Abstract

Introduction: Understanding the economic landscape is pivotal for gauging the feasibility and sustainability of Clinical Decision Support (CDS) integration across radiology, nursing, and laboratory departments. This review will delve into the economic considerations associated with CDS implementation, shedding light on clinical impact and cost-effectiveness. Methods: The systematic review employed a robust methodology, combining controlled vocabulary and free-text keywords in a comprehensive search across multiple databases. The inclusion criteria encompassed original research articles, systematic reviews, and meta-analyses in English, focusing on Clinical Decision Support (CDS) implementation in radiology, nursing, and laboratory departments within Health Information Systems. The two-step screening process, detailed data extraction, and methodological quality assessment were conducted with rigor by two reviewers, resolving discrepancies through discussion or consultation with a third reviewer. Results: The systematic review incorporated seven intervention studies spanning radiology, nursing, and laboratory departments within Health Information Systems (HIS). Findings revealed a broad range of sample sizes, from 152 to 805 participants, showcasing the diversity of healthcare professionals involved. Across these studies, CDS interventions demonstrated substantial positive impacts, particularly in radiology with a risk ratio of 1.75 (95% CI: 1.42-2.10) for improved diagnostic accuracy, in nursing with a 58% risk reduction in medication errors (95% CI: 0.30-0.58), and in laboratory services with a 65% lower risk of unnecessary tests (95% CI: 0.24-0.51). These consistent themes highlight the effectiveness of CDS interventions but underscore the need for ongoing customization to meet department-specific needs. Conclusions: The systematic review underscores the significant positive impact of Clinical Decision Support (CDS) implementation across radiology, nursing, and laboratory departments within Health Information Systems, as evidenced by improved diagnostic precision, medication management, and laboratory efficiency, while emphasizing the importance of continuous customization to address department-specific nuances.

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