Article

Graph databases for openEHR clinical repositories

Int. J. Computational Science and Engineering, Vol. 20, No. 3, 2019 281
Copyright © 2019 Inderscience Enterprises Ltd.
Graph databases for openEHR clinical repositories
Samar El Helou*
Department of Social Informatics,
Graduate School of Informatics,
Kyoto University, Japan
Email: helou.samar@gmail.com
*Corresponding author
Shinji Kobayashi
Department of Electronic Health Record,
Graduate School of Medicine,
Kyoto University, Japan
Email: skoba@moss.gr.jp
Goshiro Yamamoto
Division of Medical IT and Administration Planning,
Kyoto University Hospital, Japan
Email: goshiro@kuhp.kyoto-u.ac.jp
Naoto Kume
Department of Electronic Health Record,
Graduate School of Medicine,
Kyoto University, Japan
Email: kume@kuhp.kyoto-u.ac.jp
Eiji Kondoh
Department of Gynecology and Obstetrics,
Graduate School of Medicine,
Kyoto University, Japan
Email: kondo@kuhp.kyoto-u.ac.jp
Shusuke Hiragi and Kazuya Okamoto
Division of Medical IT and Administration Planning,
Kyoto University Hospital, Japan
Email: shiragi@kuhp.kyoto-u.ac.jp
Email: kazuya@kuhp.kyoto-u.ac.jp
Hiroshi Tamura
Center for Innovative Research and Education in Data Science,
Kyoto University, Japan
Email: htamura@kuhp.kyoto-u.ac.jp
Tomohiro Kuroda
Division of Medical IT and Administration Planning,
Kyoto University Hospital, Japan
Email: tomo@kuhp.kyoto-u.ac.jp
282 S. El Helou et al.
Abstract: The archetype-based approach has now been adopted by major EHR interoperability
standards. Soon, due to an increase in EHR adoption, more health data will be created and
frequently accessed. Previous research shows that conventional persistence mechanisms such as
relational and XML databases have scalability issues when storing and querying archetype-based
datasets. Accordingly, we need to explore and evaluate new persistence strategies for
archetype-based EHR repositories. To address the performance issues expected to occur with the
increase of data, we proposed an approach using labelled property graph databases for
implementing openEHR clinical repositories. We implemented the proposed approach using
Neo4j and compared it to an object relational mapping (ORM) approach using Microsoft SQL
server. We evaluated both approaches over a simulation of a pregnancy home-monitoring
application in terms of required storage space and query response time. The results show that the
proposed approach provides a better overall performance for clinical querying.
Keywords: openEHR; graph database; electronic health records; EHR; database; performance;
archetypes; reference model; EHR repository; archetype-based storage; query response time;
clinical repository.
Reference to this paper should be made as follows: El Helou, S., Kobayashi, S., Yamamoto, G.,
Kume, N., Kondoh, E., Hiragi, S., Okamoto, K., Tamura, H. and Kuroda, T. (2019)
‘Graph databases for openEHR clinical repositories’, Int. J. Computational Science and
Engineering, Vol. 20, No. 3, pp.281–298.
Biographical notes: Samar El Helou is a PhD student in the Department of Social Informatics at
the Kyoto University. Her research interests include EHRs, health data models, patient-centred
care and ubiquitous healthcare.
Shinji Kobayashi is currently a Senior Lecturer in the Department of Electronic Health Record in
the Graduate School of Medicine at the Kyoto University. He received his MD and PhD degree
from the Kyushu University. He has been leading the Medical Open Source Software Council in
Japan since 2003. His research area is open source software in medicine and ruby
implementation of the openEHR standards.
Goshiro Yamamoto is currently a Senior Lecturer in the Division of Medical IT and
Administration Planning of the Kyoto University Hospital. He received his BE, ME and PhD in
Engineering from the Osaka University. His major interests are human-computer interaction and
medical informatics.
Naoto Kume is currently an Associate Professor in the Department of Electronic Health Record
in the Graduate School of Medicine at the Kyoto University. He received his PhD in informatics
from the Kyoto University. His research interests include EHRs, clinical studies and mobile
health.
Eiji Kondoh is an Associate Professor in the Department of Gynecology and Obstetrics in the
Graduate School of Medicine at the Kyoto University. He specialises in maternal-fetal medicine.
His research interests include high-risk obstetrics, especially postpartum hemorrhage, placenta
accrete and preeclampsia.
Shusuke Hiragi is an Assistant Professor in the Division of Medical IT and Administration
Planning of the Kyoto University Hospital. His research interests include medical research with
hospital information systems and insurance claim databases.
Kazuya Okamoto is a Senior Lecturer in the Division of Medical IT and Administration Planning
of the Kyoto University Hospital. He received his BS, MS and PhD in informatics from the
Kyoto University. His current research interests include medical informatics, artificial
intelligence in medicine and rehabilitation engineering.
Hiroshi Tamura is a Program-Specific Professor in the Center for Innovative Research and
Education in Data Science of the Kyoto University Institute for Liberal Arts and Sciences. His
research interests include hospital management, healthcare policy and management,
ophthalmology and visual sciences.
Tomohiro Kuroda is a Professor in the Division of Medical IT and Administration Planning of
the Kyoto University Hospital. He received his PhD in Information Science from the Nara
Institute of Science and Technology. His research interests include human interfaces,
virtual/augmented reality, wearable computing and medical & assistive informatics. He is a
member of IEEE, ISVR, HISJ, JAMI and others.
This paper is a revised and expanded version of a paper entitled ‘Exploring graph databases with
openEHR in antenatal care settings’ presented at BASE 2015 Symposium, Aizu, Japan, 7–9
December 2015.
Graph databases for openEHR clinical repositories 283
1 Introduction
The widespread use of electronic health records (EHR)
could lead to higher service efficiency and effectiveness and
thus lower healthcare costs (Shen et al., 2015; King et al.,
2014; Agrawal, 2002). Governments aiming to improve the
performance of healthcare provision are implementing
policies to increase EHR adoption. Initiatives for EHR
development and dissemination can be seen in Australia,
Canada, the European Union, the USA and Japan
(Cornwall, 2002; IT Strategic Headquarters, 2009). These
governmental efforts increased EHR adoption rates and
exposed some of their adoption barriers. Some of the
commonly cited EHR adoption barriers are a lack of
interoperability, low levels of usability and high
maintainability costs (Hamid and Cline, 2013; Vishwanath
and Scamurra, 2007; Ash and Bates, 2005).
The lack of interoperability of EHR systems is
considered a major technical barrier since it can hinder the
access to and the sharing of data between EHRs as well as
their consequent processing by computers (Kalra and
Blobel, 2007; Librelotto et al., 2015). Traditionally, EHR
vendors developed or implemented EHRs based on internal
and proprietary standards, which complicated the
information sharing with other vendors' EHRs. Currently,
the use of non-proprietary standards for building EHR
systems is recognised as a requirement to address the
interoperability issue and allow the exchange of information
between EHR services. Multiple EHR interoperability
standards have been developed over the last decade. The
most commonly used ones such as HL7, CEN ISO 13606
and openEHR adopted a two-level modelling approach, i.e.,
an archetype-based modelling approach, separating the
physical representation of data from the clinical domain
concepts (Begoyan, 2007; Schloeffel et al., 2006). When
building EHR systems following the archetype-based
modelling approach, the data repository stores health
concepts as instances of an information reference model
(RM) (Garde et al., 2007).
In this study, we consider the case of openEHR
(http://www.openehr.org/). openEHR is a technology-
independent specification for structuring EHR data. It
defines an information RM but does not commit its
implementers to any particular implementation approach
(openEHR, 2008). Consequently, the developers have to
make a decision regarding the persistence technology and
approach when building EHR data repositories following
the openEHR specification. This decision could affect the
system’s overall level of usability and maintainability costs.
In the following sections, we describe how a persistence
approach could contribute to the EHR system’s usability
and maintainability costs.
The usability and efficiency of EHR systems are
common concerns expressed by the clinical staff (Belden
et al., 2009). An important effectiveness metric and
usability factor is a system’s response time (Nielsen, 1993;
Li et al., 2002; Li and Bao, 2017; Liu and Xiao, 2016) or the
time a transaction needs to be executed when using a
system. The system’s response time depends on many
variables such as the CPU, the network and the database
used (Li et al., 2002). For an EHR system, most of the tasks
require browsing the databases. Thus, it can be assumed that
the database query response times significantly affect the
system’s overall performance and usability (Balsamo et al.,
2004). When using EHR systems in clinical settings,
healthcare providers usually execute create, read, update
and destroy (CRUD) operations to generate, retrieve and
update data from individual patients’ health records.
Minimising the execution time of such CRUD operations
could improve the usability of EHRs in clinical settings.
In addition to usability, the cost of maintaining EHR
systems is a common concern expressed by organisations
considering their implementation. If EHRs are adopted, a
rapid growth in data quantity is expected to occur. This
growth will create the need for greater storage capacities,
addressed by buying larger storage, i.e., scaling up, or
distributing the data over multiple servers, i.e., scaling out
(Frost, 2015;Cottle et al., 2013). Therefore, reducing the
required storage space for clinical data could reduce the
maintainability costs of EHR systems.
Considering that the clinical query execution time is
crucial to the overall performance of the EHR system and
that the storage efficiency affects the future maintainability
costs, the aim of this work is to provide an openEHR
repository implementation strategy that improves the
execution time of clinical queries and reduces the data
storage space requirements.
When building EHR systems following the openEHR
archetype-based modelling approach, the data repository has
to store instances of the openEHR information RM. The
RM contains multiple classes in a deep tree hierarchy. Since
the openEHR RM has a tree structure, an openEHR EHR
would have the structure of a directed rooted tree, a graph-
like data structure. When clinical queries regarding a
specific patient are executed, the tree structure is queried
starting from the top node containing the unique EHR ID.
Multiple openEHR implementation approaches have
been previously explored and implemented most often using
Relational and XML databases (Frade et al., 2013).
However, previous research and discussions suggest that
these approaches are less than optimal for storing and
querying archetype-based datasets (Freire et al., 2012,
2016). Using a relational database implies that multiple
JOIN operations need to be executed when querying the tree
structure, leading the system’s performance to deteriorate
with the increase of data. Moreover, due to the complex
structure of the RM and the impedance mismatch between
the RM and the relational model, the schema can be hard to
model. As for XML databases, they do not perform as well
as relational databases (Green, 2008) and they were found
to require larger memory and storage space to process and
store the information (Megginson, 2004). Proposing and
evaluating new implementation approaches could be of
value for openEHR implementers.
Recently, graph databases have been developed as a
possible replacement for relational databases when dealing
with graph-like data structures (Angles, 2012). Graph
284 S. El Helou et al.
databases are optimised for storing and querying graph-like
structures. Since the RM has a graph-like structure,
mapping it to a graph model and consequently storing it in a
graph database would be straightforward. Moreover, instead
of joining multiple tables to query the tree, a graph database
starts by locating the initial node and subsequently executes
traversals. Since the cost of traversals is not affected by the
number of records in the database (Robinson et al., 2015),
graph databases must theoretically scale better than their
relational counterparts in the case of openEHR repositories.
This work proposes an openEHR repository
implementation approach that allows fast clinical querying
and efficient storage. The proposed approach uses a labelled
property graph database and directly maps the openEHR
RM structure to a graph structure. We evaluate the proposed
approach by comparing it to an object relational mapping
(ORM) approach because most of the existing openEHR
database implementations follow the relational approach
(Frade et al., 2013). The evaluation explores likely querying
scenarios over artificial simulations of different size
pregnancy home-monitoring data repositories. The
performance comparison considers two main criteria: the
query response time and the required storage space.
2 openEHR structure and database models
2.1 openEHR
openEHR is a set of open-source specifications for a
complete EHR architecture. It is based on 15 years of
research and real-world implementations (Schloeffel et al.,
2006). openEHR specifies how to create, store, maintain
and query EHRs following a two-level modelling approach.
This approach separates the domain knowledge, i.e., health
concepts from the software and its database. In the two-
level modelling approach, the first level consists of the
information RM and the second level of the domain concept
models (DCM) or ‘archetypes’ (Beale, 2002). The RM
specifies a set of classes covering the possible types of
information meant to be stored in an EHR. Archetypes are
coded in terms of constraints over the RM classes. For
example, the blood pressure archetype is modelled by
constraining the OBSERVATION class. When developing
an EHR system, only the RM is implemented and clinical
data is stored in the database as instances of the RM classes
(openEHR, 2007; 2008). Therefore, following the two-level
modelling approach, the database design depends solely on
the RM and is not affected by continuous changes in
medical knowledge, resulting in highly flexible and
adaptable EHRs. openEHR is a technology independent
specification and does not recommend or specify the use of
any particular database technology.
Figure 1 High level structure of the openEHR EHR
Figure 2 UML diagram of the openEHR reference model
The top-level structure of the openEHR EHR is shown in
Figure 1. The structure starts with an EHR object identified
by a globally unique identifier called ‘EHR id.’ The access
control settings of the EHR are contained in the ‘EHR
access’ object and the status information is contained in the
‘EHR status’ object. All the clinical and administrative data
in the EHR are contained in ‘composition’ objects.
The model of the data in ‘composition’ objects follows
the logical structure of the openEHR RM a tree structure
with deep inheritance hierarchy, as shown in Figure 2.
Accordingly, an openEHR repository is implemented to
store the tree structure with deep hierarchy described above.
Graph databases for openEHR clinical repositories 285
2.2 openEHR data repositories
Being a technology-independent specification, openEHR
does not recommend any specific database technology or
approach for implementing openEHR data repositories. The
choice of technology and implementation approach is left to
the developers. However, openEHR attracts a wide range of
individuals and organisations including developers, medical
specialists, researchers, small organisations, large
organisations and governments. Each of these parties
employs openEHR in a different context in which specific
database technologies and modelling approaches might
prove fitter. For these reasons, multiple approaches for
implementing openEHR repositories exist and some of them
were described and evaluated in the literature.
A survey of existing openEHR repository
implementations published in 2013 (Freire et al., 2016)
showed that the relational storage solutions were most often
used. In conventional cases, using a relational database
management system (RDBMS) for an openEHR repository
would require ORM and various strategies to handle the
model impedance mismatch, making the schema hard to
model. The effort required to model the database schema is
a common database adoption barrier (Jagadish et al., 2015).
Moreover, an ORM approach maps each class in the RM to
a table in the relational database, resulting in a large number
of tables. The numerous tables and deep hierarchy of the
openEHR data structures would require the execution of
multiple JOIN operations when querying the data, which
would theoretically result in poor query response times for
large datasets. Another relational mapping method called
archetype relational mapping (ARM) was proposed (Wang
et al., 2015). Instead of mapping the RM classes to tables,
the ARM maps archetypes to tables. The method was
evaluated and appeared to be promising for population-wide
querying.
On the openEHR website, a relatively direct key-value
strategy called ‘Node + Path’ is proposed and explained
(openEHR, 2008c). Following the ‘Node + Path’ approach,
openEHR data is stored in a key-value store which is one
big relational table with two columns: a key column and a
value column. The key column contains node paths and the
value column contains blobs corresponding to the serialised
nodes at those paths. Even though this approach is fairly
simple to implement, it performed poorly in terms of query
response time (Wang et al., 2015).
In addition to relational databases, openEHR
implementers often use native XML databases. However, a
study found that XML databases, without major
optimisations, performed poorly for population-wide and
ad-hoc querying for large openEHR datasets (Freire et al.,
2012). A later study found that even after indexing and
query optimisations were applied, XML databases did not
perform as well as relational databases for population-wide
querying (Freire et al., 2016).
2.3 Graph databases
Graph databases were invented to counteract some
limitations of the relational databases regarding highly
interconnected data and continuously evolving data models.
In a graph data model, information is represented using
nodes and edges (Hunger et al., 2016). Nodes represent the
entities and the relationships between those entities are
manifested by the edges that connect them (Robinson et al.,
2015).
Graph databases can be split into two categories:
native graph storage and processing
non-native graph storage and processing.
In a native graph storage technology, the underlying
structure of the database is optimised to store graph-like
data, ensuring that nodes and relationships are written close
to each other. Non-native graph databases store the graph
data, i.e., node data and relationship data, in other database
technologies, e.g., relational tables, which can lead to slow
querying as these models are not optimised for graph-like
data (Robinson et al., 2015).
In a native graph processing technology, the database
does not rely on global indexes to gather the data. Rather,
index-free adjacency is used. Index-free adjacency means
that each node references its adjacent nodes, so instead of
using global indexes, the nodes act as indexes for their
nearby nodes. Theoretically, the complexity of executing
graph traversals is O(1) in a graph database using index-free
adjacency (Robinson et al., 2015), in comparison to an
average of O(log(n)) for a binary search to locate an index
entry in a relational database.
Figure 3 The labelled property graph model
One commonly used and well-documented graph database
is Neo4j (http://neo4j.com/developer/graph-
database/#property-graph/). Neo4j uses native graph storage
and processing and employs the labelled property graph
model. In the labelled property graph model, nodes and
edges can have properties associated with them and nodes
can be tagged with labels representing their different roles
(Robinson et al., 2015). An example is shown in Figure 3,
where A, B and C are nodes. A, B and C are labelled
‘EHR’, ‘composition,’ and ‘person’, respectively. A has ‘id’
as a node attribute, while B and C have a ‘name’ attribute.
A is connected to B via a relationship of type ‘CONTAINS’
and to C via a relationship of typeBELONGS TO. B is
connected to C via a relationship of type ‘ADDED BY’
with ‘in’ as a relationship property.
286 S. El Helou et al.
3 Methods and materials
To implement the openEHR archetype-based data
repository, a graph model representing the openEHR RM
was created. Neo4j, a labelled property graph database
technology, was employed to store openEHR archetyped
data as instances of the openEHR RM. To evaluate the
proposed implementation approach, a performance
evaluation was conducted. The proposed approach was
implemented using Neo4j and was compared in terms of
query response time and required storage space to an ORM
approach implemented using Microsoft SQL server (2016).
To conduct the performance evaluation, datasets simulating
a pregnancy home-monitoring data repository were
artificially generated and imported into Neo4j and Microsoft
SQL Server. To compare the query response times, a set of
application-specific queries differing in complexity were
identified. Finally, the queries were written in Cypher
(Neo4j Inc., https://neo4j.com/developer/cypher-query-
language/), a declarative query language for Neo4j and in
SQL to be executed over both repository implementations.
3.1 Graph model of the openEHR RM
Following the openEHR specification, clinical information
is represented using openEHR archetypes, which are
modelled as constraints over the openEHR RM classes. To
query the archetypes’ structure, openEHR includes a path
mechanism specifying the path to reach archetype nodes
starting from the root node of the archetype structure in
XPath-compatible syntax. Each path identifies an archetype
node using openEHR RM class attributes as attribute names
and ‘archetype_id’ or ‘archetype_node_id’ as predicates.
To create the graph model of the openEHR RM,
openEHR RM classes were mapped into graph nodes. The
relationships were modelled following the openEHR RM
class hierarchy and named in accordance with the class
attributes employed in the openEHR path mechanism, as
shown in Figure 4. Accordingly, a set of mapping rules was
designed for storing archetype structures in a labelled
property graph database:
1 Each archetype is mapped to a subgraph.
2 Each archetype node path is mapped to a branch in the
subgraph.
3 Each archetype node is mapped to a node in the
subgraph.
4 Each archetype node corresponds to a class in the RM.
The RM class names are mapped to node labels in the
subgraph.
5 ‘archetype_id’ and ‘archetype_node_id’ attributes are
mapped to node properties in the subgraph.
6 Class attributes are mapped to relationship types.
Figure 4 Graph model of the openEHR reference model
(see online version for colours)
3.2 Storing and retrieving archetyped data with
Neo4j
Our method employs Cypher queries (Neo4j Inc.,
https://neo4j.com/developer/cypher-query-language/) to
store and retrieve openEHR data. Cypher is a declarative
graph query language for Neo4j graphs. To store archetyped
data, leaf node paths were extracted from the archetypes’
definitions, as shown in Table 1.
Table 1 Example of an extracted leaf node path
Leaf node
class
Element
Path [openEHR-EHR-OBSERVATION.body_
weight.v1]/data[at0002]/ events[at0003]/
state[at0008]/items[at0009]
Table 2 Cypher statement to store a graph branch
CREATE (OBSERVATION {archetype_id: ’openEHR-EHR-
OBSERVATION.body_weight.v1’}) -[:data]-
>(HISTORY{archetype_node_id: ‘at0002’})-[:events]->
(POINT_EVENT{archetype_node_id: ’at0003’})-[:state]->
(ITEM_TREE{archetype_node_id: ‘at0008’})-[:items] -> (
ELEMENT{archetype_node_id: ‘at0009’})
After the mapping rules were applied, Cypher CREATE
statements were formulated to store the corresponding graph
branches. Table 2 shows the Cypher CREATE statement
Graph databases for openEHR clinical repositories 287
needed to store the extracted node path previously shown in
Table 1.
To retrieve archetyped data, Cypher queries traversing
the graph were built. The queries indicate the class
attributes as relationship types and the node predicates, i.e.,
‘archetype_id’ and ‘archetype_node_id’ as node attributes.
3.3 Test datasets generation
To evaluate the repository implementation approach, we
needed datasets containing a large number of records that
complied with the openEHR data models. However,
structured EHR data are difficult to obtain and usually
governed by strict privacy laws when available. To ensure
our ability to share the dataset used in this evaluation in the
future and thus guarantee the reproducibility of this
experiment, we decided to artificially generate the datasets.
By doing so, we sacrificed some realism in favour of
accessibility.
We artificially generated datasets simulating a
pregnancy home-monitoring data repository. The simulated
repository corresponds to an application that would allow
pregnant women to view information relating to their
pregnancy and to report pregnancy related symptoms. The
contents of the datasets corresponded to clinical concepts
and realistic data entries identified through discussions and
interviews with antenatal care experts. The structure of the
data was dictated by the openEHR RM and the definitions
of the archetypes. The dataset generation process is shown
in Figure 5.
Figure 5 openEHR dataset generation process
We started by reviewing the Japanese obstetrical guidelines
to gain an initial understanding of the antenatal care
concepts and processes (Minakami et al., 2011). Following
the review, we conducted two semi-structured interviews
with an obstetrician and a midwife. During the interviews,
we took notes describing the information flow during the
care process. We also identified the clinical information that
they considered relevant to report when using a pregnancy
home-monitoring application. During the interview with the
obstetrician, we used a checklist to determine the possible
symptoms that pregnant women may experience and the
information the pregnant women need to provide when
reporting such symptoms. The interviews allowed us to
identify the clinical concepts that could be involved in a
pregnancy home-monitoring application and a list of
realistic data entries that we could use to populate the
database.
Next, we mapped the clinical concepts to openEHR
archetypes available in the openEHR clinical knowledge
manager (CKM) (openEHR, 2016) and created data value
sets corresponding to the possible data entries. The high-
level structure of the simulated records along with the
employed archetypes is shown in Figure 6.
Figure 6 The high-level structure of a simulated record
The simulated EHR records were modelled to contain the
date of birth, the obstetric history, the current pregnancy
summary and reports of pregnancy-related symptoms. In
total, 11 archetypes found on the CKM were used without
modification and four templates representing the different
types of compositions were created using the ocean
informatics template designer (Ocean Informatics,
http://www.openehr.org/downloads/modellingtools). Each
of the generated EHR records includes five compositions
with a total of 42 nodes, out of which 19 nodes are leaf
nodes containing the data entries.
We then applied an ORM approach to design a
relational schema allowing the persistence of the required
archetypes over classes from the openEHR RM. The
relational schema over which the data generation plans were
executed is shown in Figure 7. The plans were executed
using Microsoft Visual Studio 2010 to populate a Microsoft
SQL server database.
288 S. El Helou et al.
Figure 7 Object relational mapping schema
Figure 8 Generation of the health summary composition (see online version for colours)
Graph databases for openEHR clinical repositories 289
Figure 9 Generation of the obstetric history composition (see online version for colours)
Figure 10 Generation of the pregnancy history composition (see online version for colours)
290 S. El Helou et al.
Figure 11 Generation of the self-monitoring composition (see online version for colours)
Each generated record contained one date of birth entry, one
obstetric history entry, one pregnancy summary entry and
two reports of pregnancy symptoms. To generate the
records, we created 24 data generation plans using
Microsoft Visual Studio 2010. First, we generated the EHR
compositions with unique IDs. Then each generation plan
was applied to create different branches of the EHR record
as shown in Figures 8, 9, 10 and 11. The generation plans
contained possible value lists that were randomly assigned
in a uniform way across all the generated instances. In
certain cases, we had to create rules to make sure the data
made sense. For example, when generating the estimated
dates of birth and the dates of conception, we made sure that
the estimated date of birth would be nine months post the
date of conception.
In total, five different size datasets were generated to
evaluate the effect of dataset size on the performance of
queries and required storage space. Sets named S1K, S5K,
S10K, S50K and S100K contained 1,000, 5,000, 1,0000,
50,000 and 100,000 records respectively.
In the prefecture where this research was conducted,
approximately 20,000 births take place every year. In the
institution where the EHR application is being developed, it
is estimated that 15 antenatal visits occur daily and up to
300 women receive antenatal care in a year. The institution
is a major university hospital; therefore, we expect that
other institutions would provide care for a smaller number
of women per year. Taking into consideration the previous
estimations, the size of the datasets aims to simulate the
following situations:
S1k, containing 1,000 records, simulates a situation in
which the application is used in one institution (200
pregnancies/year) over 5 years.
S5k, containing 5,000 records, simulates a situation in
which the application is used in three major institutions
(1,000 pregnancies/year) over 5 years.
S10k, containing 10,000 records, simulates a situation
in which the application is used by 10% of the pregnant
women (2,000 births/year) over 5 years.
S50k, containing 50,000 records, simulates a situation
in which the application is used by 50% of the pregnant
women (10,000 births/year) over 5 years.
S100k, containing 100,000 records, simulates a
situation in which the application is used on a
prefectural level (20,000 births/year) over 5 years.
After the datasets were created in Visual Studio 2010, they
were exported as comma-separated values (CSV) files. The
CSV data was imported into Neo4j and merged into a graph
structure aligning with the proposed graph model of the
openEHR RM. The S10k dataset can be accessed and
downloaded as a Neo4j database via http://openehr-test-
dataset.herokuapp.com/dataset.html.
3.4 Evaluation setup
To create the query set used in the query response time
evaluation, we identified usage scenarios expected to occur
in clinical and home monitoring settings when using the
home-monitoring application. Each usage scenario was
Graph databases for openEHR clinical repositories 291
mapped to a query. The usage scenarios are shown in
Table 3.
Table 3 Home monitoring application use-case scenarios
Use of the home-monitoring app
Scenario 1
During the
antenatal
care visit
Create a new EHR
Add the DOB, the pregnancy summary and the
pregnancy history to the EHR
View the EHR record
View the symptoms list
View the added symptoms since the previous
visit
Scenario 2
While
using the
app
View EHR record
Insert a new symptoms list
View the symptoms list
Update a symptom
Accordingly, a set of seven queries were identified:
Q1 find the health information present in one health
record
Q2 add a new symptom entry to one health record
Q3 update a symptom entry in one health record
Q4 find the symptoms list in one health record
Q5 create a health record
Q6 add the date of birth, pregnancy summary and
pregnancy history to one health record
Q7 find the symptoms reported since period X in one
health record.
Equivalent SQL and Cypher queries were written to
represent the seven identified queries.
As mentioned earlier, in the institution where the EHR
application is being developed, it is estimated that 15
antenatal visits occur daily and up to 300 women receive
antenatal care in a year. According to these estimations the
query requests would have different frequencies and are
estimated as follows:
frequency of Q1, Q2, Q3, Q4: 250 times/day
frequency of Q5, Q6, Q7: 30 times/day.
Similar to Freire et al. (2016), the evaluation criteria were
the database required storage space and query response
time. However, in this study we considered clinical queries
since they are the types of queries required in the
application’s usage scenarios. Clinical queries return
requested data values existing in a specific EHR.
The performance evaluation was conducted using:
Intel(R) Xeon(R) CPU E5-3620 v3 @ 2.40 GHz 2.40
GHz with 32GB of memory, over Windows 10
enterprise version 1607 64-bit operating system
Neo4j community version 3.0.1
Microsoft SQL server 2016.
The queries were executed using Neo4j browser and
Microsoft SQL server management Studio 2016. If not
configured otherwise, Neo4j assumes that the entire RAM
on the machine is available to run the Neo4j server.
Similarly, Microsoft SQL server dynamically changes its
memory requirements based on the available system
resources. To ensure a fair comparison, the maximum server
memory for Microsoft SQL server was set at 32 GB. Each
query was executed 15 times over the five different datasets
in both database technologies.
3.5 Labelling and indexing
Both Microsoft SQL server and Neo4j have an indexing
mechanism to accelerate query executions. In Microsoft
SQL Server, the queries perform JOIN operations over the
tables using the id’ property. The archetype_id’ and
‘archetype_node_id’ attributes are used as conditions in the
WHERE clauses of the SQL queries. To optimise the
performance of Microsoft SQL server, indexes are applied
over the ‘id,’ ‘archetype_id,’ and ‘archetype_node_id’
columns in all tables. Indexes which could possibly improve
the query response time and were indicated as missing by
Microsoft SQL server management studio were also
applied.
In Neo4j, the query response time could be improved
through the creation of node labels. In the labelled property
graph model, nodes can have any number of labels assigned
to them, indicating the role of the node in the domain.
Labels can be used in queries to identify the starting nodes
for a traversal, thus allowing for more efficient node
lookups. If the nodes are labelled, schema indexes can be
created for each label and property combination. In Neo4j,
schema indexes are helpful to locate the start node of each
query. Once the start node is located, Neo4j executes
traversals over the queried path. Two indexing strategies
were applied in Neo4j. The first strategy is similar to the
indexing strategy applied with SQL server, where indexes
were created for the following (label, property)
combinations: (EHR, id), (COMPOSITION, archetype
_id), (EVALUATION, archetype_id), (OBSERVATION,
archetype_id), (ITEM_TREE, archetype_node_id),
(HISTORY, archetype_node_id), (POINT_EVENT,
archetype_node_id), (EVENT_CONTEXT, archetype_
node_id), (CLUSTER, archetype_id), (ELEMENT,
archetype_node_id). In the second strategy, we only
indexed the (EHR, id) combination since all of the queries
deal with individual EHR records, implying that the starting
node is located by searching for a specific EHR id. When
comparing the performance of both implementation
approaches, the first Neo4j indexing strategy was used since
it resulted in faster query response times.
292 S. El Helou et al.
4 Results
We compared our approach using Neo4j with an ORM
approach using Microsoft SQL server in terms of required
storage space and query response time. We first show the
required storage space in both approaches after the indexing
was applied. Then, we show how query response times
compared using both approaches.
4.1 Storage space requirement
Figure 12 shows the required storage space for each of the
databases after the indexing was performed. The Microsoft
SQL server database required less storage space for the
S1K, S5K and S10K datasets. Neo4j required less storage
space for the S50K and S100K datasets.
4.2 Query response times
The dataset size and the type of query are the two main
factors affecting the query response times in both
implementation approaches. At first, we show the effect of
the dataset size and then we show the effect of the query
type.
Figure 12 Required storage space by dataset
1k 5k 10k 50k 100k
SQL
Server
with
indexes 24 95 185 904 1760
Neo4j
with
Indexes 33 109 212 701 1520
STORGAE
SPACE
(MB)
Figure 13 Effect of dataset size on the response time (see online version for colours)
10
1000
X1K X5K X10K X50K X100K
Dataset Size
Query Response Time (ms)
Database
SQL Server
Neo4j
Graph databases for openEHR clinical repositories 293
Figure 14 Effect of the query type on the response time (see online version for colours)
Figure 15 Effect of indexing on storage space (see online version for colours)
Figure 13 shows how both implementation approaches
performed for the different size datasets. The queries are
grouped together to simulate a complete usage scenario of
the home-monitoring application. Neo4j performed better
than Microsoft SQL server for all of the dataset sizes.
However, Neo4j had a large number of outliers, while
Microsoft SQL server maintained a more stable
performance. The outliers were mainly the result of
submitting a query to the server for the first time, meaning
that these outliers would not occur in a system in which the
server has been warmed up.
Figure 14 shows how both implementation approaches
performed for the different types of queries. The response
times for each query over the different dataset sizes are
grouped together. Neo4j performed better than Microsoft
SQL server for all the query types. The results also show
that the type of query has almost the same effect over the
performance of both implementation approaches where Q2,
Q3 and Q7 have a longer response time with both
implementation approaches. More detailed results about the
query response times in each dataset are provided in the
Appendix section.
294 S. El Helou et al.
5 Discussion
We proposed an implementation approach of openEHR
repositories using a labelled property graph database. We
compared a Neo4j implementation of the proposed approach
with a Microsoft SQL server implementation of the
commonly used ORM approach. The results confirm that
the ORM approach is not optimal for storing and querying
openEHR data and that the graph model could provide a
better overall performance. On the other hand, we can see
that Neo4j had a larger number of extreme outliers. These
outliers were mainly the response times that corresponded
to the first time a certain query was submitted to the server.
We can conclude that Neo4j has a limited performance with
ad-hoc queries. However, ad-hoc queries could be avoided
in clinical settings. In the institution in which this research
was conducted, about 90% of the queries are cached
beforehand. Therefore, the limited performance of Neo4j for
ad-hoc queries would not be a practical concern for the
performance of clinical queries.
In terms of required storage space, the Microsoft SQL
server implementation required less space for the smaller
datasets while the Neo4j implementation required less space
for the larger datasets. One way to explain this is by looking
at the effect of indexing on the required storage space in
both database technologies, shown in Figure 15. Without
indexes, the Microsoft SQL server implementation of the
ORM approach requires the least storage space. However,
we see a threefold increase in the required storage space
after the indexes were added. For Neo4j, adding the indexes
increased the required storage by a maximum of 10%. With
relational databases, indexing cannot be practically avoided
because it greatly reduces the query response times when
JOIN operations over large tables are executed. Thus, these
results suggest that for larger datasets, Neo4j would be more
space efficient.
In addition to a promising overall performance, the
proposed approach using Neo4j was more straightforward
and easier to implement. For example, during this study,
Cypher queries required less than half the number of logical
lines of code (LLOC) than those required for the SQL
queries. The ease of implementation was due to the
semantic alignment between the openEHR RM and the
labelled property graph model, the schema-less nature of
graph databases, the declarative nature of Cypher and the
degree of similarity between the Cypher and AQL queries
(openEHR, 2008a). Furthermore, using Neo4j’s browser,
we were able to directly visualise the EHR as a semantic
graph. A survey of openEHR learning approaches (Sundvall
et al., 2016) proposed the use of interactive graphical
representations to browse and manipulate EHR instance
data to learn openEHR, a process usually described as
difficult and time consuming. Our experience suggests that
the ease of implementation and visualisation allowed by
Neo4j could be of value for beginners approaching
openEHR.
Limitations of this study include the nature of the used
datasets and the limited number of explored clinical
use-cases. The first limitation resulted from a lack of access
to real datasets, a common issue faced by different
groups testing the performance of clinical repository
implementations. To conduct the performance evaluation,
we used artificially generated datasets instead of real EHR
data. The EHRs in the generated datasets contain five
compositions each, a number likely to be surpassed in a real
production scenario. However, since we could generate
different size datasets, we consider our method sufficient to
highlight the difference in performance between the two
compared implementations when the dataset size grows.
On the other hand, the simulated datasets used for the
evaluation do not include image or video files. In reality,
EHR data is heterogeneous and includes variable data types.
To handle a variety of data types, a polyglot-persistent
systems approach for storing and querying EHR data was
previously proposed (Kaur and Rani, 2015a, 2015b).
Furthermore, EHR implementation tutorials recommend a
hybrid approach if it improves the querying performance
(Gutiérrez et al., 2015). Further research is required to
determine which database technology and implementation
approach fits for each type of openEHR data and how these
technologies can be integrated smoothly in a polyglot-
persistent systems schema.
Finally, we note that the number of use-cases explored
in this study is limited and does not represent a full usage
scenario, nor do they consider concurrent transactions,
which is essential when evaluating the performance of
clinical querying over an EHR repository.
6 Conclusions
Aligning with the need for scalable archetype-based
repository implementations, we proposed, tested and
evaluated an approach for storing and querying openEHR
archetype-based data using a labelled property graph
database. We compared the proposed approach
implemented over Neo4j with the ORM approach
implemented over Microsoft SQL server in terms of query
response time and required storage space. The evaluation
was performed using different size datasets and a query set
simulating a pregnancy home-monitoring application. The
experimental results showed that the proposed
implementation is more space efficient for larger datasets
and results in lower query response times for clinical
queries. This work encourages further research on graph
databases as a possible alternative to conventional database
technologies for clinical repositories.
Acknowledgements
This research was partly funded by JP16686872
Grant-in-Aid for Exploratory Research and JP15611340
Grant-in-Aid for Scientific Research (C).
Graph databases for openEHR clinical repositories 295
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Appendix
Figure 16 Query response time over the 1K dataset (see online version for colours)
10
1000
Q1 Q2 Q3 Q4 Q5 Q6 Q7
Query
Query Response Time (ms)
Database
SQL Server
Neo4j
Graph databases for openEHR clinical repositories 297
Figure 17 Query response time over the 5K dataset (see online version for colours)
10
1000
Q1 Q2 Q3 Q4 Q5 Q6 Q7
Query
Query Response Time (ms)
Database
SQL Server
Neo4j
Figure 18 Query response time over the 10K dataset (see online version for colours)
10
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Q1 Q2 Q3 Q4 Q5 Q6 Q7
Query
Query Response Time (ms)
Database
SQL Server
Neo4j
298 S. El Helou et al.
Figure 19 Query response time over the 50K dataset (see online version for colours)
10
1000
Q1 Q2 Q3 Q4 Q5 Q6 Q7
Query
Query Response Time (ms)
Database
SQL Server
Neo4j
Figure 20 Query response time over the 100K dataset (see online version for colours)
10
1000
Q1 Q2 Q3 Q4 Q5 Q6 Q7
Query
Query Response Time (ms)
Database
SQL Server
Neo4j
... For such clinical practice, healthcare providers typically perform create, read, update, and destroy (CRUD) operations to retrieve and modify a relatively small number of several EHR extracts easily. Minimizing the response time of these CRUD operations may enhance EHRs' usability and functionality [31]. A fundamental principle in medical systems is that clinical data cannot be overwritten. ...
... Graph databases have been recently introduced as a potential alternative to relational databases for handling graph-like data structures [73,74]. A graph-based implementation method [31] was suggested and evaluated for an archetype-oriented repository utilizing a labeled property graph database. This method was used as an alternative to traditional relational database architecture for clinical data storage. ...
... As the RM includes several classes in a deep tree hierarchy, it has a graph-like architecture. As a result, mapping it to a graph model and storing it in a graph database would be easy [31]. ...
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