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Ohio has been severely impacted by the opioid crisis, with opioid overdose (OD) death rates exceeding national averages. Accurate OD death prediction supports proactive prevention and treatment allocation. Existing methods often focus on ZIP Code Tabulation Area (ZCTA)-level prediction for small-area resource allocation; however, performance at this resolution is poor due to substantial fluctuations in OD death counts, which introduce noise. This raises a critical methodological question: what is the [...]

This study compares multiple LLMs, including ChatGPT, DeepSeek, and Llama, to generate meaningful, audience-adapted labels for the existing latent classes among patients with chronic low back pain (cLBP).

The pandemic dramatically increased in the frequency of audiovisual medical visits and the rate of audiovisual visits remains higher than before the pandemic. These visits have the potential to be an important clinical modality. Researchers have assessed several aspects of audiovisual visits but they have not investigated their clinical note quality. The goal of this study is to determine the quality of audiovisual and in-person clinical notes and to compare [...]

This study aims to understand how inpatient nurses determine and prioritize necessary documentation within the context of the Excessive Documentation Burden (ExDocBurden) in Electronic Health Records (EHRs).

This proof of concept for utilizing automatic data extraction methods to extract health technology assessment (HTA) attributes from HTA reports of medicines aimed to explore which attributes could be extracted and how accurately, using different data extraction methods. This enables easy access to insights into HTA recommendations for policymaking and policy-related research.

This study aimed to provide a comprehensive understanding of factors associated with Alzheimer's disease (AD) and AD-related dementias (AD/ADRD), which could aid in studies to develop new treatments for AD/ADRD patients and identify high-risk populations for prevention.

Research in suicide risk prediction often suffers from the lack of comprehensive data on patient suicide death, which differs from suicide attempt or suicidal behaviors. This study aimed to develop a population-wide multi-source harmonized data warehouse suitable for suicide death risk prediction.

Sharing behavioral health and wearable data poses privacy challenges, as traditional de-identification remains vulnerable to re-identification. Differential privacy (DP) provides mathematical guarantees through a tunable privacy budget, ϵ . This study evaluates the feasibility of generating and releasing DP synthetic behavioral health data with high analytical utility, identifying practical ϵ values for public data sharing.

Patients with rare diseases often struggle to find accurate medical information, and large language model (LLM)-based chatbots may help meet this need. However, evaluating LLM-generated free-text answers typically requires physician review, which is time-consuming and difficult to scale. This study compared traditional natural language processing (NLP) metrics to emerging LLM-based evaluation approaches for assessing answer quality in the context of Complex Lymphatic Anomalies (CLAs).

Implemented a tool to identify high-risk patients with atrial fibrillation (AF) with a CHA2DS2-VASc score of ≥2 (males) or ≥3 (females) who are not treated with oral anticoagulation. Aimed to evaluate the acceptability and usability of the "AF or Flutter not on Anticoagulant" electronic health record-based Care Gap (AF Care Gap) alert and associated best practice advisory (BPA) for clinicians managing patients with AF.