Answer the following questions based on the topics from Chapter 6 and 7 from your (Health Information Management : Concepts, Principles, and Practice) book.
1) Name three processes that you would expect to see in place for information integrity and quality in an organization with an EIM culture.
2)What is the purpose of “clinical analytics” or “business intelligence” solutions?
3)Describe the relationship between health data stewardship and information governance.
4)List the five stages of the information life cycle, and give an example of an information management function that might be performed for each.
5) What is the Foundation of Knowledge Model? (You may use external source only for this question for full points)
6) What’s the difference of between explicit knowledge and tacit knowledge. Give specific examples of explicit and tacit knowledge in healthcare. Why would it be important to manage these?
Copyright @ 2013. AHIMA Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
chapter
6
Healthcare Data Life Cycle:
Governance and Stewardship
Linda Kloss, RHIA, CAE, FAHIMA
Learning Objectives
●● Understand foundational data and information man-
agement concepts at an organizational level and for a
health system
●● Describe a model for managing digital information
over its life cycle
●● Distinguish among data, information technology, and
information governance and describe their relationship
●● Contrast general approaches and challenges of managing
records, data, and information
●● Define data stewardship and describe the key challenges of data stewardship at an organizational level
and for a health system
●● Distinguish between stewardship and governance from
a conceptual and a practical level
●● Delineate critical information governance functions
and criteria for judging their effectiveness
●● Describe considerations in organizing for enterprise
information management, information governance and
stewardship, and life cycle management
●● Discuss the role of health information management
professionals in enterprise information management,
information governance, stewardship, and life cycle
management
Key Terms
Content and records management
Data quality
Enterprise information
management (EIM)
Information
Information asset management (IAM)
Information content
Information governance (IG)
Information integrity
Information life cycle
Information management (IM)
Information science
Information theory
Stewardship
157
EBSCO : eBook Business Collection Trial – printed on 3/17/2018 4:26 PM via LOUISIANA STATE UNIV AT SHREVEPORT
AN: 667492 ; LaTour, Kathleen M., American Health Information Management Association, Eichenwald, Shirley, Oachs, Pamela K..;
07_AB103311_ch06.indd 157
12/21/12 7:21 PM
Health Information Management : Concepts, Principles, and Practice
Account: s3563253.main.ehost
158
Chapter 6
Copyright @ 2013. AHIMA Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
Our Growing Understanding
of Information
This chapter briefly steps outside the healthcare industry and
considers information as a theory, an entity with properties,
and an asset to be preserved and enhanced. James Gleick
states that the word information began to be used in a scientific context in the late 1940s (Gleick 2011). Contemporary
thinking about information theory and information science
coincided with the invention of the transistor and the early
use of the term bit as a unit of measure for information.
Information began to be thought of as something that could
be modeled, counted, transmitted, and processed, concepts
familiar in today’s information society.
Gleick captures the foundational value of information
when he states, “We can see now that information is what our
world runs on: the blood and the fuel, the vital principle … in
the long run, history is the story of information becoming
aware of itself” (Gleick 2011). Information is the basis for
all sciences. It is now understood that information processing
is the key function of DNA and the basis for physics and the
structure of the universe. So, it really can be said that information is fundamental to life.
Understanding of information has advanced immensely in
the last six decades. Information theory today is a branch of
applied mathematics and electrical engineering and involves the
quantification of information. Information theory impacts fields
from mathematics to management, astronomy to physics, and,
of course, biology to medicine. Health information management today is the beneficiary of scientific advancement related
to information theory in tools such as natural language processing, statistical inference, and other forms of data analysis.
In addition to advancements in understanding and
applying the mathematics and engineering of information,
information science has developed as a multidisciplinary
field primarily concerned with the analysis, collection, classification, manipulation, storage, retrieval, and dissemination
of information. This is the space in which health information
management lives as an applied information science with
data and information about health and healthcare at its core.
Contemporary Information Management
Concepts
In the 21st century, information is no longer viewed as an incidental by-product of business operations to be catalogued and
archived. Today, it is the key to understanding and improving the performance of organizations. The medical record and
billing claim can be thought of as by-products of the patient
care process, but also the source for data used to improve
those processes. Health informatics and information management (HIIM) is concerned with the timely and accurate
capture and processing of this transactional information. At
the same time, HIIM needs to be concerned with managing
information in all its forms and using it to its full potential.
Contemporary information management practices rest on
three foundational principles: information asset management (IAM), information management (IM), and information governance (IG).
Principle One: Information Is an Asset
That Must Be Effectively Managed
Information is an asset of the organization that has strategic
value. Like other assets such as bricks and mortar properties, people, finances, and intellectual property, information
must be deliberately managed. The information assets of the
healthcare organization include primary and secondary medical records data, business operations data, images, personal
health records, performance review, and other content in both
physical and digital form. Information assets are more difficult to envision and quantify, so emphasis continues to be
placed on the technology rather than the information content
that IT and other technologies capture, store, and process.
The value of the information asset accrues from its
use. When information is viewed as an asset and managed
accordingly, there is greater trust in it and greater willingness
to make information-informed decisions. Without trust, the
value of information is diminished. Healthcare organizations are now expanding their use of information to improve
care to patient populations and improve organizational performance. They are providing information to patients and
the public. These vital uses require greater discipline in
managing information assets so they are reliable and available to support use by competent users.
Principle Two: Information Management Is
an Organization-wide Function
Ensuring the value of information assets requires an organization-wide perspective of information management functions. It calls for explicit structures, policies, processes,
technology, and controls that taken together describe the
discipline of enterprise information management (EIM).
The scope of EIM may be expanded as information assets
come under better control, but the nexus of healthcare EIM
is the primary and secondary patient data, structured and
unstructured, residing in enterprise and departmental systems regardless of media. Billing and payment information,
e-mail, personal health record data, employee and contractor
information, quality improvement data, health information
exchange, and other information must begin to be viewed
as elements of the information asset mosaic and managed
accordingly. This includes managing across the life cycle of
the information. The information life cycle has five stages:
●● Record creation, capture, or receipt. This phase
includes creating, editing, and reviewing work in
process as well as capture of content (such as through
document imaging technology) or receipt of content
(such as through a health information exchange).
EBSCO : eBook Business Collection Trial – printed on 3/17/2018 4:26 PM via LOUISIANA STATE UNIV AT SHREVEPORT
AN: 667492 ; LaTour, Kathleen M., American Health Information Management Association, Eichenwald, Shirley, Oachs, Pamela K..;
07_AB103311_ch06.indd
158
Health Information
Management : Concepts, Principles, and Practice
Account: s3563253.main.ehost
12/21/12 7:21 PM
Healthcare Data Life Cycle: Governance and Stewardship
Copyright @ 2013. AHIMA Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
●● Record maintenance and use. Once records are created,
they must be maintained in such a way that they are
accessible and retrievable. Components of this phase
include functions, rules, and protocols for indexing,
searching, retrieving, processing, routing, and distributing.
●● Classification and metadata. Classification is a critical
component of records management. Though not a unique
point in the lifecycle, it does support the other phases.
Record classification creates categories or groups of
records necessary for access, search, retrieval, retention,
and disposition of records.
●● Metadata are generated at various points in the records
management lifecycle, providing underlying data to
describe the document, specify access controls and rights,
provide retention and disposition instructions, and maintain the record history and audit trail.
●● Record audit and data controls. Controls and audits support a variety of phases in the record lifecycle. Functions
and processes in this component of records management
may include edit checks at the data level, decision support tools, identification of classes of records that require
auditing, and checks for record completeness.
●● Record preservation and retention. Preservation is
synonymous with storage. Issues associated with
preservation include: technology and media obsolescence, media degradation, media in an archival system,
conversion over time, and conversion of standards over
time. (AHIMA 2008)
There is still a great deal to be learned about how to manage dynamic healthcare information assets across their life
cycle. A career in health information management calls
upon professionals to find pragmatic solutions to an array of
information management challenges so the right—and right
amount—of information is available to support the evolving
needs of healthcare.
Principle Three: Information Governance Is
a Crucial Building Block of EIM
Governance is about assigning rights and responsibilities and
ensuring accountability; governance is an oversight function that makes certain that critical processes and practices
are being reasonably carried out. With regard to information
governance, experts at the Gartner Group describe enterprise information management (EIM) as an essential organizational discipline and information governance as a crucial
building block of EIM (Logan 2009). Information governance is like the accountability wrapper for EIM, and it is
more fully explained later in this chapter.
Information governance is becoming a key focus for
businesses in other information-intensive and regulated sectors, particularly those such as financial services, energy and
utilities, and pharmaceuticals. Like all effective governance,
information governance begins with the boards of trustees and
159
senior leaders. Hospital boards are now holding senior management accountable for steps being taken to avoid breaches
of data. Information exchange and greater transparency and
public accountability for outcomes and cost raise the stakes.
Taken together, IAM, EIM, and IG have potential to mitigate risk, improve organizational performance, and reduce
costs. In research conducted by The Economist, businesses
with formalized information governance report improved decision making and business results due to better access to information and improved information sharing (The Economist
2008). The researchers cite service and product quality gains
because information is more accurate and reliable. They also
report improved business risk management and enhanced
reputation due to better information security practices. They
attribute improved cost control of IT and IT-related services to
tighter and more strategic planning and acquisition processes.
The demand for high-quality data and improved governance to support patient care and critical healthcare initiatives is sharply increasing. Health delivery and payment
reform will keep information management and information
governance in the spotlight. Health informatics and information management professionals are dedicated to ensuring that
useful and useable information is available to those who need
it to care for patients, manage population and public health,
and improve health system performance. In the years to come,
some level of information management will be a core competency for all who work in healthcare and for people who manage their health and that of their families and loved ones.
Check Your Understanding 6.1
Instructions: Answer the following questions on a separate piece
of paper.
1. Explain the difference between information theory and
information science. Where does HIIM fit?
2. Name three key principles for sound contemporary
information management and the relationship of the three
principles to one another.
3. List the five stages of the information life cycle, and give an
example of an information management function that might
be performed for each.
4. Describe three implications of the adoption of information
technology (IT) by healthcare organizations on the functions
of enterprise information management and information
governance.
Building Blocks of EIM
Enterprise information management for healthcare organizations can be described as the set of components shown
in figure 6.1. Each component is comprised of processes,
policies, people, and technology.
EBSCO : eBook Business Collection Trial – printed on 3/17/2018 4:26 PM via LOUISIANA STATE UNIV AT SHREVEPORT
AN: 667492 ; LaTour, Kathleen M., American Health Information Management Association, Eichenwald, Shirley, Oachs, Pamela K..;
07_AB103311_ch06.indd 159
12/21/12 7:21 PM
Health Information Management : Concepts, Principles, and Practice
Account: s3563253.main.ehost
160
Chapter 6
Figure 6.1. Components of EIM
follow drill down one level further to explore key policy,
process, people, and technology issues associated with each
component.
Copyright @ 2013. AHIMA Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
Information Governance
Information
Design and
Capture
Content and
Records
Management
Information
Analysis
and Use
Information Integrity and Quality
Access, Security, and Confidentiality
Taken together, the EIM components comprise a managerial system with oversight through information governance.
Just as cells are to biology, data, information, and content are
to EIM. EIM demands a systems view of managing information over its life cycle. Organizations that take a systems
view will have knowledge of the types and definitions of data
and its lineage. EIM will have mechanisms to track who uses
the data and how it is used. EIM will assess the reliability
of information to ensure that it is in line with the criticality
of these uses. EIM will ensure that those who view data and
information are authorized to do so. EIM will understand
how long information should be retained and in what form.
And EIM will ensure that there is an internal learning system
or feedback loop so information management policies and
practices are being adapted and improved continuously.
Figure 6.2 states the overarching goals of each building block of an EIM management system. The pages that
Figure 6.2.
Access, Security, and
Confidentiality
For healthcare organizations, privacy, security, and confidentiality are bedrock functions foundational to EIM. Information management in healthcare is grounded in recognition
of an individual’s right to control the acquisition, use, and
disclosure of his or her identifiable health data. All who
receive and handle information have obligations to respect
the individual’s privacy rights. Security refers to the physical, technological, and other tools used to protect identifiable
data from unwarranted access or disclosure.
Privacy and security programs are often framed narrowly as the Health Insurance Portability and Accountability Act (HIPAA) compliance rather than more broadly as a
critical aspect of information asset management. Failure
to implement sound practices can result in damage to the
reputation of the organization, its employees, and its affiliates and compromise the trust of patients, stakeholders, and
communities. It can result in monetary damages and diminish the value of the information asset.
Examples of how an EIM culture might alter the value
and effectiveness of access, security, and confidentiality
practices are illustrated in figure 6.3. The value proposition
for access, security, and confidentiality practices is increased
when framed as a foundational part of EIM with governance
oversight. Compliance remains important, but it is not the
only rationale.
EIM goals
EIM Building Blocks
Access, Security, and Confidentiality
To ensure that personal health information and business information are available only
to authorized persons and used only for authorized purposes and that security risks and
vulnerabilities are proactively managed.
Information Integrity and Quality
To continuously improve the value and trustworthiness of the information asset by ensuring that
data and content are valid, accurate, reliable, up to date, and “fit for use.”
Information Design and Capture
To continuously improve the standards-based policies and practices for data capture and clinical
documentation that supports the full range of uses and enable interoperability, exchange, and
linkage.
Content and Records Management
To continuously improve the methods whereby corporate and health records and data content are
maintained across their life cycle and regardless of media, to ensure compliance with regulatory,
accreditation, and legal best practices.
Information Analysis, Use, and
Exchange
To align data and content requirements and availability to the needs of those who rely on
information for a range of clinical and business uses and ensure that those who must act on
information have the requisite tools and skills to use it effectively.
Information Governance
To ensure leadership and organizational practices, resources, and controls for effective,
compliant, and ethical stewardship of information assets to enable best clinical and business
practices and serve patients, stakeholders, and the public good.
EBSCO : eBook Business Collection Trial – printed on 3/17/2018 4:26 PM via LOUISIANA STATE UNIV AT SHREVEPORT
AN: 667492 ; LaTour, Kathleen M., American Health Information Management Association, Eichenwald, Shirley, Oachs, Pamela K..;
07_AB103311_ch06.indd
160
Health Information
Management : Concepts, Principles, and Practice
Account: s3563253.main.ehost
12/21/12 7:21 PM
Healthcare Data Life Cycle: Governance and Stewardship
Figure 6.3.
Comparing access, security, and confidentiality approaches
Pre-EIM
Access, Security,
and Confidentiality
Copyright @ 2013. AHIMA Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
161
EIM Culture
• Compliance focused
• Stewardship focused
• Gaps in policy framework
• Focus on threats of punishment for noncompliance
• Policy planning integral to process,
system changes
• Vulnerabilities in planning for disruptions
• Incentives for sound stewardship
• Limited connections between related functions,
for example, privacy and security have limited
crossover
• Business continuity planning and plan
• Safeguards against accidental or deliberate information
corruption due to accidental failures or deliberate fraud
• Routine audits of compliance leading
to improvement
Information Integrity and Quality
This second foundational component, information integrity
and quality, is arguably the most underdeveloped and at the
same time the most urgently needed of the EIM building
blocks. The explosive growth of digital information has led
to a state of disorder that makes it difficult to trust, track,
and analyze health information. The factors giving rise to
these risks are certainly not all attributable to changing the
medium from paper to computer, nor are they within the
control of the organization as some issues relate to design of
the human–machine interface. Information integrity is never
easily managed because of the range of factors that must be
understood and managed:
1. Change: Depending on how they are designed and
executed, organizational structure, regulation, personnel, hardware, and software can compromise or enhance
integrity
2. Complexity: The sheer number of systems supporting
high-stakes and high-speed work processes and communications carried out across staffs can number in the
thousands
3. Conversion: Software and systems upgrades and conversions are a high-risk time for data as well as disruption
to work process
4. Corruption: Accidental failures, deliberate fraud, and
other situations can profoundly impact the integrity of
health information
Information integrity is the dependability or trustworthiness of information—it encompasses the entire framework in
which information is recorded, processed, and used (AHIMA
2012). The concept is larger than data quality. Whereas data
quality focuses on guarding against and correcting bad data,
information integrity encompasses three domains and the
relationship among them:
●● Information content includes the data elements
including their underlying definitions and relevant
data content standards in whatever form they are held,
that is, numeric, text, structured, unstructured, digital,
paper, e-mail, transaction reports, spreadsheets, analytic reports, databases, and such. Metadata, which are
characterized as information about the information, are
included in this definition of information content.
●● Process refers to the functions used to transform an
input into a specified output and the policies that guide
how they are carried out. Vocabulary mapping, coding,
decision support algorithms, and the claims adjudication process are familiar examples of health information processes that have the potential to impact on the
quality of information, positively and negatively.
●● System in this context is the human, IT, organizational,
and regulatory environment configured to achieve a
specific purpose. Again, how systems and subsystems
work together can impact information integrity. Consider the example of a major software upgrade and the
attendant risk of new information integrity issues. The
transition from ICD-9 to ICD-10 is a classic case study
in how all parts of the system must come together to
ensure information integrity.
Healthcare organizations must invest in data quality, but
they are advised to consider the broader issues of content,
process, and system that enhance or impede information
integrity as a prerequisite for many other information management initiatives. Examples of how an EIM culture might
alter the way in which information integrity and data quality
are carried out are highlighted in figure 6.4.
Information Design and Capture
As depicted in figure 6.1, information design and capture
is a first cluster of EIM functions. The key requirements of
this process depend on who is setting them. For clinicians,
data capture and documentation facilitate decision making by individual clinicians and communication with
other members of the clinical teams. Patients want their
care teams well informed and coordinated. Risk managers require that data capture and documentation produce a
EBSCO : eBook Business Collection Trial – printed on 3/17/2018 4:26 PM via LOUISIANA STATE UNIV AT SHREVEPORT
AN: 667492 ; LaTour, Kathleen M., American Health Information Management Association, Eichenwald, Shirley, Oachs, Pamela K..;
07_AB103311_ch06.indd 161
12/21/12 7:21 PM
Health Information Management : Concepts, Principles, and Practice
Account: s3563253.main.ehost
162
Figure 6.4.
Chapter 6
Comparing information integrity and quality approaches
Pre-EIM
Copyright @ 2013. AHIMA Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
Information Integrity
and Quality
EIM Culture
• Limited systematic data quality assessment
• Standards- and definitions-based data capture
• Reactive response to data quality problems
• Knowledge of data provenance and lineage
• Lack of trust in data
• Protocols for monitoring end-to-end impact of conversions
and upgrades
• Lack of standard protocols for error
correction
• Systematic data quality monitoring based on assessed risks
• Data scrubbing and cleansing in the
creation of data warehouses
• Error correction and amendment processes
• Modification of existing systems to
accommodate changes such as new
regulations
complete and logical chronology, quality managers require
data that support the measures being tracked, and financial services must ensure that information substantiates the
billing claim.
The IM challenge, of course, is to meet the range of needs
while also ensuring that information and data standards are
being used to the fullest extent and that data are interoperable
and understandable by those who receive it. IM must also
support efficient data capture of useful data, avoiding redundancy and rework, and provide training and guidance for
staff who create the records.
Like other aspects of healthcare IM, the issues relating
to efficient capture of information are layered. If designing
healthcare information capture from a blank slate, the process
would undoubtedly start by assessing data needs, defining
data sets based on data content standards and standardized
definitions for data elements, and using an information
architecture designed for interoperability and information
liquidity. But this is not how health IT has evolved, and it is
not possible to start over.
Standards development organizations are now working on data content standards, but it will be some time
before systems are retrofitted. Meaningful use incentives
are imposing greater standardization in how some data are
collected. EIM must keep current on these developments
to make certain they are well integrated in operations. At
Figure 6.5.
the same time, IM must move forward by making incremental change in areas where there is greatest potential to
improve.
As illustrated in figure 6.5, formalized EIM should
assess and create policy and learn how best to adapt current practices that compromise data, introduce risk, or
impede efficient data capture. Some examples include
setting policy for the use of the electronic health record
(EHR) copy and paste functionality, improving on formats for standard reports to spotlight important data, and
introducing speech recognition and natural language processing solutions to permit clinicians a choice in how they
capture data.
There are great opportunities to improve the information
design and capture process, improvements that will have
quantifiable positive impact on the quality and reliability of
information and its value.
Content and Records Management
Health information management professionals understand
the goals of the records management component as this has
been a core focus for the profession since its inception. The
need for more effective management of information content,
generally defined as unstructured information, is a newer
area of IM focus. This is important because valuable health
Comparing information design and capture approaches
Information
Design and Capture
Pre-IAM/EIM
EIM Culture
• Adapted work process to technology constraints
• Process for assessing data needs and improving data capture
and documentation over time
• Highly customized solutions with wide personal
preference latitude
• Documentation improvement as an overlay to
the process of data capture
• Productivity barriers leading to poor morale
• User interface issues impeding safe and
effective care
• Knowledgeable and proactive users behind coordinated
efforts to improve the design of technology and the tools
used for data capture
• Understood and addressed impact on productivity and user
acceptability of how structured and unstructured data are
captured and available
EBSCO : eBook Business Collection Trial – printed on 3/17/2018 4:26 PM via LOUISIANA STATE UNIV AT SHREVEPORT
AN: 667492 ; LaTour, Kathleen M., American Health Information Management Association, Eichenwald, Shirley, Oachs, Pamela K..;
07_AB103311_ch06.indd
162
Health Information
Management : Concepts, Principles, and Practice
Account: s3563253.main.ehost
12/21/12 7:21 PM
Healthcare Data Life Cycle: Governance and Stewardship
163
Figure 6.6. Content and records management
Pre-IAM/EIM
Copyright @ 2013. AHIMA Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
Content and Records
Management
EIM Culture
• Records management not addressed
in specifications for technology acquisition
• Policies expanded to include the range of record
types
• Permanent retention of all the data
• Policies linked to business strategy
• Data silos and data owners unaware of rules for
records management
• Retention policies and planning
• Unmanaged content accumulates in an increasing
number of files and applications
• The official health record has information holes
because valuable unstructured data are inaccessible
• Audit practices to ensure that policies and practices
are being carried out
• Records management issues considered as part of
technology acquisition
• Data redundancy expands
record information lives outside the EHR. There is a spectrum of unstructured data content that needs to be considered, including
●● Paper source documents such as record forms
received from other providers, consents, and other
forms
●● Electronic source documents whose data of interest are
not structured; e-mail and transcribed reports fall into
this category
●● Sources that are not documents but rather video,
voice, or pictures
While the goals of effective records management remain
constant, the transition from paper to computer necessitates
revising and restoring some traditionally well-managed practices that are disrupted in the transition. For example, the
hospital industry historically agreed on policies for records
retention, and these were reflected in law. The definitions of
the standard content of the health record have also been set
aside and new standards are only now being advanced. Content management is less well developed; taken together, the
content and records management functions of IM are in
need of redesign and greater standardization because digital
health information is characterized as follows:
●● Rather than a physical record, the health record today
is comprised of linked multimedia files and this
changes the focus and scope of content and records
management.
●● The content management functions extend beyond
the metaphorical cover of the health record to include
secondary data and other sources such as registries,
warehouses, e-mail, personal health records, and other
content that is not ordinarily part of the legal electronic
health record.
●● Payment and health delivery reform will further
reshape our frame for records management. As
ambulatory and inpatient data are merged for population management and bundled payment, traditional
inpatient and ambulatory content and record boundaries are breaking down.
The content and records management functions of IM
encompass the official health records and range of other
ancillary corporate records regardless of media. They focus
on the life cycle of these records and content to ensure that
such content is being systematically reviewed, classified,
retained, and disposed of according to policies and practices
that reflect standards, regulations, and laws. Content and
records management also addresses the specific protocols
and requirements for managing records involved in litigation. Electronic discovery is a critical, complex, and costly
process that must be done very well. Figure 6.6 offers some
contrast between the current state of content and records
management and the desired state possible through an EIM
approach.
Information Analysis and Use
Information management plays an important role in supporting the analysis and use of information directly and
indirectly. First, major high-stakes applications such as
computerized provider order entry (CPOE), patient portals,
quality measurement, and information exchange require
a systematic approach to IM. This must begin at the definitional stage when data needs are assessed, data content
definitions are developed, and decisions are made about
the information architecture. It is very difficult to fulfill the
user’s requirements if the requisite data are not captured or
are captured in a way that limits their usefulness.
Information management contributes to the upstream
value of information by focusing on the user’s data needs and
requirements (as distinct from user interface or functionality)
and contributing to the design of solutions that enable capture and access to data that meet integrity requirements for
that application. It is important to underscore that data quality
and integrity requirements may differ depending on the application. CPOE and other applications that are used for realtime clinical decision support require the highest standards,
whereas aggregate data applications for general trend analysis
can tolerate some data errors.
Information management strives to understand the
data issues, including data integrity requirements in each
EBSCO : eBook Business Collection Trial – printed on 3/17/2018 4:26 PM via LOUISIANA STATE UNIV AT SHREVEPORT
AN: 667492 ; LaTour, Kathleen M., American Health Information Management Association, Eichenwald, Shirley, Oachs, Pamela K..;
07_AB103311_ch06.indd 163
12/21/12 7:21 PM
Health Information Management : Concepts, Principles, and Practice
Account: s3563253.main.ehost
164
Figure 6.7.
Chapter 6
Information analysis and use
Pre-IAM/EIM
EIM Culture
Copyright @ 2013. AHIMA Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
Information
• Users maintain the information they need
Analysis and Use • Tools and products are purchased without scrutiny of
data properties and information integrity capabilities
• Greater trust in the data and willingness to act on
information
• Greater consistency in information and information
policy across the system, lowering the risk and the cost
• Information changes in one system are not replicated
in others
• Information value increases with use because issues are
proactively identified and resolved
• New error is introduced because upgrades and
interfaces are not consistently tested using standard
test databases and test scripts
• Users get minimal training in how to capture, analyze,
and use information
application. Nothing compromises user acceptance as fast
as encountering data errors and finding that no one knows
or is managing the quality of data. IM also supports the end
users with training, guidance, and feedback loops so the
experiences of users are brought back through the IM process. See figure 6.7.
Information management helps those who use the data for
operations and business management and various secondary
uses to understand its availability, meaning, and limitations.
“Clinical analytics” and “business intelligence” are contemporary terms describing solutions designed to extract useful
properties from data to enable clinical and organizational
performance improvement. There is a strong IM component
to these functions as the data must be fit for use, available,
and understandable to users.
Check Your Understanding 6.2
Instructions: Answer the following questions on a separate piece
of paper.
1. Name the five components of EIM.
2. Name three processes that you would expect to see in place
for access, security, and confidentiality in an organization
with an EIM culture.
3. Name three processes that you would expect to see in place
for information integrity and quality in an organization with
an EIM culture.
4. What is the purpose of “clinical analytics” or “business
intelligence” solutions?
Information Governance
This section addresses information governance and reasons
why it is a crucial building block of EIM. In corporate and
nonprofit organizations, boards of trustees and senior leaders understand governance principles and practices and
• Competence in the capture, analysis, and use of
information is defined by stakeholder; suitable training
and support are available
• Upgrades and interfaces do not introduce unanticipated
information issues
have put in place governance practices for the organization’s fiscal, property, compliance, human resource, and
other aspects of managing complex organizations. Most do
not yet include the organization’s information assets within
the scope of their governance duties, though this is changing quickly in information-intensive corporations. Healthcare lags, but the explosive growth of digital information
with poor information governance impedes trust and willingness to act on data.
A working definition of information governance is
The leadership and organizational structures, policies, procedures, technology, and controls that ensure that patient and
other enterprise data and information sustain and extend the
organization’s mission and strategies, deliver value, comply
with laws and regulations, minimize risk to all stakeholders,
and advance the public good (Kloss 2011).
Governance is not about doing; it is about assuring,
assessing, and enabling. Boards of directors and senior leaders at organizations that understand the value of information
asset management exercise governance by first asking questions and then seeing to it that plans are put in place where
they do not exist or are inadequate. Such questions include
the following:
●● Is there a policy, strategy, and management plan in
place for information life cycle management?
●● How do we assess the organization’s capabilities
regarding information security, and what is the plan
advancing these capabilities?
●● Are our privacy practices sufficient to avoid breaches,
and if a breach occurs, what is our plan for managing
it?
●● How do we inform patients about their information
rights, and how we are protecting and using their
information?
Information governance also assesses the IM needs in
relation to other organizational priorities to make informed
decisions about what priorities can be advanced. Constrained and competing resources suggest an incremental
EBSCO : eBook Business Collection Trial – printed on 3/17/2018 4:26 PM via LOUISIANA STATE UNIV AT SHREVEPORT
AN: 667492 ; LaTour, Kathleen M., American Health Information Management Association, Eichenwald, Shirley, Oachs, Pamela K..;
07_AB103311_ch06.indd
164
Health Information
Management : Concepts, Principles, and Practice
Account: s3563253.main.ehost
12/21/12 7:21 PM
Healthcare Data Life Cycle: Governance and Stewardship
Copyright @ 2013. AHIMA Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
approach, and health information management (HIM) leaders should be prepared to outline priorities and sequence and
pace change.
Principles for Information Governance and
Stewardship
Information governance might best be viewed as a stewardship duty. Stewardship is the responsible management of
something entrusted to one’s care. Healthcare organizations
are entrusted with managing patient data, and that responsibility is best performed if the organization’s stewardship
165
values are laid out. Starting with guiding principles and the
ends to be achieved begins to shape practices in the desired
direction. A number of references can serve as a starting
point.
The generally accepted recordkeeping principles (GARP)
define and describe eight key principles relating to records
and information management practices. GARP also include
a maturity model to help organizations assess the stage of
their development. The Maturity Model describes measures
reflecting five levels of development of information governance for each principle. These range from Level 1, Substandard, to Level 5, Transformational. Figure 6.8 overviews
Figure 6.8. Analysis of the applicability of GARP principles
Cross-Industry GARP Principle
Applicability to Health Information Governance
Accountability
A senior executive oversees the recordkeeping program and delegates
program responsibility to appropriate individuals. The organization
adopts policies and procedures to guide personnel and ensure the
program can be audited.
Consistent with the recommendation to engage the board and
develop an IAM mindset. Healthcare organizations will take
different approaches to assigning senior level responsibility
depending on program emphasis.
Transparency
Highly relevant. Healthcare organizations have very visible
The processes and activities of an organization’s recordkeeping
program are documented in a manner that is open and verifiable and is privacy policies, but other processes and activities are often not
transparent.
available to all personnel and appropriate interested parties.
Integrity
A recordkeeping program shall be constructed so the records and
information generated or managed by or for the organization have a
reasonable and suitable guarantee of authenticity and reliability.
This imperative is far more important in healthcare because the
consequence of poor data quality can be life and death. The
obligation must include the data content in the record.
Protection
A recordkeeping program shall be constructed to ensure a reasonable
level of protection to records and information that are private,
confidential, privileged, secret, or essential to business continuity.
Personally identifiable health information is governed by HIPAA
privacy and security laws and regulations and state law. Hence,
this imperative too carries great importance for healthcare
organizations.
Compliance
The recordkeeping program shall be constructed to comply with
applicable laws and the other binding authorities, as well as the
organization’s policies.
Highly relevant. Healthcare organizations have complex
compliance obligations and the information management and
records management is a key element.
Availability
An organization shall maintain records in a manner that ensures
timely, efficient, and accurate retrieval of needed information.
Highly relevant. Electronic health records and related IT can aid
retrieval of information, but the design of IT sometimes impedes
rapid availability. Access to aggregate data or linking data across
systems can be similarly challenging.
Retention
An organization shall maintain its records and information for
an appropriate time, taking into account legal, regulatory, fiscal,
operational, and historical requirements.
Highly relevant. Healthcare organizations must redesign
retention principles in light of digital media, changing
e-discovery and other laws.
Disposition
An organization shall provide secure and appropriate disposition for
records that are no longer required to be maintained by applicable
laws and the organization’s policies.
Highly relevant and related to lifecycle and retention planning.
© 2010 ARMA International.
EBSCO : eBook Business Collection Trial – printed on 3/17/2018 4:26 PM via LOUISIANA STATE UNIV AT SHREVEPORT
AN: 667492 ; LaTour, Kathleen M., American Health Information Management Association, Eichenwald, Shirley, Oachs, Pamela K..;
07_AB103311_ch06.indd 165
12/21/12 7:21 PM
Health Information Management : Concepts, Principles, and Practice
Account: s3563253.main.ehost
166
Figure 6.9.
Chapter 6
NHS acute trust requirements
National Health Service, United Kingdom, Acute Trust Version 8
Information Governance Management
Copyright @ 2013. AHIMA Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
Requires an information governance framework, approved policies with strategies and improvement plans and training for staff; also
require in all contracts.
Confidentiality and Data Protection Assurance
The information management agenda is supported by adequate confidentiality and data protection skills, knowledge, and experience
including appropriate procedures for informing and securing consents from patients, release of information, monitoring access to
personal health information.
Information Security Assurance
The information management agenda is supported by adequate security skills, knowledge, and experience; formal security risk
assessment and management programs for key information assets and security incident process and business continuity planning.
Clinical Information Assurance
The information management agenda is supported by adequate information quality and records management skills; procedures for patient
and provider identification; and multi-disciplinary audit of clinical records.
Secondary Use Assurance
Applies heavily to the accuracy of coding; national data definitions and standard are incorporated into systems; external data quality
reports are used for monitoring and improving data quality; and regular audit cycle for accuracy.
Corporate Information Assurance
Corporate records are handled in a manner consistent with law, including an information lifecycle management strategy.
Source: NHS 2011.
the GARP principles and comments on their applicability
to healthcare information governance (ARMA 2010). They
are certainly all highly relevant, but healthcare organizations carry additional obligations because their essential
business is patient care, which has its own laws, regulations,
and standards.
Another source of sample principles is the United Kingdom’s National Health Service, which require information
governance programs in healthcare organizations. Because
this was designed for and is applicable to healthcare information management and governance, it is closely related
to the components of the EIM model described previously.
The principles for acute-care services are summarized in
figure 6.9 (NHS 2011). Note the important focus on the adequacy of skills, knowledge, and experience to carry out the
various obligations. It is a key focus of information governance to ensure that the functions are appropriately staffed.
Joint Commission (2009) accreditation standards can also
serve as a guide for framing principles for information stewardship and governance, as can the principles for stewardship
and secondary use of information prepared by the National
Committee on Vital and Health Statistics (NCVHS).
Stewardship values should be reflected in health information governance principles and practices to ensure that
they reflect the special nature—and special obligations—of
safeguarding health data. The benefits accrue not only to
the healthcare enterprise but also to patients and to society. The NCVHS stewardship principles are summarized
in figure 6.10. These are deliberately written from the
patient’s perspective.
Change Leadership and Information
Management
Taking a systems approach to enterprise information management and governance is not a quick fix for any organization, particularly for healthcare organizations that are not
yet fully transitioned to digital records and are often characterized by siloed IT and IM functions. The transition will
be incremental and targeted but should be deliberate and
guided by an EIM and IG improvement plan.
This work should not be viewed as a project to be commissioned or a technology to be acquired. It is a discipline
to be built and improved upon over time. Like all change
management, the first step is raising awareness of the benefits and risks of the current state and then initiating a dialogue about points of vulnerability. Given pressure for scarce
IM resources, it will be important to follow the tenets of
change management, starting with small wins and building
from there.
Check Your Understanding 6.3
Instructions: Answer the following questions on a separate piece
of paper.
1. Describe the relationship between health data stewardship
and information governance.
2. Describe the value of laying out principles to guide
information governance.
3. Identify the eight GARP principles.
EBSCO : eBook Business Collection Trial – printed on 3/17/2018 4:26 PM via LOUISIANA STATE UNIV AT SHREVEPORT
AN: 667492 ; LaTour, Kathleen M., American Health Information Management Association, Eichenwald, Shirley, Oachs, Pamela K..;
07_AB103311_ch06.indd
166
Health Information
Management : Concepts, Principles, and Practice
Account: s3563253.main.ehost
12/21/12 7:21 PM
Healthcare Data Life Cycle: Governance and Stewardship
Figure 6.10.
167
NCVHS health data stewardship key principles
Health Data Stewardship: An NCVHS Primer
Individual Rights
• Access for an individual to his or her own health data
Copyright @ 2013. AHIMA Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
• Opportunity to correct one’s own data
• Transparency for the individual about the use(s) of his or her data
• Individual participation and consent for the use of the data
• Education
• Other rights to privacy of personal health information as set forth in state and federal laws and regulations
Responsibilities of the health data steward—Either a formal position or assigned accountability with responsibility
• Adherence to privacy and confidentiality principles and practices
• Appropriate use and interpretation of data
• Limits on the use, disclosure, and retention of information
• Appropriate deidentification of data
• Data quality
Security, safeguards, and controls
• Protect information and minimize the risks of unauthorized or inappropriate access, use or disclosure
Accountability, enforcement, and remedies
• Detection mechanisms for failure to follow policy
• Remediation for the individual whose data are involved
Source: NCVHS 2009.
Summary
Healthcare organizations that understand information as
an asset will take steps to introduce principles and practices of effective enterprise information management and
information governance. There are substantial benefits to be
realized in terms of operational efficiency, cost control, and
compliance. There are risks for failing to formally advance
the management and governance of healthcare information
across the enterprise.
Health information management professionals can
advance the vision and benefits of improving the management and governance of information assets and convene the
stakeholders needed to shape sound policies and practices.
HIM professionals should take part in assessing and developing the IM competencies across the organization. They
should advance high-priority improvements and have a firm
understanding of areas of vulnerability. Finally, they should
be role models for information stewardship.
Healthcare organizations have special obligations
as stewards of important patient information. The values of stewardship should be reflected in every aspect of
information governance and management. In the end,
information asset management, enterprise information
management, and information governance will ensure that
healthcare organizations are effective stewards of health
information to benefit patients and serve the public good.
References
American Health Information Management Association. 2008.
Enterprise Content and Record Management for Healthcare.
Journal of AHIMA (79)10: 91–98.
American Health Information Management Association. 2012.
AHIMA pocket glossary for HIM and technology.
Chicago: AHIMA.
ARMA International. 2010. Information Governance Maturity
Model. http://www.arma.org/GARP.
The Economist. 2008 (October). The future of enterprise information governance. Economist Intelligence Unit. http://www.eiu.com.
Gleick, J. 2011. The Information: A History, A theory, A flood.
New York: Pantheon Books.
Joint Commission. 2009. Comprehensive Accreditation Manual
for Hospitals: The Official Handbook. Oakbrook Terrace, IL: Joint
Commission Resources.
Kloss, L. 2011 (February). Obligation and opportunity. Trustee
64(2): 24–25.
Logan, D. 2009 (November). Organizing for information governance. Research ID number G00172224. Gartner, Inc.
National Committee for Vital and Health Statistics. 2009
(December). Health data stewardship: What, why, who, and how.
http://www.ncvhs.hhs.gov.
National Health Service. 2011. Acute Trust Version 8 (2010–2011).
http://www.igt.connectingforhealth.nhs.uk.
EBSCO : eBook Business Collection Trial – printed on 3/17/2018 4:26 PM via LOUISIANA STATE UNIV AT SHREVEPORT
AN: 667492 ; LaTour, Kathleen M., American Health Information Management Association, Eichenwald, Shirley, Oachs, Pamela K..;
07_AB103311_ch06.indd 167
12/21/12 7:21 PM
Health Information Management : Concepts, Principles, and Practice
Account: s3563253.main.ehost
Copyright @ 2013. AHIMA Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
This page intentionally left blank
EBSCO : eBook Business Collection Trial – printed on 3/17/2018 4:26 PM via LOUISIANA STATE UNIV AT SHREVEPORT
AN: 667492 ; LaTour, Kathleen M., American Health Information Management Association, Eichenwald, Shirley, Oachs, Pamela K..;
07_AB103311_ch06.indd
168
Health Information
Management : Concepts, Principles, and Practice
Account: s3563253.main.ehost
12/21/12 7:21 PM
Copyright @ 2013. AHIMA Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
chapter
7
Data Capture, Maintenance,
and Quality
Valerie J.M. Watzlaf, PhD, RHIA, FAHIMA
Learning Objectives
●● Understand how and what type of data are captured
●● Develop standard practices, policies, and procedures
and structured at an organizational level and for a
health system
●● Examine how data are maintained at the organizational
and health system levels
●● List and give examples of 10 characteristics of quality
data as outlined by AHIMA
●● Distinguish among data quality assessment, evaluation,
and integrity
●● Demonstrate how documentation in the health
record supports the overall continuum of care for
the patient, including secondary data sources such
as registries, databases, data sets, surveys, and core
measures
●● Differentiate between methods of capturing,
maintaining, and evaluating the quality of health
information
that support effective and efficient capture, maintenance, and quality of data
●● Design data quality and integrity validation strategies
and methods
●● Describe appropriate protocols to support secondary
data uses in research, patient safety, risk assessment,
epidemiology, and public health
●● Discuss how healthcare data sets such as the Healthcare Effectiveness Data Set (HEDIS), Uniform Hospital Discharge Data Set (UHDDS), and the Outcome
and Assessment Information Set (OASIS) are used
to support data capture, maintenance, and quality of
healthcare data
●● Describe the healthcare information management
(HIM) profession Core Model as developed by
AHIMA, and explain the functional components
Key Terms
10 characteristics of data quality
Attribute
Authorization management
Clinical documentation improvement (CDI)
Crosswalk
Data
Data dictionary
Data integrity
Data map
Data quality model
Database administrator (DBA) or data administrator (DA)
Database management system (DBMS) data dictionary
Documentation
Enterprise master patient index (EMPI)
Explicit knowledge
Information
Integrity constraint
Knowledge
Knowledge management
Measure applications partnership (MAP)
Patient-centered medical home (PCMH)
Referential integrity
System catalog
Tacit knowledge
169
EBSCO : eBook Business Collection Trial – printed on 3/17/2018 4:27 PM via LOUISIANA STATE UNIV AT SHREVEPORT
AN: 667492 ; LaTour, Kathleen M., American Health Information Management Association, Eichenwald, Shirley, Oachs, Pamela K..;
08_AB103311_ch07.indd 169
12/21/12 7:21 PM
Health Information Management : Concepts, Principles, and Practice
Account: s3563253.main.ehost
Copyright @ 2013. AHIMA Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
170
Chapter 7
Health information managers have numerous roles within
the healthcare industry. Most, if not all, of these roles involve
managing data and information shared by a diverse and
sometimes widely dispersed group of users. These data are
the heart of the healthcare environment and vital to decisionmaking processes surrounding both patient care and the business of healthcare. Meeting the challenge of managing the
data and information for these purposes is not a simple task.
Users have different information needs and may even have
different data definitions. These different needs and definitions must be addressed in developing effective healthcare
information systems.
This chapter introduces data capture and maintenance
within the healthcare system and how information as an organizational asset must be managed effectively to provide and
sustain its value. It examines the relationship between data
and information, several models for managing healthcare
information, other principles of healthcare information management, issues related to measuring data quality in a healthcare setting, data management roles, ensuring the integrity
and validity of data, and some basic principles of data standardization through clinical documentation improvement
policies and procedures.
From Data to Information
to Knowledge
Where does information come from? The simple answer is
that information is processed data. Data are the raw facts,
generally stored as characters, words, symbols, measurements, or statistics. Unprocessed data are not very useful for
decision making. Take, for example, the letters and numerals Z, 4, 6, and 1. What do they mean? If seen together as
Z461, the data might be processed to resemble the model of
a specific car. If one looks further, looking it up in the International Classification of Diseases, Tenth Revision, Clinical
Modification (ICD-10-CM) codebook or entering it into an
encoder software program, it takes on even more meaning.
It is now known that Z46.1 is the code that represents an
encounter for a fitting and adjustment for a hearing aid.
Is this information? That depends. When looking for a
particular patient’s diagnosis to process a claim, Z46.1 may,
Table 7.1.
in fact, be providing the information an insurance representative needs. However, for the medical researcher looking
for patient characteristics that contribute to hearing aid use,
Z46.1 on one patient’s chart is not yet “processed” enough to
provide useful information.
Where does data end and information begin? How are the
two concepts related? Do the data collected and stored affect
the information available within the organization? To answer
these questions, one must first know who needs the data or
information to perform what job function or functions. What
people and what decisions are involved?
Information as an Organizational Resource
Information is a valuable asset at all levels of the healthcare
organization. Personnel, both clinical and support staff, who
perform the day-to-day operations related to patient care
or administrative functions rely on information to do their
jobs. This is truly the information age, and nowhere is this
more apparent than in healthcare. Healthcare managers at
both the middle management level and the executive level
make extensive use of information, both to carry out dayto-day operations and in strategic planning for the organization. An interesting point to think about is that the same data
may actually provide different information to different users.
In other words, one person’s data may be another person’s
information.
To illustrate this point, consider a small data set that represents some patient demographic data. It might be a subset of data from a hospital’s electronic master patient index
(MPI) system. (See table 7.1.)
This single set of data could be used at all levels of the
hospital, beginning with the admissions process, a dayto-day operation of the facility. Admissions personnel would
use the data set to verify previous admissions or the spelling of a patient name. They also would be responsible for
data entry and updating the MPI to ensure that it contains
accurate, timely data. The MPI data set, as is, would provide
useful information to the admissions personnel.
At the middle management level, the director of outpatient services might want information about where recent
patients live and how far they travel to use this hospital. He
or she might further process or query the data set to classify
Subset of a master patient index table
MRN
Last Name
First Name
Middle
Name
DOB
Payment
Type
Zip Code
096543
Jones
Georgia
Louise
11/21/1957
Self
29425
065432
Lexington
Milton
Robert
08/12/2000
Private
29425
467345
Lovingood
Jill
Karen
10/14/1992
Medicaid
29401
678543
Martin
Chloe
Mary
05/30/1978
Private
29465
234719
Martin
John
Adams
06/22/1961
Private
29401
786543
Nance
Natalie
JoAnn
11/27/1922
Medicare
29464
EBSCO : eBook Business Collection Trial – printed on 3/17/2018 4:27 PM via LOUISIANA STATE UNIV AT SHREVEPORT
AN: 667492 ; LaTour, Kathleen M., American Health Information Management Association, Eichenwald, Shirley, Oachs, Pamela K..;
08_AB103311_ch07.indd
170
Health Information
Management : Concepts, Principles, and Practice
Account: s3563253.main.ehost
12/21/12 7:21 PM
Copyright @ 2013. AHIMA Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
Data Capture, Maintenance, and Quality
patients by zip code. After the query is completed, the director of outpatient services has useful information to help identify where patients live.
The chief executive officer (CEO) is interested in patient
mix as well, but wants to see trend data over time showing
the percentage of Medicare patients vs. the percentage of
private-pay and nonpaying patients. Again, the same data set
is used, but the query process is more complex and the data
set must be linked to another data set that contains payment
information. The data in the MPI data set must be processed
more extensively before any truly useful information is available to meet the CEO’s needs. Moreover, the data from the
MPI might be used in strategic planning for the hospital.
Any number of complex queries about the patient population
could contribute to strategic marketing or development decisions. The organization might even combine the MPI data set
with external data sets using sophisticated decision support
systems (DSSs) that compare its performance with the performance of other facilities in the region or state. Decision
support systems are discussed in chapter 19.
As information systems have evolved and become more
complex, organizations are more aware of the importance
of managing electronic data among numerous information
systems. Enterprise master patient index (EMPI) systems
include the assignment of an enterprise identifier to link
health information systems together across corporations or
enterprises. This identifier works behind-the-scenes to identify a patient at the corporate level while the medical record
number or other patient identifier links patients together at
the local or facility level (AHIMA 2010b).
Knowledge Management
Some texts include a third, higher level in the data-toinformation hierarchy: knowledge (see figure 7.1). Lau
(2004, 2) defines knowledge as “information combined with
experience, context, interpretation, and reflection.” AHIMA
e-HIM Workgroup on Computer-Assisted Coding (2004, 2)
defines knowledge management as “capturing, organizing, and storing knowledge and experiences of individual
workers and groups within an organization and making this
Figure 7.1. From data to knowledge
P
r
o
c
e
s
s
i
n
g
Knowledge
(Information with context,
experience, and interpretation)
171
information available to others in the organization.” This
definition illustrates that there are two types of knowledge:
explicit and tacit. Explicit knowledge is knowledge that is
easily communicated and stored, for example, documents
and procedures. Tacit knowledge is personal knowledge
that is not easily communicated or stored. Employees’ experiences, habits, and skills are examples of tacit knowledge.
Unless employee tacit knowledge is captured and stored, it
is lost when an employee leaves the organization—hence the
need for knowledge management.
People use knowledge to make decisions. In the preceding Z46.1 example, the medical researcher might use his or
her experience (tacit knowledge), diagnostic rules (written
or not), and statistical rules (explicit knowledge) to determine the relationships between patient characteristics and the
Z46.1 diagnosis. Computer systems that combine an expert
knowledge base and some type of rule-based decision analysis component are sometimes referred to as knowledge management systems to differentiate them from more traditional
transaction-based or analytical information systems. Although
computerized DSSs in healthcare are not always knowledge
management systems, knowledge management systems are
almost always used for decision support. (See chapter 19 for
more information on decision support systems.)
The value of knowledge management in the healthcare
setting is obvious when healthcare explicit and tacit knowledge are identified. To illustrate this, consider the explicit
knowledge that may be available in the healthcare setting:
policy and procedure manuals, evidence-based research,
clinical practice guidelines, computer programs, and training
materials. This explicit knowledge is easily recorded, stored,
and shared in electronic databases or libraries. Now consider
the tacit knowledge, most of which is in the minds of individuals: employee experience, skills, judgment, and guiding
principles. The predicted large number of healthcare worker
retirements and the shortage of healthcare workers (Bureau
of Labor Statistics 2010) illustrate the need to capture, store,
and distribute the tacit knowledge of employees.
Dr. Francis Lau (2004) has developed a knowledge management framework for the healthcare environment that
addresses both explicit and tacit knowledge. It includes the
core concepts of knowledge production, use, and refinement
within a social context influenced by individual and organizational values and preferences. Figure 7.2 illustrates how
the concepts are interrelated and iterative:
●● Knowledge production includes the creation, organi-
zation, and storage of knowledge.
Information
(Useful processed data)
●● Knowledge use includes the distribution and sharing of
knowledge.
●● Knowledge refinement includes the evaluation, adap-
tation, and sustainability of knowledge.
Data
(Raw facts, images, and sounds)
Healthcare faces constant change, and that can be challenging. Consumers are more informed; there is a need to
increase efficiency, reduce costs, and improve quality; the
EBSCO : eBook Business Collection Trial – printed on 3/17/2018 4:27 PM via LOUISIANA STATE UNIV AT SHREVEPORT
AN: 667492 ; LaTour, Kathleen M., American Health Information Management Association, Eichenwald, Shirley, Oachs, Pamela K..;
08_AB103311_ch07.indd 171
12/21/12 7:21 PM
Health Information Management : Concepts, Principles, and Practice
Account: s3563253.main.ehost
172
Chapter 7
Figure 7.2. A conceptual knowledge management
framework in healthcare
Social Context (structures, values, preferences)
Copyright @ 2013. AHIMA Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
Production
integration, evaluation,
reflection,
sustainability
collection, generation,
synthesis, identification
also
Explicit/Tacit codification,
storage,
Knowledge packaging,
coordination
Refinement
Use
distribution, sharing, application, adaptation
Source: Lau 2004, 3.
industry is changing its focus from curing to preventing illnesses; there is a healthcare worker shortage; and there has
been an increase in the generation of medical data, information, and knowledge. Managing this data, information, and
knowledge is essential to addressing these changes.
Check Your Understanding 7.1
Instructions: Answer the following questions on a separate piece
of paper.
1. Give an example of data that are found in a patient medical
record. How could these data become information?
2. Explain the statement that “one person’s information can be
another person’s data.”
3. Give specific examples of explicit and tacit knowledge in
healthcare. Why would it be important to manage these?
support systems. Each of these systems contains methods
to capture critical data that can be used for patient scheduling, treatment options, reimbursement, and overall quality of
patient care. However, the methods used to capture pertinent
data must be standardized.
The Certification Commission for Health Information
Technology (CCHIT) provides criteria that an inpatient
EHR must contain in order to be certified. Some of the criteria relate to data capture. The criteria are standards that are
consistent, organized, reachable, measurable, and valid. For
example, one of the criteria states, “The system shall capture
and maintain demographic information as discrete data elements as part of the patient record” (CCHIT 2011). Examples of demographic data elements that should be captured
as discrete variables include name, address, phone number,
and date of birth. Capturing these data elements as discrete
variables means being able to reduce the data to whole numbers or some type of categorization so that patient data can
be easily queried by more than one form of identification.
Allergy information is another example of CCHIT criteria
that must be captured by the EHR. It states “The system shall
provide the ability to capture and maintain, as discrete data
the reason for inactivating or revising an item from an allergy
and adverse reaction list” (CCHIT 2011). This can include
revising, marking as erroneous, or marking as inactive rather
than deleting this information from the EHR entirely. Again,
a specific code or number can be designated so that when the
system is mined or queried, the information will be retrieved.
For example, 1 = allergy no longer valid, 2 = erroneous allergy
information, 3 = allergy is inactive. Once the data are included
as discrete or nominal variables they are easy to capture.
Data Capture
Data Maintenance
Data capture requires the development and implementation of
standard practices, policies, and procedures. There are different types of tools that can be used to capture healthcare data.
According to AHIMA (2008), data capture methods identify
which methods are permitted for use and who is permitted
to use them. For example, structured data capture should be
used for positive and negative findings, such as the documentation of positive or negative responses to questions about
past history, family history, social history, and the review of
systems. Structured text is also appropriate for documentation
of diagnostic procedures ordered and the patient’s presenting
problem. Free-text narrative is appropriate for evaluation and
management (E/M) compliance, medical necessity, history
of present illness, details about past history, clinical impressions, and treatment options or whenever there needs to be
more thorough explanations of findings.
There are many electronic health record (EHR) built-in
tools, such as data dictionaries, automated quality measures,
patient registries, electronic-referral systems (e-referrals),
electronic visit systems (e-visits), and clinical decision
It is extremely important to be able to employ effective methods to maintain the data once they are captured. Methods to
preserve the data include developing appropriate policies and
procedures for preservation of data, length of time to maintain the data, who should be responsible for data maintenance
and meeting regulations, as well as privacy and security provisions. One method of preserving data is to develop a data
dictionary that includes the data elements, variables, descriptions, data type, and format for each of the data elements collected in the EHR. According to Kallem et al. (2007), a data
dictionary is a file that defines the organization of the database. It does not contain any actual data, only information
about what is in the database so that it will be easy to preserve
and maintain. In order to effectively maintain an EHR system, data content standards are crucial. There are many data
content standards organizations such as Health Level Seven,
ASTM International, Logical Observation Identifiers, Names
and Codes (LOINC); and Systematized Nomenclature of
Medicine (SNOMED). (See chapter 8 for more information
on these organizations.) Standards like these are imperative to
EBSCO : eBook Business Collection Trial – printed on 3/17/2018 4:27 PM via LOUISIANA STATE UNIV AT SHREVEPORT
AN: 667492 ; LaTour, Kathleen M., American Health Information Management Association, Eichenwald, Shirley, Oachs, Pamela K..;
08_AB103311_ch07.indd
172
Health Information
Management : Concepts, Principles, and Practice
Account: s3563253.main.ehost
12/21/12 7:21 PM
Data Capture, Maintenance, and Quality
bridge the gap between qualitative documents and structured,
computable data (Kallum et al. 2007).
Copyright @ 2013. AHIMA Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
Data Quality
The concept of data quality is closely tied to the ability of
a healthcare information system to support decision making
at all levels of the organization. The adage “garbage in, garbage out” is true. This section discusses the importance of
establishing data quality standards and introduces two sets of
guidelines that can be used for this purpose.
How can one know when data quality has been achieved?
The quality of data is tied to the use, or application, of the
data. Again, high-quality data are the foundation of highquality information, and the value of information lies in its
application to decision making within the organization.
Consider for a moment an organization with sophisticated
healthcare information systems that affect every type of
healthcare information, from patient-specific to knowledgebased. What if the quality of the documentation going into
the systems is poor? What if there is no assurance that the
reports generated from the systems are accurate or timely?
How would the users of the systems react? Are those information systems beneficial or detrimental to the organization
in achieving its goals? (Wager et al. 2005, 43)
A healthcare organization cannot have high-quality
healthcare information without first establishing that it has
high-quality healthcare data. We know that clinical providers and administrative staff gather healthcare information.
Much of this clinical information is recorded in patient
records and subsequently coded for purposes of reimbursement and research. Poor-quality data collection and reporting
can affect patient care, communication among providers and
patients, documentation, revenue generation (due to problems with reimbursement), outcomes evaluation, research
activities, or public reporting.
The problems with poor-quality patient care data are
not strictly limited to the patient health record. In a wellcirculated report, the Medical Records Institute (MRI) identified five major functions that are affected by poor-quality
documentation (MRI 2002). These functions are found not
only at the healthcare organizational level but also throughout the healthcare environment.
Patient safety is affected by inadequate information, illegible
entries, misinterpretations, and insufficient interoperability.
Public safety, a major component of public health, is
diminished by the inability to collect information in a coordinated, timely manner at the provider level in response to
epidemics and the threat of terrorism.
Continuity of patient care is adversely affected by the
lack of shareable information among patient care providers.
Healthcare economics are adversely affected, with
information capture and report generation costs currently
estimated to be well over $50 billion annually.
173
Clinical research and outcomes analysis are adversely
affected by a lack of uniform information capture that is
needed to facilitate the derivation of data from routine patient
care documentation. (MRI 2002, 2)
The MRI report identifies healthcare documentation as
having two basic parts: information capture and report generation. Information capture is “the process of recording
representations of human thought, perceptions, or actions in
documenting patient care, as well as device-generated information that is gathered and/or computed about a patient as
part of healthcare” (MRI 2002, 2). Some means of information capture in healthcare organizations are handwriting,
speaking, word processing, touching a screen, pointing and
clicking on words or phrases, videotaping, audio recording,
and generating digital images through x-rays and scans.
Report generation “consists of the formatting and/or structuring of captured information. It is the process of analyzing, organizing, and presenting recorded patient information
for authentication and inclusion in the patient’s healthcare
record” (MRI 2002, 2). In order to have high-quality documentation that results in high-quality data, both information
capture and report generation must be considered.
Data Quality Standards
Before an organization can measure the quality of the information it produces and uses, it must establish data standards.
That is, data can be identified as high-quality only when they
conform to a recognized standard. Ensuring this conformance
is not as easy as it might seem because no universally recognized set of healthcare data quality standards exists today. One
reason for this is that the quality of the data needed in any situation is driven by how the data or the information that comes
from the data will be used. For example, in a patient care setting the margin of error for critical lab tests must be zero or
patient safety is in jeopardy. However, a larger margin of error
may be acceptable in census counts or discharge statistics.
Healthcare organizations must establish data quality standards
specific to the intended use of the data or resulting information.
Although no universally adopted healthcare data quality
standards exist, two organizations have published guidance
that can assist healthcare organizations in establishing their
own data quality standards. In Healthcare Documentation: A
Report on Information Capture and Report Generation, the
MRI (2002, 9) has published a set of “essential principles of
healthcare documentation,” and the American Health Information Management Association (AHIMA) has published
the data quality management model (Wager et al. 2005).
MRI Principles of Healthcare
Documentation
AHIMA defines documentation as “the methods and activities of collecting, coding, ordering, storing, and retrieving
information to fulfill future tasks” (AHIMA 2007, 66). The
EBSCO : eBook Business Collection Trial – printed on 3/17/2018 4:27 PM via LOUISIANA STATE UNIV AT SHREVEPORT
AN: 667492 ; LaTour, Kathleen M., American Health Information Management Association, Eichenwald, Shirley, Oachs, Pamela K..;
08_AB103311_ch07.indd 173
12/21/12 7:21 PM
Health Information Management : Concepts, Principles, and Practice
Account: s3563253.main.ehost
174
Copyright @ 2013. AHIMA Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
Figure 7.3.
Chapter 7
MRI Consensus Workgroup Essential Principles of Healthcare Documentation
For optimal information capture and report generation, it is important to establish a set of documentation principles to be implemented on
a national/international basis. This report recommends that all healthcare documentation must meet the following “Essential Principles of
Healthcare Documentation.”
• Provide for automatic, unalterable time, date, and place stamp
Unique identification of patient
of each:
Systems, policies, and practices should:
—— Documentation entry, such as dictation, uploading, scanning
• Provide unique identification of the patient at the time of
(original, edits, amendments).
recording or accessing the information.
—
—
Access to the documentation.
• Provide within and across organizations:
—
—
Transmittal of the documentation.
—— Simple and easy methods to identify individuals and correct
duplicate identities of the same individual.
—— Methods to distinguish among individuals, including those
with similar names, birth dates, and other demographic
information.
—— Linkages between different identifications of the same
individual.
Accuracy
Systems, policies, and practices should:
• Promote accuracy of information throughout the information
capture and report generation processes as well as during its transfer
among systems.
• Require review to assure accuracy prior to integration in the
patient’s record.
• Include a means to append a correction to an authenticated
document, without altering the original.
• Require the use of standard terminology so as to diminish
misinterpretations.
Completeness
Systems, policies, and practices should:
• Identify the minimum set of information required to completely
describe an incident, observation, or intent.
• Provide means to ensure that the information recorded meets
the legal, regulatory, institutional policy, or other requirements
required for specific types of reports (for example, history and
physical, operative note).
• Link amendments to the original document (that is, one
should not be able to retrieve an original document without
related amendments [or vice versa] or notification that such
amendments exist and how to access them).
• Discourage duplication of information.
• Discourage nonrelevant and excessive documentation.
Timeliness
Systems, policies, and practices should:
• Require and facilitate that healthcare documentation be done
during or immediately following the event so that:
—— Memory is not diminished or distorted.
—— The information is immediately available for subsequent care
and decision making.
• Promote rapid system response time for entry as well as
retrievability through:
—— Availability and accessibility of workstations.
—— User-friendly systems and policies that allow for rapid user
access.
Interoperability
Systems, policies, and practices should:
• Provide the highest level of interoperability that is realistically
achievable.
• Enable authorized practitioners to capture, share, and report
healthcare information from any system, whether paper- or
electronic-based.
• Support ways to document healthcare information so that it can
be correctly read, integrated, and supplemented within any other
system in the same or another organization.
Retrievability (the capability of allowing information to be found
efficiently)
Systems, policies, and practices should:
• Support achievement of a worldwide consensus on the structure
of information so that the practitioner can efficiently locate
relevant information. This requires the use of standardized
titles, formats, templates, and macros, as well as standardized
terminology, abbreviations, and coding.
• Enable authorized data searches, indexing, and mining.
• Enable searches with incomplete information (for example, wild
card searches, fuzzy logic searches).
Authentication and accountability
Systems, policies, and practices should:
• Uniquely identify persons, devices, or systems that create or
generate the information and that take responsibility for its
accuracy, timeliness, etc.
• Require that all information be attributable to its source (that is,
a person or device).
• Require that unsigned documents be readily recognizable as such.
• Require review of documents prior to authentication.
“Signed without review” and similar statements should be
discouraged.
• Auditability
Systems, policies, and practices should:
• Allow users to examine basic information elements, such as data
fields.
• Audit access and disclosure of protected health information.
• Alert users of errors, inappropriate changes, and potential
security breaches.
• Promote use of performance metrics as part of the audit
capacity.
EBSCO : eBook Business Collection Trial – printed on 3/17/2018 4:27 PM via LOUISIANA STATE UNIV AT SHREVEPORT
AN: 667492 ; LaTour, Kathleen M., American Health Information Management Association, Eichenwald, Shirley, Oachs, Pamela K..;
08_AB103311_ch07.indd
174
Health Information
Management : Concepts, Principles, and Practice
Account: s3563253.main.ehost
12/21/12 7:21 PM
Data Capture, Maintenance, and Quality
• Confidentiality and Security
• Demonstrate adherence to related legislation, regulations,
guidelines, and policies throughout the healthcare
documentation process.
• Alert the user to potential confidentially and security breaches.
RECOMMENDATION #1: Fund, create, and promote a
practical implementation guide for the dissemination, teaching,
and adoption of the “Essential Principles of Healthcare
Documentation” by practitioners, providers, vendors, and
healthcare organizations, as well as regulatory bodies and
medical schools.
Source: MRI 2002.
Table 7.2. Documentation styles and major information
capture methods
Structured
Text
Interactive
Text
Mostly free
text
Mostly free
text
Free text
Paper forms
N/A
Macros and
normals
Templates
N/A
Free text
Mostly
structured
text
Free Text
Handwriting
Transcription
Speech
Recognition
Direct Input
Source: MRI 2002.
Interactive
templates
More
interactive
For example, when the patient’s insurance type is
recorded as Security Blue, it is accurately recorded as
Security Blue and not Medical Assistance.
●● Accessibility: Data items should be easily obtainable
and legal to access with strong protections and controls
built into the process.
●● Comprehensiveness: All required data items are
included. Ensure that the entire scope of the data is
collected and document intentional limitations.
●● Consistency/Reliability: Data quality needs to be consistent and reliable. For example, if a patient’s blood
Figure 7.4. AHIMA data quality management model
Ap
pli
ca
t
t
lec
ol
ion
Data Quality
Wa
AHIMA (2012) has published a data quality model and an
accompanying set of general data characteristics. The model
is used as a framework for the design of management processes and data quality measures. There are some similarities
between the AHIMA characteristics and the MRI essential
principles (refer to figure 7.3). However, one difference is
●● Accuracy: Data that are free of errors are accurate.
C
AHIMA Data Quality Model
that AHIMA strives to include all healthcare data and limits
characteristics to clinical documentation. (See figures 7.4
and 7.5.)
The AHIMA (2012) model includes the following
10 characteristics of data quality:
reh
i
ous
lys
i ng
Ana
Characteristics of Data Quality
s
MRI report states that many steps must be taken to ensure
the quality of healthcare documentation (and, thus, the quality of healthcare data). It lists the essential principles to
which healthcare organizations should adhere as they establish healthcare documentation and information systems (and
their accompanying policies). (See figure 7.3.) The MRI
recommends that these principles be uniformly adopted by
healthcare organizations.
It is noteworthy that the MRI takes the position that when
practitioners interact with electronic resources, their ability
to adhere to these principles is increased. All documentation
records data and information, which need to be retrieved in
order to be used. The MRI argues that all healthcare information should be indexed “to facilitate both clinical and
administrative retrieval” (MRI 2002, 16). This is difficult to
do with unstructured, free text, such as handwriting, e-mails,
and transcription. As electronic medical records are implemented and information capture methods become more
interactive, the ability to retrieve information will improve.
Table 7.2 shows documentation styles and major information
capture methods.
n
io
Copyright @ 2013. AHIMA Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
Systems, policies, and practices should:
175
• Accessibility
• Consistency
• Currency
• Granularity
• Precision
• Accuracy
• Comprehensiveness
• Definition
• Relevancy
• Timeliness
Data Quality Management Domains
Application The purpose for which the data are collected.
Collection
The processes by which data elements are
accumulated.
Warehousing Processes and systems used to archive data and
data journals.
Analysis The process of translating data into information
utilized for an application.
Source: AHIMA 2012.
EBSCO : eBook Business Collection Trial – printed on 3/17/2018 4:27 PM via LOUISIANA STATE UNIV AT SHREVEPORT
AN: 667492 ; LaTour, Kathleen M., American Health Information Management Association, Eichenwald, Shirley, Oachs, Pamela K..;
08_AB103311_ch07.indd 175
12/21/12 7:21 PM
Health Information Management : Concepts, Principles, and Practice
Account: s3563253.main.ehost
176
Chapter 7
Figure 7.5. AHIMA characteristics of data quality
Copyright @ 2013. AHIMA Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
Data Quality Management Model Domains and Characteristics
Characteristic
Application
Collection
Warehousing
Analysis
Data Accuracy
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.
Ensuring accuracy involves
appropriate education and
training and timely and
appropriate communication
of data definitions to those
who collect data. The
applications should constrain
entry to allowable values
where possible.
To warehouse data,
appropriate edits should be in
place to ensure accuracy, such
as basic field length checks.
To accurately analyze data,
ensure that the algorithms,
formulas, programming,
and translation systems are
correct.
For example, data
entry of height into
EHRs should flag or
highlight very small
(less than 12 inches)
or very tall (over
7 feet) heights.
For example, data accuracy
will help ensure that a
patient height cannot be
entered erroneously as
5 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).
The extent to which
the data are free of
identifiable errors.
Also, error reports are
generated related to transfers to For example, ensure
that the encoder assigns
and from the warehouse.
correct codes and that
All warehouses should have
the appropriate DRG is
a correction and change
assigned for the codes
management policy to track any
entered.
changes.
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.
Access to complete,
Technology and hardware
When developing the data
collection instrument, explore impact accessibility. Establish current data will
data ownership and guidelines better ensure accurate
methods to access needed
for who may access or modify analysis and data
data and ensure that the
data and/or systems. Inventory mining. Otherwise
best, least costly method
results and conclusions
data to facilitate access.
is selected. The amount
may be inaccurate or
of accessible data may be
In the EHR it may be
inappropriate.
increased through system
advisable to establish data
interfaces and integration of
For example, use of
ownership or governance
systems.
the Medicare case mix
at the data element level,
For example,
index (CMI) alone does
especially data which are
recording the date of For example, the best and
re-used. For example, allergies not accurately reflect
easiest method to obtain
birth and race in the
are recorded by many different total hospital CMI.
demographic information
EHR is appropriate
Consequently, strategic
clinicians and come in many
and should only occur may be to obtain it from an
planning based solely on
forms. Who defines what an
once with verification. existing system. Another
Medicare CMI may not be
method may be to assign data allergy is? How does this
Then, the values
appropriate.
impact the use of allergies
collection by the expertise
should roll forward.
in the EHR, especially for
of each team member. For
example, the admission staff clinical decision support?
collects demographic data,
the nursing staff collects
symptoms, and the HIM staff
assigns codes.
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.
Data entry should undergo a
cost-benefit analysis process
to determine which method
provides the best data most
efficiently.
EBSCO : eBook Business Collection Trial – printed on 3/17/2018 4:27 PM via LOUISIANA STATE UNIV AT SHREVEPORT
AN: 667492 ; LaTour, Kathleen M., American Health Information Management Association, Eichenwald, Shirley, Oachs, Pamela K..;
08_AB103311_ch07.indd
176
Health Information
Management : Concepts, Principles, and Practice
Account: s3563253.main.ehost
12/21/12 7:21 PM
Data Capture, Maintenance, and Quality
177
Copyright @ 2013. AHIMA Press.
All rights reserved. May not be reproduced in any form without permission from the publisher, except fair uses permitted under U.S. or applicable copyright law.
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 costbenefit or impact
study when collected
data are increased.
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.
The use of data definitions,
extensive training,
standardized data collection
(procedures, rules, edits,
and process) and integrated/
interfaced systems facilitate
consistency.
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.
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.
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.”
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.
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).
This is especially
important when EHR
clinical decision
support is utilized.
Incomplete data can
result in underreporting a
numerator or denominator.
For example, in
addition to outcome
it may be important
to gather data that
impact outcomes.
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.
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 some
preceding time yet
incorrect at a later
time.
The appropriateness
or value of an
application changes
over time.
In EHRs it is
imperative that
the guidelines and
algorithms be up-todate. For example,
acceptable blood
pressure ranges have
lowered, as have
target HbA1C levels.
The availability of current
data impac…