Leveraging explainable artificial intelligence to optimize clinical decision support.

Objective: To develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches.

Methods: We extracted data on alerts generated from January 1, 2019 to December 31, 2020, at Vanderbilt University Medical Center. We developed machine learning models to predict user responses to alerts.

A Prospective, Randomized, Open-Label Study Comparing an Opioid-Sparing Postsurgical Pain Management Protocol With and Without Liposomal Bupivacaine for Full-Arch Implant Surgery.

Purpose: Mild to moderate pain is common following dental implant surgeries, although severe pain is reported in some patients. This randomized, open-label trial was designed to determine the efficacy and safety of an opioid-sparing postsurgical pain management protocol with or without local infiltration of liposomal bupivacaine for full-arch implant surgery. This procedure is used in edentulous and/or failing dentition patients and involves securing four or more implants to the maxilla and/or mandible to serve as anchors for dental prostheses.

Parenteral Protein Decision Support System Improves Protein Delivery in Preterm Infants: A Randomized Clinical Trial.

Background: Management of neonatal parenteral protein intake for preterm infants is challenging and requires daily modifications of the dose to account for the infant's postnatal age, birth weight, current weight, and the volume and protein concentration of concurrent enteral nutrition. The objective of this study was to create and evaluate the Parenteral Protein Calculator (PPC), a clinical decision support system to improve the accuracy of protein intake for preterm infants who require parenteral nutrition (PN).

Materials And Methods: We integrated the PPC into the computerized provider order entry system and tested it in a randomized controlled trial (routine or PPC).

Reducing Inappropriate Testing for the Evaluation of Diarrhea Among Hospitalized Patients.

Background: Diarrhea is one of the most common illnesses in the United States. Evaluation frequently does not follow established guidelines. The objective of this study was to evaluate the effectiveness of a computerized physician order entry-based test guidance algorithm with regard to the clinical, financial, and operational impacts.

Clinical decision support for atypical orders: detection and warning of atypical medication orders submitted to a computerized provider order entry system.

The specificity of medication-related alerts must be improved to overcome the pernicious effects of alert fatigue. A systematic comparison of new drug orders to historical orders could improve alert specificity and relevance. Using historical order data from a computerized provider order entry system, we alerted physicians to atypical orders during the prescribing of five medications: calcium, clopidogrel, heparin, magnesium, and potassium.