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Value-based care (VBC) represents a fundamental shift from volume-driven reimbursement to models focused on improving patient outcomes and reducing costs. Informatics plays an essential, but often underappreciated, role in enabling VBC. Traditional discussions of informatics emphasize data and technology; however, a broader sociotechnical view highlights how people, organizations, workflows, and policies interact with technology to influence the success of VBC initiatives. In this article, we apply the Informatics Stack as [...]

To assess patient awareness, trust, perceived benefits, and risks of artificial intelligence (AI) in clinical care within an urban safety-net health system.

To address the gap between artificial intelligence (AI) model development and clinical implementation by developing a machine-learning model to predict cochlear implant (CI) eligibility based on routine hearing tests, while illustrating 3 practical lessons: reformulation for clinical utility, selection of problem-specific metrics, and handling input data variability.

Patients often receive health care from multiple organizations. Privacy Preserving Record Linkage (PPRL) is a technology for linking patient records without releasing personally identifiable information. We compared a commercial PPRL tool that uses the XGBoost machine learning algorithm with Care Everywhere (CE), a widely used rule-based patient linkage module.

To develop and validate a prespecified logistic model for detecting mild cognitive impairment (MCI) using MyCog Mobile, a self-administered smartphone-based screening application, and to evaluate a structured simplification that reduces patient burden while maintaining diagnostic accuracy.

Predictive models of suicide risk have focused on features extracted from structured data found in electronic health records, with limited consideration of predisposing life events (LE) expressed in unstructured clinical text such as housing instability and marital troubles. This study aims to expand upon previous research, demonstrating how high-performance computing (HPC) and machine learning methodologies can be used to extract and annotate 8 LE across all Veterans Health Administration (VHA) [...]

Medical product safety surveillance efforts, whether using electronic health record (EHR) or claims data, typically rely on structured codes. Utilizing unstructured EHR data, particularly information extracted from clinical text through natural language processing (NLP), enriches information available for data mining, phenotyping, and surveillance. To assess overlapping and distinct information across structured and unstructured EHR data, we mapped both to a common vocabulary (Medical Dictionary for Regulatory Activities, MedDRA). We assess [...]

This study evaluates the completeness of diagnostic information captured in electronic health records (EHRs) compared to administrative claims data across multiple U.S. healthcare organizations between 2010 and 2022.

To evaluate CigStopper, a machine learning algorithm designed to predict billing eligibility for tobacco cessation counseling (CPT 99406/99407), addressing persistent underbilling and documentation gaps in health systems.

This study examined the use of machine learning (ML) and domain-specific enrichment in patient-generated health data, in the form of free-text meal logs, to classify meals on alignment with different nutritional goals.