An object-oriented design for automated navigation of semantic networks inside a medical data dictionary
Introduction
Modern hospital information systems (HIS) strive to comprise a complete electronic patient record. They can however no longer be considered as singular applications. Van de Velde [38] defines a modern hospital information system with the following words:
An HIS serves essentially as a medium for communication between the diverse collaborating functional subsystems or units in a hospital. It acts therefore as skeleton to facilitate the integration of various systems operating at subsystem level.
In addition to patient data such information systems will offer access to an increasing variety of medical information sources. Such sources can comprise medical textbooks and guidelines as well as literature search programs such as MEDLINE. They can provide full library access, drug data sources, teaching programs and research databases. In some institutions the integration of medical contents has been formalized, e.g. in the American IAIMS (integrated advanced information management systems) program [17]. However, with more and more medical information sources available, information searching becomes increasingly tedious and cannot be performed quickly during daily routine any more. Approaches have been proposed in the shape of so called infobuttons [15], [11]. The generic idea behind this approach is that data from an application the user is working with, are fed into a search mechanism which narrows the search results to the most appropriate information sources in the given circumstance.
Considering van de Velde’s definition of an HIS as an integration skeleton it becomes clear that such infobutton mechanisms must be independent from any specific HIS application. Only then they can be integrated in many different existing or future HIS applications dealing with patient data. Furthermore, infobutton mechanisms should also be independent of any specific information source or guideline. They should rather be able to deal with as many different kinds of information sources as possible. Within this paper, we present navigation mechanisms and an implemented solution based on a medical data dictionary as means to achieve those goals. We coined the expression ‘context sensitive information presentation’ for this solution which allows us to follow links to different appropriate information sources based on terms derived from the HIS application [5].
In our case the clinical application must be merely complemented with an infobutton. Clinical staff, which record e.g. clinical findings or intensive care plans on a computer, can press this infobutton to retrieve information for a clinical term they have marked in their application. Such clinical terms may comprise, e.g. medical findings, medical events or procedures. The infobutton activates an Internet browser and sends the term of interest to the Gießen data dictionary server (GDDS). Receiving the search term the dictionary server starts searching the semantic network of the data dictionary and returns the semantically related website information to the Internet browser.
Gießen University hospital is a 1300 bed facility caring for about 38 000 admissions and 300 000 outpatient treatments per year. The Gießen University Hospital Information System is a comprehensive and continuously evolving HIS with its roots dating back to 1989. A central clinical database, maintained on a Tandem mainframe, still forms the backbone and contains most of the clinical patient data [35]. The information system itself has been converted to a client server architecture integrating a variety of commercial and non-commercial departmental applications as described by van de Velde [27]. A total of 2000 clinical workstations and printers spread throughout the hospital in wards, doctors’ offices, outpatient clinics, theatres and partially at bedsides have access to the HIS. Starting in 1993, a variety of commercial information sources such as MEDLINE, clinical textbooks, etc. as well as in-house compiled information sources called the electronic books have been made available on clinical workstations as separate applications [1], [8], [32], [33]. The concept has always been that all required clinical information sources should be made available on each single workstation, rendering possible what we termed one stop information shopping [32]. Many information sources have been converted to HTML (hypertext markup language), the presentation format used also in the Internet and an intranet has been established with Web browsers available on most clinical workstations.
To provide an independent mechanism for the linking of on-line information sources to clinical applications a medical data dictionary has been used. Medical data dictionaries can be defined as
A central thesaurus for the controlled definition of the medical vocabulary to be applied in an HIS, which is also capable to represent the semantic relationships existing between all HIS objects and to link the local vocabulary to standardized international nomenclatures and knowledge sources [34].
There are two common structures to arrange terms in medical data dictionaries (MDDs) [3], [13]. The terms in early data dictionaries are grouped in strict hierarchies [22], [26], [39]. More modern data dictionary concepts allow semantic links besides hierarchy [16], [23], overcoming the limitations imposed by purely hierarchic relationships. Another shortcoming of some elder data dictionaries was a limitation in the depth of a hierarchy and the number of items per hierarchy level [29].
Semantic structure is flexible and has no structural limitation. Medical terms can be linked with semantic relationships as needed. It allows for a simple and straightforward representation of real world conditions. Therefore it is comparatively easy to link any given medical term or procedure derived from clinical applications to any information source. On the other hand, navigating semantic networks is somewhat complicated. The method to represent the semantic network for navigation purposes should have no limitation either.
Gaining its first experience with the US derived PTXT (pointer to text) data dictionary [22] during an experimental HELP HIS installation, the staff of Gießen University medical information processing department has developed several of its own data dictionaries [3], [28], [30], [31]. The data dictionary used in our project is based on the architecture of MDD-GIPHARM [31] which was originally designed to support drug charting and the implementation of knowledge based functions to monitor the prescription [7]. The MDD-GIPHARM architecture allows the definition of a vocabulary of medical terms, which can be linked by semantic relationships. In the following sections whenever we reference MDD this architecture is meant.
To allow flexible linking between on-line information sources and different clinical applications an independent web based dictionary server has been implemented on top of the MDD. When passive data dictionaries can be described as a thesaurus which can be used merely for lookup purposes, a dictionary server can perform active duties offering some kind of application programming interface (API) to different applications. Therefore it is independent of any given information system. The concept of the active dictionary service is described in Refs. [4], [5]. We will reference our Gießen data dictionary server as GDDS.
Section snippets
Graph theory
Graphs, by providing a means of explicitly presenting relations using arcs and nodes, have proved to be an ideal vehicle for formalizing association theories of knowledge. A semantic network represents knowledge as a graph with the nodes corresponding to facts or concepts and the arcs to relations or associations between concepts. Both nodes and links are generally labeled [25]. Therefore in the following sections, the terminology of graph theory is used to explain our method and algorithms.
A
Applications
For the first prototype an HIS application for nurses was chosen to supply active help functions. This application is used on intensive care units to document nursing acuity and medical procedures which have been performed for a patient [2], [6]. Nursing acuity is documented using the TISS scheme (therapeutic intervention scoring system). TISS comprises a set of 75 medical and nursing procedures [20], e.g. dressing changes or urinary catheterization. We provide context sensitive information
Structural limitation
A generic method of searching all the possible paths in a semantic network from a randomly chosen start node to a fixed destination node is described in this paper. The method uses an object-oriented representation of a semantic network and accommodates any semantic network layout as long as it can be represented as a directed, unilaterally linked, simple graph.
In our method, a semantic network is represented as a group of objects called node object that encapsulates a node, its neighbors and
Conclusions
This paper described an object-oriented representation of a generic semantic network. Based on this representation a method of automated navigation of the network has been implemented. The method does not depend on any given layout of the semantic network. The resulting module called GDDS (Gießen data dictionary server) can act independent of our hospital information system. Any clinical application can be supplied with context sensitive help functions using a simple API. On the other hand, any
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