Human-Supervised, Large Language Model-Based Clinical Decision Support Aligned to National Newborn Protocols in Kenya: A Pragmatic, Early-Stage Evaluation dataset

This repository contains the datasets generated during the development and evaluation of AIFYA, a human-supervised, large language model (LLM)–based clinical decision support system (CDSS) specifically aligned with the Kenya Consolidated Newborn Clinical Protocols.The repository comprises the following three distinct datasets:Newborn Clinical Scenario Dataset: De-identified clinical parameters and management inputs corresponding to newborn patient encounters that were processed by the AIFYA platform for clinical decision support recommendations. Provides the input context for evaluating the AI system's performance.Expert Neonatal Review and Concordance Dataset: Contains the consensus ratings and detailed qualitative comments provided by independent experts in neonatal care. This dataset quantifies the inter-rater reliability and the final adjudicated agreement/concordance scores for AIFYA's recommendations against national guidelines. Supports the primary outcome analysis regarding guideline adherence and recommendation correctness.Clinician Knowledge, Attitudes, and Practices (KAP) Survey Response Data: Anonymised response data collected from the post-implementation survey administered to healthcare workers (HCWs) regarding their perceptions of AIFYA's usability, workflow integration, and the acceptance of AI in their clinical practice. Supports the secondary outcome analysis on user perception and system implementation factors.