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As the use of artificial intelligence (AI) in healthcare is rapidly expanding, there is also growing recognition of the need for ongoing monitoring of AI after implementation, called algorithmovigilance. Yet, there remain few systems that support systematic monitoring and governance of AI used across a health system. In this study, we identify end-user needs for a novel AI monitoring system-the Vanderbilt Algorithmovigilance Monitoring and Operations System (VAMOS)-using human-centered design (HCD).

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.

Clinical trial data is still predominantly manually entered by site staff into Electronic Data Capture (EDC) systems. This process of abstracting and manually transcribing patient data is time-consuming, inefficient and error prone. Use of Electronic Health Record to Electronic Data Capture (EHR-To-EDC) technologies that digitize this process would improve these inefficiencies.

To engage end-users to develop and evaluate an algorithm to convert electronic adherence monitoring device (EAMD) output into the adherence data required for analyses.

Deprescribing is the clinically supervised process of stopping or reducing medications that are no longer beneficial. MedSafer is an electronic decision support tool that guides healthcare providers (HCPs) through the deprescribing process. We recently developed a novel patient-facing version of the software, allowing patients and caregivers to generate a personalized deprescribing report to bring to their prescriber.

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.

This study evaluates the performance and deployment feasibility of a machine learning (ML) model to identify adult-onset type 1 diabetes (T1D) initially coded as type 2 on electronic medical records (EMRs) from a health information exchange (HIE). To our knowledge, this is the first evaluation of such a model on real-world HIE data.

To implementation an automated multi-institutional pipeline that delivers breast-cancer risk integrated with polygenic risk scores, monogenic variants, family history, and clinical factors, emphasizing operational challenges and their solutions.