Utilizing a novel unified healthcare model to compare practice patterns between telemedicine and in-person visits.

Objective: Telemedicine practice has been shown to vary from clinical guidelines. Variations in practice patterns may be caused by disruptions in the continuity of care between traditional and telemedicine providers. This study compares virtual and in-person visits in Stanford's ClickWell Care (CWC) - where patients see the same provider for both visit modalities.

ClinicNet: machine learning for personalized clinical order set recommendations.

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 templates clinicians will use more accurately than existing decision support tools.

Materials And Methods: We process 57 624 patients worth of clinical event EHR data from 2008 to 2014. We train a feed-forward neural network (ClinicNet) and logistic regression applied to the traditional problem structure of predicting individual clinical items as well as our proposed workflow of predicting existing institutional order set template usage.

Neural Networks for Clinical Order Decision Support.

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 decision support suggestions for inpatient care through a feed-forward neural network. Given a patient's current status based on information data-mined and extracted from the Electronic Health Record (EHR), our model predicts clinical orders a physician enters for a patient within 24 hours.

Healthcare Service Utilization under a New Virtual Primary Care Delivery Model.

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