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This article focuses on the role of the electronic health record (EHR) to generate meaningful formative feedback for medical students in the clinical setting. Despite the scores of clinical data housed within the EHR, medical educators have only just begun to tap into this data to enhance student learning. Literature to-date has focused almost exclusively on resident education.

Accurately identifying clinical phenotypes from Electronic Health Records (EHRs) provides additional insights into patients' health, especially when such information is unavailable in structured data. This study evaluates the application of OpenAI's Generative Pre-trained Transformer (GPT)-4 model to identify clinical phenotypes from EHR text in non-small cell lung cancer (NSCLC) patients. The goal was to identify disease stages, treatments and progression utilizing GPT-4, and compare its performance against GPT-3.5-turbo, Flan-T5-xl, Flan-T5-xxl [...]

Artificial intelligence tools such as Chat Generative Pre-trained Transformer (ChatGPT) have been used for many health care-related applications; however, there is a lack of research on their capabilities for evaluating morally and/or ethically complex medical decisions. The objective of this study was to assess the moral competence of ChatGPT.

The Department of Veterans Affairs (VA) is transitioning from its legacy electronic health record (EHR) to a new commercial EHR in a nationwide, rolling-wave transition. We evaluated clinician and staff experiences to identify strategies to improve future EHR rollouts.

Pharmacists in over half of the United States can prescribe contraceptives; however, low pharmacist adoption has impeded the full realization of potential public health benefits. Many barriers to adoption may be addressed by leveraging an electronic health records (EHR) system with clinical decision support tools and workflow automation. We conducted a feasibility study to determine if utilizing a contraceptive-specific EHR could improve potential barriers to the implementation of pharmacist-prescribed contraceptive [...]

The increasing prevalence of electronic health records (EHRs) in healthcare systems globally has underscored the importance of data quality for clinical decision-making and research, particularly in obstetrics. High-quality data is vital for an accurate representation of patient populations and to avoid erroneous healthcare decisions. However, existing studies have highlighted significant challenges in EHR data quality, necessitating innovative tools and methodologies for effective data quality assessment and improvement.

Various data quality issues have prevented healthcare administration data from being fully utilized when dealing with problems ranging from COVID-19 contact tracing to controlling healthcare costs.

 Multitasking, defined as performing two or more interventions simultaneously, increases the cognitive burden of clinicians. This may, in turn, lead to higher risk of medication and procedural errors. Time motion study (TMS) data for nurses in nursing homes revealed an extensive amount of multitasking while managing medications. Further investigation of multitasked nursing interventions will provide a foundation for optimizing medication management workflows.

Accelerating demand for patient messaging has impacted the practice of many providers. Messages are not recommended for urgent medical issues, but some do require rapid attention. This presents an opportunity for artificial intelligence (AI) methods to prioritize review of messages. Our study aimed to highlight some patient portal messages for prioritized review using a custom AI system integrated into the electronic health record (EHR).

 We developed and implemented a customized internal clinical informatics (CI) curriculum for the UC Irvine CI Fellowship program. The goal was to transition from an externally sourced curriculum to a more focused, internally developed program, aligning with feedback from early fellows and the evolving practical needs of clinical informatics. By designing this curriculum in-house, we sought to provide a more efficient, cost-effective, and relevant educational experience for fellows.