Applied AI in Transinstitutional Health Information Systems

Dear Colleagues,

Health information systems deal with a socio-technical subsystem of settings which comprise all data, information and knowledge processing as well as the associated human and technical actors in their respective data, information and knowledge processing roles.

The information system of a healthcare network is transinstitutional in nature, as it connects different healthcare contexts such as territory, hospitals and intermediate care, medical offices, rehabilitation centers, nursing homes or ambulatory nursing organizations, personal environments (a patient's home), university medical centers, centers for statistics and health economics and centers of public health.

While managing health information systems, based on data, information and knowledge, legal and other requirements must be taken into account. Legal requirements encompass data protection and reimbursement aspects. Furthermore, management decisions require the building of a common electronic health record (EHR) in transinstitutional health information systems.

In general, a well-crafted health information system might perform the following functions:

1. It must make information about patients available on time, at the right location, to authorized staff and in an appropriate and usable form. Then, in order to avoid fatal biases, data must be correctly collected, stored, processed and systematically documented. They must also be kept up-to-date.
2. It might change the available knowledge on diseases, side effects and interactions of drugs to support decision-making in diagnostics and therapy.
3. It might make information available on the quality of patient care and the performance and cost parameters within healthcare settings.

Information and knowledge logistics aims at making the right information and knowledge available at the right time, at the right place, to the right people and in the right form, always in accordance with legal and privacy concerns.

The most important people in a healthcare setting are the patients and their caregivers. The most important groups of professionals working in healthcare settings are physicians, nurses, midwives, pharmacists, administrative staff, technical staff, medical informaticians, health information management staff and managers. Within each of these stakeholder groups, different needs and demands, different natural language tools or different languages in a multiethnic environment  may exist, all on the same transinstitutional health information system. That is, depending on the role, tasks and responsibilities.

Artificial intelligence engineering is developing technologies that could enhance patient care across healthcare settings and information systems. However, challenges related to data privacy, bias and the need for human expertise, and for the safeguarding of an effective therapeutic alliance between patients and physicians, might be addressed regarding the responsible and effective implementation of AI in healthcare.

This Special Issue, "Applied AI in Transinstitutional Health Information Systems", is a call for the leading experts in the fields of artificial intelligence engineering, informatics and clinical/medical sciences to address various crucial topics on the matter.

We truly hope that together we can successfully complete this Special Issue of Electronics, focusing on the principle applications of AI as part of the main transinstitutional health information systems.

Dr. Annamaria Vianello
Dr. Giuseppina Sgandurra
Prof. Dr. Luca Fanucci
Guest Editors

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• prediction models
• ChatGPT healthcare NLP
• privacy preservation models
• electronic health record building and management
• rule-based medical expert systems for diagnosis and care
• semi-supervised learning
• model training of multi-hospital data settings
• deep learning models
• AI applications in different fields of medicine
• AI in management of public health
• artificial general intelligence, superintelligence and ethics
• AI-based anomaly detection for clinical-grade histopathological diagnostics
• prospective evaluation of use of AI in diagnosis and care
• AI language models and semantic algorithms used to bring artificial intelligence into medicine
• medical robotics and humanoid robots in healthcare systems
• pediatric rehabilitation