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OBJECTIVES: This study aimed to design and evaluate the usability of an electronic health record (EHR)-integrated clinical decision support prototype supporting pediatric surgical site infection (SSI) risk estimation and preoperative optimization using a multiphase human-centered design approach.

BACKGROUND: Diagnostic errors are common in medical care and can significantly affect patient outcomes. Symptom checker apps promise to help patients understand their symptoms, possible diagnoses, and actions to take, but lack evidence on accuracy, safety, and usability.

BACKGROUND: Sepsis is a life-threatening condition in which delayed recognition and treatment are associated with increased mortality. While predictive models such as Epic's Early Detection of Sepsis Model (ESM) were developed to support early intervention, their real-world effects after integration into clinical workflows remain difficult to evaluate.

BACKGROUND: Although interruptive alerts, or pop-ups, are one of the most common forms of clinical decision support (CDS) tools within the electronic health record (EHR), they are often ignored. Interruptive alerts have the potential to enhance patient care and reduce clinician burden, but end-users frequently experience alert fatigue and override alerts, defeating their purpose. Few studies have elicited the perspectives of all stakeholder groups involved in the development, experience, and [...]

OBJECTIVE: Assess inter-rater agreement on clinician- and clinical decision support (CDS)-driven diuretic titrations and risk classifications to benchmark a semi-autonomous CDS (OTTO-FM).

OBJECTIVES: The objective of this study is to evaluate the effect of a continuous remote patient monitoring (CRPM) program on reducing 30-day readmissions in a case-versus-retrospective-propensity-matched-control study of heart failure (HF) patients.

BACKGROUND: Patient portals are crucial for successful transition after hospitalization, especially for cancer patients with complex treatment plans.

BACKGROUND: No studies have examined the effect of artificial intelligence (AI)-generated hospital courses on discharge summary documentation time.

BACKGROUND: Electronic health record (EHR) clinical decision support (CDS) systems generate excessive, non-contextual alerts, causing clinician desensitization with override rates exceeding 90%. This alert fatigue disrupts workflows, contributes to burnout, and creates patient safety risks. Traditional survey-based methods fail to capture real-time end-user feedback at scale.