The meaningful use of electronic medical records (EMR) will come from effective clinical
decision support (CDS) applied to physician orders, the concrete manifestation of clinical …

Objective: To answer a “grand challenge” in clinical decision support, the authors produced
a recommender system that automatically data-mines inpatient decision support from …

Physician orders, the concrete manifestation of clinical decision making, are enhanced by
the distribution of clinical expertise in the form of order sets and corollary orders …

Uncertainty and variability is pervasive in medical decision making with insufficient evidence-
based medicine and inconsistent implementation where established knowledge exists …

Objective Determine how varying longitudinal historical training data can impact prediction
of future clinical decisions. Estimate the “decay rate” of clinical data source relevance …

Consistent and high quality medical decisions are difficult as the amount of literature, data,
and treatment options grow. We developed a model to provide automated physician order …

Objective Evaluate the quality of clinical order practice patterns machine-learned from
clinician cohorts stratified by patient mortality outcomes. Materials and methods Inpatient …

Objective This study assesses whether neural networks trained on electronic health record
(EHR) data can anticipate what individual clinical orders and existing institutional order set …

Clinical decision support tools that automatically disseminate patterns of clinical orders have
the potential to improve patient care by reducing errors of omission and streamlining …

Clinical decision support is essential for achieving the maximum value from electronic
medical records. Content for decision support systems is usually developed manually, and …