(untitled) (The Review of Diabetic Studies, 2024)
Background: In the contemporary healthcare landscape, the management and interpretation of laboratory results constitute a fundamental component of the nursing process, directly influencing patient safety, diagnostic trajectories, and therapeutic timeliness. Laboratory testing provides approximately 70-85% of the objective data utilized in clinical decision-making. However, the efficiency with which this data is processed is heavily contingent upon the underlying information infrastructure. In many healthcare settings, particularly in low-resource or legacy environments, the standard of care remains the manual or hybrid paper-based management of laboratory results [Intervention 2]. This conventional strategy is characterized by linear, synchronous workflows where nurses must physically retrieve printed reports, transcribe values onto flowsheets, and rely on telephonic communication for critical value notifications. These manual processes are fraught with limitations, including high susceptibility to transcription errors, delayed communication of life-threatening values, and significant administrative burdens that divert time from direct patient care. Integrated Health Informatics Systems (IHIS) [Intervention 1], specifically the interoperability between Laboratory Information Systems (LIS) and Electronic Health Records (EHR), offer a promising alternative. By automating data flow, providing real-time alerts, and offering advanced visualization tools, IHIS aims to streamline the "brain-to-brain" loop of laboratory testing. Objective: The primary aim of this systematic review is to systematically compare the effectiveness of Integrated Health Informatics Systems [Intervention 1] versus manual or hybrid paper-based laboratory management systems [Intervention 2] on key outcomes for nursing professionals [Population] managing patients with acute and chronic conditions [Condition] requiring frequent laboratory monitoring. Methods: A comprehensive systematic review was conducted adhering to the PRISMA 2020 guidelines. A structured search was performed across major databases including PubMed, CINAHL, Scopus, and Web of Science for studies published between 2000 and 2024. The PICO framework was utilized to define eligibility: Population (Nurses), Intervention (Integrated LIS/EHR), Comparison (Paper/Manual/Hybrid systems), and Outcomes (Primary: Efficiency defined by Turnaround Time and Time-to-Action; Secondary: Patient safety including missed critical values, and user satisfaction including alert fatigue). The risk of bias was assessed using the Cochrane RoB 2.0 tool for randomized trials and the Newcastle-Ottawa Scale for observational studies. Results: The review identified and synthesized data from 112 discrete studies and data sources. The findings indicate a robust statistically significant improvement in primary efficiency outcomes associated with integrated systems. Specifically, automated critical value reporting systems reduced the turnaround time for result acknowledgement from a baseline of approximately 30 minutes in manual systems to under 11 minutes in integrated systems. Secure text messaging integration further reduced this to as low as 3.03 minutes. In terms of documentation, integrated systems were associated with a 23-24% reduction in time spent on administrative data entry. However, secondary outcomes revealed a complex dynamic; while objective safety improved (e.g., 18% reduction in medication errors), user satisfaction was polarized. Nurses reported high levels of "alert fatigue" and increased cognitive load due to interface complexity, with 32% citing EHRs as a contributor to burnout. Conclusion: Integrated Health Informatics Systems demonstrate superior effectiveness compared to manual systems in reducing the latency of information transfer and improving the reliability of critical value communication. The transition to digital integration is associated with significant gains in operational efficiency and patient safety. However, the "digital paradox" of increased cognitive load and alert fatigue presents a new challenge. Clinical practice must prioritize workflow-centric interface design and "smart" alert logic to sustain these gains. Future research should focus on longitudinal assessments of nurse burnout and the efficacy of AI-driven clinical decision support in reducing signal noise.