This study aims at developing SuperOrder, an order recommendation system for outpatient clinics. Using the electronic health record data available at midnight, SuperOrder predicts the order contents for each upcoming appointment on a daily basis. A t...
Journal of the American Medical Informatics Association : JAMIA
31390471
BACKGROUND: Drug prescription errors are made, worldwide, on a daily basis, resulting in a high burden of morbidity and mortality. Existing rule-based systems for prevention of such errors are unsuccessful and associated with substantial burden of fa...
In the first part of this series, we introduced the tools of Big Data, including Not Only Standard Query Language data warehouse, natural language processing (NLP), optical character recognition (OCR), and Internet of Things (IoT). There are nuances ...
In a general inpatient population, we predicted patient-specific medication orders based on structured information in the electronic health record (EHR). Data on over three million medication orders from an academic medical center were used to train ...
Journal of the American Medical Informatics Association : JAMIA
32984901
OBJECTIVE: To improve patient safety and clinical outcomes by reducing the risk of prescribing errors, we tested the accuracy of a hybrid clinical decision support system in prioritizing prescription checks.
Current approaches to understanding medication ordering errors rely on relatively small manually captured error samples. These approaches are resource-intensive, do not scale for computerized provider order entry (CPOE) systems, and are likely to mis...
Journal of the American Medical Informatics Association : JAMIA
38641410
OBJECTIVE: Current Clinical Decision Support Systems (CDSSs) generate medication alerts that are of limited clinical value, causing alert fatigue. Artificial Intelligence (AI)-based methods may help in optimizing medication alerts. Therefore, we cond...
Journal of the American Medical Informatics Association : JAMIA
38452289
OBJECTIVES: To evaluate the capability of using generative artificial intelligence (AI) in summarizing alert comments and to determine if the AI-generated summary could be used to improve clinical decision support (CDS) alerts.
Studies in health technology and informatics
39176549
Continuous unfractionated heparin is widely used in intensive care, yet its complex pharmacokinetic properties complicate the determination of appropriate doses. To address this challenge, we developed machine learning models to predict over- and und...