An Approach Towards Information Quality Management of Electronic
Health Records
Mohammad Mahdi Mahdavi Amjad, Kamyar Rasta, Martin Gerdes and Rune Fensli
Department of Information and Communication Technology, University of Agder, Grimstad, Norway
Keywords:
Electronic Health Record, Information Quality, EHR Analysis.
Abstract:
Electronic Health Records (EHR) management systems, have gained attention in many countries as public
organizations working in health domain (such as hospitals and municipalities) use them to lift the quality of
health care. EHRs are considered as the basis for establishing Health Information Service (HIS) systems. In
spite of remarkable advantages of EHRs, the lack of quality metrics can reduce the efficiency of high level
systems that are based on them. Particularly, it is smart to design a mechanism to assure the quality of data
generated by sensors. In this paper, we propose an architecture in which EHRs are enriched with metadata
to provide the information quality. Our architecture emphasizes on the quality of EHRs since we believe that
the quality aspect of health records has not been contemplated in many commercial systems. We utilize some
quality dimensions to produce the total quality metric. Moreover, we show that this architecture can provide
quality-based systems with more appropriate inputs.
1 INTRODUCTION
As computers are increasingly used in health care
realm, more applications are developed for managing
electronic health records (EHR). Utilizing the EHRs
have lifted the quality of health care (Gunter and
Terry, 2005)(DesRoches et al., 2008). There are nu-
merous applications out there and each one has its
own method to store and analyze EHRs. We have
studied some of them e.g. OpenMRS and OpenEHR
and noticed that in almost all of them there is no
mechanism for handling information quality. (Kalra
et al., 2005)(Seebregts et al., 2009) As EHRs are typ-
ically raw data of health sector analyzing systems, the
information quality of them is extremely important to
improve.
The quality of EHRs must be ensured before be-
ing used in other higher level systems. On the other
hand, the use of sensors has been drastically in-
creased in the e-Health area and it means that there
can be some errors in automatically generated mea-
surements. Hence, the necessity of normalizing the
automatically generated records shows itself.
In general, in case of using sensor networks, the
probability of receiving wrong data from the sub-
ject can dramatically increase, especially if the error
detection and error correction mechanisms have not
been implemented (Akyildiz et al., 2002). Addition-
ally, remarkable changes of the vital signs measure-
ments are not usual incidents and beside other param-
eters, can be signs of emergency condition. This sig-
nificant feature, can be used in notification systems
but currently implemented systems do not support
it, although there exist applications that have some
thresholds for data or triggered alarm systems.
In this paper, we present an architecture which en-
hances EHR management systems by mechanisms to
determine the information quality in terms of different
quality dimensions. This architecture can be used to
ensure that EHR data meet some basic criteria. More-
over, our system produces some metadata for each
EHR that helps analyzing systems to achieve better
performances.
2 INFORMATION QUALITY OF
EHRS
To the best of our knowledge, almost none of the ex-
isting EHR management systems have mechanisms to
determine the information quality of health records.
This can degrade the result of EHR analyses espe-
cially when machines are responsible for generating,
storing, and using them.
Majority of the EHR Management (EHRM) sys-
270
Mahdavi Amjad M., Rasta K., Gerdes M. and Fensli R..
An Approach Towards Information Quality Management of Electronic Health Records.
DOI: 10.5220/0004752402700275
In Proceedings of the International Conference on Health Informatics (HEALTHINF-2014), pages 270-275
ISBN: 978-989-758-010-9
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
Figure 1: Conventional two-tier EHR management system.
tems use the conventional approach to manage the
medical records. As Figure 1 shows, typically there
is a relational database as a data storage space and a
web/form application as the user interface which en-
ables users to store and retrieve data into the storage
and creates reports.
We have developed an application which can nor-
malize EHRs in a relational database. This applica-
tion has a collection of rules that can be applied to al-
most all types of EHR. For instance, assume that we
have a system in which a sensor measures the body
temperature of a subject and sends it to the recording
system. The sensor stops functioning after a while
and generates fake data. This can severely affect the
quality of the health record and consequently the va-
lidity of analysis.
In spite of conventional approach, we propose
an architecture in which a Quality-Enhanced Broker
Server (Q-EBS) controls the EHR’s quality. The Q-
EBS is responsible for calculating and assigning the
quality metric of each EHR. The quality metric is a
value that represents the outcome of the quality di-
mension values. From information quality point of
view, there are several popular quality dimensions
(Wand and Wang, 1996) such as accuracy, timeliness,
completeness, consistency, access security, relevancy,
understandability, etc. which are used in many sys-
tems. However, in this paper we just deal with the
first four and since connectivity is important in mobile
networks, we consider it as another quality dimension
as well. Other quality dimensions can be engaged if
necessary.
2.1 Quality Dimensions
The computation of the final quality metric requires
the values of the quality dimensions. In this section
we briefly define our quality dimensions and the cor-
responding calculation equations. There is no consen-
sus on the definitions of quality dimensions, and in
most cases the definitions are customized according
to the requirements of the system (Wand and Wang,
1996) (Baumgartner et al., 2010) (Strong et al., 1997).
2.1.1 Accuracy
The first quality dimension that we propose is accu-
racy. In our system, accuracy shows the ratio of the
observed value and the real value. We name this real
value as reference value. In real world there is no way
to retrieve the reference (or the real) value. As any
other measurement, ours is just a reflection of truth
and therefore cannot be exactly true. One possible
way for generating the reference value in health do-
main is to ask a nurse to manually produce it. Hence,
we can consider this value as the reference and simply
ignore the measurement errors.
The reference value can be generated by the sys-
tem automatically as well. To do this, we should con-
sider a proper time for system to reach to a reference
value which can be simply the average of the values
received in a past period of time. Nevertheless, the
production process of a reliable reference value is out
of the scope of this paper and so we do not focus on
how to get the reference value. We just want to show
how to calculate the accuracy of EHRs based on a
given reference value.
Whether using an automatically or manually gen-
erated reference, we can compute the accuracy di-
mension values to ensure that incoming data is not
produced by a malfunctioning sensor. Eq. (1) is used
to calculate the accuracy of an observed value where
r represents the reference value and o represents the
observed value. Min and Max functions are utilized
for calculating the minimum and maximum values of
the reference and the observed values respectively.
Accuracy =
Min(r, o)
Max(r, o)
100 (1)
Note that in reality there is no practical way to
generate reference values for each EHR. Neverthe-
less, reference number is a concept that can be used
for evaluating the quality of an observed data.
2.1.2 Completeness
The second quality dimension we use is the complete-
ness. The completeness dimension, demonstrates the
proportion of the number of received data items and
the expected number of data items as a number be-
tween zero and hundred. In other words, the com-
pleteness shows how many data items are received
from the sensor and how many of them we expect to
receive. For example, assume that we have a device
sending five values of blood oxygen saturation every
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one minute and a listener expecting to receive it. Us-
ing the completeness quality dimension, we can de-
termine if the entire information package is received
by the listener or not.
As our system knows about the number of data
items in a package, it can evaluate the completeness of
the received information and assign a quality dimen-
sion value to it. Therefore, the completeness of an in-
formation package can be calculated utilizing Eq. (2)
in which N
received
shows the number of received data
items by the system and N
sent
represents the number
of sent data items from the device.
Completeness =
N
received
N
sent
100 (2)
2.1.3 Timeliness
The timeliness is the third proposed quality dimension
when generating EHRs. Timeliness refers to the fact
that the quality of an EHR can be sometimes strictly
associated to the time it has been generated. Some
EHRs must be committed to the system in a proper
time to be useful. For instance, you can think of a
notification system in which EHRs are used to detect
the emergency condition of the subject.
In our systems, there is a time stamp for genera-
tion time and another one for the commitment time
of each EHR. Technically, if the difference between
these two times is more than the threshold, the EHR
is not meaningful. The threshold, cab be defined ac-
cording to the level of importance of the time in our
system. For example, it is completely acceptable (but
not desirable) for an SMS to be delivered twenty four
hours after being sent but what about the report of a
heart attack? Typically in mobile networks, frequent
connections and disconnections are inevitable proper-
ties of the environment (Huang and Garcia-Molina,
2001). Therefore, the timeliness is very important
when determining the quality of an EHR.
The timeliness quality dimension can have vari-
ous definitions in different contexts. However, what
we generally need in this paper is a number (prefer-
ably between 0 and 100) which shows the quality di-
mension value. All we need to compute the timeliness
value, is to subtract the generation time (T generate)
from the commitment time T
commit
as it is shown in
Eq. (3).
Timeliness = T
commit
T
generate
(3)
Table 1, explains how to convert the timeliness
value to the quality dimension values between 0 and
100. With the aid of this mapping table, the timeliness
becomes unified with other quality dimensions so that
we can calculate the final quality metric.
Table 1: An example of timeliness mapping table.
T1 T2 T2 - T1 Timeliness
0 5 5 95%
0 10 10 90%
0 15 15 85%
Note that the timeliness dimension can be defined
using other equations and mapping tables in respect
to the system environment. To design a mapping ta-
ble, the maximum value of the acceptable delay must
be defined. This value is calculated considering the
importance level of the expected data, required time
for taking appropriate action, the type of network in-
frastructure (e.g. mobile, fixed, etc.), and/or any other
influencing parameters.
2.1.4 Connectivity
Although connectivity is not among the popular qual-
ity dimensions, we believe that it can be useful in our
architecture. As mentioned earlier, frequent connec-
tions and disconnections of components are inalien-
able properties of mobile environments. Additionally,
mobile networks are very good environment for de-
ployment of our architecture. Therefore, connectivity
is very important and somehow it should be measured
and stored as metadata of EHR.
To make it more clear, assume that we have some
sensors measuring the heart pulse rate of a subject and
a system monitoring the values they generate (i.e. the
ECG curves). After a while, our monitoring system
receives no signal from the sensor. This incident, can
be interpreted at least in two ways: the loss of signal
or the subject’s heart attack. The loss of signal can be
caused by sensor failure, network issues, etc. but the
subject’s heart attack must be confronted differently.
The connectivity quality dimension demonstrates
the connection status of the network when an EHR is
being generated and committed. We assign 0 to the
connectivity dimension value when the sensor and/or
data gateway are disconnected from quality manager
and 100 when the connection is perfectly fine.
2.1.5 Consistency
The last quality dimension we use is the consistency.
Consistency has a wide range of definitions in differ-
ent environments, but we use it as a quality dimension
which shows that whether EHR values are produced
in proper range of validity. In many health records,
especially those which are associated with measure-
ments, there is a range of validity. For example, in
EHR management systems there is a validity range
for heart pulse rate (e.g. 0-230).
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Although the probability of experiencing a heart
rate out of this range is very low, it is possible to have
an anomalous value due to sensor failure. In such a
case, consistency is a very good dimension to deter-
mine the quality of the EHR. According to Eq. (4),
the consistency is calculated by the proportion of the
number of in-range data (N
inrange
) and the total num-
ber of data (N
total
).
Consistency =
N
inrange
N
total
100 (4)
2.1.6 Quality Metric
Now that we have all required quality dimension val-
ues, we can calculate the quality metric. Quality met-
rics are shown as numbers between 0 and 100 so that
bigger values show higher quality of the information.
In fact, the quality is an outcome of different qual-
ity metric. As mentioned, several different quality di-
mensions can be considered. The final quality metric
is computed using Eq. (5) in which N is the number
of quality dimensions, (Q
i
) is the quality dimension
value and the (W
i
) is the weight of each quality di-
mension.
QualityMetric =
1
N
N
i=1
Q
i
Wi (5)
As you can see in Eq. (5), we define a weight
value (W
i
) for each quality dimension. These values
are used to control the effect of each quality dimen-
sion on the final quality metric. For instance, in some
systems accuracy is more important than the timeli-
ness. In such a system we assign lesser weight to
the timeliness so that timeliness value can less affect
the quality metric in comparison with accuracy value.
This feature provides our system with more flexibil-
ity and causes more compatibility to diverse environ-
ments.
3 QUALITY-ENHANCED EHR
APPROACH 1
Once we have calculated our quality metrics, we can
concentrate on our architecture. As shown in Figure
2, we propose an architecture in which a quality man-
ager is responsible for computing and assigning the
quality metric to the health record. Typically, a stan-
dard EHR consists some information about the pa-
tient, measured values, date and time, but we need
an EHR containing quality data to calculate the qual-
ity dimensions and quality metric. If a new system is
going to be developed, then these quality data can be
Figure 2: Quality-enhanced EHR management system.
easily considered in the system. But if we want to add
quality to an existing system, then extra information
must be added to the original EHR and it means that
some modification in the data gateway is necessary.
Additionally, we need to modify the existing storage
space of EHRs.
As mentioned earlier, the majority of EHRM sys-
tems, use relational databases. Therefore, when
adding the quality metadata to the original EHR, mod-
ification of involved tables, views, stored procedures
and functions and other database objects must be per-
formed. Moreover, web/form interfaces which are
used for data entry should be updated as well.
When the data gateway sends quality-enhanced
data to the Q-EBS, the quality manager extracts the
quality data, calculates the quality dimensions and the
quality metric and stores them to the database. This
approach enables us to have the quality information
right beside the original EHR. Therefore, there is no
need to add any extra component to the current system
except the quality manager. The quality manager, can
be implemented as a single (while big) stored proce-
dure or function inside the database or an application
manipulating it.
4 QUALITY-ENHANCED EHR
APPROACH 2
As a major possible modification of the first approach,
we can design another independent component on the
sensor side which manages the generation of infor-
mation quality metadata. We name this component as
quality agent. The quality agent can accept various
roles in our architecture. For instance, it can just re-
trieve quality data from the data gateway and send it
to the quality manager in order to be processed. The
quality agent can also calculate the quality metric and
only send the results.
Figure 3 shows a possible scenario of using this
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component in our architecture. The principles of this
scenario are the same as what is shown in Figure
2, except that the quality agent component has been
added to it.
5 DISCUSSION
It is very important to decide on the kind of informa-
tion that should be sent to the quality manager since
this can affect the performance of the whole system.
Moreover, network traffic, process distribution on the
smartphone and the EHRM server and the complex-
ity of implementation in term of updates propaga-
tion are directly associated with this decision. Table
2, demonstrates the effects of distribution strategies
on these aspects of the system, where traffic means
the required bandwidth for communications between
quality agent and the quality manager, process means
the smartphone process load, and the number of up-
date refers to the number of necessary updates in case
of modification of quality computation rules.
Table 2: The effects of sending quality data and quality met-
ric on various aspects of system performance.
Sent Item Traffic Process No. of Updates
Q Data
Q Metric
There are some disadvantages with the quality
agent approach. However, it should be considered
as a possible option in mobile environments. Let
us briefly describe some of its advantages and dis-
advantages. First of all, we must consider that hav-
ing a quality agent on sensor side (i.e. smartphone)
means that we have to completely split our architec-
ture into two sections: EHR management and infor-
mation quality management. It can result in a lot of
overheads in terms of process, communication, stor-
Figure 3: QEHRM system with quality agent.
age, security, etc. On the other hand this approach
enables us to manage the information quality in an in-
dependent way so that there will be no need to modify
the inner structure of existing systems. The only ma-
nipulation needed, is to store the metadata (i.e. qual-
ity dimensions and quality metric) in another database
with a link to corresponding EHR.
Secondly, computation of quality metric and then
sending it, may impose more process on the smart-
phone since the quality agent can be implemented
inside a smartphone. On the contrary, if the quality
agent calculates and sends the quality metric, then we
can decentralize the process and gain better network
efficiency since smaller data packets are transferred
through the network.
6 CONCLUSIONS
In this paper we proposed an architecture for assuring
the information quality of EHRs. Generally, our ar-
chitecture is based on five quality dimensions that can
be calculated using some metadata which are added
to the original EHR. We engaged four simple equa-
tions for calculation of EHR quality dimensions and
another one for computation of quality metric. Ad-
ditionally, we proposed two different implementation
alternatives for diverse environments. According the
section 5, the first architecture is more suitable for
fixed networks where the network bandwidth is not
our most important concern. Instead, we probably
prefer to distribute the process load of the centralized
server to the clients.
The second approach is more appropriate for mo-
bile networks where we want to use our valuable
bandwidth in the most efficient way. Nowadays,
smartphones with high performance processors can
be found almost everywhere, so putting the burden of
some computation on smartphones seems trivial.
6.1 Future Work
As semantic web grows, more semantic-enhanced ap-
plications are developed and migrating from rela-
tional databases to ontologies becomes a must since
new customers have requirement that make conven-
tional approaches to face serious challenges (Berners-
Lee et al., 2001). Our proposed system uses some
statistical techniques to ensure the quality of informa-
tion. A magnificent improvement of our system can
be the enhancement with semantic data and functions.
This can lead us to a system that can more precisely
detect the anomalous information and treat them in a
better way (Chandola et al., 2009).
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Another component that can be used in our sys-
tem, is an intelligent EHR controller which is able to
improve itself to make better decision in case of fac-
ing abnormal records.
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