This document provides an overview of various tools and concepts related to data quality management models. It discusses key aspects of data quality including accuracy, accessibility, comprehensiveness, consistency, currency, definition, granularity, precision, relevancy, and timeliness. Specific quality management tools are also outlined, such as check sheets, control charts, Pareto charts, and scatter plots. Examples are given for how these data quality principles and tools apply across different domains like healthcare records and data warehousing.

1. Data quality management model
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I. Contents of data quality management model
==================
Data Accuracy
The extent to which the data are free of identifiable errors.
To facilitate accuracy, determine the application's purpose, the question to be answered, or the
aim for collecting the data element.
Standard acceptable values should be used where available. Where possible value flags and
constraints should be implemented.
For example, data entry of height into EHRs should flag or highlight very small (less than 12
inches) or very tall (over seven feet) heights.
Ensuring accuracy involves appropriate education and training along with timely and appropriate
communication of data definitions to those who collect data. The applications should constrain
entry to allowable values where possible.
For example, data accuracy will help ensure that a patient height cannot be entered erroneously
as five inches when it is in fact 50 inches. In addition to a primary data error, this would impact
any calculated fields such as Body Mass Index (BMI).
To warehouse data, appropriate edits should be in place to ensure accuracy, such as basic field
length checks.
Also, error reports are generated related to transfers to and from the warehouse.
All warehouses should have a correction and change management policy to track any changes.

2. To accurately analyze data, ensure that the algorithms, formulas, programming, and translation
systems are correct.
For example, ensure that the encoder assigns correct codes and that the appropriate DRG is
assigned for the codes entered.
Continual data validation is important to ensure that each record or entry within the database is
correct.
Data Accessibility
Data items that are easily obtainable and legal to access with strong protections and controls built
into the process.
The application and legal, financial, process, and other boundaries determine which data to
collect. Ensure that collected data are legal to collect for the application.
For example, recording the date of birth and race in the EHR is appropriate and should only
occur once with verification. Then the values should roll forward.
When developing the data collection instrument, explore methods to access needed data and
ensure that the best, least costly method is selected. The amount of accessible data may be
increased through system interfaces and integration of systems.
For example, the best and easiest method to obtain demographic information may be to obtain it
from an existing system. Another method may be to assign data collection by the expertise of
each team member. For example, the admission staff collects demographic data, the nursing staff
collects symptoms, and the HIM staff assigns codes.
Data entry should undergo a cost-benefit analysis process to determine which method provides
the best data most efficiently.
Technology and hardware impact accessibility. Establish data ownership and guidelines for who
may access or modify data and/or systems. Inventory data to facilitate access.
In the EHR it may be advisable to establish data ownership or governance at the data element
level, especially data which are reused. For example, allergies are recorded by many different
clinicians and come in many forms. Who defines what an allergy is? How does this impact the
use of allergies in the EHR, especially for clinical decision support?
Access to complete, current data will better ensure accurate analysis and data mining. Otherwise
results and conclusions may be inaccurate or inappropriate.
For example, use of the Medicare case mix index (CMI) alone does not accurately reflect total
hospital CMI. Consequently, strategic planning based solely on Medicare CMI may not be
appropriate.
Data
Comprehensiveness
All required data items are included. Ensures that the entire scope of the data is collected with
intentional limitations documented.
Clarify how the data will be used and identify end users to ensure complete data are collected for
the application. Include a problem statement and cost-benefit or impact study when collected

3. data are increased.
For example, in addition to outcome it may be important to gather data that impact outcomes.
Cost-effective comprehensive data collection may be achieved via interface to or download from
other automated systems.
Data definition and data precision impact comprehensive data collection (see these
characteristics below).
Warehousing includes managing relationships of data owners, data collectors, and data end-users
to ensure that all are aware of the available data in the inventory and accessible systems. This
also helps to reduce redundant data collection.
Ensure that all pertinent data impacting the application are analyzed in concert.
This is especially important when EHR clinical decision support is utilized. Incomplete data can
result in underreporting a numerator or denominator.
Data Consistency
The extent to which the healthcare data are
reliable and the same
across applications.
Data are consistent when the value of the data is the same across applications and systems, such
as the patient's medical record number. In addition, related data items should agree.
For example, data are inconsistent when it is documented that a male patient has had a
hysterectomy.
The use of data definitions, extensive training, standardized data collection (procedures, rules,
edits, and process) and integrated/interfaced systems facilitate consistency.
Static data should be moved between users. For example, once date of birth has been definitively
established, age at the time of treatment should be calculated, not entered by a user who might
make an error.
Warehousing employs edits or conversion tables to ensure consistency. Coordinate edits and
tables with data definition changes or data definition differences across systems. Document edits
and tables.
When new data are loaded it should be checked against existing data for consistency. For
example, is someone reporting a different race for a patient?
Analyze data under reproducible circumstances by using standard formulas, scientific equations,
programming, variance calculations, and other methods. Compare "apples to apples."
Any manipulation of data, aggregating or otherwise, should be documented thoroughly. For
example, how is BMI calculated and has the formula been checked?
Data Currency
The extent to which data are up-to-date; a datum value is up-to-date if it is current for a specific
point in time. It is outdated if it was current at a preceding time yet incorrect at a later time.
The appropriateness or value of an application changes over time.

4. In EHRs it is imperative that the guidelines and algorithms be up-to-date. For example,
acceptable blood pressure ranges have lowered, as have target HbA1C levels.
Data definitions change or are modified over time. These should be documented so that current
and future users know what the data mean. These changes should be made in accordance with
data governance policies and practices. Further, they must be communicated in a timely manner
to those collecting data and to the end users.
To ensure current data are available, warehousing involves continually validating systems,
tables, and databases. The dates of warehousing events should be documented.
The availability of current data impacts the analysis of data.
For example, analyzing the long-term incidence or prevalence of disease requires data in a
different timeframe than when trying to track a disease outbreak for biosurveillance purposes.
Validating data from various fiscal and calendar years should also be considered.
Data Definition
The specific meaning of a healthcare related data element.
The application's purpose, the question to be answered, or the aim for collecting the data element
must be clarified to ensure appropriate and complete data definitions.
Does the system use the Office of Management and Budget (OMB) standard for race and
ethnicity? If not, what are the definitions and acceptable values?
Clear, concise data definitions facilitate accurate data collection.
For example, the definition of patient disposition may be "the patient's anticipated location or
status following release or discharge." Acceptable values for this data element should also be
defined. The instrument of collection should include data definitions and ensure that the
application limits data collection to the allowed values.
Warehousing includes archiving documentation and data. Consequently, data ownership
documentation and definitions should be maintained over time. Inventory maintenance activities
(purging, updates, and others), purpose for collecting data, collection policies, information
management policies, and data sources should be maintained over time also.
For appropriate analysis, display data needs to reflect the purpose for which the data were
collected. Appropriate comparisons, relationships, and linkages need to be shown.
Data Granularity
The level of detail at which the attributes and values of healthcare data are defined.
A single application may require varying levels of detail or granularity.
For example, census statistics may be utilized daily, weekly, or monthly depending upon the
application. Census is needed daily to ensure adequate staffing and food service. However, the
monthly trend is needed for long-range planning.
Similarly, lab test results may be trended at various levels of detail.
Collect data at the appropriate level of detail or granularity.
For example, the temperature of 100° may be recorded. The granularity for recording outdoor
temperatures is different from recording patient temperatures. If patient Jane Doe's temperature
is 100°, does that mean 99.6° or 100.4°?

5. Appropriate granularity for this application dictates that the data need to be recorded to the first
decimal point while appropriate granularity for recording outdoor temperatures may not require
it.
Warehouse data at the appropriate level of detail or granularity.
For example, exception or error reports reflect granularity based on the application. A spike
(exception) in the daily census may show little or no impact on the month-to-date or monthly
reports.
Appropriate analysis reflects the level of detail or granularity of the data collected.
For example, a spike (exception) in the daily census resulting in immediate action to ensure
adequate food service and staffing may have had no impact on analysis of the census for long-
range planning. Of particular note for analysis is the impact of any rounding which might be
done for numerical data.
Data Precision
Data values should be strictly stated to support the purpose.
The application's purpose, the question to be answered, or the aim for collecting the data element
must be clarified to ensure data precision.
What level of detail is needed for the data collection purpose? Are age ranges or four U.S.
regions sufficient?
To collect data precise enough for the application, define acceptable values or value ranges for
each data item.
For example, limit values for gender to male, female, and unknown; or collect information by
age ranges or allow more detailed collection to fully meet the needs.
Are warehouses receiving and storing all data elements being transferred from the source
system?
If the precision of the data has been altered in the analysis is the process understood and well
documented?
Data Relevancy
The extent to which healthcare-related data are useful for the purposes for which they were
collected.
The applications purpose, the question to be answered, or the aim for collecting the data element
must be clarified to ensure relevant data.
To better ensure relevancy, complete a pilot of the data collection instrument to validate its use.
A "parallel" test may also be appropriate, completing the new or revised instrument and the
current process simultaneously. Communicate results to those collecting data and to the end
users. Facilitate or negotiate changes as needed across disciplines or users.
Establish appropriate retention schedules to ensure availability of relevant data. Relevancy is
defined by the application.
It may be appropriate for warehouses to subset data related to its relevancy for certain uses.
For appropriate analysis, display data to reflect the purpose for which the data were collected.
This is defined by the application. Show appropriate comparisons, relationships, and linkages.

6. Data Timeliness
Concept of data quality that involves whether the data is up-to-date and available within a useful
time frame. Timeliness is determined by how the data are being used and their context.
Timeliness is defined by the application.
For example, patient census is needed daily to provide sufficient day-to-day operations staffing,
such as nursing and food service. However, annual or monthly patient census data are needed for
the organization's strategic planning.
In the EHR, vitals may be taken once per visit for ambulatory care patients, but every 15 minutes
or more often for critically ill patients.
Timely data collection is a function of the process and collection instrument.
In the EHR, system performance plays an important role in data timeliness. Data display should
be sub-second and data entry should occur instantaneously.
Warehousing ensures that data are available per information management policy and retention
schedules.
For EHR or clinical data warehouses, is the data updated concurrently or does it occur in a batch
process?
Timely data analysis allows for the initiation of action to avoid adverse impacts. For some
applications, such as allergy-drug or drug-drug interactions, timely may be seconds. For others,
such as the prevalence of a disease over time, it may be years.
==================
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

7. 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

8. 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

9. 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

10. 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

11. 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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