Search AMIA.org

 Clinical decision support systems (CDSSs) are computer applications, which can be applied to give guidance to practitioners in antimicrobial stewardship (AS) activities; however, further information is needed for their optimal use.

 The method of documentation during a clinical encounter may affect the patient-physician relationship.

To present a general framework providing high-level guidance to developers of computable algorithms for identifying patients with specific clinical conditions (phenotypes) through a variety of approaches, including but not limited to machine learning and natural language processing methods to incorporate rich electronic health record data.

To use workflow execution models to highlight new considerations for patient-centered clinical decision support policies (PC CDS), processes, procedures, technology, and expertise required to support new workflows.

The current medical paradigm of evidence-based medicine relies on clinical guidelines derived from randomized clinical trials (RCTs), but these guidelines often overlook individual variations in treatment effects. Approaches have been proposed to develop models predicting the effects of individualized management, such as predictive allocation, individualizing treatment allocation. It is currently unknown whether widespread implementation of predictive allocation could result in better population-level outcomes over guideline-based therapy. We sought to simulate [...]

The integration of these preventive guidelines with Electronic Health Records (EHRs) systems, coupled with the generation of personalized preventive care recommendations, holds significant potential for improving healthcare outcomes. Our study investigates the feasibility of using Large Language Models (LLMs) to automate the assessment criteria and risk factors from the guidelines for future analysis against medical records in EHR.

To identify impacts of different survey methodologies assessing primary care physicians' (PCPs') experiences with electronic health records (EHRs), we compared three surveys: the 2022 Continuous Certification Questionnaire (CCQ) from the American Board of Family Medicine, the 2022 University of California San Francisco (UCSF) Physician Health IT Survey, and the 2021 National Electronic Health Records Survey (NEHRS).

Numerous programs have arisen to address interruptive clinical decision support (CDS) with the goals of reducing alert burden and alert fatigue. These programs often have standing committees with broad stakeholder representation, significant governance efforts, and substantial analyst hours to achieve reductions in alert burden which can be difficult for hospital systems to replicate.

To address challenges in large-scale electronic health record (EHR) data exchange, we sought to develop, deploy, and test an open source, cloud-hosted app "listener" that accesses standardized data across the SMART/HL7 Bulk FHIR Access application programming interface (API).