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

Abstract

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.

Adaption BERT for Medical Information Processing with ChatGPT and Contrastive Learning

“…Medical semantic similarity calculations can enhance DDSSs by providing precise and efficient medical information processing, offering reliable advice and insights for clinical decisionmaking. However, due to differences in expertise and backgrounds, the representation of medical terms exhibits complexity, diversity, and dynamics, posing challenges to understanding and utilizing medical information [10,11]. For instance, in the open Electronic Health Record (OpenEHR) dataset [12], the term "care plan" has several semantically similar words, such as "Treatment plan", "Care regimen", and "Health management plan".…”