Search AMIA.org

The CONCERN Early Warning System (CONCERN EWS) is an artificial intelligence based clinical decision support system (AI-CDSS) for prediction of clinical deterioration leveraging signals from nursing documentation patterns. While a recent multi-site randomized controlled trial demonstrated its effectiveness in reducing inpatient mortality and length of stay, evaluating implementation outcomes is essential to ensure equitable results across patient populations.

To evaluate and compare the diagnostic responses generated by two artificial intelligence models developed 54 years apart and to encourage physicians to explore the use of large language models (LLMs) like GPT-4o in clinical practice.

Health equity is greatly impacted by the systems and processes with which health systems deliver care. Given the minimal guidance on measurement and reporting of health inequities specific to key population health outcomes, a solution for measurement of health equity is proposed.

Quantify the effect of ambient artificial intelligence (AI) scribe technology on work experience, clinical operations, and patient experience in pediatric primary care.

Health professional (HP) trainee burnout is hard to capture. There are many validated quantitative tools to assess trainee burnout, but fewer qualitative methodological tools that can elicit rich and trustworthy qualitative data on HP trainee burnout.

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.

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.

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.

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 [...]