Information Technology Reference
In-Depth Information
interoperability regulation in Europe. Next, a proposal for contextualized access to
EHR and the adaptation to interoperability is presented. The last section is dedicated
to conclusions.
23.2
Background
First of all we must indicate that the proposal presented here has been developed in
collaboration with the University Hospital San Cecilio from Granada, and that we
have based their Electronic Health Record System, and used it as reference.
This system stores around 800.000 EHR, containing more than 50 millions doc-
uments. In the future it is expected to have a fast increase in the size, due to the
inclusion of new types of documents from two sources: old documents that still
have not been digitalized (scanned images, MRI, etc.) and new documents gener-
ated from the recently and future acquired devices and equipments like PAC's.
In this section we briefly show the characteristics of this EHR system, as well as
the structure of the Electronic Health Records stored on it.
23.2.1
Electronic Health Records Structure
The information stored in the EHR is structured according to the
Reference Model
given by the [28]. According to this standard, the elements of the hospital infor-
mation systems are organized according to an Ontology with a class
structure
that
gives rise to the following classes:
•
Fo l d e r
: This class represents the divisions at the highest level inside the clinical
history. In our case these divisions are the
assistance acts
and the
pathologies
,
so all the documents in the EHR are grouped into assistance acts or pathologies,
and both classifications coexist.
•
Section
: This class of the standard represents logical groupings of information,
each one representing a set of data with an uniform informative clinical guidance
(Figure 23.1), and corresponds to each
document
stored in the EHR. Examples
of documents are from a blood analysis to a preanaesthetic study, or from an
admission document to a X-ray test.
•
Entry
: According to the standard each entry represents a clinical observation or
a set of them. It corresponds to what we call
data groups
(i. e. the hematology
information in a blood analysis).
•
Cluster
and
Element
: These classes correspond to what we call
data items
.
The difference between these classes is that the first one is used to represent
an unique observation or action (a data item) that requires a complex structure
like a list, a table or a temporal series (i.e. an electrocardiogram); whereas the
second class represents a unique and simple value, instance of some of the types
defined by it (i.e. the percentage of hematocrit in a blood analysis).
