Objective: Evaluation of the effect of six optimization strategies in a clinical decision support system (CDSS) for drug-drug interaction (DDI) screening on alert burden and alert acceptance and description of clinical pharmacist intervention acceptance.
Methods: Optimizations in the new CDSS were the customization of the knowledge base (with addition of 67 extra DDIs and changes in severity classification), a new alert design, required override reasons for the most serious alerts, the creation of DDI-specific screening intervals, patient-specific alerting, and a real-time follow-up system of all alerts by clinical pharmacists with interventions by telephone was introduced. The alert acceptance was evaluated both at the prescription level (i.e. prescription acceptance, was the DDI prescribed?) and at the administration level (i.e. administration acceptance, did the DDI actually take place?). Finally, the new follow-up system was evaluated by assessing the acceptance of clinical pharmacist's interventions.
Results: In the pre-intervention period, 1087 alerts (92.0 % level 1 alerts) were triggered, accounting for 19 different DDIs. In the post-intervention period, 2630 alerts (38.4 % level 1 alerts) were triggered, representing 86 different DDIs. The relative risk forprescription acceptance in the post-intervention period compared to the pre-intervention period was 4.02 (95 % confidence interval (CI) 3.17-5.10; 25.5 % versus 6.3 %). The relative risk for administration acceptance was 1.16 (95 % CI 1.08-1.25; 54.4 % versus 46.7 %). Finally, 86.9 % of the clinical pharmacist interventions were accepted.
Conclusion: Six concurrently implemented CDSS optimization strategies resulted in a high alert acceptance and clinical pharmacist intervention acceptance. Administration acceptance was remarkably higher than prescription acceptance.
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http://dx.doi.org/10.1016/j.ijmedinf.2021.104393 | DOI Listing |
J Int Soc Respir Prot
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Georgia Institute of Technology, School of Materials Science and Engineering, Atlanta GA USA.
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School of Information Science and Technology, ShanghaiTech University, Shanghai, China; Shanghai Engineering Research Center of Intelligent Vision and Imaging, Shanghai, China. Electronic address:
Advanced Driver Assistance Systems (ADAS) are crucial for enhancing driving safety by alerting drivers to unrecognized risks. However, traditional ADAS often fail to account for individual decision-making processes, including drivers' perceptions of the environment and personal driving styles, which can lead to non-compliance with the provided assistance. This paper introduces a novel Cognitive-Digital-Twin-based Driving Assistance System (CDAS), leveraging a personalized driving decision model that dynamically updates based on the driver's control and observation actions.
View Article and Find Full Text PDFPediatr Qual Saf
January 2025
Department of Pediatrics, Washington University in St. Louis School of Medicine, St. Louis, Mo.
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View Article and Find Full Text PDFJ Acad Consult Liaison Psychiatry
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School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan; Department of Nursing, Taipei Medical University Hospital, Taipei, Taiwan. Electronic address:
Background: Delirium is a common symptom following a traumatic brain injury (TBI) that is often overlooked by healthcare professionals. Early detection of post-traumatic delirium is crucial to improving patient outcomes and quality of life. The four As Test (4AT: alertness, attention, abbreviated mental test-4, and acute mental changes) is a brief and rapid tool for delirium assessment with acceptable reliability and validity.
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