Healthcare Information System Modelling

Healthcare Information System Modelling

Jean-Luc Hainaut (University of Namur, Belgium), Anne-France Brogneaux (University of Namur, Belgium) and Anthony Cleve (University of Namur, Belgium)
DOI: 10.4018/978-1-4666-6339-8.ch022
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Abstract

This chapter studies the requirements for a wide range of healthcare information systems, including, but not limited to, clinical pathways management, patient record management, home care management, and medical personnel and resource management. The analysis concentrates on the description and management of medical activities, leaving aside the standard management processes common to all enterprises. It develops a generic architecture for these information systems comprising four central submodels devoted to the description, respectively, of organizational structures, care processes, information, and resources. Each submodel is analysed independently of the others then integrated into a consistent global model. Extensions of this model to other facets of the healthcare information system are discussed and some practical applications are suggested.
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Background

Many models and standards do exist in the broad domain of healthcare information systems (HIS). Each of these models focuses on one or several particular aspect(s) of the system including the modelling of care guidelines and processes, clinical information, clinical resources and organization, and information/process security. In this section we briefly summarize the major HIS models and standards by identifying their main underlying concepts.

Care Guidelines

EON (Tu & Musen, 2001) is a guideline modelling and execution system that includes an extensible, component-based suite of models to represent parts of a clinical guideline, domain ontologies, a view of patient data, and other entities (e.g., those defining roles in an organization). The guideline model defines guideline knowledge structures such as eligibility criteria, abstraction definitions, guideline algorithm, decision models, and recommended actions. A guideline algorithm consists of a set of scenarios, action steps, decisions, branches, and synchronization nodes that are connected through followed-by relationships.

GLIF (Boxwala et. al., 2004) is a language for modeling and executing clinical guidelines. In addition to defining an ontology for representing guidelines, it also defines a medical ontology for representing medical data and concepts. The guideline ontology covers several kinds of guideline steps such as Action, Decision, Patient_state, Branch and Synchronization.

PRODIGY (Johnson et. al., 2000) is a guideline model that was initially designed to support the management of chronic diseases such as asthma, angina or hypertension. According to this model, a guideline is organised as a network of patient scenarios, management decisions and action steps which, in turn, may produce further scenarios. The sequencing of action steps is achieved by followed-by relations.

PROforma (Sutton & Fox, 2003) is a guideline representation language supporting the management of medical procedures and decision systems. According to this language, a guideline application is modelled as a set of tasks and data items. The notion of a task is central - the PROforma task model divides generic tasks in four subcategories: plans, decisions, actions and enquiries.

GUIDE (Ciccarese, 2004) is a multi-level architecture that integrates:

  • 1.

    A formalized model of the medical knowledge contained in clinical guidelines,

  • 2.

    A workflow/care process management system, and

  • 3.

    An electronic patient record system.

The message-based interaction between the GUIDE subsystems is defined through specific contracts, and relies on common ontologies, terminologies and datatypes. The care process model of GUIDE is based on Petri nets.

SAGE (Tu et. al., 2007) is a guideline model that integrates guideline-based decision support with care processes. The model includes organizational knowledge to capture workflow information and resources. The guideline-driven processes are modelled by means of Activity Graphs, while Decision Maps are used to represent recommendations involving decisions. SAGE is based on existing standard models and terminologies such as the HL7 Reference Information Model and the SNOMED Clinical Terms.

As identified by Peleg et. al. (2003), the major differences between the clinical guideline modelling languages reside in the underlying decision models, the representation of goals, the use of scenarios, and the structured medical actions.

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