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A growing body of evidence suggests that testing for influenza virus alone is more appropriate than multiplex respiratory viral panel (RVP) testing for general populations of patients with respiratory tract infections. We aimed to decrease the proportion of RVPs out of total respiratory viral testing ordered during influenza season.

Failure to complete recommended diagnostic tests may increase the risk of diagnostic errors.

With the consequences of inadequate dosing ranging from increased bleeding risk to excessive drug costs and undesirable administration regimens, the antihemophilic factors are uniquely suited to dose individualization. However, existing options for individualization are limited and exist outside the flow of care. We developed clinical decision support (CDS) software that is integrated with our electronic health record (EHR) and designed to streamline the process for our hematology providers.

Due to reimbursement tied in part to patients' perception of their care, hospitals continue to stress obtaining patient feedback and understanding it to plan interventions to improve patients' experience. We demonstrate the use of natural language processing (NLP) to extract meaningful information from patient feedback obtained through Press Ganey surveys.

Newborns are often assigned temporary names at birth. Temporary newborn names-often a combination of the mother's last name and the newborn's gender-are vulnerable to patient misidentification due to similarities with other newborns or between a mother and her newborn. We developed and implemented an alternative distinct naming strategy, and then compared its effectiveness on reducing the number of wrong-patient orders with the standard distinct naming strategy.

Physicians may spend a significant amount of time using the electronic health record (EHR), but this is understudied in the pediatric intensive care unit (PICU). The objective of this study is to quantify PICU attending physician EHR usage and determine its association with patient census and mortality scores.

Sepsis is an uncontrolled inflammatory reaction caused by infection. Clinicians in the pediatric intensive care unit (PICU) developed a paper-based tool to identify patients at risk of sepsis. To improve the utilization of the tool, the PICU team integrated the paper-based tool as a real-time clinical decision support (CDS) intervention in the electronic health record (EHR).

Current machine learning models aiming to predict sepsis from electronic health records (EHR) do not account 20 for the heterogeneity of the condition despite its emerging importance in prognosis and treatment. This work demonstrates the added value of stratifying the types of organ dysfunction observed in patients who develop sepsis in the intensive care unit (ICU) in improving the ability to recognize patients at risk of sepsis from their EHR [...]