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Fast Healthcare Interoperability Resources–Based Support System for Predicting Delivery Type: Model Development and Evaluation Study

Fast Healthcare Interoperability Resources–Based Support System for Predicting Delivery Type: Model Development and Evaluation Study

However, to the best of our knowledge, there was no model tested in clinical practice with an interoperable format of communication such as Fast Healthcare Interoperability Resources (FHIR), which tried to not only predict delivery type but also provide support about possibly wrong deliveries, and none with simulation about financial implication, making our paper a potential novelty on different dimensions.

João Coutinho-Almeida, Alexandrina Cardoso, Ricardo Cruz-Correia, Pedro Pereira-Rodrigues

JMIR Form Res 2024;8:e54109

Design and Development of Learning Management System Huemul for Teaching Fast Healthcare Interoperability Resource: Algorithm Development and Validation Study

Design and Development of Learning Management System Huemul for Teaching Fast Healthcare Interoperability Resource: Algorithm Development and Validation Study

A critical requirement for universal access to health is to have interconnected and interoperable health systems that guarantee effective and efficient access to quality data, strategic information, and tools for decision-making and people’s well-being [1]. One of the most relevant areas in medical informatics is the interoperability between health information systems. The interoperability eliminates duplication and errors in health data.

Sergio Guinez-Molinos, Sonia Espinoza, Jose Andrade, Alejandro Medina

JMIR Med Educ 2024;10:e45413

Machine Learning–Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review

Machine Learning–Enabled Clinical Information Systems Using Fast Healthcare Interoperability Resources Data Standards: Scoping Review

Specific disease classes may lack an interoperable ontology. For cancer, there are active efforts in the Code X HL7 FHIR Accelerator community to capture oncologic data from the EHR by using the m CODE (minimal Common Oncology Data Elements) ontology [39,40]. Advanced CDSSs have been integrated with machine learning algorithms to process data, especially unstructured data, such as clinician notes.

Jeremy A Balch, Matthew M Ruppert, Tyler J Loftus, Ziyuan Guan, Yuanfang Ren, Gilbert R Upchurch, Tezcan Ozrazgat-Baslanti, Parisa Rashidi, Azra Bihorac

JMIR Med Inform 2023;11:e48297

A Data Transformation Methodology to Create Findable, Accessible, Interoperable, and Reusable Health Data: Software Design, Development, and Evaluation Study

A Data Transformation Methodology to Create Findable, Accessible, Interoperable, and Reusable Health Data: Software Design, Development, and Evaluation Study

The Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles have been conceptualized to enable machine-accessible and actionable data interoperability [13]. FAIR refers to a set of principles that should be perceived as guidelines; in other words, FAIR is not a standard and does not constrain implementation-related decisions. Worldwide, there has been a rapid uptake of the FAIR principles [14-17].

A Anil Sinaci, Mert Gencturk, Huseyin Alper Teoman, Gokce Banu Laleci Erturkmen, Celia Alvarez-Romero, Alicia Martinez-Garcia, Beatriz Poblador-Plou, Jonás Carmona-Pírez, Matthias Löbe, Carlos Luis Parra-Calderon

J Med Internet Res 2023;25:e42822