The document discusses the AHIMA data quality management model. It provides definitions for data quality management and data quality measurement. The model aims to ensure integrity of healthcare data during collection, use, storage and analysis. It addresses challenges from initiatives like EHR adoption, ICD-10 implementation, and quality reporting. The model evaluates data quality across collection, application, warehousing and analysis. HIM professionals can use the model and its assessment tools to improve data governance and take on expanded roles in healthcare data management.

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I. Contents of ahima data quality management model
==================
Healthcare leaders face many challenges, from the ICD-10-CM/PCS transition to achieving
meaningful use, launching accountable care organizations (ACOs) and value-based purchasing
programs, assuring the sustainability of health information exchanges (HIEs), and maintaining
compliance with multiple health data reporting and clinical documentation requirements—
among others. These initiatives impacting the health information management (HIM) and
healthcare industries have a common theme: data.
As electronic health record (EHR) systems have become more widely implemented in all
healthcare settings, these systems have developed and employed various methods of supporting
documentation for electronic records. Complaints and concerns are often voiced—whether via
blogs, online newsletters, or listservs—regarding the integrity, reliability, and compliance
capabilities of automated documentation processes. Several articles in the Journal of AHIMA
establish guidelines for preventing fraud in EHR systems. Documentation practices are within
the domain of the HIM profession, for both paper and electronic records.1
As a result, the need for more rigorous data quality governance, stewardship, management, and
measurement is greater than ever.
This practice brief will use the following definitions:
Data Quality Management: The business processes that ensure the integrity of an
organization's data during collection, application (including aggregation), warehousing, and
analysis.2 While the healthcare industry still has quite a journey ahead in order to reach the
robust goal of national healthcare data standards, the following initiatives are a step in the right
direction for data exchange and interoperability:
 Continuity of Care Document (CCD), Clinical Documentation Architecture (CDA)
 Data Elements for Emergency Department Systems (DEEDS)

2.  Uniform Hospital Discharge Data Set (UHDDS)
 Minimum Data Set (MDS) for long-term care
 ICD-10-CM/PCS, Systemized Nomenclature of Medicine—Clinical Terms (SNOMED
CT), Logical Observation Identifiers Names and Codes (LOINC), RxNorm
Data Quality Measurement: A quality measure is a mechanism to assign a quantity to quality
of care by comparison to a criterion. Quality measurements typically focus on structures or
processes of care that have a demonstrated relationship to positive health outcomes and are under
the control of the healthcare system.3 This is evidenced by the many initiatives to capture
quality/performance measurement data, including:
 The Joint Commission Core Measures
 Outcomes and Assessment Information Set (OASIS) for home health care
 National Committee for Quality Assurance's (NCQA) Health Plan Employer Data and
Information Set (HEDIS)
 Meaningful Use-defined core and menu sets
Key Terms
Understanding of the following term definitions is key to ensure clarity in this article.
Data Quality Management: The business processes that ensure the integrity of an
organization's data during collection, application (including aggregation), warehousing,
and analysis.
Data Quality Measurement: A quality measure is a mechanism to assign a quantity to
quality of care by comparison to a criterion. Quality measurements typically focus on
structures or processes of care that have a demonstrated relationship to positive health
outcomes and are under the control of the healthcare system.
These data sets will be used within organizations for continuous quality improvement efforts and
to improve outcomes. They draw on data as raw material for research and comparing providers
and institutions with one another.
Payment reform and quality measure reporting initiatives increase a healthcare organization's
data needs for determining achievement of program goals, as well as identifying areas in need of
improvement. The introduction of new classification and terminology systems—with their
increased specificity and granularity—reinforce the importance of consistency, completeness,
and accuracy as key characteristics of data quality.4 The implementation of ICD-10 CM/PCS will
impact anyone using diagnosis or inpatient procedure codes, which are pervasive throughout
reimbursement systems, quality reporting, healthcare research and epidemiology, and public
health reporting. SNOMED CT, RxNorm, and LOINC terminologies have detailed levels for a
variety of healthcare needs, ranging from laboratory to pharmacy, and require awareness of the
underlying quality from the data elements.
Healthcare data serves many purposes across many settings, primarily directed towards patient
care. The industry is also moving towards an increased focus on ensuring that collected data is
available for many other purposes. The use of new technologies such as telemedicine, remote
monitoring, and mobile devices is also changing the nature of access to care and the manner in
which patients and their families interact with caregivers. The rates of EHR adoption and
development of HIEs continue to rise, which brings attention to assuring the integrity of the data

3. regardless of the practice setting, collection method, or system used to capture, store, and
transmit data across the healthcare continuum of care.
The main outcome of data quality management (DQM) is knowledge regarding the quality of
healthcare data and its fitness for applicable use in the intended purposes. DQM functions
involve continuous quality improvement for data quality throughout the enterprise (all healthcare
settings) and include data application, collection, analysis, and warehousing. DQM skills and
roles are not new to HIM professionals. As use of EHRs becomes widespread, however, data are
shared and repurposed in new and innovative ways, thus making data quality more important
than ever.
Data quality begins when EHR applications are planned. For example, data dictionaries for
applications should utilize standards for definitions and acceptable values whenever possible. For
additional information on this topic, please refer to the practice brief "Managing a Data
Dictionary."5
The quality of collected data can be affected by both the software—in the form of value labels or
other constraints around data entry—and the data entry mechanism whether it be automated or
manual. Automated data entry originates from various sources, such as clinical lab machines and
vital sign tools like blood pressure cuffs. All automated tools must be checked regularly to
ensure appropriate operation. Likewise, any staff entering data manually should be trained to
enter the data correctly and monitored for quality assurance.
For example, are measurements recorded in English or metric intervals? Does the organization
use military time? What is the process if the system cannot accept what the person believes is the
correct information?
Meaningful data analysis must be built upon high-quality data. Provided that underlying data is
correct, the analysis must use data in the correct context. For example, many organizations do
not collect external cause data if it is not required. Gunshot wounds would require external cause
data, whereas slipping on a rug would not. Developing an analysis around external causes and
representing it as complete would be misleading in many facilities. Additionally, the copy
capabilities available as a result of electronic health data are likely to proliferate as EHR
utilization expands. Readers can refer to AHIMA's Copy Functionality Toolkit for more
information on this topic.6Finally, with many terabytes of data generated by EHRs, the quality of
the data in warehouses will be paramount. The following are just some of the determinations that
need to be addressed to ensure a high-quality data warehouse:
 Static data (date of birth, once entered correctly, should not change)
 Dynamic data (patient temperature should fluctuate throughout the day)
 When and how data updates (maintenance scheduling)
 Versioning (DRGs and EHR systems change across the years—it is important to know
which grouper or EHR version was used)
Consequently, the healthcare industry needs data governance programs to help manage the
growing amount of electronic data.
Data Governance
Data governance is the high-level, corporate, or enterprise policies and strategies that define the
purpose for collecting data, the ownership of data, and the intended use of data. Accountability
and responsibility flow from data governance, and the data governance plan is the framework for
overall organizational approach to data governance.7

4. Information Governance and Stewardship
Information governance provides a foundation for the other
data-driven functions in AHIMA's HIM Core Model by providing parameters based on
organizational and compliance policies, processes, decision rights, and responsibilities.
Governance functions and stewardship ensure the use and management of health information is
compliant with jurisdictional law, regulation, standards, and organizational policies. As stewards
of health information, HIM professionals strive to protect and ensure the ethical use of health
information.8
The DQM model was developed to illustrate the different data quality challenges. The table
"Data Quality Management Model" includes a graphic of the DQM domains as they relate to the
characteristics of data integrity, and "Appendix A" includes examples of each characteristic
within each domain. The model is generic and adaptable to any care setting and for any
application. It is a tool or a model for HIM professionals to transition into enterprise-wide DQM
roles.
Assessing Data Quality Management Efforts
Traditionally, healthcare data quality practices were coordinated by HIM professionals using
paper records and department-based systems. These practices have evolved and now utilize data
elements, electronic searches, comparative and shared databases, data repositories, and
continuous quality improvement. As custodians of health records, HIM professionals have
historically performed warehousing functions such as purging, indexing, and editing data on all
types of media: paper, images, optical disk, computer disk, microfilm, and CD-ROM. In
addition, HIM professionals are experts in collecting and classifying data to support a variety of
needs. Some examples include severity of illness, meaningful use, pay for performance, data
mapping, and registries. Further, HIM professionals have encouraged and fostered the use of data
by ensuring its timely availability, coordinating its collection, and analyzing and reporting
collected data. To support these efforts, "Checklist to Assess Data Quality Management Efforts"
on page 67 outlines basic tenets in data quality management for healthcare professionals to
follow.
With AHIMA members fulfilling a wide variety of roles within the healthcare industry, HIM
professionals are expanding their responsibilities in data governance and stewardship.
Leadership, management skills, and IT knowledge are all required for effective expansion into
these areas.
Roles such as clinical data manager, terminology asset manager, and health data analyst
positions will continue to evolve into opportunities for those HIM professionals ready to upgrade
their expertise to keep pace with changing practice.
==================
III. Quality management tools
1. Check sheet

5. 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

6. 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

7. 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

8. 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

9. 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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