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Electronic patient-reported outcome measures (ePROMs) can improve care for people with cancer, but effectiveness hinges on well-supported integration in clinical settings.We evaluated clinician use of specific clinical decision support (CDS) tools in the electronic health record (EHR) designed to facilitate timely, clinically appropriate responses to ePROM scores for six symptoms commonly experienced by cancer patients.The parent pragmatic trial, which took place at Mayo Clinic (Rochester, Minnesota, United States) and its affiliated community health care system between March 2019 and January 2023, evaluated the population-level effectiveness and implementation of an ePROM surveillance and EHR-facilitated collaborative care symptom management intervention. The present evaluation used a case study approach with four data sources: (1) clinician interactions with CDS tools abstracted from the EHR; (2) clinician notes identified with an institution-specific textual search tool; (3) qualitative interviews and group discussions with care teams; and (4) administrative records reviewed to identify training and outreach to care teams.EHR metrics showed very low adoption of CDS tools including alerts and symptom-specific order sets, despite educational outreach and information technology support provided to clinical care teams. Qualitative findings revealed that CDS use was not easy to integrate into busy clinical workflows and highlighted clinician perceptions that the collaborative care intervention provided additional patient support that reduced clinicians' need to utilize CDS tools. They also highlight the importance of contextual factors, including institutional priorities and EHR changes.This pragmatic clinical trial case study found limited adoption of EHR CDS tools that had been developed to increase clinicians' awareness of and responses to ePROM data. Findings suggest the need to align clinician and organizational implementation strategies, simplify CDS tools to fit practice expectations, and identify and address contextual factors that could undercut strategies like education and peer support. This may be especially important for teams who aim to iteratively evaluate and refine CDS and implementation strategies for multicomponent interventions or introduce new strategies that are responsive to barriers while maintaining scalability.
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http://dx.doi.org/10.1055/a-2587-6081 | 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.