Cross-Institutional Data Harmonization for AI in Nursing Care Using the OMOP CDM.

No prior work had resolved the challenge of integrating fragmented nursing information across diverse healthcare settings. This gap motivated the need for standardized data structures to support digital innovation. It was already known that artificial intelligence requires high-quality, consistent inputs to function effectively. However, existing documentation often remains unstructured and semantically inconsistent across different facilities. That uncertainty drove researchers to investigate how common data models might bridge these institutional divides. Prior research has shown that nursing documentation varies significantly between hospitals and long-term care environments. This variability prevents the secondary use of clinical records for advanced technological development. The current landscape lacks a unified framework for aggregating these disparate data sources into a single, usable repository.

This work aims to harmonize heterogeneous nursing data from hospitals and nursing homes to create an AI-ready data foundation. The researchers sought to address the fragmentation and semantic inconsistency that currently hinder the secondary use of nursing records. By establishing a standardized approach, the team intended to make clinical information more accessible for technological innovation. The study focuses on mapping diverse datasets into a common model to facilitate cross-institutional utility. This effort is motivated by the potential for artificial intelligence to support and empower nurses in their daily practice. The investigators recognized that high-quality, standardized data is a prerequisite for developing effective digital tools. They aimed to define a core dataset that captures essential elements such as patient demographics and vital signs. Ultimately, the project seeks to lay the groundwork for future informatics applications within the nursing profession.

The review approach involved a systematic process to harmonize heterogeneous clinical information from two separate healthcare organizations. Researchers first extracted datasets from distinct software systems to initiate the mapping procedure. The team then performed dataset specification to define the scope of the information being integrated. Vocabulary identification followed to ensure semantic consistency across the different institutional sources. A coverage analysis assessed the compatibility of the existing records with the target model. Structural mapping translated the fragmented data into the standardized format required for the common framework. The investigators implemented an initial extraction, transformation, and loading sequence to test the integration. This comprehensive design allowed for the evaluation of mapping feasibility for key clinical elements.

The strongest finding indicates that mapping and integrating basic nursing data into the common model is feasible. The researchers successfully defined a core dataset encompassing demographics, vital signs, and medication information. Initial test transfers confirmed that these elements could be effectively standardized across different institutional systems. However, the results highlight that complex nursing constructs remain difficult to integrate. Specifically, care plans and assessments continue to pose significant challenges for semantic and structural alignment. The study provides a clear demonstration of how fragmented records can be transformed into a unified format. These findings support the viability of the proposed methodological approach for cross-institutional data harmonization. The evidence confirms that a standardized foundation is achievable for essential clinical data points.

The authors propose a structured methodological approach for harmonizing nursing information across multiple institutions. This synthesis suggests that mapping disparate records to a common model is feasible for basic clinical elements. The researchers indicate that demographics, vital signs, and medication records represent the most accessible data for integration. They acknowledge that complex nursing constructs like care plans present ongoing difficulties for standardization. This review implies that future efforts must address these intricate semantic challenges to improve data utility. The authors maintain that their work establishes a necessary foundation for subsequent artificial intelligence development in nursing. They emphasize that standardized data remains a prerequisite for empowering clinicians through digital tools. The study concludes that cross-institutional integration is a viable path toward enhancing nursing informatics capabilities.