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Clinical quality management
In this file, you can ref useful information about clinical quality management such as clinical
quality managementforms, tools for clinical quality management, clinical quality
managementstrategies … If you need more assistant for clinical quality management, please
leave your comment at the end of file.
Other useful material for clinical quality 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 clinical quality management
==================
Improving the quality, safety and efficiency of healthcare is a national priority. While clinical
quality measures have been around for some time, they are now taking center stage through
federal, state and commercial quality/pay for performance (P4P) initiatives. Regulations such
as Meaningful Use (MU), Physician Quality Reporting System (PQRS), Hospital Inpatient
Quality Reporting Program (HIQRP), Accountable Care and Value-Based Payment Programs are
driving healthcare organizations to implement enterprise-wide strategies to meet clinical quality
reporting and evidence-basedclinical decision support requirements.
While most healthcare organizations support the goals of quality improvement—enhancing
patient outcomes and reducing healthcare costs—meeting those objectives across the enterprise
is challenging. While organizations solve commonly acknowledged problems around data
capture and complex measure calculations, there are no consistent, systematic means for
measuring clinical quality management.
Multiple measures for the same condition
Organizations such as the National Quality Forum (NQF), Centers for Disease Control and
Prevention and the Center forMedicare and Medicaid Services (CMS) are doing significant work
toward standardizing clinical measures irrespective of the quality initiative, but there is still a
long way to go. The problem is that various quality improvement programs, such as PQRS or
HEDIS, are developing, standardizing and adopting evidence-based clinical quality measures on
their own, which results in multiple clinical measures with slight variations for a given condition
or outcome. For instance, when tracking HbA1c levels in diabetic patients, performance
measures include HbA1c testing, HbA1c poor control, HbA1c control (<7%) and HbA1c control
(<8%) depending on the program.
Initiative-specific nuances in measure implementation
Different quality initiatives select a set of measures based on industry best practices and their
endorsement by nationally recognized organizations such as NQF or CMS. This results in
common measures—say, tracking HbA1c poor control in diabetes—being tracked across
different quality initiatives, such as MU or PQRS. And because each quality initiative has its
own nuances in the actual specification and implementation of the measures, provider
organizations need to track the same measure multiple times for different initiatives.
Additionally, organizations need to be cognizant of the fact that the eligible patient population
for the same measure may also vary across quality initiatives based on health plan coverage
(Medicare or commercial), allowable evaluation and management (E&M) services and so forth.
Periodically changing measure definitions
Even within a quality initiative such as PQRS, HIQRP or HEDIS, the specifications for clinical
quality measures change over time, driven by factors such as the addition/retirement of E&M
codes, medication codes, endorsement of new evidence and changes in exclusions/exceptions.
Consequently, organizations are forced to expend significant time and effort in periodically
tracking and upgrading measure calculation logic as well as associated workflows, data sources
and IT systems based on the extent of change. Moreover, changes in measure calculations make
it very difficult (and potentially risky) for organizations to accurately trend and benchmark the
performance of clinical measures over a period of time.
==================
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
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.
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
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
 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 Clinical quality management (pdf download)
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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
quality management policy
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Clinical quality management

  • 1. Clinical quality management In this file, you can ref useful information about clinical quality management such as clinical quality managementforms, tools for clinical quality management, clinical quality managementstrategies … If you need more assistant for clinical quality management, please leave your comment at the end of file. Other useful material for clinical quality 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 clinical quality management ================== Improving the quality, safety and efficiency of healthcare is a national priority. While clinical quality measures have been around for some time, they are now taking center stage through federal, state and commercial quality/pay for performance (P4P) initiatives. Regulations such as Meaningful Use (MU), Physician Quality Reporting System (PQRS), Hospital Inpatient Quality Reporting Program (HIQRP), Accountable Care and Value-Based Payment Programs are driving healthcare organizations to implement enterprise-wide strategies to meet clinical quality reporting and evidence-basedclinical decision support requirements. While most healthcare organizations support the goals of quality improvement—enhancing patient outcomes and reducing healthcare costs—meeting those objectives across the enterprise is challenging. While organizations solve commonly acknowledged problems around data capture and complex measure calculations, there are no consistent, systematic means for measuring clinical quality management. Multiple measures for the same condition Organizations such as the National Quality Forum (NQF), Centers for Disease Control and Prevention and the Center forMedicare and Medicaid Services (CMS) are doing significant work toward standardizing clinical measures irrespective of the quality initiative, but there is still a long way to go. The problem is that various quality improvement programs, such as PQRS or HEDIS, are developing, standardizing and adopting evidence-based clinical quality measures on their own, which results in multiple clinical measures with slight variations for a given condition or outcome. For instance, when tracking HbA1c levels in diabetic patients, performance
  • 2. measures include HbA1c testing, HbA1c poor control, HbA1c control (<7%) and HbA1c control (<8%) depending on the program. Initiative-specific nuances in measure implementation Different quality initiatives select a set of measures based on industry best practices and their endorsement by nationally recognized organizations such as NQF or CMS. This results in common measures—say, tracking HbA1c poor control in diabetes—being tracked across different quality initiatives, such as MU or PQRS. And because each quality initiative has its own nuances in the actual specification and implementation of the measures, provider organizations need to track the same measure multiple times for different initiatives. Additionally, organizations need to be cognizant of the fact that the eligible patient population for the same measure may also vary across quality initiatives based on health plan coverage (Medicare or commercial), allowable evaluation and management (E&M) services and so forth. Periodically changing measure definitions Even within a quality initiative such as PQRS, HIQRP or HEDIS, the specifications for clinical quality measures change over time, driven by factors such as the addition/retirement of E&M codes, medication codes, endorsement of new evidence and changes in exclusions/exceptions. Consequently, organizations are forced to expend significant time and effort in periodically tracking and upgrading measure calculation logic as well as associated workflows, data sources and IT systems based on the extent of change. Moreover, changes in measure calculations make it very difficult (and potentially risky) for organizations to accurately trend and benchmark the performance of clinical measures over a period of time. ================== III. Quality management tools 1. Check sheet
  • 3. 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 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
  • 4. 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. 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
  • 5. 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 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
  • 6. 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  Environment: The conditions, such as location, time, temperature, and culture in which the process operates 6. Histogram method
  • 7. 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 Clinical quality 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 quality management policy quality management strategy quality management books