Information systems such as Electronic Health Record (EHR) systems are susceptible to data quality (DQ) issues. Given the growing importance of EHR data, there is an increasing demand for strategies and tools to help ensure that available data are fit for use. However, developing reliable data quality assessment (DQA) tools necessary for guiding and evaluating improvement efforts has remained a fundamental challenge. This review examines the state of research on operationalising EHR DQA, mainly automated tooling, and highlights necessary considerations for future implementations. We reviewed 1841 articles from PubMed, Web of Science, and Scopus published between 2011 and 2021. 23 DQA programs deployed in real-world settings to assess EHR data quality (n = 14), and a few experimental prototypes (n = 9), were identified. Many of these programs investigate completeness (n = 15) and value conformance (n = 12) quality dimensions and are backed by knowledge items gathered from domain experts (n = 9), literature reviews and existing DQ measurements (n = 3). A few DQA programs also explore the feasibility of using data-driven techniques to assess EHR data quality automatically. Overall, the automation of EHR DQA is gaining traction, but current efforts are fragmented and not backed by relevant theory. Existing programs also vary in scope, type of data supported, and how measurements are sourced. There is a need to standardise programs for assessing EHR data quality, as current evidence suggests their quality may be unknown.

The heterogeneity of health data systems remains a major barrier to interoperability and data integration, and a challenge for clinical decision support and AI-driven research. The coexistence of multiple Common Data Models (CDMs) complicates integration, requiring complex manual or rule-based schema mapping, which is labor-intensive, error-prone, and difficult to scale. We evaluated three leading Large Language Models (LLMs): ChatGPT, Gemini, and DeepSeek, for their ability to match data elements from four widely used CDMs (PCORnet, OMOP, Sentinel, and i2b2) to FHIR R4 via web-based chat interfaces. Reference mappings from the HL7 Common Data Models Harmonization Implementation Guide were used as the benchmark. Standardized prompts were submitted across 36 total runs (3 LLMs × 4 schemas × 3 repetitions), and accuracy was measured using F1-scores with 95% confidence intervals, alongside strict (AND) and relaxed (OR) match criteria. DeepSeek obtained the highest numerical mean F1-scores (0.78-0.99) with strong consistency (AND Match = 0.87). Gemini showed good coverage (OR Match = 0.96) but greater variability (AND Match = 0.64), while ChatGPT achieved moderate accuracy with lower consistency (AND Match = 0.51). A two-way ANOVA indicated no statistically significant differences by LLM type (p = 0.138), CDM schema (p = 0.194), or their interaction (p = 0.451). Performance varied by domain, with Demographics and Condition mapped most accurately, while Observation showed the highest error rates (44% of total errors). Out of 2,583 total matchings, 314 errors were observed and categorized into four failure patterns: semantic confusion (42%), structural misalignment (28%), ambiguous terminology (18%), and terminology variance (12%). This study demonstrates the feasibility of using LLMs to automate health data integration as a complementary approach to traditional ontology-based methods, particularly for well-structured, context-rich CDMs. However, safe deployment requires human-in-the-loop validation, governance, prompt optimization, metadata integration, and validation to ensure clinical and research reliability.

Digital transformation and network transactions have brought about more complex network connectivity and complexity in controlling network traffic, leading to a significant rise in network vulnerabilities. Hence, it opens a gap in networking control strategy, which needs to be equated to rapid digital advancement and the impending numerous malicious attacks. Therefore, adopting proactive, intelligent mechanisms to monitor and manage network communication is imminent. In this paper, deep learning techniques are embedded in our chosen intrusion detection system to monitor and track malicious network traffic proactively. Hence, based on the network behaviour and certain set thresholds, an alert will be triggered while gathering transactions and managing the network activities by event and time. This proposed model then collects the dataset and trains the network to detect transactional traffic flow toward alertness and an efficient detection strategy for network anomalies. This technique will be used on different datasets with a defined malicious traffic suspicion perimeter to evaluate the impact of our proposed technique in mitigating vulnerabilities that led to network anomaly behaviour.