Predictive models of long COVID.

Background: The cause and symptoms of long COVID are poorly understood. It is challenging to predict whether a given COVID-19 patient will develop long COVID in the future.

Methods: We used electronic health record (EHR) data from the National COVID Cohort Collaborative to predict the incidence of long COVID.

Clinical decision support to reduce unnecessary diagnostic testing for heparin-induced thrombocytopenia.

Appropriate evaluation of heparin-induced thrombocytopenia (HIT) is imperative because of the potentially life-threatening complications. However, overtesting and overdiagnosis of HIT are common. Our goal was to evaluate the impact of clinical decision support (CDS) based on the HIT computerized-risk (HIT-CR) score, designed to reduce unnecessary diagnostic testing.

Relationship between a risk score for QT interval prolongation and mortality across rural and urban inpatient facilities.

Objectives: To evaluate the relationship between a modified Tisdale QTc-risk score (QTc-RS) and inpatient mortality and length of stay in a broad inpatient population with an order for a medication with a known risk of torsades de pointes (TdP).

Background: Managing the risk of TdP is challenging due to the number of medications with known risk of TdP and the complexity of precipitating factors. A model to predict risk of mortality may be useful to guide treatment decisions.

Clinician Responses to a Clinical Decision Support Advisory for High Risk of Torsades de Pointes.

Background Torsade de pointes (TdP) is a potentially fatal cardiac arrhythmia that is often drug induced. Clinical decision support (CDS) may help minimize TdP risk by guiding decision making in patients at risk. CDS has been shown to decrease prescribing of high-risk medications in patients at risk of TdP, but alerts are often ignored.

Clinician Satisfaction With Advanced Clinical Decision Support to Reduce the Risk of Torsades de Pointes.

Objectives: Clinical decision support (CDS) can potentially help clinicians identify and manage patients who are at risk for torsades de pointes (TdP). However, computer alerts are often ignored and might contribute to alert fatigue. The goals of this project were to create an advanced TdP CDS advisory that presents patient-specific, relevant information, including 1-click management options, and to determine clinician satisfaction with the CDS.