Verifying the Feasibility of Implementing Semantic Interoperability in Different Countries Based on the OpenEHR Approach: Comparative Study of Acute Coronary Syndrome Registries

Min, Lingtong; Atalag, Koray; Tian, Qi; Chen, Yani; Lü, Xudong

doi:10.2196/31288

Cited by 7 publications

(6 citation statements)

References 30 publications

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“…They also sometimes have several different ways to structure the same information on varying levels of detail, leading to internal variability. This has been shown in evaluations of implementations of information models [ 13 , 44 ]. The standards for secondary use had a more rigid structure, only permitting 1 way to structure per type of information.…”

Section: Discussionmentioning

confidence: 89%

Structure of Health Information With Different Information Models: Evaluation Study With Competency Questions

Rossander¹,

Karlsson²

2023

JMIR Med Inform

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Background There is a flora of health care information models but no consensus on which to use. This leads to poor information sharing and duplicate modelling work. The amount and type of differences between models has, to our knowledge, not been evaluated. Objective This work aims to explore how information structured with various information models differ in practice. Our hypothesis is that differences between information models are overestimated. This work will also assess the usability of competency questions as a method for evaluation of information models within health care. Methods In this study, 4 information standards, 2 standards for secondary use, and 2 electronic health record systems were included as material. Competency questions were developed for a random selection of recommendations from a clinical guideline. The information needed to answer the competency questions was modelled according to each included information model, and the results were analyzed. Differences in structure and terminology were quantified for each combination of standards. Results In this study, 36 competency questions were developed and answered. In general, similarities between the included information models were larger than the differences. The demarcation between information model and terminology was overall similar; on average, 45% of the included structures were identical between models. Choices of terminology differed within and between models; on average, 11% was usable in interaction with each other. The information models included in this study were able to represent most information required for answering the competency questions. Conclusions Different but same same; in practice, different information models structure much information in a similar fashion. To increase interoperability within and between systems, it is more important to move toward structuring information with any information model rather than finding or developing a perfect information model. Competency questions are a feasible way of evaluating how information models perform in practice.

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Section: Discussionmentioning

confidence: 89%

Structure of Health Information With Different Information Models: Evaluation Study With Competency Questions

Rossander¹,

Karlsson²

2023

JMIR Med Inform

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“…However, as discussed in (Bestek, 2021) and (Min et al, 2021), OpenEHR suffers from problems related to the governance of structured and standardised medical content (semantic resources) -especially at local and national levels. Semantic resource governance is a general problem in healthcare (not only related to OpenEHR) that has not yet been properly addressed.…”

Section: Electronic Health Records and Openehrmentioning

confidence: 99%

“…For example, it is difficult to achieve long-term commitment to semantic resources that are not limited to specific projects but are treated as a cross-cutting issue for multiple projects. This hinders the achievement of a high level of semantic interoperability (Bestek, 2021;Min et al, 2021) or FAIRness in general.…”

Section: Introductionmentioning

confidence: 99%

“…This leads to a better understanding of (1) shared semantic resources as a global commons, (2) the relationship between the global and local levels of semantic resource governance, and (3) the potential application of the concept of commoning in the context of semantic interoperability. Second, we use commons, and in particular commoning, to justify our proposal on how to address the governance problems of semantic resources, also put forward in (Bestek, 2021) and (Min et al, 2021), in a continuous and sustainable way.…”

Section: Introductionmentioning

confidence: 99%

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Commoning Semantic Interoperability in Healthcare

Bestek

Grönvall

Saad-Sulonen

2022

International Journal of the Commons

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Commons traditionally refer to shared natural resources that are at risk of being depleted or even destroyed. The rules established by commoners offer a way to manage such scarce resources. Through a series of projects on national eHealth patient health records, we have identified another type of shared resources -semantic resources -that would benefit from commons-based governance. Semantic resources are standardised definitions of data that ensure a common understanding of data during processes of data sharing between different actors and systems. In this paper, we first use commons as a lens to explore shared semantic resources and the governance challenges associated with them (e.g. lack of cross-cutting support across many projects). This leads to a better understanding of (1) shared semantic resources as global commons, (2) the relationship between the global and local levels of semantic resource governance and, by observing traces of commoning at the local level, (3) the role of commoning as a crucial concept in the context of semantic interoperability. Second, we introduce the notion of semantic commons to address the governance problems of semantic resources that needs to be continuous and sustainable. In addition, we also contribute to the commons literature by proposing a new area of research for commons scholars. We identify semantic resources in healthcare as an interesting and important type of resource that offers a new perspective on the dynamics of the relationship between global and local commons. We also identify a central role for commoning in healthcare in general as well as in the governance of semantic resources there.

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“…Semantic similarity computation of medical information, as one of the cores in medical information processing, aims to calculate the similarity of medical professional terms within massive datasets [1][2][3][4]. Semantic similarity computation of medical information finds essential applications in various areas, including medical information modeling [5], semantic retrieval [6], intelligent decision support [7], and medical knowledge graph construction [8].…”

Section: Introductionmentioning

confidence: 99%

Adaption BERT for Medical Information Processing with ChatGPT and Contrastive Learning

Min,

Fan,

Dou

et al. 2024

Electronics

Self Cite

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Calculating semantic similarity is paramount in medical information processing, and it aims to assess the similarity of medical professional terminologies within medical databases. Natural language models based on Bidirectional Encoder Representations from Transformers(BERT) offer a novel approach to semantic representation for semantic similarity calculations. However, due to the specificity of medical terminologies, these models often struggle with accurately representing semantically similar medical terms, leading to inaccuracies in term representation and consequently affecting the accuracy of similarity calculations. To address this challenge, this study employs Chat Generative Pre-trained Transformer (ChatGPT) and contrastive loss during the training phase to adapt BERT, enhancing its semantic representation capabilities and improving the accuracy of similarity calculations. Specifically, we leverage ChatGPT-3.5 to generate semantically similar texts for medical professional terminologies, incorporating them as pseudo-labels into the model training process. Subsequently, contrastive loss is utilized to minimize the distance between relevant samples and maximize the distance between irrelevant samples, thereby enhancing the performance of medical similarity models, especially with limited training samples. Experimental validation is conducted on the open Electronic Health Record (OpenEHR) dataset, randomly divided into four groups to verify the effectiveness of the proposed methodology.

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Verifying the Feasibility of Implementing Semantic Interoperability in Different Countries Based on the OpenEHR Approach: Comparative Study of Acute Coronary Syndrome Registries

Cited by 7 publications

References 30 publications

Structure of Health Information With Different Information Models: Evaluation Study With Competency Questions

Structure of Health Information With Different Information Models: Evaluation Study With Competency Questions

Commoning Semantic Interoperability in Healthcare

Adaption BERT for Medical Information Processing with ChatGPT and Contrastive Learning

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