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Chapter 6.
Flow Processes Improvement:
Reengineering & Lean Management
Chapter 6: Flow Process Improvement
Yasar A. Ozcan
1
1
Outline
Reengineering vs. Other Methods
Lean Management
Work Design in Health Care Organizations
Work Design
Job Design
Work Measurement-Standard Times
Stopwatch Time Studies
Standard and Predetermined Times
Work Measurement Using Work Sampling
Determination of Sample Size
Development of Random Observations Schedule
Training Observers
Work Simplification
Work Distribution Chart
Flow Chart & Flow Process Chart
Value Stream Map
Spaghetti Diagram
Worker Compensation
Yasar A. Ozcan
Chapter 6: Flow Process Improvement
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Introduction
Organizational Performance is a major concern for health care managers
Performance is usually measured financially by looking at profits, market share, reimbursement, but also can be measured by market share compared to other institutions or healthcare systems.
Performance is usually classified as:
Those who perform adequately with no imminent risk in their finances or market share
Those whose performance is marginally adequate
Those whose performance is less than less than expected
Yasar A. Ozcan
Chapter 6: Quantitatve Methods in Health Care Management
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How do you improve institutional Performance?
4
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Yasar A. Ozcan
Improve Finances (Restructuring, downsizing/Layoffs, mergers)
What problems are created by restructuring /downsizing?
Will this impact Quality of care?
Pareto Principle “While improving a part of the organization, one should not make other parts of the organization worse off”
Improved Productivity (automation, implement process improvements, cross training staff, etc.)
Improve Quality of Care
Value = Quality/Cost (Increase value by Improving Quality and by reducing cost)
TQM/CQI
To improve both performance and quality one can use TQM (Total Quality Management) and CQI (Continuous Quality Improvement)
This should be a Long-term goal
Make incremental changes (often over 5 to 6 years)
Requires management commitment to quality
Q. Why TQM and CQI end up in failure?
Management’s commitment can become diluted
Responsibility is only assigned to a limited number of people
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Reengineering & Lean Management vs. Other Methods
Healthcare managers have often sought organizational change, restructuring, and downsizing. Although those methods may improve the financial base of the organization or productivity at least temporarily by “cutting the fat,” namely by reducing the staff across the board, yet they create other problems. In particular, reducing staff can lead to major problems in the quality of care.
Two other contemporary and popular methods that aim to improve both performance and the quality are total quality management (TQM) and continuous quality improvement (CQI) which are geared to make incremental changes over time.
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Chapter 6: Flow Process Improvement
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Reengineering and Lean management
What is Reengineering and Lean Management?
Yasar A. Ozcan
Chapter 6: Quantitatve Methods in Health Care Management
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Flow Process Improvement via Reengineering and Lean Management
Reengineering and Lean Management methods are process improvement methodologies intended to overcome the difficulty in realizing TQM/CQI performance over a long duration, as well as the myopic conduct of organizational change, restructuring and downsizing.
To improve the system flow process, healthcare managers must be able to understand work-design, jobs, job measurement, process activities, and reward systems – all well known concepts of industrial engineering. With that knowledge, they can recognize the bottlenecks in the old system, identify unnecessary and repetitive tasks, and eliminate them in the reengineered system of care.
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Chapter 6: Flow Process Improvement
Reengineering
Reengineering in the early 1990 aimed to improve:
Quality
Cost
Service
Speed
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Reengineering
What should Reengineering do?
Eliminate Delays in healthcare delivery
Eliminate duplication in healthcare delivery/Eliminate unnecessary tasks
Implement automation or IT
Retrain employees to provide a comprehensive and undisruptive care
This will help reduce cost and speed up recovery
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Example of Comprehensive and undisruptive care
Example is patient focused or patient centered care. Hospital offering patient-focused cardiac care for a patient recuperating from a heart attack or bypass surgery.
Nurses are trained to perform EKGs and Draw blood, so fewer staff are involved in the patient’s care
Patients are given one on one education about heart disease and cardiac rehab.
Families receive education about their health
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Final Thought: Why reengineering Fails
12
Chapter 6: Quantitatve Methods in Health Care Management
Yasar A. Ozcan
One reason such efforts fail is that leaders assume that reengineering is no more than cost reduction. In fact, reengineering must go beyond simple cost reduction and create processes that, by adding value to the product, are attractive to customers.
As one writer has put it, “No company ever shrank to greatness”
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Lean Management
Similar to reengineering in many aspects, Lean Management conceives of business processes as value streams in which value flows through various process steps to the customer.
Lean is a systematic approach intended to identify and eliminate non-value added process steps, or process waste, and reduce lead time in order to create value for the customer using fewer resources.
Lean emphasizes a model of continuous, incremental improvement to the process.
Based on the Toyota Production System, the early applications of lean management focused on manufacturing, but its applications have since expanded into service industries such as health care and software development (Kim et al, 2006).
Chapter 6: Flow Process Improvement
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Lean Management Process
15
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Define the Value
Map the Process
Identify Process Waste
Identify Improvements
Map the Future State
Implement Improvements
Repeat the Cycle
Define the value
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The first step in lean management is to identify the value that each step in the process provides to the customer.
This may involve engaging the customer and/or stakeholders to understand their perceptions of the value received from the product or service.
8-Process Waste
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Chapter 6: Quantitatve Methods in Health Care Management
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Examples
8 Wastes of Lean Management:
Defects: Data entry errors, mislabeled specimens, incomplete documentation
Overproduction: Providing Meals that is not eaten by patients, providing reports that are not utilized by staff
Waiting: Time spent being idle, waiting for exam room or waiting for blood darw
Over-processing: completing unnecessary testing or labs performing surgery instead of minimally invasive procedure
Transportation: transportation of specimens or blood products
Motion: unnecessary walking between departments
Inventory: excess inventory: risk of damage, expiration, becoming obsolete
Under-utilized talent: inappropriate utilization of the talents of the staff
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Map the Process
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Next, a visual map of the process is created using a tool called a value stream map (discussed later).
A value stream map incorporates the results of time observation studies, documenting the amount of time it takes to complete each step of the process.
Ultimately, the value stream map serves as a benchmark of current process performance (Kim et al, 2015).
Once the value stream has been mapped, it can be examined to identify performance issues and process waste.
Identify Improvements
Once process waste is identified, the project team can leverage lean management tools to design improvements that eliminate waste and standardize the process.
5-Why Analysis: drill down to the root cause of a problem by asking “why” a problem is occurring at least five times.
5S: Sort, Straighten, Shine, Standardize, and Sustain. Technique for organizing the workplace.
Mistake-proofing: Involves putting process controls in place that prevent defects from occurring.
Kitting: Items needed to execute a step or steps in the workflow are gathered and packaged as a kit.
Autonomation: Implementation of technology solutions to replace, or automate, specific tasks that may be repetitive or too complicated for staff to complete.
Kanban: Visual signaling system used to ensure a continuous flow of supplies, finished goods, or customers.
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Example of the 5-Why?
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Example of 5 S
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Mistake Proofing
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Kitting
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Map the Future State
Any improvements are then incorporated into a future state process design, which is visualized in a future state value stream map.
The revised value stream map includes a time component that indicates how long each activity will take to complete once the improvements are implemented, providing an estimate of the overall reduction in lead time under the improved process.
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Yasar A. Ozcan
Implement Improvements
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Once the best solutions have been identified and agreed upon by stakeholders, the project team may implement a pilot project to test the improvements on a smaller scale.
The results of the pilot project are then reviewed to ensure the effectiveness of the solution and also collect any lessons learned.
If the pilot project is deemed successful, there is a full scale implementation of the solutions.
Repeat the Cycle
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Lean management does not end once improvements are implemented.
An organization that implements lean management will continue to look for opportunities to improve the process and reduce process waste.
Importance of Lean In HealthCare
Concerns over how process waste has contributed to rising health care costs in the United States have increasingly led health care organizations to implement lean management.
By implementing lean management, health care organizations can:
identify redundant processes,
pinpoint process steps that increase the probability of error, and
categorize processes in terms of importance.
Health care managers can then leverage the numerous lean tools to design and implement process improvements and ultimately improve process efficiency.
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Importance and Purpose Of Human Resources Management
Human resources represents over 40% of healthcare facility budgets
Productivity and satisfaction of staff involves an understanding of the work environment
Work must be designed so that employees are happy, organizational productivity is high, and costs are minimized
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Work Design- A Systems Perspective
Work Design
Work
Measurement
Time Study
Predetermined
Standard
Work Sampling
Job Design
Who?
How?
Where?
Job Simplification
Worker
Compensation
Time Based
Output Based
Incentive Plans
External
Factors
Job Design:
Who does what, how, and where?
31
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Consistent with organizational goals
Write it down!
Understand and communicate it
Involve employees and management
Frederick Winslow Taylor
Developed Scientific Management Approach
Focused on time studies
Conflicts between labor and management occurred because management had no idea how long jobs actually took
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For what types of jobs would this
approach work best? Are there
Healthcare applications?
Efficiency School–
Logical and Systematic
Best for simple, repetitive routine, and separable tasks
Healthcare Examples:
lower level administrative duties
division of labor
standardized forms and paperwork
robots in laboratories
automation of routine tasks
Not good for judgmental/unpredictable tasks
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Advantages and Disadvantages of Specialization
Management
Employees
Advantages
a. Simplifies training
b. Higher productivity
c. Low wage costs
a. Low education/skill
b. Minimum responsibilities
c. Little mental effort needed
Disadvantages
Difficult to motivate
quality
b. Worker dissatisfaction,
absenteeism, high turn-
over, disruptive tactics,
poor attention to quality
a. Monotonous and boring
b. Limited opportunities for
advancement
c. Little control over work
d. Little opportunity for self-
fulfillment
Behavioral School
Satisfaction of Wants/Needs
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Intrinsic and extrinsic motivators
Specialization leads to monotony and worthlessness
Socio-technical School Approach
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Efficiency School
(Technical Focus)
Behavioral School
(Human Focus)
Socio-Technical School
How can jobs be improved?
37
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What are examples of each?
1
Job enlargement – give workers a larger portion of the total task (horizontal loading– additional work at same level of skill and responsibility)
II
Job enrichment – increasing responsibility for planning and coordinating tasks (vertical loading)
III
Job rotation – workers periodically exchange jobs
Work Measurement Using Time Standards
Time standards are important in establishing productivity measures, determining staffing level and schedules, estimating labor costs, budgeting, and designing incentive systems
A time standard represents the amount of time needed for the average worker to do a specific job working under typical conditions
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The First Step. . .
The amount of time it should take a qualified worker to complete a specified task, working at a sustainable rate, using given methods and equipment, raw materials, and workplace arrangements is called a Standard Time.
A Standard Time can be developed through:
Stop-watch studies
Historical times
Predetermined data
Work sampling
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Stopwatch Time Studies
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Take time over a number of trials (cycles)
Workers should be educated regarding the process to avoid suspicion and avoid the Hawthorn Effect
Number of cycles to time (i.e., sample size)
variability in observed times
desired accuracy
desired level of confidence for the estimate
Determining Sample Size
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Desired
Confidence Z-value
90 1.65
95 1.96
98 2.33
99 2.58
Accuracy desired may be explained by the percentage of the mean of the observed time. For instance, the goal may be to achieve an estimate within 10 percent of the actual mean. The sample size is then determined by:
where:
z = number of std. dev.
needed for desired
confidence
s = sample std. dev.
a = desired accuracy
x = sample mean
An Alternative Formula
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Desired accuracy may be expressed as an amount (e.g.,
within one minute of the true mean). The formula for
sample size becomes:
where
e = Accuracy or
maximum error
acceptable
To make an initial estimate of sample size, you should
take a small number of observations and then compute
the mean and std. dev. to use in the formula for n.
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Example 6.1:
A heath care analyst wishes to estimate the time required
to perform a certain job. A preliminary stopwatch study
yielded a mean of 6.4 minutes and a standard deviation of
2.1 min. The desired confidence level is 95 percent. How many
observations will be needed (including those already taken)
if the desired maximum error is:
a) +/- 10 percent?
b) one-half minute?
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a. Using formula (6.1) and z = 1.96, we get:
b. Similarly, using formula (6.2), we get:
Solution:
Time Standard
Once the sample size is determined, observations can be made.
The activity is timed and the standard time is computed.
To compute a time standard, three times must be calculated:
Observed Time
Normal Time
Standard Time
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OT, ST, NT
OT: Observed Time = (∑Xi Observed time for worker i)/n (number of observations for worker i).
The observed time must be adjusted for workers performance to yield the normal time
NT: Normal Time = OT (observed time) x PR (workers performance) (This formula assumes that single performance rating has been made for the entire job); however, Each element or task that composes a job may have a different performance rating
Example testing a clinical sample has many different elements: Transportation, spinning the sample, labeling, testing, resulting, charting etc.
NT = ∑Ej (observed time of element j) X PR (Performance rating for element j)
Normal time is the time it takes a worker to perform the job without interruptions, but no one can be asked to work 100% of the time. Therefore the Normal Time is adjusted by an allowance factor to reach the standard time
ST: standard Time = NT X AF (Allowance Factor)
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Determining the Standard Time
Observed Time — average of observed times
OT = åxi/n
Normal Time — observed time adjusted for worker performance
NT = OT * PR (where PR = performance standard measured for the entire job)
NT = å(Ej*PRj) (where PR is measured element by element)
PR equals 1 for the average worker; PR< 1 is for a slower worker
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ST = NT * AF
Standard time equals normal time multiplied by an allowance factor
Allowance Factor
Accounts for personal delays, unavoidable delays, and/or rest breaks
AFjob = 1+A, where A= allowance percentage based on job time
AFday = 1/(1-A), where A = allowance percentage based on work day
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Table 6.1 Typical Allowance Percentages for Varying Healthcare Delivery Working Conditions
Allowance Level
Percent
1. Basic-low (personal, fatigue, standing)
11
2. Basic-moderate (basic-low and mental strain)
12
3. Basic-high (basic-moderate and slightly uncomfortable heat/cold or humidity
14
4. Medium-low (basic high and awkward position)
16
5. Medium-moderate (medium-low and lifting requirements up to 20 lbs.)
19
6. Medium-high (medium-moderate and loud noise)
21
7. Extensive-low (medium-high and tedious nature of work)
23
8. Extensive-medium (extensive-low and with complex mental strain)
26
9. Extensive-high (extensive-medium and lifting requirement up to 30 lbs.)
28
Source: Adapted from B.W. Niebel, 1988.
The Allowance Factor
Compute the allowance factor if:
The allowance is 20 percent of job time.
The allowance is 20 percent of work day.
A) AF = 1 + A = 1.20, or 120%
B) AF = 1/(1-A) = 1/(1-.2) = 1.25 = 125%
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Example 6.2:
The nursing unit manager at HEALTH FINDER HOSPITAL wants to evaluate the activities in the patient care unit. The manager hired an analyst, who timed all the patient care activities for this job, which has twenty elements. The observed times (OT) and the performance ratings for six samples of a particular employee are recorded in Table 6.2. From those measurements the nursing manager wants to know the standard time for the whole job with its 20 tasks with extensive-medium level allowance. Assume that nursing tasks differ from other clinical and ancillary operations.
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Nursing Unit Activities
Performance Rating
Observed time in minutes
1
2
3
4
5
6
1. Patient assessment
1.08
12
11
12
9
13
12
2. Care planning
0.95
9
7
6
8
7
9
3. Treatments
1.12
8
8
7
9
10
11
4. Medication
1.05
4
3
4
5
6
4
5. Collecting blood/lab specimens
1.10
8
7
6
9
10
7
6. Passing/collecting trays, snacks, feeding patients
1.20
18
21
18
19
21
20
7. Shift report
0.97
5
6
5
7
8
6
8. Charting/ documentation
0.98
8
5
6
8
9
10
9. Responding to patients’ call lights
1.15
4
3
3
5
6
5
10. Staff scheduling phone calls
0.95
5
4
4
5
6
7
11. Phone calls to/from other departments
0.96
6
5
5
4
6
7
12. Transporting patients, specimens etc.
1.05
9
11
12
11
9
10
13. Patient acuity classification
1.11
5
6
5
6
7
4
14. Attending educational in-services
1.00
75
75
75
75
75
75
15. Order transcription and processing
0.94
5
6
4
6
7
6
16. Ordering/stocking supplies and lines
0.98
6
4
5
6
7
4
17. Equipment maintenance and cleaning
0.95
9
11
8
9
11
10
18. General cleaning/room work (garbage, making beds)
1.15
12
9
12
10
9
11
19. Assisting with the admission process
1.06
11
9
10
9
8
9
20. Breaks/ personal time (not including lunch)
1.00
15
15
15
15
15
15
TABLE 6.2. Observed Times and Performance Rating for Nursing Unit Activities
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Solution:
Table 6.3 displays the calculations summary for all 20 job elements involved in nursing care. Column (4) is the average of the six observations from column (3). Column (5) uses the normalizing formula (6.5):
NT = Sum of [(Avg. time for element j) x (Performance rating for element j)]
To calculate the standard time, an allowance factor should be determined using Table 6.1, in this case 26 percent.
The allowance factor for this job:
AFjob =1 + A = 1 + 0.26 = 1.26.
Finally, the standard time for the nursing activities:
ST = NT x AF = 243.49 x 1.26 = 306.80 minutes or 5.1 hours.
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(1)
(2)
(3)
(4)
(5)
Nursing Unit Activities
Performance Rating
Sample Observed Times
Observed
Normal Time
in Minutes
Time
(NT)
(PR)
1
2
3
4
5
6
(OT)
OT * PR
1. Patient assessment
1.08
12
11
12
9
13
12
11.50
12.42
2. Care planning
0.95
9
7
6
8
7
9
7.67
7.28
3. Treatments
1.12
8
8
7
9
10
11
8.83
9.89
4. Medication
1.05
4
3
4
5
6
4
4.33
4.55
5. Collecting blood/lab specimens
1.10
8
7
6
9
10
7
7.83
8.62
6. Passing/collecting trays, snacks, feeding patients
1.20
18
21
18
19
21
20
19.50
23.40
7. Shift report
0.97
5
6
5
7
8
6
6.17
5.98
8. Charting/documentation
0.98
8
5
6
8
9
10
7.67
7.51
9. Responding to patients’ call lights
1.15
4
3
3
5
6
5
4.33
4.98
10. Staff scheduling phone calls
0.95
5
4
4
5
6
7
5.17
4.91
11. Phone calls to/from other departments
0.96
6
5
5
4
6
7
5.50
5.28
12. Transporting patients, specimens etc.
1.05
9
11
12
11
9
10
10.33
10.85
13. Patient acuity classification
1.11
5
6
5
6
7
4
5.50
6.11
14. Attending educational in-services
1.00
75
75
75
75
75
75
75.00
75.00
15. Order transcription and processing
0.94
5
6
4
6
7
6
5.67
5.33
16. Ordering/stocking supplies and lines
0.98
6
4
5
6
7
4
5.33
5.23
17. Equipment maintenance and cleaning
0.95
9
11
8
9
11
10
9.67
9.18
18. General cleaning/room work(garbage, making beds etc)
1.15
12
9
12
10
9
11
10.50
12.08
19. Assisting with the admission process
1.06
11
9
10
9
8
9
9.33
9.89
20. Breaks/ personal time (not including lunch)
1.00
15
15
15
15
15
15
15.00
15.00
234.83
243.49
Job - OT
Job - NT
TABLE 6.3. Observed and Normal Time Calculations for Nursing Unit Activities
What are the problems with time studies?
Subjective performance ratings and allowances
Only observable jobs can be studied
Highly costly -- best for repetitive tasks
Disrupts worker routine
May cause worker resentment
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Other Methods
Historical/Standard Elemental Times
Firms collect data on standard job elements
Put these data together to determine job times
Less costly and disruptive
Limited applications in healthcare
Predetermined Standards
Obtained from trade publications
Need no performance of allowance factor
Operations are not interrupted
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Technique for estimating the proportion of time that a worker or machine spends on various activities
Observers make brief observations of a worker or a machine at random intervals over a period of time and simply note the nature of the activity
Purpose:
To estimate percentage of unproductive or idle time for repetitive jobs
To estimate the percentage of time spent on various tasks for non-repetitive jobs
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Work Measurement Using Work Sampling
Work Sampling Steps
1) Determine the sample size
2) Train the observers
3) Develop random sample schedule
4) Take observations, and re-compute the desired sample size several times if initial estimates are not reliable
5) Determine the estimated proportion of time spent on specified activity
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Step 1: Sample Size
CI = confidence interval,
e = error,
z = number of standard deviations needed to achieve desired confidence,
sample proportion (number of occurrences divided by sample size),
n = sample size.
The goal of work sampling is to obtain an estimate that provides a specified confidence not differing from the true value by more than a specified error
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Sample Size, cont.
Example 6.3: A hospital administrator wants an estimate of X-ray idle time that has a 95.5 percent confidence of being within 4 percent of the actual percentage. What sample size should be used?
e = 0.04 z = 2.00
Desired
Confidence Z-value
90 1.65
95 1.96
95.5 2.00
98 2.33
99 2.58
n = (z/e)2p(1-p)
When p is unknown, a
preliminary estimate of
sample size can be obtained
using p = 0.5. Then after 20
observations, a new estimate
can be obtained.
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Solution:
Given: e = 0.04; z = 2.00 (see Appendix A);
= 0.5 (preliminary).
If for 20 observations, it is observed that the x-ray machine was idle only once, the revised estimate is then
= 1/20 = 0.05.
= 0.05, n = (2.00/0.04)2 x .05 x (1-..05) = 118.75 or 119 observations.
The revised estimate of sample size is:
= 0.5: n = (2.00/0.04)2 * .50 * (1-.50) = 625 observations.
Step 2: Train Observers
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A comprehensive training program of three steps should be standardized for all data collectors. Data collectors should be first educated as to the study’s goals, protocol, collection procedures, and data submission procedures, and the guidelines for their behavior. Then, the observers should be trained in data collection. Training may include sessions using videotaped activities for practice in identifying and recording actual nursing services. In the third phase, observers participate with a project member, in explaining the nature of the project to those who will be observed, in the observation setting.
Step 3: Random Observation Schedule
Need random number for day, hour, and minute, with the number of digits needed for each number equaling the number of days in the study, hours per day, and minutes per hour.
Excel has a dynamic random generation method
Formula = RAND() * (End Date ‑ Begin Date) + Begin Date
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EXAMPLE 6.4:
The manager of Transesophageal Echocardiogram laboratory would like to improve efficiency of the processes in this department. To observe the proportion of time spent in various processes, a pilot work sampling study with an initial 20 observations will be taken during March 2017. Laboratory is open 8:00 a.m. to 5:00 p.m. during the week days only. Determine the random observation schedule using Excel.
SOLUTION:
Using the steps described earlier, beginning date and time of the observation schedule is placed in column A as a date function “=DATE(2017,3,1)+TIME(8,0,0).” First parenthesis indicates year, month and day to begin observations, and the second parenthesis show 8:00 a.m. as the start time. Similarly, column B includes the last permissible observation date/time using “=DATE(2017,3,31)+TIME(17,0,0).” Finally, the formula (6.12) is entered in column C as “=RAND()*(B3-A3)+A3.” The next step would be copying these three columns to the following rows. Figure 6.3 displays the resulting random selections.
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FIGURE 6.3 RANDOM OBSERVATION SCHEDULE
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FIGURE 6.4 STABILIZED DATES AND TIMES
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FIGURE 6.5 VALID DATES AND TIMES
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FIGURE 6.6 FINAL OBSERVATION SCHEDULE
69
Table 6.4 Abridged Patient Care Tasks in a Nursing Unit
Patient Care Tasks
Professional
Non-Professional
Direct
Indirect
1. Ace bandage application
*
*
2. Admit – patient orientation
*
*
3. Assist to/from bed, chair
*
*
4. Bed bath
*
5. Bed change – empty
*
*
6. Bed change - occupied
*
*
7. Bed pan
*
*
8. Blood pressure
*
*
9. Catheterization of bladder
*
*
10. Census count
*
*
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Table 6.5 Work Sampling Data Collection Form for Nursing Unit
Unit: 4 West
Observer: CL
Date: 11/02/17
Shift: AM
Time: 10:04
Observed
Staff
Name& Title
Prof.
Direct
Non-Prof.
Direct
Prof.
Indirect
Non-Prof.
Indirect
In Communication with
On Break
Patient
Staff
Physician
G. Smith, RN
V. Black, RN
E. Mason, RN
Z. Sander, RN
P. Bills, RN
Work Sampling Steps
4) Take observations, and re-compute the desired sample size several times if initial estimates are not reliable
5) Determine the estimated proportion of time spent on specified activity
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Advantages of Work Sampling
Observations less susceptible to short term fluctuations
Little or no work disruption
Workers are less resentful
Less costly and time-consuming
Many studies can be conducted simultaneously
Useful for non-repetitive tasks
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Disadvantages of Work Sampling
Less detail on elements/tasks of a job
Workers may alter patterns
Often no record of method used by worker
Observers may fail to adhere to random observation schedule
Not useful for short, repetitive tasks
Much time required to move from observation area to observation area to ensure randomness
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Nobody likes to do things the hard way! Work Simplification
Work Simplification -- process of changing work methods:
Eliminate unnecessary parts of work
Combine and rearrange parts of work
Simplify work when possible
Work Simplification Tools
Work Distribution Chart
Flow Process Chart
Flow Chart
Value Stream Map
Spaghetti Diagram
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The Work Distribution Chart
Shows what a department does to identify each of its major activities and to pinpoint the contribution of each employee to those activities
Must be specific!
Spotting Trouble
Which activities consume the most time?
Are tasks evenly distributed?
Is there under-specialization?
Are employees assigned too many unrelated tasks?
Are talents utilized efficiently?
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Table 6.6 Partial Work Distribution Chart for Nursing Unit
Activity
Hours
Nurse Manager
Hours
Nurse I
Hours
Nurse II
Hours
Patient admissions
12
Coordination with Admissions Dept.
8
2
2
Communications
16
Physicians and patient family
8
Patient family
4
Patient family
4
Direct patient care
48
8
Medication administration
20
20
Indirect patient care
16
Monitor charts
4
Meals
6
Update Charts
6
Discharge planning
14
2
6
6
Scheduling & Adm.
4
4
Miscellaneous
10
Supervisory meeting
Sessions with trainees
42
Emergency coverage
2
2
TOTAL
120
40
40
40
Flow Process Chart
Records a procedure in a graphic form, using a sort of shorthand to simplify and unify the record
Ensures every significant detail of the work process in its proper sequence is recorded
Highlights inconsistencies and redundancies
Can eliminate, combine, change (sequence, place, person), or improve activities
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Operation
Move
Inspect
Delay
Store
OPERATION
MOVE
INSPECT
DELAY
Flow Process Chart for Emergency Room Specimen Processing
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Process
Decision
Start/Terminate
Preparation
Document
Manual
Operation
Commonly Used Flow Chart Symbols
Off page connector
On page connector
Patient
entry
Flow Chart for Emergency Room Specimen Processing
Triage:
need blood?
Nurse draws
blood
MD orders
lab
IS order
entry
Label &
package
Verification
Lab
Accession &
analysis
IS double
entry
MD
terminates
lab order
(End)
Patient
entry
Triage:
need blood?
Nurse draws
blood
MD orders
lab
IS entry
label & package
Lab
Accession &
analysis
Results
arrive in ER
(End)
Initial Process
After Improvement
Yes
End
No
End
No
Value Stream Map
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Value Stream Maps are the primary tool in Lean, providing a more detailed view of the current state workflow and its corresponding time components.
Process steps are typically depicted using a process box symbol, while wait time or delays are depicted as a triangle.
A lead time ladder is displayed below the process flow, documenting the time it takes to complete each process step.
Any wait or delay is recorded as non-value added time, while time completing tasks is recorded as value-added time
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Value Stream Map
Adding the total non-value added time and the total value-added time gives the total Lead Time, or Cycle Time, for a product or person to make it through the entire workflow.
Value Stream Maps can be expanded to include information flows, inventory information, daily demand and other related factors to provide a more comprehensive view of the workflow.
Value Stream Maps are frequently used to highlight lag time and bottlenecks in a workflow.
Once such process waste has been identified, health care managers can identify opportunities for improvement, and then create a future state Value Stream map that incorporates these process improvements.
Using the future state Value Stream Map, health care managers can then assess what the expected reduction in lead time will be once an improvement is implemented.
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82
Value Stream Map for Prescribing and
Dispensing Medication
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Spaghetti Diagram
A Spaghetti Diagram captures the actual physical flow of a product or person through a process.
A continuous flow line is used to trace the path and distance traveled on a floor plan layout, such as a hospital floor or unit. The name “Spaghetti Diagram” comes from the resulting flow line, which typically looks like cooked spaghetti (and not a straight line).
The Spaghetti Diagram is used to locate unnecessary travel, redundancies, and areas of congestion in the process.
This information can then be leveraged to design a more efficient process layout that shortens travel distance and reduces process lead time.
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Front Desk Check-In Spaghetti Diagram
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EXAMPLE 6.5:
After the results of a patient satisfaction survey indicated that patients felt wait times were unacceptable, the director of a primary care resident clinic put together a lean management project team to identify inefficiencies and opportunities for improvement of patient flow.
Solution:
The team followed the lean management systematic approach for identifying process waste and opportunities for improvement, beginning with defining the value.
1. Define the Value – The value this process provides to the patient is direct care in the form of primary care services. Accordingly, the goal of this project was to minimize any time not spent providing direct patient care.
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2. Map the Process – In order to map the process, the project team first conducted time motion observations to understand the patient’s course through the clinic as well as estimate the length of each process step. The average of the observed times for each activity was calculated, and then multiplied by the appropriate performance and allowance factors to calculate the standard time for each activity in the patient’s workflow, as shown in Table 6.7. The team also determined whether each process step was value added or non-value added. From this information, the team was able to construct a value stream map of the patient’s flow through the clinic, shown in Figure 6.12.
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Activity
Value/ Non-Value
Standard Time (mins)
Waiting to Sign-In
Non-Value
3.5
Signing-In
Value
0.58
Waiting to Check-In
Non-Value
4.77
Checking In - Height & Weight
Value
0.78
Checking In – Vitals
Value
5.25
Checking In - History & Chief Complaint
Value
7.95
Waiting in Waiting Room after Check-In
Non-Value
21.97
LPN Preps Patient in Exam Room
VA
1.25
Waiting in Exam Room
Non-Value
11.38
Time with Physician in Exam Room
VA
14.67
Wait for Procedures/Testing
Non-Value
15
Procedures
VA
1.95
Testing
VA
5.75
Waiting to Check-Out
Non-Value
8.03
Check-Out
VA
8.82
TABLE 6.7. Time Study Results
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Figure 6.12. Value Stream Map – Patient Flow
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The team also constructed a Spaghetti Diagram to illustrate patient travel through the clinic. Each observed patient’s travel path was layered on top of the clinic floor plan, as shown in Figure 6.13.
Figure 6.13. Spaghetti Diagram
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3. Identify Process Waste – Analysis of the Value Stream Map and Spaghetti Diagram revealed several bottlenecks in the clinic.
A patient spends 58% of their time, or 65 minutes, in the clinic waiting. There are three key process steps serving as bottlenecks to patient flow:
waiting for an exam room,
waiting for the physician to conduct the exam, and
waiting for a clinician to complete any procedures or testing.
Observation revealed that these bottlenecks result from an inefficient resident reporting process.
This reporting process produces process waste in the form of waiting, motion, as well as overprocessing.
Eight resident physicians report to two attending physicians, and each resident sees up to eight patients each. This means that 2 attendings are responsible for the care of up to sixty-four patients per day. This inefficiency is ultimately passed along to the patient, creating increased wait-time in the exam room and further backing up patient flow through the clinic.
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Figure 6.14. Bottlenecks – Value Stream Map
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Figure 6.15. Bottlenecks - Spaghetti Diagram
Identify Process Waste
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Review of the Spaghetti Diagram revealed congestion near the registration area, where sign-in, check-in, and check-out occur.
When examined in conjunction with the Value Stream Map, it was apparent that this congestion may be contributing to wait times.
Additionally, some patients mistakenly come to the resident clinic instead of the faculty
International Journal for Quality Research 15(3) 1007–1022
ISSN 1800-6450
1 Corresponding author: Nayara Nicole de Sene Pereira
Email:
nayara_senne@hotmail.com
1007
Nayara Nicole de Sen
e
Pereira1
Evandro Eduardo
Broday
Article info:
Received 24.03.2020
.
Accepted 15.07.2020.
UDC – 005.6
DOI – 10.24874/IJQR15.03-20
APPLICATION OF CONTROL CHARTS
FOR MONITORING THE WAIT ING TIME
IN A BASIC HEALTHCARE UNIT IN
BRAZIL
Abstract: Brazilian public healthcare service is highly
demanded. However, the system has been through a restrained
scenario, with a long waiting time to have a doctor’s
appointment and scans. This research aims to analyze the
waiting time in the services of a Basic Healthcare Unit (BHU)
in a small city in the state of Sao Paulo, Brazil, by using
statistical control charts. The field research relays on
Taguchi’s loss function smaller-the-better, in other words, the
shorter the waiting time for patients, better is the perception of
quality. A direct observation was carried out in order to
acquire the patients’ waiting time for a medical appointment
and to evaluate the quality of the service. The patient waiting
time was monitored with Control Chart for Individual
Measurements and Moving Range and then it was determined
the capability of the service by using the 𝐶𝑝𝑘 index. It was
concluded that the service is inefficient based on Process
Capability Index (𝐶𝑝𝑘=-0.15), being the average waiting time
for a doctor’s appointment around 121.88 minutes
(approximately 2 hours).
Keywords: Quality Management; Control Charts; Public
Health Service; Waiting Time.
1. Introduction
The Brazilian Constitution (1988) says that
access to healthcare is a right of all Brazilian
citizens, either through the National Health
Service, which is the public service offered by
the government, or through private
agreements with private companies.
According to Tieghi (2013), the National
Health Service serves 200 million people, of
which 152 million are exclusive users of this
system. Brazil has more than 6000 hospitals,
45000 Basic Healthcare Units (BHU) and
30300 family health teams.
It is evident, then, that public health services
are the most demanded by the population.
However, the system has flaws in its main
programs and, as a consequence, there are
crowded hospitals, lack of manpower, lack of
training for professionals and problems
related to National Health Service financing
(Rossi, 2015). Thus, preserving a free
universal health system is an obstacle for
Brazil, mainly due to its territorial extension
(Tieghi, 2013).
These flaws can also be evidenced through
research carried out by the Brazilian Institute
of Geography and Statistics (IBGE) (2015),
which points out that 40.4% of the population
cannot get care due to the absence of doctors
and dentists, 32.7% do not have access to a
BHU, 6.4% do not find specialized
professionals to attend, 5.9% waited a long
time and gave up, 2.3% due to unavailability
1008 N.N.S. Pereira, E.E. Broday
of equipment, 2.1% due to the health service
not working, 0.5% for not being able to pay
for the consultation and 9.7% for other
reasons.
Al-Shdaifat (2015) conducted a survey where
TQM (Total Quality Management) was
implemented in hospitals in Jordan. Results
showed that less than 60% of hospitals
implemented, being the main principle to be
implemented costumer focus. Kalaja et al.
(2016) conducted a study at the regional
public hospital in Durrës, Albania, and
reported that healthcare is on the rise in the
country, receiving the attention of researchers
and doctors, due to deficiencies that the sector
faces and the challenges to be overcome. This
situation is very similar to that faced by
Brazil, which also faces deficiencies in the
sector.
In this way, quality tools can help the
limitations of the health sector. Control charts
are an example of a quality tool used in this
sector. According to Fry et al. (2012), even
though these graphs have been developed to
assist manufacturing quality control, control
charts have been suggested for assessing
clinical outcomes. It can be shown that
Statistical Process Control (SPC) can bring
many benefits to the health sector, reducing
waste, reducing costs and making better use
of resources, in order to prioritize patient
satisfaction. In order to bring benefits to the
services, the use of quality methods is
increasing, concerned with the quality of
service and improvements, with the consumer
satisfaction as the main goal (Rosa and
Broday, 2018).
The present research sought to evaluate the
capability of the Health System, based on the
Cpk index, in a small city in the state of Sao
Paulo, Brazil. The patients’ waiting time was
monitored with Control Charts for Individual
Measurements and Moving Range using data
from the waiting time of patients collected in
a Basic Healthcare Unit.
2. Literature Review
2.1. Statistical Process Control (SPC)
Statistical Process Control (SPC) is focused
on quality improvement
processes.
This
refers to the use of statistical methods, in
order to monitor and supervise a process so
that it can produce a product according to
predetermined specifications (Madanhire and
Mbohwa, 2016).
According to Costa, Epprecht and Carpinetti
(2005) the intermittent control of the
processes is the minimum condition to
maintain the quality of the goods and services
offered. Therefore, quality is paramount in
processes and services. Montgomery (2009)
states that the process of awareness of the
need and the insertion of formal methods and
tools to obtain control aimed at improving
quality are progressive procedures.
SPC brings several benefits to organizations
that use it. Nordström et al. (2012) point out
that one of the benefits is that it allows a
quantitative analysis of the variability of the
process, with emphasis on the early
verification and prevention of possible
problems. According to Ho and Aparisi
(2016), intervening in the production process
seeks to minimize the production of
nonconforming items.
For Montgomery (2009), the evolutionary
process of this tool had its origin with
Frederick W. Taylor with his first studies of
division of tasks, which led to improvements
in productivity and work patterns. These
studies, however, sometimes took the focus
away from the characteristics of the quality of
work, thus opening gaps in aspects of quality,
product and the work developed. Thus, it was
only in 1924 that SPC started with the
development of control charts by Walter
Shewhart at Bell Telephones Laboratories.
According to Fry et al. (2012), Shewhart
noted the existing variability in the processes
and developed the charts in order to
understand and improve the production
processes.
1009
According to Ahmad et al. (2014), the control
charts assist in the investigation of the process
and in the differentiation of control and out-
of-control situations for different parameters
of interest. From this tool, it is possible to
state whether or not a process is under
statistical control. Dupont et al. (2014) claims
that a process will be in statistical control
when the value of its indicator varies between
the lower and upper control limits. If it
crosses one of the limits it will express the
presence of a cause to be investigated,
corrected and used for future improvements.
Fry et al. (2012) state that Shewhart divided
this variation in two ways: variations of
common causes and variations of special
causes.
Common causes are an intrinsic part of the
process, that is, a process that operates with
these causes will be under statistical control.
Special causes are when internal or external
failures occur in the processes. Montgomery
(2009) complements stating that these causes
can come from machines adjusted or
mistakenly controlled, errors by the operators
or even defects found in the raw material,
being this out of statistical control.
Nordström et al. (2012) state that there are
several types of control charts and the
selection of the most suitable becomes a
difficult task. This scope made it possible to
reach new areas: the control charts at the
beginning were exclusive to industrial
processes, currently they are not limited to
this sector, they are also used in the service
sector (Nascimento and Broday, 2018).
Control charts can be divided into attributes
(quality characteristic that cannot be
measured on a continuous scale) or variables
(everything that can be measured on a
continuous scale).
Shewhart Control Charts for Individual
Measurements and Moving Range were used
in this study, since the goal is to monitor one
variable (waiting time). This chart is used
when the sample size is equal to 1. For the I-
MR graph the moving range is given by
Equation 1 (Montgomery, 2009):
𝑀𝑅𝑖 = |𝑥𝑖 − 𝑥𝑖−1 | (1)
Equations 2, 3 and 4 present the control limits
for the individual measurements chart:
𝑈𝐶𝐿 = �̅� + 3
𝑀𝑅̅̅ ̅̅̅
𝑑2
(2)
𝐶𝑒𝑛𝑡𝑒𝑟 𝑙𝑖𝑛𝑒 = �̅� (3)
𝐿𝐶𝐿 = �̅� − 3
𝑀𝑅̅̅ ̅̅̅
𝑑2
(4)
where:
UCL = upper control limit;
LCL = lower control limit;
MR = moving range;
d2 = constant value.
The moving range chart has center line 𝑀𝑅̅̅̅̅̅
and the Upper Control Limit as UCL =
D4𝑀𝑅̅̅̅̅̅. In this graph, normally, LCL = 0.
When using control charts for individual
measurements, Montgomery (2009) claims
the importance of performing a Normality
Test, since this chart is sensitive to lack of
normality. In order to obtain the potential
capability, Cp index is used as shown in
Equation 5:
𝐶𝑝 =
𝑈𝑆𝐿 − 𝐿𝑆𝐿
6𝜎
(5)
where:
USL = upper specification limit;
LSL = lower specification limit;
𝜎 = standard deviation.
As can be seen in equation 5, Cp does not take
into consideration where the process mean is
located relative to the specifications.
Equations 6, 6.1 and 6.2 present the index Cpk
(minimum value between Cpu and Cpi) that
takes process centering into account:
𝐶𝑝𝑘 = min (𝐶𝑝𝑢, 𝐶𝑝𝑙) (6)
𝐶𝑝𝑢 =
USL − 𝜇
3𝜎
(6.1)
𝐶𝑝𝑖 =
𝜇 − 𝐿𝑆𝐿
3𝜎
(6.2)
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By using the Cp index, it is possible to obtain
the potential capability, that is, how much the
process uses its capacity according to
specification limits. While the Cpk index
measures the effective (real) capability of the
process, it then checks whether the activity is
centered or not. Thus, the degree of similarity
between Cp and Cpk has direct relation and
indicates the magnitude of the centrality of
the process (Montgomery, 2009).
2.2 Service Sector
Service sector has been gaining a large space
within today’s society. Borges (2007) states
that it is possible to visualize the growth of
this sector in recent years, as well as its
contribution to the growth of the economy. In
this way, it is possible to define the
importance of services since it has a major
contribution to the Gross Domestic Product
and in the generation of jobs in developed and
developing countries, including Brazil.
For Kotler, Hayes and Bloom (2002), services
originated in the Middle Ages with
professions focused on the law, since these
together with the armed forces and the church
were an acceptable social way of earn a
living. The growth took place in the 16th
century, with new professions originating
from capitalism and the increase in industrial
technology. Over time these activities have
been improving and diversifying to escape
from the market competition.
Meesala and Paul (2018) state that the
concession of high-quality services is the
basis for success in the service sector.
Services can range from renting hotel rooms,
making deposits at banks, consulting doctors,
getting a haircut, traveling by plane, renting
movies, etc. They may include physical
components such as meals or not include
physical components, such as consultancy
services (Kotler, Hayes and Bloom, 2002).
In this way, it is possible to perceive the
intangibility, simultaneity and the
participation of the customer throughout the
process in the service sector. For Grönroos
(1990), the service refers to the activity or set
of activities with a usual relationship between
customers, employees, physical resources or
goods and service providers, and may be
intangible in nature with the aim of solving
customer problems. Services are intangible
because they are experienced by customers,
while products can be purchased. As a result,
the evaluation of quality becomes more
complex for the customer, as it is based on the
opinion of third parties and the image of the
company, which is responsible for the service
provided (Carpinetti, 2012).
Customer has a role in the service process,
since the consumer is present in the front
office of the companies, thus the quality of the
service provided is influenced according to
the environment where it is offered. The
services are simultaneous due to the lack of
an intermediate stage between production and
delivery. Thus, services cannot be stored, that
is, when the productive capacity of the system
is not used, it will be wasted and lost forever
(Fitzsimmons and Fitzsimmons, 2014).
The service sector also has a service package
that must be offered to the customer, and this
refers to a set of products and services, which
are offered in a given environment and have
five aspects, as shown in Table 1.
Hora, Moura and Vieira (2009) emphasize
that organizations and companies must aim
for the excellence of the services provided,
aiming at satisfaction and even exceeding
customer expectations. The service provider
system should be able to meet these
expectations in a short time, since it is in
relation to them that the service will be
judged. Minimizing customer waiting time
makes it possible to improve the quality of
service and increase satisfaction with the
service (Nascimento and Broday, 2018).
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Table 1. Service Package (Adapted from Fitzsimmons and Fitzsimmons, 2014)
Aspect Characteristic Example
Support
Facilities
Physical resource needed to offer the
service
Hospital, Airplane, Mall
Facilitating
Goods
Items to be offered or purchased by
customer
Snacks during the flights, meals
Informations
Offered to the customer or supplier, in
order to obtain an effective and
customized service
Information on hotels in the region, medical
records, seat availability, GPS location
Explicit
Services
Inherent and essential characteristic
perceived by
the customer
Car runs smoothly, no pain after medical
procedure, waiting time for firefighters after
an emergency call
Implicit
Services
Features beyond the service, that is,
psychological advantages perceived by
the customer
Status obtained after obtaining a degree,
privacy obtained by a credit company
2.3 Healthcare Services
Despite the fact that healthcare services may
be private or public origin, Kalaja et al.
(2016) claim that is important measuring,
evaluating and monitoring the quality of
health services. Mohebifar et al. (2016) state
that since the 90s one of the methods to
measure patients’ perception of the quality of
the health service provided takes into account
satisfaction.
Thus, Kalaja et al. (2016) state that the sector
that targets health care is a growing sector
receiving attention from doctors and
researchers. Caldwell (2008) reports that one
of the goals of improving quality in relation
to healthcare is through the increase in human
factors engineering and systems engineering
principles in the predominance of adverse
events.
Meesala and Paul (2018) claim that hospitals
seek to identify the most critical factors and
that solving and managing these problems
will guarantee survival and success in the
future, and that it is necessary to identify
strategic factors. But, in addition, it is
necessary to have the satisfaction of your
patients in order to ensure the quality of the
service. It is important than to identify the
specific characteristics of a service so that
they contribute especially to patient
satisfaction, with which the hospital can focus
on these characteristics.
Mohebifar et al. (2016) add that customers
(patients) assess quality by comparing their
expectations with understandings of actual
performance. If the patient’s understanding
exceeds their expectations, then the services
provided are of a high quality. However,
Senot et al. (2016) state that patients have
different needs in a hospital environment,
varying in relation to severity, such as a
simple cold, heart attack, fracture, among
others. Thus, quality is a multidimensional
concept, but it has customer satisfaction as
one of the essential aspects that reflects the
quality of service in a hospital environment.
Greer et al. (2014) claim that the perceptions
of service quality of customers and service
providers may not be aligned, as in the
perception of employees they may judge that
there was no failure, that is, that the service
was delivered with quality.
Many companies in the manufacturing or
service sector use quality management and
quality assurance as a way to achieve the
desired quality of their product or service and
meet customer needs and expectations. Rath
(2008) reports that in order to have a
successful management and quality
assurance, it is necessary to focus on the
processes and empower the people involved
in it with the necessary tools and give them
the responsibility to improve the quality of
the service. However, this approach is not
adopted due to the non-use or misuse of the
1012 N.N.S. Pereira, E.E. Broday
proposed tools; most organizations adopt
only the removal of the defective product or
rework or hope that there will be a failure so
that they can subsequently search for its
causes so that they do not occur again.
Thus, service companies in the health sector
seek tools with the objective of improving the
quality of the services provided, thus
obtaining greater satisfaction from their
customers. As reported by Plantier et al.
(2017), the processes of using quality
indicators in hospitals are being encouraged
as a way to assess quality in these
environments in order to improve the quality
care and patient safety.
3. Methods
3.1 Data Collection
Waiting time data were collected in a BHU in
a small city located in the state of Sao Paulo,
Brazil. This city is located in the
administrative region of Bauru, as shown in
Figure 1.
Figure 1. BHU’s Location (Adapted from
Portalpower, 2016)
The city has five HBUs strategically
distributed to serve the residents and anyone
in the city can have access to these units. They
operate from Monday to Friday from 7am to
5pm. The HBU has a team of six nursing
technicians, one nurse, three janitors, one
pharmacy technician, five community agents,
one dentist, one speech therapist, one
psychologist and eight doctors, being: an
otorhinolaryngologist, an orthopaedist, a
cardiologist, a gynecologist, two
pediatricians, a gastroenterologist and a
general practitioner.
Among the services offered in the HBU, there
are consultations and scans such as
electrocardiogram, rapid tests, PAP test and
vaccination, which are performed weekly.
Medical, dental, psychologist and speech
therapist consultations work with
appointments. In this case, the system is
computerized, when the patient arrives, a
number is generated in order of arrival, then
it goes through a pre-consultation, only to
perform the scheduled consultation
afterwards. Vaccination works under the
condition of scheduling or spontaneous
demand, whereas rapid tests and PAP tests are
performed by means of scheduling with a
password.
Regarding the population, patients can come
from all regions of the city, however due to
the presence of more units in the city, the
majority of them serve an audience from the
nearest neighborhoods. Only people who had
preferential care were not counted for the
study, since their time at the UBS is reduced.
Regarding sex, both genders will be included
in the research, since there is no distinction in
treatment in relation to waiting time.
3.2 Data Analysis Procedures
After obtaining the waiting time of patients at
the HBU, two types of analysis were
performed: a qualitative analysis and a
quantitative analysis.
The qualitative analysis
was made using the Ishikawa Diagram, in
order to verify the reasons for the waiting
time in the analyzed HBU being so high. The
Kolmogorov-Smirnov normality test was
performed, with 95% confidence, using the
SPSS 23 software. After verifying the
normality of the data, the control charts of the
individual measurements and moving range
were then constructed, in order to perform a
quantitative analysis.
10
13
For the control chart for individual
measurements, the parameters were
calculated using equations 1-4, as described
in section 2.1. Calculations can be performed
using �̅� which is the mean, MR which is the
moving range and d2 which is a constant value
according to the number of observations in
the sample. Since the sample size is one, d2
always takes the value of 1.128. In order to
perform the moving range calculation, sample
must take two by two.
It is noteworthy that for this research,
Taguchi’s loss function smaller-the-better is
used, because smaller the time the patient
waits to be seen, the better it will be for the
quality perception. For the preparation of the
control charts, Action Stat 3.2 was used.
Subsequently, from the waiting times, the
UCL (Upper Control Limit), CL (Center
Line) and LCL (Lower Control Limit) were
calculated. In this way, it was possible to
carry out a more detailed analysis
.
Finally, the effective process capability index
(Cpk) was calculated. In this situation, it has
only the upper specification limit (USL),
since there is no minimum value for the
patient to wait for a medical consultation. So,
there isn’t a lower specification limit. As an
upper limit of specification, the waiting time
was considered, since in Brazil there is no
legislation determining the maximum waiting
time for patients in the HBU.
4 Data Analysis and Discussion
4.1 General Data
Sixty patients who would have medical
appointments in the three days of field study
were analyzed (otorhinolaryngology (A),
pediatrics (B), orthopedics (C), dentistry (D)
and general practitioner (E)). Through the
waiting times obtained, it was possible to
perform a statistical analysis by medical
specialty and a general value for the HBU, as
shown in Table 2.
Table 2. Mean Waiting time (The Authors, 2020)
Statistical Analysis by medical specialty
A B C D E HBU
Sample
(n) 12 12 11 7 18 60
Minimum waiting
time
(min)
50 125 33 101 40 33
Maximum waiting
time (min)
200 255 275 260 194 275
Mean waiting time
(min)
114 187 110 159 100 134
Standard
Deviation (min)
42.91 42.72 78.22 59.82 40.09 60.78
When observing data in Table 2, it is possible
to conclude that the specialty that obtained the
longest average waiting time was Pediatrics.
It is also noted that pediatrics and dentistry
have an average waiting time greater than the
average waiting time at the BHU in general.
It was also concluded through Table 2 that
orthopedics is the specialty that has the
shortest minimum waiting time; however, it is
also the one that has the longest maximum
waiting time. Consequently, it is the specialty
that has the highest standard deviation.
1014 N.N.S. Pereira, E.E. Broday
Figure 2. The Ishikawa Diagram (The Authors, 2020)
4.2 Ishikawa Diagram
To better understand the reasons that
generates a long waiting time, the Ishikawa
Diagram was used, with the main objective of
listing the root causes of this problem. The
Ishikawa Diagram is illustrated in Figure 2.
Possible main causes for the problem were
divided in the following categories:
measurement, method, manpower,
machinery, mother nature and materials.
From these causes and the conversations with
patients at the HBU, it was possible to obtain
the secondary causes that originated the main
problem.
In relation to the mother nature
(environment), it was possible to list the lack
of structure as secondary causes, since there
is no infrastructure for the queues before and
when opening the HBU. Lack of material and
financial resources, because if there was a
password system when the patient arrives at
the HBU it could be easier to organize the
screening and with that the service would be
faster. The screening time increases the
patient’s waiting time and occurs on the same
day as the consultation. As, for example, in
the case of pediatric consultations, children
must be weighed beforehand, so this
weighing could occur on the previous day in
order to optimize services on the day of the
consultation.
Regarding to the manpower, the secondary
causes related are the lack of professionals,
their delay and even the lack of specialties. In
view of the high demand for people, it appears
that there is a lack of professionals to serve
everyone, as well as specialties. The HBU in
question has only an otorhinolaryngologist,
gastroenterologist, orthopedist, pediatrician
and gynecologist, when interviewing the
patients, they reported the need for more
specialties such as dermatologists, urologists,
neurologists, among others, as well as more
doctors of the specialties already offered.
The method, that is, the way services are
offered at the HBU stands out as secondary
causes for political changes, which refer to
the strategy that will be adopted by the health
department. In addition to problems already
addressed, such as lack of organization, since
patients line up outside the HBU, however
when it opens the first in line, it is not always
the first to be attended. As well as, the lack of
passwords and inadequate scheduling, since
more appointments are often scheduled than
the medical service hours tolerate.
In relation to the measurement, the
complementary causes are attributed to the
lack of prioritization, that is, in addition to
scheduling, a new prioritization scale could
1015
be created according to the urgency of the
service according to the patients’ symptoms
and with that the assistance would be more
agile. As well as, the operation above
capacity, that is, due to the high demand,
doctors are few for the number of patients to
be treated.
In addition, old equipments to perform the
exams and the lack of some basic materials,
such as gloves and masks, complete the
problems that make the waiting time so long.
In this way, it is possible to attribute the long
waiting time to the most varied causes and
with this setting priorities in order to
eliminate root causes.
4.3 Normality Test
In order to be able to use the Control Charts
for Individual Measurements and Moving
Range, data must be normal. Therefore, the
Kolmogorov-Smirnov normality test, with
95% confidence, was performed. The results
obtained are shown in Table 3.
Thus, it is observed through the software
result table that the data have a normal
distribution, since the significance is greater
than 0.05 (0.089 > 0.05). Therefore, data can
be used to build the control charts for
individual measurements and moving range.
Table 3. Kolmogorov-Smirnov test for data
Waiting
Time
N 60
Normal
Parameters
Mean 127.95
Std. Deviation 59.825
Most
Extreme
Differences
Absolute 0.106
Positive 0.106
Negative -0.067
Test Estatistic 0.106
Asymp. Sig. (2-tailed) 0.089
4.4 Control Charts
For the construction of the charts, the Action
Stat 3.2 software was used. Initially, a table
was built with the waiting time data obtained
through observations to patients in the three
days analyzed. The data obtained are shown
in Table 4.
Table 4. Waiting Time data (The Authors, 2020)
Sample
Waiting time
(min)
Sample
Waiting time
(min)
Sample
Waiting time
(min)
1 65 21 135 41 33
2 45 22 165 42 168
3 115 23 220 43 135
4 90 24 165 44 260
5 120 25 177 45 218
6 109 26 240 46 101
7 65 27 152 47 110
8 50 28 215 48 122
9 120 29 225 49 182
10 137 30 255 50 95
11 200 31 275 51 139
12 137 32 227 52 93
13 155 33 103 53 40
14 55 34 55 54 94
15 110 35 88 55 70
16 115 36 55 56 85
17 117 37 90 57 194
18 105 38 58 58 92
19 165 39 163 59 88
20 125 40 67 60 88
1016 N.N.S. Pereira, E.E. Broday
The Action Stat generated for the sample of
the values of the time that the users waited for
the attendance in the Health Care Unit the
following parameters: UCL = 255.80, center
line = 128.95 and the LCL = 2.10 for the
Chart of Individual Values. Using the same
data, but for the Moving Range chart, the
UCL values = 155.87, the center line = 47.74,
the LCL = 0 and the σ = 42.28. Figure 3
illustrates
both charts:
Figure 3. Control Charts
When analyzing the control charts
constructed from the waiting time of patients
at the BHU, it is possible to verify that for the
moving range graph all values were within
the determined control limits and show a
behavior random. However, for the graph of
individual measurements it is possible to
check two points outside the control limits,
which refer to samples 31 (275min) and 44
(260 min) indicating special causes.
When investigating the special cause found in
sample 31, it was found that it refers to a
patient who was waiting for a withdrawal to
undergo a medical consultation with the
orthopaedist. So, he arrived at HBU earlier in
order to ensure that he would be attended to
that day. However, as this is a case of
withdrawal, this is the last patient to go
through the consultation, so it is the one that
presented a long waiting time. When
investigating the special cause identified in
the sample 44, it was found that it also refers
to a patient who was waiting for withdrawal,
however to undergo a dental consultation.
Like the other patients who are waiting to
give up, he arrived early to guarantee the
appointment that day. In addition to the
In
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1017
longer waiting time, it was found that the
times for dental consultations are also long.
As a result, samples 31 and 44 were
eliminated and the control limits for the
control charts of individual measurements
and moving range were recalculated.The new
values obtained were: UCL = 249.69, center
line = 124.17 and the LCL = 0 for the Chart
of Individual Values. Using the same data,
but for the Moving Range chart, the UCL
values = 154.24, the center line = 47.21, the
LCL = 0 and the σ = 41.84. Figure 4 illustrates
both charts:
Figure 4. Control charts without samples 31 and 44
When analyzing the control graphs of
individual measurements and moving range
without the samples 31 and 44 presented in
Figure 4, it is possible to verify that their
control limits were shifted downwards, that
is, the values of upper control limit and
average limit decreased. Thus, it is possible to
identify that for the moving range control
graph all points are within the established
control limits and that they behave in a
random manner. However, for the control
chart of individual measurements, it is
possible to check a point outside the limits,
which refers to the sample 30 (255 min).
When investigating the special cause found in
sample 30, it was found that it refers to the
last child who went to the pediatric
consultation on the first day of research, who
was also awaiting withdrawal. The mother
arrived very early with the child to guarantee
the withdrawal and waited until everyone was
attended to. When eliminating sample 30, the
control limits were recalculated again.
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1018 N.N.S. Pereira, E.E. Broday
The following parameters were obtained:
UCL = 246.98, center line = 121.88 and the
LCL = 0 for the Chart of Individual Values.
Using the same data, but for the Moving
Range chart, the UCL values = 153.75, the
center line = 47.05, the LCL = 0 and the σ =
41.70. Figure 5 illustrates both charts:
Figure 5. Control charts without samples 30, 31 and 44 (The Authors, 2020)
When analyzing the control charts illustrated
by Figure 5, it is possible to verify that both
are in control, since all waiting times are
within the control limits and these are
randomly arranged. Thus, it is possible to
conclude that the waiting times are between 0
to 246.98 minutes (4.12 hours) and that the
difference between the current patient’s time
and the one previously analyzed varies from
0 to 153.72 minutes (2,56 hours).
Thus, it is possible to conclude that by
eliminating patients waiting for withdrawal, it
can be said that the waiting time for patients
is in statistical control. Thus, we classify
dropouts as special causes of this process and
should be eliminated. As a way of eliminating
these causes, patients should be shown that
there is no need to arrive at HBU 4 or 5 am
for care. To do this, it should have an order
for the waiting list too, so that the patient
already has a password among the patients
who are waiting to give up for their care, that
is, a queue must be created between the
patients who are waiting withdrawal.
4.5 Capability of the HBU Service
To calculate the process capability, the pre-
determined specification limits are
necessary.
In the case of waiting time, there is no lower
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specification limit, since the ideal is that the
patient did not wait to be seen. With this, there
is a unilateral specification presenting only
the upper specification limit.
The upper specification limit will be the
maximum time the patient must wait for a
medical appointment. This limit is related to
the function smaller-the-better, meaning, the
shorter the waiting time for patients, better is
the perception of quality. In Brazil, there is
currently no law that limits the maximum
waiting time for a patient for a medical
consultation in HBU, hospitals or even in
private offices. In this sector, there is only the
resolution of the Federal Council of
Medicine, CFM 2.077/14. This resolution
states that the maximum waiting time for
patients with less urgency should be around
120 minutes.
Therefore, considering that the patient is
waiting for a screening and a medical
evaluation to classify his degree of urgency,
the waiting time of 120 min was considered
as the upper specification limit for the
calculation of the effective process index
(Cpk). The mean and standard deviation of the
process are described in Figure 5, with the
mean being the center line of the control chart
of individual measurements (121.88 minutes)
and the standard deviation (41.70 minutes).
By using equation (6) it is possible to obtain
the effective capability index of the HBU:
𝐶𝑝𝑘 = min (𝐶𝑝𝑢, 𝐶𝑝𝑙)
𝐶𝑝𝑢 =
USL − 𝜇
3𝜎
Cpk =
120 − 121,88
3 x 41,70
= −0,015
The Cpk index demonstrates that the service in
the HBU is not capable to attend patients in
120 minutes, since its value is less than one
(Cpk < 1). Changes in the way of attendance
are required in order to improve the service.
This result goes against the resolution of the
Federal Council of Medicine.
4. Final Considerations
According to the established objectives, it is
verified that the present research made it
possible to analyze the waiting time by means
of statistical control charts and how they
influence the capability of the service offered.
Through the research, the functioning of the
HBU was better understood through the direct
observation, since it was possible to
experience the unit’s routine.
It was found that the health service has been
going through some changes in order to
improve the services offered and increase the
satisfaction of its patients. The introduction of
the time clocking system, for example, is an
improvement to be made in the unit, since it
will be possible to avoid delaying
professionals and thereby reduce the waiting
time for patients. Another improvement that
has been observed is the reduction in the daily
workload of doctors, as this way they will be
available on more days of the weeks and will
be less idle, in order to provide care more
quickly.
With that, it is possible to observe that
changes in the sector are appearing in order to
reduce the waiting time of patients for
medical care. However, there is also a cultural
change in patients that needs to occur. Many
of them arrive at the HBU before it even
opens. Currently, as consultations are
scheduled there is no longer a need for
patients to arrive at the unit so early, however
it is still a common habit among them,
especially when it comes to dropouts.
Therefore, through the research it was
possible to verify that the HBU still has
restrictions and improvements to be carried
out in order to improve and optimize the
services offered. However, it was found that
there are already some actions being taken to
reduce this waiting time and improve the
quality of the service. In general, it was found
that patients evaluate the service offered as
good.
1020 N.N.S. Pereira, E.E. Broday
Thus, it was proved that in times of crisis or
difficulties, quality engineering becomes
more important, because through tools it
seeks to bring solutions in order to improve
the services offered, bringing a new view of
the system. It was found that the health sector
has space for future projects involving
engineering tools and that through them it is
possible to improve the quality of services
offered and optimize material and financial
resources. In this way, in future research it is
possible to go deeper into the waiting time by
making a comparison between units or
applying in new locations, but different
studies involving forecasts and demand
monitoring, materials, exams and other
resources can be carried out. make analyzes
necessary.
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http://www.usp.br/espacoaberto/?materia=a-saude-brasileira-tem-cura>
Nayara Nicole de Sene
Pereira
Federal University of
Technology – Paraná
(UTFPR),
Ponta Grossa,
Brazil
nayara_senne@hotmail.com
Evandro Eduaardo
Broday
Federal University of
Technology – Paraná
(UTFPR),
Ponta Grossa,
Brazil
broday@utfpr.edu.br
http://www.usp.br/espacoaberto/?materia=a-saude-brasileira-tem-cura
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