Publications by authors named "M Hravnak"
Article Synopsis
- The Telecritical Care Collaborative Network aimed to establish best practice recommendations for delivering critical care through telehealth technologies, addressing the variability and lack of evidence in the field.
- Using a modified Delphi methodology, an oversight panel developed and refined 79 practice statements based on expert feedback across three voting rounds, achieving consensus on 78 statements.
- The recommendations cover ten core domains, including care delivery models and staffing, emphasizing that effective telecritical care is best provided by specialized care teams and well-structured programs.
Importance: Persistent hypothermia after cardiopulmonary bypass (CPB) in neonates with congenital heart defects (CHD) has been historically considered benign despite lack of evidence on its prognostic significance.
Objectives: Examine associations between the magnitude and pattern of unintentional postoperative hypothermia and odds of complications in neonates with CHD undergoing CPB.
Design: Retrospective cohort study.
Objectives: Early signs of bleeding are often masked by the physiologic compensatory responses delaying its identification. We sought to describe early physiologic signatures of bleeding during the blood donation process.
Setting: Waveform-level vital sign data including electrocardiography, photoplethysmography (PPG), continuous noninvasive arterial pressure, and respiratory waveforms were collected before, during, and after bleeding.
Background: Critical instability forecast and treatment can be optimized by artificial intelligence (AI)-enabled clinical decision support. It is important that the user-facing display of AI output facilitates clinical thinking and workflow for all disciplines involved in bedside care.
Objectives: Our objective is to engage multidisciplinary users (physicians, nurse practitioners, physician assistants) in the development of a graphical user interface (GUI) to present an AI-derived risk score.
A significant proportion of clinical physiologic monitoring alarms are false. This often leads to alarm fatigue in clinical personnel, inevitably compromising patient safety. To combat this issue, researchers have attempted to build Machine Learning (ML) models capable of accurately adjudicating Vital Sign (VS) alerts raised at the bedside of hemodynamically monitored patients as real or artifact.
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