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To construct a data quality (DQ) system that incorporates combinations of methods to evaluate data characteristics and analytic fitness across research questions for multiple uses.

Acupuncture, a key modality in traditional Chinese medicine, is gaining global recognition as a complementary therapy and a subject of increasing scientific interest. However, fragmented and unstructured acupuncture knowledge spread across diverse sources poses challenges for semantic retrieval, reasoning, and in-depth analysis. To address this gap, we developed AcuKG, a comprehensive knowledge graph that systematically organizes acupuncture-related knowledge to support sharing, discovery, and artificial intelligence-driven innovation in the field.

Artificial intelligence (AI) has the potential to transform medical informatics by supporting clinical decision-making, reducing diagnostic errors, and improving workflows and efficiency. However, successful integration of AI-based decision support systems depends on careful consideration of human-AI collaboration, trust, skill maintenance, and automation bias. This work proposes five central questions to guide future research in medical informatics and human-computer interface (HCI).

Privacy preserving record linkage (PPRL) refers to techniques used to identify which records refer to the same person across disparate datasets while safeguarding their identities. PPRL is increasingly relied upon to facilitate biomedical research. A common strategy encodes personally identifying information for comparison without disclosing underlying identifiers. As the scale of research datasets expands, it becomes crucial to reassess the privacy risks associated with these encodings. This paper highlights the [...]

Ambient listening tools powered by generative artificial intelligence (GenAI) offer real-time, scribe-like support that reduce documentation burden and may help alleviate burnout. This study assesses physician-perceived benefits and challenges of ambient AI implementation through surveys and evaluates its effectiveness in clinical workflows using automatically recorded electronic health record (EHR) time-efficiency metrics.

The number of ethical frameworks designed to guide artificial intelligence (AI) use has grown substantially over the past decade, yet their real-world effect remains unclear. We aimed to synthesize existing evidence to analyze the practical impact of AI ethics frameworks (AIEFs) operationalized in healthcare.

Clinical registries advance healthcare by tracking patient outcomes and intervention safety. Manually extracting information from clinical text for registries is labor- and resource-intensive and often inaccurate. Therefore, this systematic review aims to evaluate the use and effectiveness of natural language processing (NLP) methods in extracting information from clinical text for populating clinical registries.

We created a probabilistic maternal-child electronic health record (EHR) linkage algorithm to promote clinical research in maternal-child health.

To address the challenges of data heterogeneity and manual feature engineering in clinical predictive modeling, we introduce FHIR-Former, an open-source framework integrating Fast Healthcare Interoperability Resources (FHIR) with large language models (LLMs) to automate and standardize clinical prediction tasks.

To evaluate the accuracy, computational cost, and portability of a new natural language processing (NLP) method for extracting medication information from clinical narratives.