5.6 5.8 5.2 6.2 5.5

6 5.8 5.9 5.4 5.7 5.1

7 5.3 5.5 6.4 4.8 5.9

8 4.9 5.8 7.5 6.3 5.3

9 6.2 5.5 5.8 5.9 4.8



SolutionObservation means for each day (sample) are calculated and are shown in the last rows of the following table.

Control Charts for Variables: Mean Chart, σ Method

x

Sample Day-1 Day-2 Day-3 Day-4 Day-5

5.51 5.88 5.73 5.86 5.46

s 0.6

x = (5.51+5.88+5.73+5.86+5.46) ÷ 5 = 5.69.

with z = 3, n = 9 observations per sample (day), and s = 0.6, we obtain:

.29.6)2.0(369.5)9/6.0(369.5 UCL

.09.5)2.0(369.5)9/6.0(369.5 LCL



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.

.

RAxUCL 2

RAxUCL 2

Where A2 is a factor from Table 12.1



Table 12.1 Factors for Determining Control Limits for Mean and Range Charts (for 3-sigma or 99.7% confidence level)

Sample Sizen

Factor for Mean Chart, A2

Factors for Range Chart

LCL, D3 UCL, D4

2 1.88 0 3.27

3 1.02 0 2.57

4 0.73 0 2.28

5 0.58 0 2.11

6 0.48 0 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.



Example 12.4During 5 weekdays, each day the number minutes spent for each of 10 patient registration operations were observed in a time study as follows:

Control Charts for Variables: Mean Chart, Range Method

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 10.5 9.7 10.2

3 10.3 11.1 8.9 10.5 10.3

4 8.9 8.9 10.5 9.8 10.9

5 10.5 10.5 9.8 8.9 11.1

6 9.8 9.7 10.2 10.5 9.8

7 10.0 8.9 8.9 10.4 9.5

8 11.3 10.5 10.5 8.9 9.7

9 10.7 9.8 9.7 10.5 10.5

10 9.8 11.3 10.5 9.8 8.8



SolutionThe 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:

Control Charts for Variables: Mean Chart, Range Method

x

Sample Day-1 Day-2 Day-3 Day-4 Day-5

Maximum 11.3 11.3 10.5 10.5 11.1

Minimum 8.9 8.9 8.9 8.9 8.8

Range 2.4 2.4 1.6 1.6 2.3

10.12 10.19 9.84 9.85 10.13

x = (10.12+10.19+9.84+9.85+10.13) ÷ 5 = 10.03.

R = (2.4+2.4+1.6+1.6+2.3) ÷ 5 = 2.06.

UCL = 10.03 + 0.31 (2.06) = 10.67.

LCL = 10.03 – 0.31 (2.06) = 9.39.



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:

.

RDUCL 4

RDLCL 3



Example 12.5Use 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:

Control Charts for Variables: Range Chart

UCL = 1.78 (2.06) = 3.67.

LCL = .22 (2.06) = 0.45.



Investigation of Control Chart Patterns

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

.

Run-Based Pattern Tests.



Figure 12.8 Identification of Runs

UCL

LCL

Sample number

1 3 4 5 6 7 8 92 10 11 12

A B B BB BA A A A A A

D D DDDDUU DU U*

Observed runs

6

5

1 2 43 65

1 2 3 54

CL




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:

.


runsofdeviationdardS

runsExpectedrunsObservedz

tan

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.




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:

.


12

)( / N

runE BA4

1)( /

Nrun BA

3

12)( /

NrunE DU 90

2916)( /

Nrun DU



Example 12.6Determine the presence/absence of non-randomness for the example presented in Figure 12.8, with 95.5% confidence limits. SolutionThe example has twelve observations, so N=12. Using the formulas we get:


0.712

12)( / BArunE 66.175.2

4

11

4

112)( /

BArun

67.73

1)12*2()( /

DUrunE 35.181.1

90

163

90

29)12*16()( /

DUrun

60.066.1

76/

BAz

98.135.1

67.75/

DUz conclude that U/D runs exhibit randomness.

conclude that the A/B runs exhibit randomness.




An alternative method that is often used by quality control software packages is called the “zone test.” The essence of the zone test rests on deviation from the center line by 1-sigma, 2-sigma, or 3-sigma limits. Zone C, zone B and Zone A are identified by these limits, respectively.

To create the zones, the formulas for constructing mean chart with range approach formulas are used. Those formulas, presented earlier, use A2 from Table 12.1, and the values for this table were calculated for 3-sigma levels (or 99.7% confidence level). Thus, in constructing the zones, one must reduce the A2 factor proportionately, according to the sigma level. Since A2 is designated for 3-sigma, for 2-sigma 2/3 of A2, and for 1-sigma 1/3 of A2 would be appropriate. The zone formulas can be written as:

.

Zone Tests.

RAxAZone 2

RAxBZone 22

1

RAxCZone 23

1



Figure 12.9 Zone test

UCL

LCL

Sample number

1 3 4 5 6 7 8 92 10 11 12

CL x

+1σ

-1σ

-2σ

-3σ

+2σ

+3σ RAxAZone 2

RAxBZone 22

1

RAxCZone 23

1

RAxBZone 22

1

RAxCZone 23

1

RAxAZone 2



Process Improvement

Methods for Generating New Ideas:

The 5W2H Approach Brainstorming Nominal Group Technique Interviewing Focus Groups Quality Circles “Kaizen Teams” Benchmarking.

.



Process Improvement

Tools for Investigating the Presence of Quality Problems and Their Causes

Check SheetHistogram Scatter Diagram Flow ChartCause-and-Effect Diagram Pareto Chart

.



Figure 12.10. A Check Sheet and Corresponding Histogram for Emergency Room Wait Times

WeeksWeeks

AA

Wait time to Wait time to register >10 register >10

minutesminutes

BB

Registration Registration time > 5 time > 5 minutesminutes

CC

Wait time for Wait time for MD > 15 MD > 15 minutesminutes

11 ////// ////////////

22 //////// // //

33 //////////// ////// ////////////

44 // //// //////////

55 //////////// //// //////////

0

1

2

3

4

5

6

Week 1 Week 2 Week 3 Week 4 Week 5

A

B

C



0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0 5 10 15 20

Number of Infections per Month

Mo

rbid

ity

Ra

teFigure 12.11 Scatter Diagram



E.D. MDRequests X-ray

Obtain Form

Hand WritePatient

DemographicInformation

NO

YES

1

Physician Completes Form

Computer-Prepared FormAvailable?

Figure 12.12 A Flow Chart for the X-Ray Order Process in an Emergency Department



PatientWait TooLong

Equipment/MaterialRewards

People

Methods/Processes FunctionsStructure

Tests not coordinated

Delays in ordering tests

Test Errors

Boring Environment

Lack of Supplies

Lack of ER Beds

Design isnot efficient

Lack of automatedsystem

Lab/Rad./ERDepts. report to different VPs

Lack of Feedback

Lack of incentives

Too many steps

Hospital roomnot available ifadmitted

Private MDsnot on site

Lack of transporters

Figure 12.13 Cause and Effect Diagram



100%

75%

50%

25%

0Lack of info.

to patientToo many

stepsDelays intest orders

Lack of automation

Ineffective/voluminous

documentation

Other

80%

Figure 12.14 Pareto Diagram



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Chapter 12: Quantitatve Methods in Health Care Management Yasar A. Ozcan 1 Chapter 12. Quality...

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