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Objective: Little is known about how clinical decision support (CDS) tools can support care teams in changing clinical decisions to account for patients' social risks. We piloted a suite of electronic health record (EHR)-based CDS tools designed to facilitate social risk-informed care decisions to assess how the tools were used in practice and how they could be improved.
Materials And Methods: After developing CDS tools through a process involving clinic staff and patient engagement, the tools were implemented in three community health center clinics. Data from staff interviews, observations of meetings with clinic staff, and the EHR were used to understand tool use patterns, and to yield insights that were then used to inform tool revisions.
Results: The overarching suggestion derived from the study data was that the tools should shift from making care recommendations to instead supporting documentation of social risk-related actions that clinical team members had already taken. Other revisions were guided by four additional insights: the CDS tools should: (1) facilitate documentation in standardized, short formats, (2) make documentation easy and consistent, (3) support work distribution across care team members, and (4) ensure documentation could serve multiple purposes.
Discussion: The CDS tools were revised to improve usefulness and acceptability for primary care teams in community clinics that serve patients with social risks. Numerous challenges exist in designing tools that can accommodate diverse clinics and workflows.
Conclusion: These findings provide insights on how CDS tools can be optimized for social risk-informed care while minimizing care team burdens.
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http://dx.doi.org/10.1093/jamiaopen/ooaf045 | DOI Listing |
Int J Med Inform
September 2025
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA. Electronic address:
Background: Identifying patient-specific barriers to statin therapy, such as intolerance or deferral, from clinical notes is a major challenge for improving cardiovascular care. Automating this process could enable targeted interventions and improve clinical decision support (CDS).
Objective: To develop and evaluate a novel hybrid artificial intelligence (AI) framework for accurately and efficiently extracting information on statin therapy barriers from large volumes of clinical notes.
Appl Clin Inform
September 2025
Department of Medicine, Oregon Health & Science University, Portland, United States.
Background Hypertension is a chronic condition defined by persistent high blood pressure (BP) that leads to significant health impacts. Evidence-based clinical guidelines provide recommendations for the diagnosis and treatment of hypertension. These recommendations are frequently incorporated into clinical decision support (CDS) systems used by clinicians.
View Article and Find Full Text PDFJ Adv Prosthodont
August 2025
Department of Statistical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy.
Purpose: This study aims to compare the occlusal trueness and precision of teeth manufactured using two modern digital milling processes.
Materials And Methods: A total of 38 complete dentures (CDs) were fabricated and analyzed. CDs in Group 1 (monolithic) (n = 19) were produced using a monolithic bicolor resin disk, whereas in Group 2 (oversize) (n = 19) were fabricated using the oversize process, which involves two separate resin disks of different colors.
Appl Clin Inform
August 2025
Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States.
Clinical decision support (CDS) tools in electronic health records (EHRs) often face low uptake due to limited usability, workflow integration, and other implementation issues. We recently designed and implemented the STRATIFY-CDS tool, which calculates a validated risk-prediction model and recommends disposition for emergency department (ED) patients with acute heart failure. Despite applying human-centered design and implementation science strategies, initial utilization in the first 3 months of the STRATIFY-CDS tool was just 3%.
View Article and Find Full Text PDFAuris Nasus Larynx
September 2025
Objective: To systematically evaluate the diagnostic accuracy, educational utility, and communication potential of generative AI, particularly Large Language Models (LLMs) such as ChatGPT, in otolaryngology.
Data Sources: A comprehensive search of PubMed, Embase, Scopus, Web of Science, and IEEE Xplore identified English-language peer-reviewed studies from January 2022 to March 2025.
Review Methods: Eligible studies evaluated text-based generative AI models used in otolaryngology.