This study identified barriers to implementing the FAIR (Findable, Accessible, Interoperable, Reusable) principles in child and adolescent mental health research.

A total of 45 barriers were identified, including those related to organizational policies and software. Key recommendations include adding a FAIR data steward to the research team, providing accessible guides, and ensuring sustainable funding to overcome these barriers, ultimately enhancing data reusability.

 Rowdy de Groot, et el DOI: 10.2196/59113

Machine learning models often struggle to generalize to data distributions that differ from their training dataset, known as "out-of-distribution" (OOD) data. It is important to detect OOD because prediction on such data can be unreliable.

While OOD detection methods have been explored in various fields, their effectiveness for medical data remains unclear. This study aims to evaluate and compare different OOD detection methods in the context of medical tabular data.

 The visual summary and interview with Giovanni Cina explains OOD.

 Mohammad Azizmalayeri, et al DOI:10.1016/j,ijmedinf.2024.105762

This study evaluates the completeness of mappings from 5th-level (most fine-grained level, the chemical substance) Anatomical Therapeutic Chemical (ATC) classification classes to RxNorm ingredient concepts in two vocabulary repositories, OHDSI Standardized Vocabularies (OSV) and Unified Medical Language System (UMLS) Metathesaurus. The results show that OSV provides more mappings and is therefore recommended over UMLS, though UMLS may perform better in concrete applications.

 Rowdy de Groot, et el DOI: 10.1016/j.ijmedinf.2024.105777

Sustainability is playing an increasingly important role in healthcare organizations, but improving the environmental impact in healthcare remains a challenge. The aim of this research is to develop a model that allows healthcare professionals to assess and improve the sustainability performance of their organization or department. Do you want to learn more? Click here for more information.

Marieke Sijm et al

This study looked at two different methods to measure the effect of organizational factors on ICU performance in Brazil and Uruguay. The used methods were causal random forest and linear regression modelling. Both methods found that the number of nurses per ten beds improved ICU efficiency. Causal random forest was able to spot areas where overall results might not seem significant. This approach could help hospital managers to improve ICU efficiency.

Ferishta Raiez, et el. DOI: 10.1016/j.ijmedinf.2024.105568