1. Quality and risk management
In this file, you can ref useful information about quality and risk management such as quality and
risk managementforms, tools for quality and risk management, quality and risk
managementstrategies … If you need more assistant for quality and risk management, please
leave your comment at the end of file.
Other useful material for quality and risk management:
• qualitymanagement123.com/23-free-ebooks-for-quality-management
• qualitymanagement123.com/185-free-quality-management-forms
• qualitymanagement123.com/free-98-ISO-9001-templates-and-forms
• qualitymanagement123.com/top-84-quality-management-KPIs
• qualitymanagement123.com/top-18-quality-management-job-descriptions
• qualitymanagement123.com/86-quality-management-interview-questions-and-answers
I. Contents of quality and risk management
==================
The Department of Diagnostic Imaging (DI) relentlessly pursues innovative solutions to meet
the needs of each and every patient and achieve the highest quality of patient care. We are
committed to the provision of exceptional services while recognizing and respecting the
diversity of the patients and other customers we serve. We take a systematic approach to
visualize the future in the context of internal and external business environment, build
organizational capacity for innovation and formulate strategies to overcome challenges and
maximize future opportunities. Comprehensive strategies are developed to continually
improve quality of care, enable equitable and timely access to services, further enhance
patient safety, introduce advanced technology and equipment, create value for patients and
their families, and promote clinical, academic and research excellence.
The key drivers of the DI Quality and Risk Management program include the Quality
Assessment and Improvement Committee, DI and Image Guided Therapy (IGT) Morbidity
and Mortality Review Rounds, MR Safety Committee, Equipment Quality Control
Committee, Radiation Safety Committee, dedicated Quality Management Leader, and various
project teams and work groups. Peer review process is integrated seamlessly into the daily
workflow to ensure high level of competence amongst our radiologists, safeguard patient
safety and improve overall standards by identifying unperceived discrepancies and
educational needs. The peer review process allows for the random selection of studies to be
reviewed on a regularly scheduled basis, supports rapid identification of trends and reveals
opportunities for quality improvements. Effective application of quality concepts, principles,
and methods requires an inclusive, engaging, and empowering team environment, systems
thinking, information sharing, interdepartmental collaboration, and decisive action based on
systematically obtained evidence. Continually refined quality indicators used to measure,
2. evaluate, and improve effectiveness of our processes are at the core of the DI Quality and
Risk Management Program. Every effort is made to enable sound decision making and
promote evidence-based solutions by creating selective, reliable, responsive, valid, and cost-
effective measures of performance.
In order to adapt to a rapidly changing operating environment, align resources with key
priorities, support hospital strategic directions, and ensure a coordinated approach to continual
quality improvement, we have developed and implemented a customized, dynamic and
integrated Quality Management System (QMS). The QMS provides a framework for seamless
integration of quality planning, quality assurance (QA), quality control (QC), process
improvement, risk management, innovation, and a number of other structured, systemic and
planned activities designed to improve quality and patient safety. The QMS operates in
conjunction with other organizational systems, enables efficient planning, allocation and
utilization of resources, provides robust structure to facilitate generation of new ideas, and
creates a harmonized network of interdependent processes, procedures and elements required
to drive organizational performance improvement.uccess in achieving the highest quality of
patient care and providing timely access to services at lower cost depends significantly on
effective management and continuous improvement of complex, interconnected and cross-
functional processes. Therefore, we have developed a robust, data-driven and patient-focused
process improvement framework to engage employees in a meaningful way, reduce process
variation, improve flow, solve recurring problems, minimize wasteful activities, and create
value from the viewpoint of patients and other stakeholders. We strive to adapt, integrate and
appropriately contextualize Lean, Six Sigma, Human Factors, Project Management, Plan-Do-
Study-Act (PDSA) Cycle, and other complementary methodologies, disciplines, and best
practices while taking into account specific goals, organizational culture and overall
capabilities. The various process improvement methodologies are not mutually exclusive and
they bring unique perspectives, approaches, tools, and techniques that can be effectively
combined to achieve and sustain operational excellence. Rapid changes in imaging and
information technology, increasing complexity of patient care, high cognitive demands, and
large volume of information arising from research present unique challenges to radiologists,
technologists, nurses, and administrative staff. In response to these challenges, the discipline
of Human Factors Engineering is applied to optimize system performance, reduce
opportunities for errors, and design systems, processes, tasks, jobs, and work environment
that take into consideration the needs and abilities of people.
Diagnostic Imaging has adapted and introduced the Quality and Safety Leadership Walkarounds
across all modalities. This tool was originally developed by the Institute for Healthcare
Improvement (IHI) and Dr. Allan Frankel. The DI Quality and Safety Leadership Walkarounds
are designed to foster an environment of trust by engaging leaders and front-line staff in an open
dialogue concerning quality and patient safety, directly inform the leaders about existing
organizational barriers, and demonstrate commitment to building a culture of safety, service
excellence, innovation, and continuous quality improvement. Results of this process are
communicated to all relevant stakeholders and corrective or preventive actions are initiated as
required.
==================
3. III. Quality management tools
1. Check sheet
The check sheet is a form (document) used to collect data
in real time at the location where the data is generated.
The data it captures can be quantitative or qualitative.
When the information is quantitative, the check sheet is
sometimes called a tally sheet.
The defining characteristic of a check sheet is that data
are recorded by making marks ("checks") on it. A typical
check sheet is divided into regions, and marks made in
different regions have different significance. Data are
read by observing the location and number of marks on
the sheet.
Check sheets typically employ a heading that answers the
Five Ws:
Who filled out the check sheet
What was collected (what each check represents,
an identifying batch or lot number)
Where the collection took place (facility, room,
apparatus)
When the collection took place (hour, shift, day
of the week)
Why the data were collected
2. Control chart
Control charts, also known as Shewhart charts
(after Walter A. Shewhart) or process-behavior
charts, in statistical process control are tools used
to determine if a manufacturing or business
process is in a state of statistical control.
If analysis of the control chart indicates that the
process is currently under control (i.e., is stable,
with variation only coming from sources common
4. to the process), then no corrections or changes to
process control parameters are needed or desired.
In addition, data from the process can be used to
predict the future performance of the process. If
the chart indicates that the monitored process is
not in control, analysis of the chart can help
determine the sources of variation, as this will
result in degraded process performance.[1] A
process that is stable but operating outside of
desired (specification) limits (e.g., scrap rates
may be in statistical control but above desired
limits) needs to be improved through a deliberate
effort to understand the causes of current
performance and fundamentally improve the
process.
The control chart is one of the seven basic tools of
quality control.[3] Typically control charts are
used for time-series data, though they can be used
for data that have logical comparability (i.e. you
want to compare samples that were taken all at
the same time, or the performance of different
individuals), however the type of chart used to do
this requires consideration.
3. Pareto chart
A Pareto chart, named after Vilfredo Pareto, is a type
of chart that contains both bars and a line graph, where
individual values are represented in descending order
by bars, and the cumulative total is represented by the
line.
The left vertical axis is the frequency of occurrence,
but it can alternatively represent cost or another
important unit of measure. The right vertical axis is
the cumulative percentage of the total number of
occurrences, total cost, or total of the particular unit of
measure. Because the reasons are in decreasing order,
the cumulative function is a concave function. To take
the example above, in order to lower the amount of
late arrivals by 78%, it is sufficient to solve the first
three issues.
5. The purpose of the Pareto chart is to highlight the
most important among a (typically large) set of
factors. In quality control, it often represents the most
common sources of defects, the highest occurring type
of defect, or the most frequent reasons for customer
complaints, and so on. Wilkinson (2006) devised an
algorithm for producing statistically based acceptance
limits (similar to confidence intervals) for each bar in
the Pareto chart.
4. Scatter plot Method
A scatter plot, scatterplot, or scattergraph is a type of
mathematical diagram using Cartesian coordinates to
display values for two variables for a set of data.
The data is displayed as a collection of points, each
having the value of one variable determining the position
on the horizontal axis and the value of the other variable
determining the position on the vertical axis.[2] This kind
of plot is also called a scatter chart, scattergram, scatter
diagram,[3] or scatter graph.
A scatter plot is used when a variable exists that is under
the control of the experimenter. If a parameter exists that
is systematically incremented and/or decremented by the
other, it is called the control parameter or independent
variable and is customarily plotted along the horizontal
axis. The measured or dependent variable is customarily
plotted along the vertical axis. If no dependent variable
exists, either type of variable can be plotted on either axis
and a scatter plot will illustrate only the degree of
correlation (not causation) between two variables.
A scatter plot can suggest various kinds of correlations
between variables with a certain confidence interval. For
example, weight and height, weight would be on x axis
and height would be on the y axis. Correlations may be
positive (rising), negative (falling), or null (uncorrelated).
If the pattern of dots slopes from lower left to upper right,
it suggests a positive correlation between the variables
6. being studied. If the pattern of dots slopes from upper left
to lower right, it suggests a negative correlation. A line of
best fit (alternatively called 'trendline') can be drawn in
order to study the correlation between the variables. An
equation for the correlation between the variables can be
determined by established best-fit procedures. For a linear
correlation, the best-fit procedure is known as linear
regression and is guaranteed to generate a correct solution
in a finite time. No universal best-fit procedure is
guaranteed to generate a correct solution for arbitrary
relationships. A scatter plot is also very useful when we
wish to see how two comparable data sets agree with each
other. In this case, an identity line, i.e., a y=x line, or an
1:1 line, is often drawn as a reference. The more the two
data sets agree, the more the scatters tend to concentrate in
the vicinity of the identity line; if the two data sets are
numerically identical, the scatters fall on the identity line
exactly.
5.Ishikawa diagram
Ishikawa diagrams (also called fishbone diagrams,
herringbone diagrams, cause-and-effect diagrams, or
Fishikawa) are causal diagrams created by Kaoru
Ishikawa (1968) that show the causes of a specific
event.[1][2] Common uses of the Ishikawa diagram are
product design and quality defect prevention, to identify
potential factors causing an overall effect. Each cause or
reason for imperfection is a source of variation. Causes
are usually grouped into major categories to identify these
sources of variation. The categories typically include
People: Anyone involved with the process
Methods: How the process is performed and the
specific requirements for doing it, such as policies,
procedures, rules, regulations and laws
Machines: Any equipment, computers, tools, etc.
required to accomplish the job
Materials: Raw materials, parts, pens, paper, etc.
used to produce the final product
Measurements: Data generated from the process
that are used to evaluate its quality
7. Environment: The conditions, such as location,
time, temperature, and culture in which the process
operates
6. Histogram method
A histogram is a graphical representation of the
distribution of data. It is an estimate of the probability
distribution of a continuous variable (quantitative
variable) and was first introduced by Karl Pearson.[1] To
construct a histogram, the first step is to "bin" the range of
values -- that is, divide the entire range of values into a
series of small intervals -- and then count how many
values fall into each interval. A rectangle is drawn with
height proportional to the count and width equal to the bin
size, so that rectangles abut each other. A histogram may
also be normalized displaying relative frequencies. It then
shows the proportion of cases that fall into each of several
categories, with the sum of the heights equaling 1. The
bins are usually specified as consecutive, non-overlapping
intervals of a variable. The bins (intervals) must be
adjacent, and usually equal size.[2] The rectangles of a
histogram are drawn so that they touch each other to
indicate that the original variable is continuous.[3]
III. Other topics related to Quality and risk management (pdf download)
quality management systems
quality management courses
quality management tools
iso 9001 quality management system
quality management process
quality management system example
quality system management
quality management techniques
quality management standards
