Acute internal medicine physicians' clinical intuition based on acute care telephone referral: A prospective study.

Introduction: In the Netherlands, most emergency department (ED) patients are referred by a general practitioner (GP) or a hospital specialist. Early risk stratification during telephone referral could allow the physician to assess the severity of the patients' illness in the prehospital setting. We aim to assess the discriminatory value of the acute internal medicine (AIM) physicians' clinical intuition based on telephone referral of ED patients to predict short-term adverse outcomes, and to investigate on which information their predictions are based.

Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department.

Background: Many prediction models have been developed to help identify emergency department (ED) patients at high risk of poor outcome. However, these models often underperform in clinical practice and their actual clinical impact has hardly ever been evaluated. We aim to perform a clinical trial to investigate the clinical impact of a prediction model based on machine learning (ML) technology.

Explainable Machine Learning Models for Rapid Risk Stratification in the Emergency Department: A Multicenter Study.

Background: Risk stratification of patients presenting to the emergency department (ED) is important for appropriate triage. Diagnostic laboratory tests are an essential part of the workup and risk stratification of these patients. Using machine learning, the prognostic power and clinical value of these tests can be amplified greatly.

Head-to-head comparison of 19 prediction models for short-term outcome in medical patients in the emergency department: a retrospective study.

Introduction: Prediction models for identifying emergency department (ED) patients at high risk of poor outcome are often not externally validated. We aimed to perform a head-to-head comparison of the discriminatory performance of several prediction models in a large cohort of ED patients.

Methods: In this retrospective study, we selected prediction models that aim to predict poor outcome and we included adult medical ED patients.

Performance of prediction models for short-term outcome in COVID-19 patients in the emergency department: a retrospective study.

Introduction: Coronavirus disease 2019 (COVID-19) has a high burden on the healthcare system. Prediction models may assist in triaging patients. We aimed to assess the value of several prediction models in COVID-19 patients in the emergency department (ED).

An 86-year-old man with acute abdominal pain.

An 86-year-old man presented with severe pain in the upper abdomen along with fever. On physical examination, we found an arterial blood pressure of 84/43 mm Hg, a heart rate of 80 bpm and a temperature of 38.3°C.

Symptomatic cholecystolithiasis after cholecystectomy.

A 43-year-old woman was admitted to the gastroenterology department with colicky pain in the upper abdomen. Four years earlier, she had undergone a laparoscopic cholecystectomy because of cholecystitis. She recognised her current complaints from that previous episode.