Document Type

Theses, Ph.D

Rights

Available under a Creative Commons Attribution Non-Commercial Share Alike 4.0 International Licence

Publication Details

Thesis submitted for the degree of Doctor of Philosophy to Technological University Dublin, May 2016.

Abstract

The sharing and retrieval of health information for an electronic health record (EHR) across distributed systems involves a range of identified entities that are possible subjects of documentation (e.g., specimen, clinical analyser). Contemporary EHR specifications limit the types of entities that can be the subject of a record to health professionals and patients, thus limiting the use of two level models in healthcare information systems that contribute information to the EHR. The literature describes several information modelling approaches for EHRs, including so called “two level models”. These models differ in the amount of structure imposed on the information to be recorded, but they generally require the health documentation process for the EHR to focus exclusively on the patient as the subject of care and this definition is often a fixed one. In this thesis, the author introduces a new identity modelling approach to create a generalised reference model for sharing archetype-constrained identity information between diverse identity domains, models and services, while permitting reuse of published standard-based archetypes. The author evaluates its use for expressing the major types of existing demographic reference models in an extensible way, and show its application for standards-compliant two-level modelling alongside heterogeneous demographics models.

This thesis demonstrates how the two-level modelling approach that is used for EHRs could be adapted and reapplied to provide a highly-flexible and expressive means for representing subjects of information in allied health settings that support the healthcare process, such as the laboratory domain. By relying on the two level modelling approach for representing identity, the proposed design facilitates cross-referencing and disambiguation of certain demographics standards and information models. The work also demonstrates how it can also be used to represent additional clinical identified entities such as specimen and order as subjects of clinical documentation.

DOI

https://doi.org/10.21427/D7XG72

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