Search AMIA.org

To compare clinical summaries generated from simulated patient primary care electronic health records (EHRs) by GPT-4, to summaries generated by clinicians on multiple domains of quality including utility, concision, accuracy, and bias.

Venous thromboembolism (VTE) prophylaxis in hospitalized patients must balance risks of bleeding and thrombosis. Clinical changes such as bleeding or renal injury can also trigger changes or delays in thromboprophylaxis. Electronic health record alerts (EHRAs) can allow for targeted notification to providers to improve venous thromboembolism prophylaxis and improve patient outcomes at the risk of alert fatigue if not carefully designed and implemented.This study aimed to develop and refine an [...]

Patient mobility during hospitalization is essential for high-quality healthcare as mobility is linked to physical function and quality of life. The Johns Hopkins Highest Level of Mobility (JH-HLM) scale is a validated method to assess mobility in hospitalized patients. Although the JH-HLM is widely utilized, it has limitations including ceiling effects, unobserved mobility events going unrecorded, and the staff time needed to observe and document.We explored the feasibility of using [...]

To address biased representation in Electronic Health Records (EHRs) across subpopulations (SPs), which leads to predictive models underperforming for underrepresented groups, we propose a framework to enhance equitable predictive performance.

Clinical informatics (CI) is a dynamic field at the intersection of health care, technology, and information science, aimed at enhancing health care delivery and patient outcomes. CI fellowship programs prepare physicians to lead and apply health information technologies that drive improvements in clinical care, system efficiency, and innovation. Achieving Accreditation Council for Graduate Medical Education (ACGME) accreditation for such programs is crucial for standardizing training and ensuring the development of [...]

Large language models (LLMs) have demonstrated high levels of performance in clinical information extraction compared to rule-based systems and traditional machine-learning approaches, offering scalability, contextualization, and easier deployment. However, most studies rely on proprietary models with privacy concerns and high costs, limiting accessibility. We aim to evaluate 14 publicly available open-source LLMs for extracting clinically relevant findings from free-text echocardiogram reports and examine the feasibility of their implementation in information [...]

Major depression in adolescents is recognized as a serious psychiatric illness and a risk factor for suicide. It is important for providers to screen for depression and suicidality and have clinical decision support (CDS) for evaluation, risk assessment, and initial management. Our efforts focused on leveraging the electronic health record (EHR) to ensure that high-risk adolescents received appropriate interventions before leaving the office.This study aims to improve notification of positive [...]

Disparities exist in patient portal use among non-English-speaking caregivers of pediatric patients. This study aims to evaluate the reasons behind Spanish-speaking caregivers' use of patient portals and identify facilitators and barriers, focusing on those caring for children with chronic conditions.We conducted semi-structured interviews and surveys with Spanish-speaking caregivers of pediatric patients with chronic conditions at an academic pediatric health network in California. Data were transcribed, coded, and analyzed using inductive [...]

Studies on symptom concordance between patients and their caregivers often use cross-sectional designs, which may fail to capture the longitudinal, dynamic symptom experience. The Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C) is a remote health monitoring system that utilizes smartwatches and ecological momentary assessments (EMAs) to empower patients and caregivers to monitor and manage cancer pain at home. BESI-C collects real-time symptom data in naturalistic settings, enabling longitudinal [...]

Traditional early warning systems (EWS) have shown uncertain efficacy in real-world settings. More recently, machine learning models like the Epic Deterioration Index (DTI) have been developed, promising greater accuracy. Recognizing the potential of DTI, but also the pervasive issue of alert fatigue with interruptive (i.e., pop-up) EWS alerts, our institution implemented a DTI-enabled EWS with passive alerts (colored icons visible in prespecified locations within the electronic health record). We hypothesized [...]