International Journal of Medical Informatics

Official journal of the European Federation for Medical Informatics (EFMI) and IMIA.

International Journal of Medical Informatics provides an international medium for dissemination of original results and interpretative reviews concerning the field of medical informatics. The journal emphasizes the development, use, implementation, deployment, and evaluation of information and communication technologies (ICT) in healthcare settings, including consumer health, public health, clinical care, telehealth, education, clinical research, policies, and regulations considering security, privacy, and the ethical use of ICT resources at local, national, and global levels to improve population care delivery.

The scope of journal covers:

• Information systems, including national or international registration systems, hospital information systems, departmental and/or clinician’s office systems, document handling systems, clinical information systems, electronic medical record systems, personal health records, public and population health informatics, consumer health systems, digital health, digital ecosystem, terminologies and standardization, systems integration and interoperability, among other topics on the scenario of delivering patient care through information and communication technology devices.

• Computer-assisted medical decision support systems that use heuristics, data structures, algorithms, and statistical methods exemplified in decision theory, protocol development, and artificial intelligence, including techniques for discovering new knowledge. Note that studies in AI will be considered if they are integrated into clinical systems or evaluate the impact caused on clinical implementations.

• Educational information and communication technology-based programs pertaining to medical informatics in general, including organizational, educational framework, curriculum content, economics evaluation, social and clinical impact, ethical and cost-benefit aspects of ICT applications in health care.

• Patient safety, data privacy, security and confidentiality requirements, legal regulation, policies process, system assessment, maturity models evaluation and governance for ICT.

The journal is in the healthcare domain. As an international journal, methodology plays a crucial role in allowing researchers from different countries to acquire knowledge and benefits from the steps followed to obtain results and evidence in using ICT in healthcare delivery. Results are often not transferable, but researchers can pursue and use methods in various scenarios.

If the article is on artificial intelligence using machine learning, deep learning, natural language processing, predictive modelling and related topics, authors MUST add relevant information as an appendix to allow better evaluation, transparency, and reproducibility. Many studies currently use models, algorithms, and in silico evaluation. In this case, we recommend a practical clinical application or a test with a different dataset than the one used to design the algorithm or model – an evaluation performed with data from other facilities is recommended. In other words, the dataset must be different (origin and testing). In addition, it would be helpful for authors to highlight the study’s limitations, particularly in cases where the testing is not feasible.

Predictive modeling (based on deep learning or machine learning techniques) applied to image analysis can significantly enhance diagnostic accuracy when integrated into hospital electronic health records. IJMEDI's scope allows the acceptance of primary research articles or reviews on image analysis that have been validated on external datasets (different from those used for development and training) and, possibly, integrated into clinical workflow or that have developed novel approaches using multimodality data. Articles that only focus on methodological approaches, analytical frameworks, or studies reporting algorithm performance in medical image analysis without a clear clinical contribution described above are considered out of scope for IJMEDI.

Again, note that IJMEDI is not interested in the model description or the comparison of framework performance with no novelty on the method or deployment. The scope is to focus on the results of ICT in clinical practice. The IJMI is interested in robust evaluation and intervention studies in clinical practice applications. Faced with so many studies using models, algorithms, in silico evaluation, it is demanded a practical clinical application (different dataset for validation) with significant meaning for providers and patients. Studies on models and descriptions must be advanced and show how much the contribution is evident for international readers and researchers. Most studies only evaluated model performance, but no usability and impact evaluation. It is necessary to advance and demonstrate impact on clinical practices.

Please also consult the IJMEDI editorials which details the checklist for machine learning checklist (https://www.sciencedirect.com/science/article/pii/S1386505621001362?via%3Dihub) and the scope of AI for the IJMEDI journal (https://www.sciencedirect.com/science/article/pii/S1386505623001685). Authors may also consider other journals addressing artificial intelligence in medicine and methods (Medical Informatics Journals | Elsevier).

The scope does not cover bibliometric or scientometric studies or similar studies using those methods.