The use of HL7 in the domain of Medical Devices
Fabrizio Pecoraro and Daniela Luzi
Institute for Research on Population and Social Policies, National Research Council
Via Palestro 32, 00185, Rome, Italy
f.pecoraro@irpps.cnr.it
d.luzi@irpps.cnr.it
Abstract— The paper describes the approach used to design and
develop a Medical device information system (MEDIS) focusing
in particular on the description of the Domain Analysis Model
(DAM) of medical devices as well as on the conversion of the HL7
RIM meta-model into a logical data model.
Keywords—Medical devices, Clinical investigations, HL7, logical
data model
I. INTRODUCTION/BACKGROUND
Progress in clinical research depends largely on the results
of Clinical Investigations (CIV), i.e. any “study of human
subjects that is designed to answer specific questions about
biomedical or behavioural interventions (drugs, treatments,
devices, or new ways of using known drugs, treatments, or
devices)” [1]. CIVs are complex processes encompassing
different steps, from specification and planning to execution
and final result analysis. There is an increasing number of
applications related to data and trial management systems
(CDMS and CTMS) that support different aspects of trial
design [2] (protocol authoring systems, Case Report Form
generation) as well as its performance (electronic data capture
for both data analysis and regulatory submission, patient
recruitment, scheduling of clinical activities, site management,
etc.) [3,4]. In this context challenges still concern the
adaptation of data models on different clinical domains and
pathologies as well as on various organizational frameworks.
This makes it often necessary to develop tailored databases
from scratch to manage new clinical studies. Moreover, the
integration between these different legacy systems is still a
crucial issue, considering, the variety of both data and
knowledge to be managed, especially in multi-centric clinical
studies [5]. Besides the achievement of interoperability within
the framework of clinical research systems, there is an
increasing interest in linking clinical research data to hospital
and laboratory information systems as well as Electronic
Health Records (EHRs).
Within this scenario, a specific role is played by trial
registries, defined [6] as “a database of planned, ongoing or
completed trials, published or unpublished, containing details
of the trial’s objectives, patient population, sample size and
tested interventions”. The set of data contained in these
registries as well as the process of acquiring them fulfils
specific users’ information need and purposes. Nevertheless,
the integration among national and international registries
and/or between them and other e-health information system
still represents a crucial concern.
In Italy the National Research Council supported by the
Italian Ministry of Health developed an information system,
MEDIS (Medical Device Information System) that manages
the approval process of CIV on Medical Devices (MD) and
monitors its performance. In particular MEDIS supports both
applicants in the submission of data and regulatory documents
and the National Competent Authority (NCA) in the
evaluation and monitoring of CIVs.
A concise overview of the methods used in the design and
development of a national registry is provided in paragraph 3,
while the medical device domain analysis model is described
in paragraph 4, and the conversion of the HL7 meta-data
model into the logical data model of the MEDIS system is
reported in paragraph 5.
II. OBJECTIVES
This paper describes the approach used to design and
develop MEDIS focusing, in particular, on the description of
the Domain Analysis Model (DAM) and how it has been
transformed into a logical data model. The novelty of our
approach resides on the development of a conceptual model
describing MDs in framework of CIVs as well as on the
application of HL7 DAM to develop a logical data model.
III. METHODS
Taking into account the necessity to interoperate with other
systems, MEDIS design and development was based on HL7
v.3 standards following its methodology in the description of
the use cases for the identifications of the actors involved and
interactions between them. Moreover, the process description
and the identification of both data and documents necessary
for the evaluation of CIVs performed by NCAs are based on
the European Directives, ISO standards and ICH Good
medical practice guidelines.
The analysis of the already balloted HL7 domains [7], and
in particular the BRIDG project [8] was the starting point to
define MEDIS domain analysis model and the relevant
conceptual classes, attributes and data types. The BRIDG
DAM focuses in particular on the process of a clinical
investigation capturing concepts and data from the clinical
protocol, that is the source document that describes the
planned investigation in detail. On the contrary, MEDIS DAM
has to consider this process from a different perspective,
taking into account the information needs of a National
competent authority that has to assess investigation proposals
as well as monitoring their performance. For these reasons the
process to be described has to encompass also the submission
and evaluation process, while data captured during the
performed clinical investigation have to be used for
monitoring purposes.
Therefore, we adopted some BRIDG conceptual entities
that can be easily mapped with our domain. For instance, we
modeled classes describing actors considering: (1)
E_Organization CMET that identifies an organization
including its employees and its relationship with other
organizations; (2) the Study Participation model that describes
information related to the sites in which clinical investigations
are conducted and investigators involved in each site as well
as the information on the scientific, ethical, and regulatory
committees. However, to capture particular aspects of our
domain we had to develop a customized model such as
medical devices, regulatory documents, adverse event
reporting. A detailed description of the DAM is reported in a
previous publication [9], while this paper focuses on the
device domain model.
Moreover, in order to design the MEDIS database we
adopted a novel approach, which converts the DAM schema
into a logical data model based on HL7 Reference Information
Model (RIM) (see § 5).
IV. MEDICAL DEVICE DOMAIN MODEL
To model the description of the products involved in the
whole clinical investigation lifecycle a set of characteristics of
the MD domain has been taken into account. The variety of
MD types (ranging from implantable to imaging devices) as
well as their different application in the delivery of care
(diagnostic, treatment, prevention) makes it difficult to have a
MD comprehensive conceptual description. Moreover, in the
framework of an approval process of a CIV the investigational
device is described in detail in regulatory documents such as
clinical protocol, risk assessment document and Investigator’s
brochure. Other MDs are often mentioned in the regulatory
documentation to prove for instance the safety requirements of
the investigational MD based on the characteristics of a
similar MD already commercialised. Another example
concerns the case when a comparator is used in a CIV to
measure the efficacy of the MD under investigation. Given
this premise, the use of HL7 RIM resulted to be a valid means
to develop a product conceptual model (fig. 1) that can be
decomposed into the following conceptual areas:
 The static area represented by the RIM classes Entity and
Role that describe a general artefact involved in a
clinical investigation processes and, its role (i.e. an
artefact has the role of a MD);
 The dynamic area represented by the RIM classes
Participation and Act, that describe the different subprocesses of the clinical investigation lifecycle (i.e.
Notification, Evaluation and Clinical Investigation) and
how any artefact takes part in each sub-process (i.e. a
MD participates as a investigational device in the
activity of performing a clinical investigation).
Figure 1. Medical Device DAM
The static area entails only two Entities: ArtefactKind and
ArtefactInstance. Using the Entity ArtefactKind it is possible
to model a common description of any product, i.e. name,
description, models. This class is related to a set of Role
classes that are the basis to distinguish the different types of
products involved in the clinical investigation and to
specialize the description of each artefact. For example the
Roles MD and Drug have a set of attributes that describe
respectively the specificity of a medical devices class (such as
risk class, national and international classification codes) and
pharmaceutical products (such as drug active principle, drug
classification). As highlighted in figure 1 this model considers
data necessary to evaluate safety requirements of a device
under investigation extracted from regulatory documents (see
Evaluated MD box) as well as information used to track MD
in the CIV performance (see Tested MD). The
determinerCode of the RIM class Entity has been used to
distinguish whether any given artefact stands for a specific
object (determinerCode = Instance, i.e. an explicit stent
implanted in a given patient) or an object description
(determinerCode = Kind, i.e. the class of stent investigated in
the clinical trial described in clinical protocol). This model
creates an isomorphism in which the Roles related with the
Entity ArtifactKind are mirrored in the Entity ArtifactInstance.
The dynamic area allows defining the functions covered by
each artefact in the whole clinical investigation lifecycle.
Using the RIM stereotype Participation it is possible to
distinguish, for example, whether a MD described in the static
area is an Investigational, a Comparator or a Similar device.
This approach is also used to describe a drug that can be either
dispensed by a MD (Pharmaceutical product) or used as a
Comparator during a clinical investigation. Finally, the
stereotype Act has been used to model the three main subprocesses of the clinical investigation lifecycle: Notification,
Evaluation and Clinical Investigation.
V. LOGICAL DATA MODEL
The integration of the HL7 standards in healthcare
information systems need to map the HL7 RIM and more
specifically the RMIM (Refined Message Information Model)
classes with the tables of the database schema. This task is
very complicated due to the heterogeneity of data models,
schema structures and query language they support [10].
Moreover, the association of the HL7 RIM to a database table
requires the identification of the multiple relationship that link
HL7 classes. For this purpose different mapping techniques
are used [10,11], but usually they do not provide satisfactory
results. Generally, automatic tools that map HL7 still require
human intervention as well as a profound knowledge of both
HL7 and the domain of application. Moreover, these tools
developed by middleware applications are usually timeconsuming and expensive parts of in the design and
development of interoperable information systems.
In this paper we propose a novel method to design a logical
data model based on the DAM to develop the MEDIS
relational database. The purpose of this technique is to create
a logical data model that closely matches the RIM logical
meta-model so that the mapping between HL7 messages and
the database is straightforward.
Similar approaches are proposed in the literature.
Eggebraaten [12] uses both the entity-relationship (ER) and
the entity-attribute-value (EAV) methodologies to map the
RIM classes into tables of the physical data model. The EAV
model is applied in particular to RIM Observation class to
capture different data types of the attribute value, while ER
model is used to map the other RIM classes. Based on
Eggebraaten approach, Yang et al. [13] map the RMIM and
CMET classes into a physical database in the domain of
ambulatory encounters.
HL7 has established a working group (RIMBAA, RIM
Based Application Architecture) focused on the use of the
RIM model to design clinical application and databases,
promoting the development of HL7 version 3-compliant
applications. RIMBAA proposes a technology matrix that
contains all possible “roadmaps” to transition between the
persistence layer and the serialized representation (e.g. XML
messages) of the data [14].
Our approach took advantage of the RIM meta-model - thus
privileging an ontological point of view - to represent a
domain where data and data types had to be identified almost
from scratch being the standardisation in this domain still at
an initial stage. Therefore, compared to other approaches, our
mapping concerns the high level RIM classes to define both
the multiple association between the objects of the MD
domain and the rules that represent their behaviour. Similarly
with the RIMBAA reference implementation [15], we used
Hibernate framework to map Java objects into a relational
database. In figure 2 a portion of the MEDIS logical data
model showing the MD representation is depicted. It is
divided in two different conceptual areas:
 The instance area that exactly transforms each DAM
class into a table of the database. These tables store all
information collected during the whole clinical
investigation process. Moreover, this area also explicitly
maps the six base classes of the HL7 RIM backbone.
 The rule area that represents the logic governing the
relationship allowed between the instance area tables as
well as the business process of the clinical investigation
lifecycle.
In the instance area for each class of the DAM a separate
table is created containing all the attributes for that specific
class as well as those inherited for its parent classes.
Furthermore, each DAM tables is associated with a RIM table
as a generalization depending on its stereotype, for instance
the table ArtifactKind is associated with the table Entity.
Moreover, the clinical investigation sub-processes are stored
in the instance area in the Notification, Evaluation and
Clinical_Investigation tables. In our approach the relationship
between these tables is stored in the Act_Relationship table
that tracks the evolution of the business process of a single
CIV proposal. In this way it is possible to identify all steps of
the entire process as well as the state reached by each CIV
proposal.
Figure 2. MEDIS logical data model
The rule area is composed by a set of tables that store the
different type of the instance objects depending on their
stereotype, for example the Act_Type table contains the
different category of the CIV sub-processes tables (i.e.
Notification,
Evaluation
and
Clinical_Investigation).
Moreover, the Allowed tables store the rules that express the
allowed relationship between the instance area tables as
modeled in the DAM (for example the Allowed_Role_Entity
table express that the relationship between the Artifact_Kind
and the MD_Kind is permitted). Moreover, the tables of the
rule area are used by the business logic layer of the MEDIS
system to determine the functionalities active in a given time.
This makes it possible for example to prevent CIV applicant
to notify a serious adverse event before the clinical
investigation has started.
Despite these benefits, some limits appear evident in
respect of the complexities of the overall database schema
realized, both in terms of the expression of complex queries as
well as the computational costs of the execution of the queries
themselves.
Given the central role played by regulatory documents in
the framework of NCA approval of CIVs, our next step is to
analyse clinical protocols and risk analysis documents in order
to identify their structure and information content using CDA
representation.
VI. CONCLUSION AND FUTURE WORKS
This paper describes the use of HL7 standards to design
and develop an interoperable system in the CIV on MD
domain. This is an important issue considering that the
interoperability is going to be fundamental in this domain due
to the exponential increasing of both clinical investigations
and MD deployment. The adoption of HL7 RIM to represent
MDs allowed us to describe their characteristics and in
particular the use of stereotypes makes it easier to highlight
the different artefacts involved in a CIV as well as their role in
the process. Moreover, the proposed approach to develop a
logical data model simplifies the development of a tool that
easily converts the data stored in the database in HL7 standard
messaging. Given that regulations in this domain frequently
change and MDs are continuously evolving in terms of their
technological complexity, this approach makes it also possible
to update and modify the database schema as well as the rules
governing it.
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ACKNOWLEDGMENT
This study was supported by the Italian Ministry of Health
through the MEDIS project (MdS-CNR collaboration contract
n° 1037/2007).
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