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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).

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.

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.

Phone surveys are crucial for collecting health data but are expensive, time-consuming, and difficult to scale. To overcome these limitations, we propose a survey collection approach powered by conversational Large Language Models (LLMs).

Describing the development of a visual dashboard leveraging available tools for efficient recruitment for patient centered clinical trials in resource constrained settings.

Medical examiners and coroners (ME/C) oversee medicolegal death investigations which determine causes of death and other contextual factors that may have influenced a death. We utilize open data releases from ME/C offices covering 6 different geographic areas to demonstrate the strengths and limitations of ME/C data for forensic epidemiology research.

Health information exchanges (HIE), tools that electronically share clinical data across healthcare organizations, provide the opportunity to improve patient care. While widely available, HIE is utilized in only 2%-10% of patient encounters. Few studies have explored current barriers to use. The goal of this study was to evaluate current clinician perspectives on HIE and barriers to use at the point of care.

To develop and evaluate a patient-centric medication module within a personal health record (PHR) app for capturing medication use, focusing on accuracy, usability, and concordance.

To develop a more accurate fall prediction model within the Veterans Health Administration.