Title: Evaluating the Impact of Clinical Decision Support Systems on Diagnostic Time and Accuracy in Hospital-Based Care
Authors: Kendra Mueller, RHIA and Dr. Bruce Lazar, MBA, DM
Volume: 9
Issue: 8
Pages: 19-30
Publication Date: 2025/08/28
Abstract:
Timely and accurate diagnoses are essential for quality patient care, yet traditional diagnostic methods often fall short due to evolving diseases and complex clinical environments. This systematic literature review aimed to explore whether clinical decision support systems, compared to traditional diagnostic methods, reduce diagnostic time and improve diagnostic accuracy during patient hospitalization. A thorough search was conducted through PubMed and MEDLINE Ultimate EBSCO, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Thirty-three relevant peer-reviewed articles published between 2020 and 2025 were identified and thematically analyzed. Five main themes emerged from the literature analysis: enhanced early diagnosis and risk prediction (33%), real-time clinical integration (27%), the importance of interpretability and trust (21%), broad applicability across specialties (24%), and the need for continuous evaluation (18%). Findings indicated that clinical decision support systems that use artificial intelligence and machine learning generally outperform traditional methods in speed and precision, though limitations such as ethical concerns, usability challenges, and the need for validation remain. This review supports clinical decision support systems as a promising tool to augment clinical decision-making and improve healthcare outcomes in hospital settings. This review supports health informatics leaders in educating and adopting clinical decision support systems as a promising tool to augment clinical decision-making and improve healthcare outcomes in hospital settings.