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Clinical decision support systems (CDSS) are central to modern healthcare, but their effectiveness is compromised during system downtimes, which affect 96% of healthcare organizations. During these failures, clinicians lose access to critical decision-making tools like order sets, increasing the risk of medical errors. Traditional downtime solutions, such as paper-based protocols, are often impractical and difficult to maintain.

Background In recent years, there has been an expansion in the literature on the effects of computerized alerts on prescribing and patient outcomes. The aim of our study was to examine the impact of these systems on clinician prescribing and patient outcomes. Methods We searched three databases (Medline, Embase and PsychINFO) for studies that had been conducted since 2009 and included studies that examined the effects of alerts at the [...]

Electronic health records (EHR) are widely implemented and consume nearly half of physicians' work time. Despite the importance of efficient data entry, physicians' typing skills - potential contributors to documentation burden - remain poorly studied.

Electronic Health Record (EHR) patient portal messaging has become as essential tool for patient-clinician communication by improving accessibility to primary care. While messaging is beneficial for patients, it can increase clinicians' workloads. Female clinicians receive a greater number of EHR messaging, resulting in an increased workload.

Accurate discharge summaries are essential for effective communication between hospital and outpatient providers but generating them is labor-intensive. Large language models (LLMs), such as GPT-4, have shown promise in automating this process, potentially reducing clinician workload and improving documentation quality. A recent study using GPT-4 to generate discharge summaries via concatenated clinical notes found that while the summaries were concise and coherent, they often lacked comprehensiveness and contained errors. To [...]

Objective In dentistry, social determinants of health (SDoH) are potentially recorded in the clinical notes of Electronic Dental Records (EDRs). The objective of this study was to examine the availability of SDoH data in dental clinical notes and evaluate NLP methods to extract SDoH from dental clinical notes. Methods A set of 1,000 dental clinical notes was sampled from a dataset of 105,311 patient visits to a dental clinic and [...]

Background Inpatient hypoglycemia is associated with increased length of stay and mortality. There have been several models developed to predict a patient's risk of inpatient hypoglycemia. Objectives Describe the barriers to implementing a model that we developed to predict inpatient hypoglycemic events informing a clinical decision support tool. Methods A logistic regression model was trained on inpatient hospitalizations of diabetic patients receiving insulin at Atrium Health Wake Forest Baptist Medical [...]

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Central line-associated bloodstream infections (CLABSIs) are associated with substantial pediatric morbidity and mortality. The capacity to predict which children with central lines are at greatest risk of CLABSI could inform surveillance and prevention efforts. Our team previously published in silico predictive models for CLABSI.

Online healthcare portals provide access to electronic health information and support clinical communication. Almost no studies have examined perspectives on parental portal access. We aimed to characterize parental and adolescent perspectives on parental portal access.