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OpenEHR Modeling Applied to Eating Disorders in Clinical Practice: OpenEHR-Archetypes in Eating Disorders

Authors: Priscila Alves Maranhão; Gustavo Marísio Bacelar-Silva; Duarte Nuno Gonçalves-Ferreira; Conceição Calhau; Pedro Vieira-Marques; Marle Alvarenga; Ricardo João Cruz Correia;

OpenEHR Modeling Applied to Eating Disorders in Clinical Practice: OpenEHR-Archetypes in Eating Disorders

Abstract

Eating disorders (ED) are described as a broad spectrum of eating-related issues, which include dysfunctional behavior related to dissatisfaction with body shape or size, as well as inadequate eating behavior such as purgative practices, binge eating and dietary restrictions for weight loss and control purposes. ED assessments are carried out through questionnaires/scales focused on ED symptoms. Meanwhile, electronic health records (EHR) should help health professionals to make decisions by providing data to support individual decisions. Aim: The current article aims to present a solution for the integration of ED tools into EHR via openEHR-archetypes and to understand the challenges involving in this process. Methods: This is an exploratory study. The literature review focused on finding the main scales applied to ED screening, which were organized and structured into openEHR-archetypes. The Ocean Archetype Editor software was used as openEHR modeling tool. Results: Three new open-EHR archetypes (Eating attitudes test - EAT-40 and its short-version - EAT-26; and Bulimic Investigatory Test, Edinburgh - BITE) were developed in the initial stage of the current study. According to a review conducted by a member of our research group, these archetypes were described as the most often adopted tests by researchers. Conclusions: The quality of medical information is essential to help improving data standards and to assure interoperability. The openEHR-archetypes developed in the current study will be essential to help to improve clinical practices and, mainly, clinical research.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Average
Average
Average
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