Development of a minimum data set for a pediatric and neonatal Medication Administration Clinical Decision Support...

Medication administration in pediatrics and neonatology is a highly complex and safety-critical process, where even minor errors can have severe consequences. Clinical Decision Support Systems (CDSS) have shown promises in reducing such errors; however, most existing systems focus primarily on the prescribing stage and are designed for physicians, leaving a critical gap in support for nurses during medication administration-the final and most error-prone stage. Moreover, heterogeneous and overly complex data structures in CDSS hinder interoperability, consistency, and scalability, limiting their effectiveness in clinical practice. To address these challenges, it is esssential to develop a Medication Administration CDSS (MACDSS) tailored to pediatric and neonatal care. A foundational step in this process is to establish a standardized Minimum Data Set (MDS) to ensure data consistency, interoperability, and alignment with international standards such as HL7 Fast Healthcare Interoperability Resources (FHIR), thereby enhancing medication safety in these vulnerable populations.

A research synthesis approach was employed to develop the MDS. The study began with a qualitative phase to identify nurses' needs for and barriers to health information technology adoption in the medication administration process. This was followed by a systematic literature review across four databases (PubMed/MEDLINE, Embase, CINAHL, and ProQuest) to extract data elements from existing systems. Data from both phases were synthesized using a mixed-methods approach proposed by Robert et al. and refined through thematic analysis. An expert panel comprising medical informatics specialists and pediatric nursing Ph.D. holders validated the final data elements to ensure relevance, accuracy, and interoperability.

The qualitative study identified 12 data elements and highlighted key themes and barriers to technology adoption in medication administration processes. The systematic literature review yielded 84 data elements, which were mapped to the FHIR standard. An expert panel contributed 17 additional elements. From a total number of 83 candidates, 33 essential data elements were finalized and categorized into Individuals (7), Diagnostics (4), and Medications (22), with 14 elements linked to researcher-defined extensions on the FHIR. Of these, 20 were text-based and 13 numeric.

By integrating qualitative research, a systematic review, and expert panel, 33 essential data elements were identified and mapped to the FHIR standard to support interoperability with EHR systems. The resulting framework offers a practical foundation for safer medication administration and can guide future system design and research in health informatics.