Digital Order System Redesign in Emergency-to-Intensive Care Unit Admissions: Prospective Observational Study.

Background: Emergency-to-intensive care unit (ICU) admissions are high-stakes transitions in care, where delays or documentation errors can compromise patient safety and disrupt operational efficiency. Although digital order systems can support these workflows, traditional platforms often lack real-time traceability and structured input logic, leaving them vulnerable to duplicate submissions, lost orders, and misaligned ICU bed reservations. Objective: The study aimed to examine whether an updated digital order system was associated with differences in emergency-to-ICU admission workflows, including order validity, documentation-error frequencies, and ICU reservation alignment, compared with those of the traditional platform. Methods: We conducted a single-center retrospective historical-control formative evaluation of emergency-to-ICU admissions processed through traditional (2023) and updated (2024) digital order systems at a tertiary medical center. Prospectively generated system log and ICU-reservation data were analyzed to quantify order validity, error subtypes (duplicate, lost, and tracking), and admission-to-reservation alignment. Proportions were compared using chi-square tests, with risk ratios (RRs) and absolute differences in proportions reported. Statistical significance was defined as Results: The updated system was associated with higher admission-order validity (54.1%-75.4%; RR 1.39, 95% CI 1.25-1.54; Conclusions: The updated digital order system was associated with more consistent emergency-to-ICU workflow patterns, including higher order validity, fewer documentation errors, and closer alignment between admission intent and ICU reservations. These findings are consistent with literature on structured electronic order systems, digital-workflow standardization, and timestamp integrity, and they highlight the potential value of structured, timestamp-driven digital infrastructures in supporting more reliable high-acuity workflows. Further evaluation using time-motion analysis, interaction-log metrics, or mixed methods assessment is warranted to examine the robustness and generalizability of these patterns.