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Limited research exists on patient knowledge/cognition or "getting inside patients' heads." Because patients possess unique and privileged knowledge, clinicians need this information to make patient-centered and coordinated treatment planning decisions. To achieve patient-centered care, we characterize patient knowledge and contributions to the clinical information space.

The sequence of intravenous infusions may impact the efficacy, safety, and cost of intravenous medications. The study describes and assesses a computerized clinical decision support annotation system capable of analyzing the sequence of intravenous infusions.

Red blood cell (RBC) transfusion is a common medical procedure. While it offers clinical benefits for many, hemodynamically stable patients are often subjected to unwarranted transfusions, with the potential to lead to adverse consequences. We created a real-time clinical decision support (CDS) tool in the electronic health record system to address this problem and optimize transfusion practice as part of an institutional multidisciplinary, team-based patient blood management program.

Personal health records (PHR) provide opportunities for improved patient engagement, collection of patient-generated data, and overcome health-system inefficiencies. While PHR use is increasing, uptake in rural populations is lower than in urban areas.

Prediabetes and type 2 diabetes mellitus (T2DM) are one of the major long-term health conditions affecting global healthcare delivery. One of the few effective approaches is to actively manage diabetes via a healthy and active lifestyle.

Alert fatigue is a common issue with off-the-shelf clinical decision support. Most warnings for drug-drug interactions (DDIs) are overridden or ignored, likely because they lack relevance to the patient's clinical situation. Existing alerting systems for DDIs are often simplistic in nature or do not take the specific patient context into consideration, leading to overly sensitive alerts. The objective of this study is to develop, validate, and test DDI alert algorithms [...]

Limited research exists in predicting first-time suicide attempts that account for two-thirds of suicide decedents. We aimed to predict first-time suicide attempts using a large data-driven approach that applies natural language processing (NLP) and machine learning (ML) to unstructured (narrative) clinical notes and structured electronic health record (EHR) data.

The objective of this study is to provide an overview of the Regenstrief Teaching Electronic Medical Record (tEMR), how the tEMR could be used, and how it is currently being used in health professions education. The tEMR is a derivative of a real-world electronic health record (EHR), a large, pseudonymized patient database, and a population health tool designed to support curricular goals. The tEMR has been successfully adopted at 12 [...]

To construct and publicly release a set of medical concept embeddings for codes following the ICD-10 coding standard which explicitly incorporate hierarchical information from medical codes into the embedding formulation.

The use of alerts from the Bronx RHIO, a health information exchange (HIE) to identify James J. Peters VAMC patients diagnosed with COVID-19 in the community was described to facilitate COVID-19 VA primary care follow-up. COVID-19 hospitalization and testing alerts were delivered on a Bronx RHIO facility report. VA COVID-19 follow-up care by telephone and video was guided by local COVID-19 clinical pathways, electronic health record (EHR) templates, and tracking [...]