1. Health quality management
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I. Contents of health quality management
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MEDVARSITY
Medvarsity, India’s first Medical e-learning venture was established in April 2000, and launched
its official online portal www.medvarsity.com in October 2000. It is an initiative by Apollo
Hospitals Group, the leader in healthcare in Asia Pacific region having the first JCI accredited
hospital in India. Medvarsity offers a variety of medical and nonmedical courses to working
executives and professionals- an education that offers professional advantage for personal
growth and career advancement.
ASTRON
Astron Institute of Quality (AIQ) has been established as an academic division of Astron
Hospital & Healthcare Consultants Pvt. Ltd, a comprehensive speciality healthcare consultancy
organization focussed on the fields of Quality & Accreditation, Hospital Planning and Design,
Community Health Research & Training. AIQ aims to address the skill and knowledge gap in the
healthcare industry pertaining to practical aspects of healthcare delivery. The organization is
empanelled with Quality Council of India (NBQP) as an academic organization. Astron’s

2. leadership has played a significant role in establishment of healthcare accreditation mechanism
in the country with Dr Y P Bhatia being the Chairman of National Task Force to establish
healthcare accreditation mechanism in India and presently serves as a Board Member of NABH
and a member of Advisory Council of JCI. Astron thus strives to have a multidimensional
approach to achieve its vision of establishing Astronomical heights for Indian Healthcare Quality
through skill development, academic, training , consultancy and research.
Medvarsity, AHERF & Astron Institue of Quality have come together to offer students an
exciting programme of study in Quality Management in Healthcare. This program is made up of
8 topics leading to an award of Certificate in Healthcare Quality Management.
Course Overview
Healthcare services today are scrutinized, measured, evaluated and publicized as never before.
An increasingly educated public expects to receive nothing less than excellent care and services
and it must be the mission of every healthcare organization to deliver that excellence. Quality
improvement is the process of continually evaluating existing processes of care and developing
new standards of practice and therefore has to be a core competency for every healthcare
manager and clinician at every level of medical practice: be they in primary care clinics, acute
hospitals, specialist centers of excellence or regulatory bodies.
This course is affiliated to Martin Luther Christian University.
Learning Objective
The Certificate Course in Healthcare Quality is designed to provide participants with a solid
foundation of all the core elements of healthcare quality improvement. This course provides
comprehensive coverage of the fundamental concepts, tools and management techniques through
which quality management and improvement can be delivered in a healthcare setting.
It will therefore be invaluable, not only to those entering healthcare quality for the first time, but
also for established healthcare managers needing an update of what is current in the field.
Course Content
Module 1

3.  Introduction to Healthcare Quality
 Quality Management Tools and Theories
 Statistics in Quality
 Accreditation in Healthcare Quality
 Quality in Healthcare Organizations
 Consumer Protection Act & Quality
Module 2
 Patient Safety and Medical Errors
 Dashboards and Scorecards
 IT and Quality
 National Health Programs of India
 The Past, Present and Future of Healthcare Quality
Admission Criteria
 A Bachelor's Degree from a recognized university
Course Duration
 6 months with 2 days Contact Program
Course Fee
 Rs.36,000 (One time Payment) or 1st Installment Rs.20,000 and 2nd Instalment
Rs.16,000 payable in favour of Medvarsity online limited.
Course Highlights:
 Course material developed by experienced specialists from Apollo Hospitals Group.
 Convenient to take the course as it is online anytime, anywhere learning with print
support.

4.  No need to dislocate yourself from existing practice & place of work as the course is
available both online & offline
Course Delivery
Our unique study approach enables you to balance your busy work schedule, social activities and
travel so you can
 Study anytime, anywhere via the internet
 Take your assessments online
 Support in the form of books and articles
 An ID and Password is provided to access the course content and online assessments
Certification
The student is required to complete all internal assessment tests. Final examination will be
conducted at the end of the course. Successful candidates will be awarded Certificate by
Medvarsity, AHERF & Astron.
Who Should Join?
This Certificate program in Healthcare Quality Management is specially tailored for healthcare
professionals, both clinical and managerial including:
 Quality managers... and all managers with a passion for quality! Patient safety and risk
management managers
 Staff involved in JCI and other accreditation processes
 Clinical directors and heads of departments
 Physicians and surgeons
 Heads of nursing and other senior nurses
 Clinical research managers
 Healthcare informatics and IT professionals
 Facilities, estates and support services managers
 Radiographers, Laboratory, Pharmacy and scientific staff

5. Joint Commission International
Accreditation to the hospitals is a significant step in establishing Indian healthcare on the global
horizon. It will contribute in the process of enhancing confidence in the Indian healthcare system
on the international front and will help in bringing in more international patients.
Quality in healthcare and Apollo hospitals have always been synonymous. Joint Commission
International (JCI) which is USA's main hospital accreditation agency is considered as the
American mark of quality health care and is the leader in accreditation and certification of health
care organizations outside USA. JCI gives ratings in various areas such as patient safety and
service standards. The process of accreditation and postaccreditation surveillance ensure
continuous focus on patient safety and quality improvement. This is the most significant
contribution of JCI and has a positive impact on patient care.
==================
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)

6.  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.

7. 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

8. 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

9. 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

10. 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]
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