A qualitative model for physiology: apart from function and abnormality.

Physiological knowledge is often described in terms of mathematical models in the domain of bioinformatics, and some ontologies have been developed to integrate these models. However, such models do not explicitly describe clinicians' qualitative knowledge, which is required for clinical applications including decision support and counseling of patients to help them understand their clinical situation. This paper proposes a description framework for a qualitative and context-independent ontology of physiology, QliP, which has three features: 1) It models physiological knowledge qualitatively without mathematical knowledge; 2) The described knowledge is independent of surrounding anatomical entities and abnormality; and 3) It targets physiological components in varying degrees of granularity, from cells to organ systems. An ontology based on this proposed model enables automatic generation of a physiological state transition, starting and ending with a given state.

An analysis of the openehr archetype semantics based on a typed lambda theory.

The openEHR has adopted the dual model architecture consisting of Reference Model and Archetype. The specification, however, lacks formal definitions of archetype semantics, so that its behaviors have remained ambiguous. The objective of this poster is to analyze semantics of the openEHR archetypes: its variance and mutability. We use a typed lambda calculus as an analyzing tool. As a result, we have reached the conclusion that archetypes should be 1) covariant and 2) immutable schema.

Evaluation of a Context-based Prescription Alert System: A Clinical Perspective.

Medication alert systems have been implemented worldwide. The purpose of this study is evaluation of the medication alert systems from a clinical perspective. We surveyed physicians with regard to their reactions to the medication alerts. We collected the revised prescription information and assessed risk avoidance in all cases. The system reviewed 51,006 prescriptions and produced 16,718 physician alerts related to 13,823 prescriptions over the course of 1 month. We identified 45 prescriptions that were revised following the alert and four cases in which patient treatment may have been discontinued or adverse drug events (ADEs) may have occurred if the alerts had not been issued. We demonstrated that the system prevented these potential medication errors. This study adopted a clinical perspective and demonstrated that a real-time alert system can contribute to prevention of ADEs.