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Despite the benefits of Clinical Decision Support (CDS), concerns of potential risks arise amidst increasing reports of CDS malfunctions. Without objective and standard methods to evaluate CDS in post-live stage, CDS performance in dynamic healthcare environment remains a black box from user perspective. In this study, we proposed a comprehensive framework to identify and evaluate post-live CDS malfunctions from the perspective of healthcare settings.

Telemedicine use has surged since the COVID-19 pandemic, offering a convenient way for patients to access healthcare. Whereas digital literacy (general comfort with and ability to use digital tools) is necessary to utilize telemedicine, digital health literacy is a subset of this, focusing on the ability to use digital tools to seek out, understand, and utilize health information. As such, barriers such as the lack of high-speed internet and limited [...]

Despite its morbidity, mortality, and financial burden, in-hospital malnutrition remains underdiagnosed and undertreated. Artificial intelligence offers a promising clinical informatics solution for identifying malnutrition risk and one that can be coupled with clinician-delivered patient care.

Interruptive clinical decision support (CDS) alerts are intended to improve patient care but can contribute to alert fatigue, diminishing their effectiveness. The alert demonstrated minimal clinical effect while contributing significantly to alert fatigue.

Appropriate Use Criteria Clinical Decision Support (AUC CDS) was legislatively mandated in the United States in 2014, and multiple CDS vendors were designated as qualified Clinical Decision Support Mechanisms by the Centers for Medicare and Medicaid Services. Little is known about the costs and benefits of these systems in real-world settings.

Background Children with a difficult airway are at high risk of decompensation in the setting of respiratory distress. Situational awareness among all team members, and a shared plan in case of an emergency, can reduce the chance of catastrophic outcomes. Methods We developed clinical decision support (CDS) to improve difficult airway situational awareness while minimizing alert burden. Three iterative designs were developed and implemented from 2015 through 2023. We measured [...]

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.

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

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.