Design and implementation of the GLIF3 guideline execution engine

Abstract

We have developed the GLIF3 Guideline Execution Engine (GLEE) as a tool for executing guidelines encoded in the GLIF3 format. In addition to serving as an interface to the GLIF3 guideline representation model to support the specified functions, GLEE provides defined interfaces to electronic medical records (EMRs) and other clinical applications to facilitate its integration with the clinical information system at a local institution. The execution model of GLEE takes the "system suggests, user controls" approach. A tracing system is used to record an individual patient's state when a guideline is applied to that patient. GLEE can also support an event-driven execution model once it is linked to the clinical event monitor in a local environment. Evaluation has shown that GLEE can be used effectively for proper execution of guidelines encoded in the GLIF3 format. When using it to execute each guideline in the evaluation, GLEE's performance duplicated that of the reference systems implementing the same guideline but taking different approaches. The execution flexibility and generality provided by GLEE, and its integration with a local environment, need to be further evaluated in clinical settings. Integration of GLEE with a specific event-monitoring and order-entry environment is the next step of our work to demonstrate its use for clinical decision support. Potential uses of GLEE also include quality assurance, guideline development, and medical education.

Ontologies in Process Systems Engineering

“…Information is shared between recipe tools, process simulation, and safety analysis tools using the ontology as a common representation of the information that is exchanged. The process of product development is modeled explicitly using an execution system called GLEE [29], which is based on GLIF, which is an ontology for medical guideline representation [30].…”

Modern methods of process management at healthcare facilities

“…One of the main limitations of existing approaches is that they often underestimate the knowledge and data dimension. As patient treatment is knowledge-driven, the focus should be not on automating the decision making process, but rather on supporting the clinician during this process, according to a "system suggests, user controls" approach [62] that makes available the appropriate data and relevant knowledge when needed or required. Any system intended to support CGs should allow for representing and integrating at a semantic level evolving medical knowledge, patient-related data (including conditions, medical history, prescribed treatments and medications, etc.…”

Modellierung und Verifikation medizinischer Leitlinien

“…Auch GLIF [53,75] Da es sich um eine Datenspezifikation handelt, werden automatisch Axiome generiert. Diese beschreiben die hier definierten Listen als frei erzeugte Datentypen.…”