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To investigate the impact of missing laboratory measurements on sepsis diagnostic delays.

 Telemetry monitoring is crucial for high-risk patients but excessive use beyond practice standards increases costs. Prior studies have shown that electronic health record (EHR) alerts reduce low-value telemetry monitoring. However, specific components of these alerts that contribute to effectiveness are unknown.

To integrate a computerized adaptive test for depression into the electronic health record (EHR) and establish systems for administering assessments in-clinic and via a patient portal to improve depression care.

The Multiple Chronic Conditions (MCCs) Electronic Care (e-Care) Plan project aims to establish care planning data standards for individuals living with MCCs. This article reports on the portion of the project focused on long COVID and presents the process of identifying and modeling data elements using the HL7 Fast Healthcare Interoperability Resources (FHIR) standard.

 Leaders in Informatics, Quality, and Systems (LInQS) is a non-ACGME (Accreditation Council for Graduate Medical Education)-accredited 2-year training program developed to enhance training in the fields of health care delivery, quality improvement (QI), clinical informatics, and leadership.

To evaluate the efficacy of ChatGPT 4 (GPT-4) in delivering genetic information about BRCA1, HFE, and MLH1, building on previous findings with ChatGPT 3.5 (GPT-3.5). To focus on assessing the utility, limitations, and ethical implications of using ChatGPT in medical settings.

Wrong-patient order entry (WPOE) is a potentially dangerous medical error. It remains unknown if patient photographs reduce WPOE in the pediatric inpatient population.

The publication of the Phoenix criteria for pediatric sepsis and septic shock initiates a new era in clinical care and research of pediatric sepsis. Tools to consistently and accurately apply the Phoenix criteria to electronic health records (EHRs) is one part of building a robust and internally consistent body of research across multiple research groups and datasets.

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 [...]

To describe the prevalence and trends in the use of social media over time and explore whether social media use is related to better self-care efficacy and thus related to better mental health among United States older adults with multimorbidity.