Guidelines for Health IT Addressing the Quality of Data in EHR
Information Systems
Nabil Georges Badr
a
Higher Institute for Public Health, USJ, Lebanon
Keywords: Data Quality, Electronic Health Records, Electronic Health Records Information Systems, Medical
Informatics.
Abstract: Quality of patient care is dependent on the quality of patient healthcare data. Electronic Health Record
Information Systems (EHR-IS) capture patient health data for diagnosis, treatment, testing, medication and
patient support. Issues in healthcare data quality comprise missing, incorrect, imprecise, and irrelevant data.
Stakeholders of health data from practitioners, to patients, governments and lawmakers have long concerned
themselves with these issues. Our paper looks at data quality in healthcare from the locus of ensuing risks,
challenges and approaches in the literature. The paper proposes a reference for designing Electronic Health
Record Information Systems and the evaluation of data quality in EHR-IS implementations.
1 INTRODUCTION
Electronic health record information systems are the
components of IS that capture, manage and host the
data in an electronic health record (EHR). Electronic
health record information systems (EHR-IS) rely on
datasets that include medical diagnoses, allergies,
demographics and laboratory test results,
computerized provider order entry for prescriptions,
decision systems, rule based alerts and reminders,
etc., and provide reporting and population health
management through secondary use of data for
research and disease control (Hoffman and
Podgurski, 2008).
For four decades, quality of data in the medical
record attracted medical researchers (Feinstein,
1970). The concept of a central database for health
information, assigning trusted sources of data in a
consolidated view of what was referred to as
“integrated clinical databases” (Kahn, 1997).
Unchecked data sources present quality risk
factors to patient care introduced by human system
errors, implementation issues and lack of standards.
These data sources must withstand quality
maintenance approaches to maintain desired levels of
data quality and standards to be able to remove the
impediments and maintain a basis of quality in the
a
https://orcid.org/0000−0001−7110−3718
data (Win et al., 2002). Adopters of EHR-IS have
made it obvious that quality of patient care is
dependent on the quality of healthcare data (Jones and
Blavin, 2013). Jha et al 2008 explain that it is difficult
for hospitals to obtain quality data in EHR-IS which
are reliant on data prone to potential mistakes. Data
quality concerns could be introduced by device borne
issues of connectivity, synchronization, volume of
data captured (Yao et al., 2011) and data formatting
(Karkouch et al., 2016) in connected or wireless data
capture devices (Zafar, 2017). Governments, policy
makers and standards bodies have supported the
development of architectures and guiding principles
for semantically interoperable infrastructures, such as
Health Level Seven (HL7) (Dolin and Alschuler,
2011) for the purpose of quality data exchange (Yun
and Kim, 2007). Practitioners are seeking and
adopting technologies to lessen the chances of errors,
such as wireless handheld devices with for timely
access to data entry and retrieval, calculation
assistance for prescription dosage aimed at error
reduction (Lu et al., 2005). Implementations of
features of EHR systems such as closed loop
medication administration have attracted significant
attention, due to the serious looming risk of
prescription errors (Singh et al., 2009). A lack of
prudence of a physician might impact downstream
healthcare quality; an error at this stage, may not
Badr, N.
Guidelines for Health IT Addressing the Quality of Data in EHR Information Systems.
DOI: 10.5220/0006941001690181
In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019), pages 169-181
ISBN: 978-989-758-353-7
Copyright
c
 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
169

show up until much later, and cause potentially a
patient health risk. Patients are advised to be aware of
this trend and take steps to ensure the accuracy of
their medical records (Ash et al., 2004).
What is data quality in Healthcare IT? Can
HealthCare IT address the quality of data in EHR
information systems?
1.1 Approach
In order to help answer these questions, the paper
reviews literature and practice publication in an
exploratory style in an attempt to suggest potential IT
architectural guiding principles for addressing issues
of data quality in EHR-IS. First, we define
dimensions for data quality relative to the context of
Electronic Health Record systems. For that, we use
the reference work on data quality by Wang and
Strong (1996). These dimensions will help guide our
exploration in the context of EHR-IS. Then, based on
these dimensions, we conduct our literature review to
explore data quality issues and ensuing risks in EHR-
IS implementations. We perform our search in
Google Scholar using a search filter of "data quality
issues" AND "electronic health record systems" AND
"risks", for publications dated since 1970. The query
returned 517 results encompassing publications in the
field of health informatics, namely the Journal of the
American Medical Informatics Association and the
International Journal of Medical Informatics,
Perspectives in Health Information Management,
Health Informatics Journal, MIS quarterly, Health
Information Science and Systems and others. The
search was limited to articles written in the English
language. After screening, we identify the articles
relevant to the study, selected based on their direct
relevance to the subject of data quality in electronic
health record systems, avoiding duplication in
findings among publication and prioritizing literature
review articles for their broad coverage of the subject.
Articles are categorized under the four dimensions of
Wang and Strong (1996) with a focus on data quality
issues. We include literature on data quality
assessment (Pipino et al., 2002; Weiskopf and Weng,
2013) and relevant practitioner journals such as
Health Affairs, triangulated with federal agency
bulletins concerned with the progress of innovations
in health IT. For our final representation of a practical
framework of guiding principles aimed at addressing
issues of Data Quality in EHR-IS, we borrow from
the four fundamental layers of data standards of
content, structure, technology, and organization, a
model introduced by Bott (2004). Our aim is not to
present an exhaustive set of principles, but rather to
under-score essential higher order guiding principles.
These Meta principles are proposed as unalienable
fundamental guidelines for developers and
implementers of EHR-IS, in instances and best
practices that maintain higher levels of Data Quality.
2 BACKGROUND
Healthcare practitioners identify an essential need for
“quality” in data collection systems to address
challenges in improving quality in healthcare (Dixon-
Woods et al., 2012). Data quality in EHR/EMR
systems holds first place in interest, importance and
relevance in electronic health care research (Coleman
et al., 2015). Patient safety and quality of care are
directly related to the quality of data in the healthcare
ecosystem (Gallego et al., 2015). More specifically,
data quality issues in healthcare were found to
comprise missing, incorrect, imprecise, and irrelevant
data (Mans et al., 2015). Other data quality concerns
for standardized EHR-IS are validity, believability,
accessibility, security, timeliness, completeness,
interpretability, ease of understanding, and
consistency (Orfanidis et al., 2004).
2.1 Dimensions of Data Quality
Data quality points to the fitness for the data to be
used (Juran, 1988). Data quality has been given
categories and dimensions (Wang and Strong 1996).
At the foundation of data quality (DQ) concepts in the
perspective of IS, Wang and Strong, 1996 have
suggested that “High quality data should be
intrinsically good, contextually appropriate, clearly
represented and accessible to data consumers”
(Wang and Strong 1996, p. 6).
For this paper, we use the four dimensions of
Wang and Strong (Table 1) as a springboard to
examine data quality issues in healthcare and provide
a summary of remedies seen in the literature and in
practice. Other researchers have built upon this
framework owing to its high degree of inclusiveness
of essential attributes of data quality that are
important to data consumers in broad contexts (Pipino
et al., 2002) and specifically in medical informatics
(Weiskopf and Weng, 2013).
Wang and Strong characterise data as having
quality in their own right referring to it as intrinsic
data quality. Intrinsic data quality dimensions stress
attributes of accuracy and objectivity (data is error
free and represents no bias), and believability and
reputation (relating to the source of data).
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Wang and Strong also stipulate that data quality
must be considered within the context of the task on
hand. Contextual data quality attributes relate to
completeness and timeliness (levels of relevancy,
value-added, and amount of data). Two other
dimensions (Representational and Accessibility DQ)
relate to fundamental information system (IS)
functions that manipulate data. These dimensions
address attributes of quality in data related to a
concise and consistent representation (data
formatting), maintaining interpretability and
understanding (meaningful data) and accessibility. IS
professionals may argue that data access
authorization is to be factored into data quality,
however, it is increasingly evident that data access
authorization (i.e. security) as a constituent of data
privacy (Wickramage et al., 2017).
Table 1: Aspects of data quality that are important to data
consumers (Wang and Strong 1996, p. 6).
Essential Attributes of the four Dimensions of DQ
Intrinsic DQ
(Data have quality in their own right)
 Accuracy (also. correctness): Data are error free
 Objectivity: Data represent no bias
 Believability and reputation (also. credibility): Data are
from a trusted source
Contextual DQ
(Data quality is to be considered within the context of the task)
 Relevancy: Data are current and provide value
 Completeness: Data are in the right amount for the need
 Timeliness: Data are available at the right time
Representational DQ
(Emphasize the importance of the role of systems)
 Data Concordance (Concise / consistent
representation): Data have no mismatch between
sources or tables of data
 Interpretability and understanding: Data are
meaningful, with no ambiguity
Accessibility DQ
(Emphasize the importance of the role of systems)
 Accessibility: Data are readily available for use by the
consumer
For the context of this paper, we presume that
accessibility implicitly refers to accessibility through
authorised means and will focus mainly on the level
of access to important data for the task.
3 FINDINGS AND DISCUSSION
3.1 Data Quality in Health Informatics
The literature on data quality in relation to electronic
health record systems is not profuse. Studies
represent healthcare data quality as a
multidimensional construct, with the most used
dimensions being completeness, accuracy,
correctness, consistency and timeliness (Liaw et al.,
2012). The literature review shows a great deal of
variability and overlap in the terms used for quality
attributes (Weiskopf and Weng, 2013). For instance,
“accuracy” was found sometimes to be used as “a
synonym for correctness, but in other articles meant
both correctness and completeness” (Weiskopf and
Weng, 2013, p.145). Nevertheless, it remains that the
most frequently studied attributes of data quality in
healthcare are of “credibility and “accuracy” (Leite et
al., 2015). Yet, there seems to be a level disparity on
what attribute is priority and on what the prevalent
definitions of data quality attributes could be
(AHIMA, 2013). Of these, incompleteness (missing
information) and inconsistency (information
mismatch between sources or tables of data) for
example, which render the specific patient records
unusable (Mikkelsen and Aasly, 2005), were
sometimes reclassified under the attribute of
“accuracy” (Gendron and D’Onofrio, 2001; Hristdis,
2009). Further, dimensions of data quality are
interrelated (De Amicis et al., 2006). The analysis of
interdependencies of dimensions of data quality has
shown trade-offs among these dimensions. For
instance, the improvement of timeliness could
adversely affect the accuracy (Ballou and Pazer,
1995); various degrees of data completeness may
affect consistency (Ballou and Pazer, 1995). Yet, the
literature review has informed this study of
interesting concepts.
The following sections provide a more succinct
classification of principal data quality attributes in the
context of healthcare informatics as found in the
literature, organized according to the four dimensions
of the framework of Wang and Strong. Suggested
approaches to address data quality issues are also
proposed. Section 3.6 introduces risk factors
associated with Data Quality, and section 3.7
produces a framework of Guiding Principles for
addressing issues of Data Quality in EHR-IS.
3.2 Intrinsic Data Quality
At a glance, in the first dimension, the intrinsic data
quality dimension (Table 2), our review of the
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literature has identified concepts of user entry of
incorrect data (Wang et al., 2015; Mans et al., 2015)
and errors in data transcription and data translation
(Meystre et al., 2008) were identified to affect data
accuracy and correctness in EHR-IS. These findings
reinforce notions that data have quality in their own
right and that quality could erode due to misuse and
potentially improve through corrective action, often
not so obvious. Objectivity, which can reflect user
bias and assumptions in data entry could be corrected
by comparing the data with other patients or historical
values (Bayley et al., 2013).
Table 2: Challenges Affecting Intrinsic DQ.
Challenges Affecting…
Accuracy (also Correctness)
 User entry of incorrect data / Errors in data
transcription and data
Objectivity
 Validity issues – Corrected by comparing the data
with other patients or historical values
Believability and reputation (also Credibility)
 Undetected issues are repeated causing loss of
credibility
 Believability issues (e.g. unrealistic blood
pressure); often more difficult to distinguish
Believability issues in data quality sometimes show
as easy to detect oddity that could be addressed (e.g.
unrealistic blood pressure), others, are harder to
detect and are still more difficult to correct. Data
provenance information of data can improve
believability (Gendron and D’Onofrio, 2001).
Undetected issues can be repeated causing loss of
credibility. Error reduction principles have focused
on regulations concerning the effective use of
technologies (Lu et al., 2005; Aimé et al., 2015).
These regulations and associated best practices
introduce concepts for error handling, normalization
and terminology mapping included in system design
(Dolin and Alschuler, 2011). Alerting functions for
incorrect data entry are recommended (Moss and
Berner, 2015; Qureshi et al., 2015). In order to reduce
the chances of error due to language barriers
(Blumenthal and Tavenner, 2010), the use of local
terminologies is suggested with a lexicon built into
the system to map terminologies used to standard
dictionaries for interoperability (Aimé, et al., 2015).
Practices involving data comparison with other
patients or values the patient history, was found to
improve the objectivity of the data collected (Bayley
et al 2013). On the other hand, data credibility is
sustained when the data collection associates data
provenance information (Gendron and D’Onofrio,
2001).
Hence, we summarize suggested approaches to
address Intrinsic DQ issues, namely, as follows:
 Establish regulations concerning technologies
for error reduction / normalization
 Ensure that error reduction and terminology
mapping included in system design.
 Implement alerting functions as a warning for
incorrect data entry
 Use of local terminologies in standard
dictionaries for interoperability
 Encourage practices of data comparison with
patients / values from patient history
(objectivity)
 Stress the importance of data provenance
information (traceability)
3.3 Contextual Data Quality
Our review has isolated data quality challenges
pertaining to the second dimension that treats the
contextual data quality dimension (Table 3), a
function on how data elements are collected, treated
and manipulated. Challenges to attributes of
contextual data quality relate to maximizing the use
of structured data for accurate interpretation.
Table 3: Challenges Affecting Contextual DQ.
Challenges Affecting…
Relevancy
 Insufficient information content of data
 Varying levels of IT literacy among care team
Completeness
 Missing / Omitted data (lack of time for data
entry)
Timeliness
 Timely collection and available data
 Delay in data entry by practitioners, nurses and
labs often due to workload
 System introduced delays / synchronization with
separate systems / distributed databases.
 Operational issues hinder timely data entry
The literature review shows that data quality in
EHR systems may withstand irrelevant (Mans et al.,
2015), insufficient and incomplete data which in
some instances could be due to lack of time for data
entry (Bayley et al., 2013). The use of classifications
and controlled vocabularies normalize the data
collected (Hennessy et al., 2013) and a versioning
capable repository keeps the proper data context
(Dolin and Alschuler, 2011).
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Timeliness of data was indicated as an important
attribute for high quality EHR systems as it directly
relates to quality of patient care (Liaw et al., 2012).
Timely collection and accumulation of available data
is a foundation data quality for quality of care (Hopf
et al., 2014). Delay in data entry by practitioners,
nurses and labs due to workload could negatively
affect timeliness and eventually lead to data
incompleteness if never performed, forgotten or fell
victim to data literacy levels of the data entry
collaborator (Lluch, 2011). Further, the literature
emphasizes changes to processes and that reduce
workload issues to avoid delay in data entry (Hopf,
2014). Hosting systems in multiple location, with
different standards for database synchronization
(Srour and Badr, 2017) could introduce a negative
effect on data timeliness and data quality (Willis-
Shattuck et al., 2008).
Therefore, we could summarize suggested
approaches to address
Contextual DQ issues, namely,
as follows:
 Maximize the use of structured data for accurate
interpretation.
 The use of classifications and controlled
vocabularies to normalize the data collected
 The use of versioning capable repository in
order to keep the proper data context
 Emphasis on changes to processes that reduce
workload to avoid delay in data entry
3.4 Representational Data Quality
The literature reviewed included challenges affecting
Representational data quality attributes (Table 4),
depicted as the third dimension, to emphasize the
importance of the role of systems functions which are
largely discussed in the context of data consistency,
and include prescriptive guidance on the need of data
accreditation standards for the removal of
inconsistency and duplication. Data could be
collected from multiple sources in the ecosystem,
even from remote mobile sensors in the use of
telemedicine (Hennessy et al., 2013). This creates
opportunities for inconsistencies in data. Multiple site
implementations (Liaw et al., 2012) and data gathered
from different sources that may use conflicting
standards for data representation (Gendron and
D’Onofrio, 2001), structure (Bott, 2004) and
definition (Bayley et al 2013) contribute greatly to
potential of inconsistencies.
System design flaws could introduce data
corruption (Hoffman and Podgurski, 2008; Phillips
and Fleming, 2009) and information mismatch
between sources or tables of data. Attributes of
interpretability (meaningful data with no ambiguity)
refer to the need to maintain user-friendliness and
proper functionality of system interfaces (Jones et al.,
2011; Phillips and Fleming, 2009) and reduce
imprecise or ambiguous metadata (Mans et al., 2015).
Ensuring interoperability through the definition of
standardized terminologies is essential to remove
ambiguity and maintain interpretability (Murff et al.,
2011; Bayley et al 2013). Discrepancies between data
fields must be virtually eliminated in order to reduce
issues with interpretability (this is improved using
constructed data sets for the user to choose from and
avoid the use unstructured text). Fundamentals based
on architectural models for semantic interoperability,
initiatives and standards are imperative to counter
data quality risk factors in human and systems
implementation errors. Well-defined ontological
foundations address semantic interoperability,
clinical decision support and complexity of
information systems models (Liaw et al., 2012).
Table 4: Challenges Affecting Representational DQ.
Challenges Affecting…
Data Concordance (Concise / consistent
representation)
 Lack of standardized terminologies
 Data corruption due to system bugs
 Data mismatch from different sources / multisite
implementations / different standards
 Data accreditation standards needed for the
removal of inconsistency and duplication
 System design flaws introduce data corruption
 Varying standards of data structure (level of
structured data implementation) 
Interpretability /Understanding (meaningful, with no
ambiguity)
 Discrepancies between data fields
 Complicated with use of unstructured text
 Interoperability - Standardized terminologies
 System interface problems
 Lack of user-friendly functionality
 Imprecise data or ambiguous metadata 
Thus, approaches to address representational DQ
pivot around:
 Implementing principles of standardization with
reference to best practices that address systems
and implementation issues
 Addressing semantic interoperability, clinical
decision support and complexity of information
systems models
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3.5 Accessibility Data Quality
Lastly, grouping challenges that affect the availability
of data into the fourth dimension, the essential
attribute of accessibility data quality (Table 5). This
dimension mandates a requirement of easy to use
interfaces (Magrabi et al., 2015), availing the user to
different data formats (Häyrinen et al., 2008), through
any system and from any location. A vulnerability can
be observed due connectivity and system availability
issues (Gendron and D’Onofrio, 2001; Bayley et al.,
2013). In an era of patient centered healthcare,
patients have control over who has access to their data
and in what circumstances (Cimino et al., 2002). The
implementation of easy to use portals and interfaces
(Gendron and D’Onofrio, 2001) should maintain
accessibility in different data formats, through any
system and from any location with secured portals for
patients to control access to their records (Häyrinen et
al., 2008). Accessibility constraints should limit
different levels of users to access the data based on
their credentials and must maintain compliance with
HIPAA (Health Insurance Portability and
Accountability Act of 1996) guidelines to ensure the
security and privacy of data. Limited authorizations
to data access can also contribute to data quality by
limiting the chances of erroneous data entry.
Table 5: Challenges Affecting Accessibility DQ.
Challenges Affecting…
Accessibility (Data readily available for use by
consumer)
 Vulnerable to system availability issues
 Ease of use issues hindering access to data
 Different levels of authorized users access
 Different data formats, systems and locations
 Lack of standardized terminologies
Recommended Approaches…
 Conducting thorough testing of EHR-IS
applications for usability
 Implementing easy to use portals / interfaces
maintain accessibility in different data formats,
 Providing levels of authorized users in compliance
with HIPAA privacy guidelines
 Limiting authorizations to data access, limiting
chances of erroneous data entry.
Therefore, in order to address accessibility DQ
issues the literature indicates:
 Conducting thorough testing of EHR-IS
applications for usability
 Implementing easy to use portals / interfaces
maintain accessibility in different data formats,
 Providing levels of authorized users in
compliance with HIPAA privacy guidelines
 Limiting authorizations to data access, limiting
chances of erroneous data entry.
3.6 EHR-IS Data Quality Risk Factors
This section looks at the literature to identify salient
risk factors associated with data quality variations
introduced by human or system errors in EHR-IS.
3.6.1 Human and User Errors
EHR-IS are reliant on data prone to potential mistakes
emanating from accidental errors in data entry (Wang
et al., 2015), in medication dosage (Kaushal et al
2003), in data transcription or even in translation,
such as in transcripts of voice recognition dictation
system (Meystre et al., 2008). Data errors such as data
that have been compromised, partially transferred
between interconnected systems, wrongfully
translated, entered in error or mixed up with someone
else’s, etc. present an issue with data quality and a risk
on patient safety (Barkhuysen et al 2014). Easy to use
interfaces with predefined archetypes could alleviate
impending risks of data entry. Other risks could be
caused by errors of data omission (Phillips et al.,
2009). This risk could manifest in the form of patient
safety and quality of care (Gallego et al., 2015), costly
medical malpractice liability (Mangalmurti and
Mello, 2010), and or health threatening prescription
errors (Singh et al., 2009), especially if multiple
repeated events are incurred before such issues are
detected. Secured portals have enabled patients to
control who can have access to their data (Cimino et
al., 2002). Patient engagement practices bring forth
potentials for enhancing the quality of care. Policies
and procedures related to record management are
required to sustain accuracy, integrity, and quality in
patient records, especially in such situations where
patient data entries are permitted and incorporated
into the record (Bonomi et al., 2016).
3.6.2 System and Data Errors
Analysis and design of data quality issues are an
integral part of the development of an EMR system
(Orfanidis et al., 2004). Problems involving human
factors were found four times as likely to result in
patient harm as technical problems; Nevertheless,
EMR system failures such as migration of records
between systems, power failures, computer viruses
and messaging failures, etc. were found to account for
the majority of IT related EMR events (Magrabi et al.,
2015). IS practitioners are urged to address safety
concerns unique to EMR technology in the contexts
of EHR-enabled health care (Rea, et al 2012). At the
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time of their research, (Bates, et al., 2003) found, that
nearly half of serious medication errors related to the
fact that clinicians had insufficient information about
the patient and the medication prescriptions (Fleming,
et al 2011), possibly due to varying levels of IT
literacy among care team (Lluch, 2011). Ease of use
is fundamental for risk reduction in EMR-IS.
“Usability errors occur as a result of system
complexity, lack of user-friendly functionality (e.g.,
confusing user interfaces) or workflow automation
incompatibility (Phillips and Fleming, 2009).
Vendors of EMR systems often add functionalities to
their interface design to assist with support and
documentation (Weir et al., 2003), such as copy and
paste, templates, use of standard phrases and
paragraphs, and automatic object insertion (e.g.,
clinical values brought in from other parts of the
electronic record). However, when used without
proper education and controls, “these features can
lead to inaccurate documentation and potentially
result in medical errors or allegations of fraud”
(AHIMA (2012). On the other hand, templates can
guide documentation so that elements essential to
demonstrating appropriate care are not ignored. Such
features could improve the efficiency of data capture,
timeliness and legibility, and consistency and
completeness of documentation (Reed et al., 2012).
In some unfortunate cases, templates automatically
fill in data elements based on certain patient
characteristics or other data entries, even though this
default information is not an accurate representation
of that particular patient encounter (Bowman, 2013).
Embedded clinical-decision support (CDS)
systems are prone to human error and cognitive
constraints (Sittig and Singh, 2012). Clinical decision
support systems can still give wrong clinical advice
even when designed and implemented according to
high-quality standards, and is working as intended
(Garg et al., 2005). Electronic records replacing
paper-based records have introduced what is referred
to as “adjacency error,” in which a provider selects an
item next to the intended one in a drop-down menu,
such as the wrong patient or medication (Ash et al.,
2004).
Programming error that incorrectly converts from
one measurement system to another (e.g., pounds to
kilograms or Celsius to Fahrenheit) could
occasionally introduce undetectable errors (Phillips
and Fleming, 2009). The implementation of alerting
mechanisms for clinical decision support tools (Moss
and Berner, 2015) are incorporated into electronic
prescription system for instance (Qureshi et al.,
2015). Disabling functions of these alerts based on the
practitioner’s perception that they are distracting or
disruptive (alert fatigue) could result in a critical
safety feature not being deployed when needed
(Wheeler, 2015).
The complexity of real life situations can disrupt
proper operation of the system and render CDS
recommendations unusable, especially in case of
frequent use of workarounds (Ash et al., 2004).
Further, atypical circumstances, such as unusual
combinations of conditions or local lack of resources,
are not always taken into consideration. The number
of decision tree options becomes too great and the
system becomes impossible to maintain and use
(Sittig and Singh, 2012). Ultimately, the
trustworthiness and integrity of the health record are
damaged.
3.6.3 Implementation Risks
Patient safety risks can certainly vary with the
implementation stages of EMR in an organization
(Lenert, 2002). Priorities for patient safety in the
midst of an EMR rollout have been noted to differ
from those of an organization that has used a fully
integrated EMR system for 5 or more years (Dean et
al., 2011). Issues with delays in data entry by
practitioners, nurses and labs due to workload and
operational issues hindering timely data entry.
Insufficient training and preparation is liable to
introduce data quality issues due to varying levels of
IT literacy among care team (Lluch, 2011). EMR and
related health information system designers and those
responsible for integrated EMR implementation and
management should be aware of the related types of
errors and should take them into account as they build
and implement such systems. These types are often
due to (1) system faults, (2) metadata setup errors, (3)
completeness of tests (how to backtrack data) and (4)
system configuration errors (Ash et al., 2004). The
risk of EHR downtime on clinical operations and
patient safety increases with tightly coupled systems
and widespread geographic areas (Sittig and Singh,
2012). Disparate systems and distributed data bases
introduce delays in data synchronization (Srour and
Badr, 2017). Patient safety could then be
compromised as a result of miscommunication
between the components of an EHR system causing a
potentially unavoidable metadata mismatch.
3.7 Framework
In order to further the sense-making of our approach,
we propose a higher order classification of the
recommended approaches identified in the literature.
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This classification is presented in a framework for
addressing issues of Data Quality in EHR-IS (Table
6) using the suggested model of Bott, 2004 who have
classified data standards under four fundamental
layers: content, structure, technology, and
organization (Bott, 2004). From our result in the
previous sections, in the context of dimensions of data
quality, potential measures to address the risks
associated with implementation and use errors, it
becomes clear that data quality standards for EMR-IS
span all of these four layers.
Therefore, for our framework, we choose to
triangulate our findings with this four-layer model in
order to add rigor and significance to our framework.
That said, for our final representation of the
framework of guiding principles aimed at addressing
issues of Data Quality in EHR-IS, we represent our
findings under these four fundamental layers:
 The Content Layer deals with terminological
issues such as classifications or controlled
vocabularies.
 The Structure Layer defines dataset related
practices improve data quality in EHR (Boyle
and Cunningham, 2002) and are required for
traceability (Yun and Kim, 2007).
 The Technology Layer contains regulations
concerning technologies in healthcare aimed at
error reduction (Lu et al., 2005).
 The Organization Layer relates to organizations
to addressing organizational challenges
associated with the introduction of EHR- IS into
patient care practices. These challenges generally
relate to structure, policies and processes.
3.7.1 Principles for Data Error Control
Tied to the content layer, we introduce the first
guiding principle of “data error control” that
supports the implementation of industry regulations
concerning technologies aimed at error reduction
through the application of concepts for error
handling, normalization and terminology mapping
included in solid system design. Thus, normalizing
data collected from multiple sources (Hennessy et al.,
2013). To that effect the Health Level Seven (HL7)
standard specifies the structure and semantics of
“clinical documents architecture” (Clinical
Document Architecture standards - CDA) for the
purpose of quality data exchange (Yun and Kim,
2007). User interfaces would implement alerting
functions for incorrect data entry designed to reduce
the potential of alert fatigue (too many system-
generated alerts that tend to be ignored).
Systems must maintain data provenance
information to reinforce data validity and credibility.
Finally, preserving the local terminologies can lower
the risk of error by keeping familiar references in the
interfaces and mapping them in the backend to
standards of semantic interoperability standards
through the exploit of meaningful use APIs.
3.7.2 Context of Maintaining Quality Data -
Structured Data Handling
Standards that define the structure layer concentrate
on data repository structures reducing unstructured
data elements such as notes and free form data that
could be limited and constrained to a structure for
dissemination and reporting. For this layer, a guiding
principle points to the “Context for maintaining
quality data”. This principle stipulates that EHR-IS
data management systems ought to be conceived in
the mindful context for maintaining quality data
namely in the preservation of data relevancy,
completeness and timeliness. This relies on designs
that maximize structured data use, classifications or
controlled vocabularies for completeness check.
A structured data approach in necessary that
incorporates formatting for patient data (personal
record). Structured data could include vital signs,
diagnosis, prescription drugs related data and event
reporting (Declerck et al., 2015). Such system would
implement versioning capable repositories for
contextual validation and reference.
3.7.3 Principles of Systems Design
A third set of guiding principles tackles the
technology layer and rests on fundamentals of
“system design”. One of this principle’s primary
edicts is to advocate lower system design complexity.
On the data management backend, this principle
supports quality attributes of interpretability and
understanding (meaningfulness, with no ambiguity).
Thorough metadata entry validation routines must
be considered to reduce metadata setup errors and use
archetypes in data definition in order to reduce issues
in terminologies. In order to sustain a concise and
consistent representation of data, attention ought to be
drawn to how to backtrack erroneous, corrupt or
damaged data. Data accreditation standards could be
applied for the removal of inconsistency and
duplication in structured data.
From an infrastructure perspective, special
architectural considerations would be associated with
the reduction of risk of system failure with an
emphasis on thorough testing during and after the
implementation. Accessibility ought to be certified in
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different data formats, through any system and from
any location. Secured portals could be used for
patients to control access and help routinely inspect
and validate their health data for HIPAA compliance.
Establishing consistency among EHR systems,
meaningful use APIs set standards for user interface
applications (patient, clinician, payer, etc.), semantics
and language translation, search and index
functionality and how chart and record data are stored
(Blumenthal and Tavenner, 2010).
3.7.4 Addressing the Organizational Context
The fourth and last set of principles brought forth in
this framework, borrows its significance from
organizational change management principles.
Organizational changes must be built into the
adoption process of an EHR-IS in order to ease
adoption resistance, avoid the obstacles of IT literacy,
reduce the workload of the staff and realign the
processes for optimal workflow in data collection.
The introduction of EHR- IS into patient care
practices challenge organizations to develop changes
in structure, policies, incentives and processes. The
organizational layer, as introduced by the literature,
treats those changes in processes, guidelines, roles
and protocols required and caused by the usage of an
EHR system in an organization (Poissant et al., 2005).
Rearranged working relationships, schedules,
authorities and prerogatives could damage the
interaction among the healthcare team or improve it
based on the team’s readiness to face the shifting in
roles and responsibilities (Willis-Shattuck et al.,
2008). Process standardization contributes to data
quality by readapting the data contributors to different
EHR data requirements and new workflows (Hopf,
2014). Notwithstanding, the required changes in
business processes, guidelines, roles and protocols
are considered baseline in improving the performance
of the healthcare team and the stakeholders of the
EMR-IS data (Willis-Shattuck et al., 2008), and may
apply as a foundation for any data quality
conversation.
4 CONCLUSION
The paper builds upon existing academic and
practitioner work to consolidate principal data quality
attributes in the context of healthcare informatics. We
perform an in depth exploratory literature review to
develop a broad overview of electronic health records
data quality risk factors, expose challenges in
assessing data quality in electronic health record
information systems (Tables 2, 3, 4 and 5) and
explore approaches in addressing issues of data
quality in electronic health record information
systems (Sections 3.1 to 3.5). Then, in order to extract
relevant architectural guidance, we consolidate the
information into guiding principles for addressing
issues of DQ in EHR-IS using categorization four
fundamental layers of data standards of content,
structure, technology, and organization (Bott, 2004).
As a final step, based on these fundamental layers
of data standards, we present the framework with 4
Meta Principles that categorize a set of Guiding
Principles for addressing issues of Data Quality in
EHR-IS (Table 6):
 P1: Principles of error control and
minimization;
 P2: Principles for maintaining data quality
through rigorous data structure and
versioning;
 P3: Key system design principles for data
quality assurance; and
 P4: Providing the organizational context for
“fit for use” data quality sustainability.
4.1 Contribution and Further Research
Software developers in health information systems
can exploit the guidelines in table 6 in order to
improve the quality of data in their design and
implementation of their product. Without necessarily
introducing new concepts, the principal contribution
of the paper aims at raising the awareness of
developers and users of EHR-IS platforms and
components regarding the importance, essential
dimensions of data quality. As a focusing lens, the
framework provides a reference for designing EHR-
IS offering a guideline for implementing measures for
data quality.
Approaches in addressing issues of data quality
(DQ) in EHR-IS have limitations. Measuring data
quality is a complex process requiring a systemic
approach to data quality assessment. The level of use
of structured data that may not be sufficient for the
assessment of data quality in an EHR setting
(Weiskopf and Weng, 2013), narrowing the scope to
data verification and validation (Sachdeva and
Bhalla, 2012). On the other hand, the use of gold
standards for assessing data quality can be hindered
by multisite systems and databases (Bae et al., 2015).
Failure to extract data from all locations and to
transform into a common format would result in
incomplete data. Assessing completeness and
concordance of the data set may prove more
successful.
Guidelines for Health IT Addressing the Quality of Data in EHR Information Systems
177

Hence, further research could be useful in
evaluating approaches to measure data quality in
Healthcare and relate it to out framework to close the
feedback loop into the success of its implementation.
4.2 Limitations
This paper succeeded in connecting academic
knowledge with practitioner and legislative
approaches to achieve data quality. Nevertheless, in
order to manage the risks associated with the quality
of data, assessment must be improved.
We recognize also that this paper has limitations
in that it looked at only English publications in peer-
reviewed journals and renowned practitioner
publications.
Another limitation could also be in researcher bias
in the classification of certain concepts. Though this
was a successful approach to build a thorough
research product, improvements are always possible.
Table 6: Proposed Framework for Approaches Addressing Issues of Data Quality in EHR-IS.
Meta Principles Guiding Principles (Related DQ Dimension)
P1. Principles of Error
Control and Minimization
(Content Layer)

Establish regulations concerning technologies for error reduction / normalization - (Intrinsic DQ)
 Implement alerting functions for incorrect data entry - (Intrinsic DQ)
 Use of local terminologies in standard dictionaries for interoperability - (Intrinsic DQ)
 Stress the importance of data provenance information (traceability) - (Intrinsic DQ)
P2. Principles for
Maintaining Data Quality
(Structure Layer)

Maximize the use of structured data for accurate interpretation - (Contextual DQ)
 Use classifications and controlled vocabularies to normalize the data collected - (Contextual DQ)
 Use of versioning capable repository in order to keep the proper data context - (Contextual DQ)
P3. Key System Design
Principles for Data quality
Assurance
(Technology Layer)

Ensure that error reduction and terminology mapping included in system design (Intrinsic DQ)
 Focus of features to reduce Metadata Errors - (Intrinsic DQ)
 Implement semantic interoperability, clinical decision support / Error reduction - (Contextual DQ)
 Reduce complexity of information systems models - (Representational DQ)
 Implement standards with reference to best practices to reduce systems and implementation issues
and reduce the risk of system failure - (Representational DQ)
 Provide levels of authorized users in compliance with HIPAA privacy guidelines (secured portals
for patients to control access) - (Accessibility DQ)
 Limit data access for reduced chances of erroneous data entry - (Accessibility DQ)
 Lower complexity of system design - (Accessibility DQ)
 Conduct thorough testing for usability- including how to backtrack data) - (Accessibility DQ)
 Implementing easy to use portals / interfaces maintain accessibility in different data formats,
through any system and from any location - (Accessibility DQ)
P4. Providing the
Organizational Context
for Data Quality
Sustainability
(Organization Layer)

Identify and implement required process changes for data entry accuracy - (Intrinsic DQ)
 Encourage practices of data comparison with patients / values from patient history - (Intrinsic DQ)
 Emphasize process changes to that reduce workload / avoid delay in data entry - (Contextual DQ)
 Address workload issues induced by the insertion of the EHR-IS, possibly through quick reference
guides, online help and easy navigation - (Accessibility DQ)
 Address IT literacy issues through training and features for adoption support - (Accessibility DQ)
4.3 Compliance with Ethical Standards
This research represents no conflict of interest and did
not receive any specific grant from funding agencies
in the public, commercial, or not-for-profit sectors.
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