Digital Health, Tele-ICU, Remote Monitoring & ICU Capacity Planning
Аннотация
Background: The blistering nature of healthcare digitalization has replicated the critical-care provisioning using technologies in the form of Tele-ICU systems, remote monitoring, and data-guided ICU-capacity planning. These developments cater to the rising need for efficient, real-time utilization of intensive care resources, and this is more so in situations where manpower is scarce and where patients are increasingly becoming complicated. Although there is an interest in it, empirical studies that have associated digital health adoption with ICU operational performance are scarce. The paper explores the impacts of digital health preparedness, integrating Tele-ICUs, and remote monitoring in managing the capacity of ICUs in healthcare facilities. Methods: A cross-sectional, quantitative research design was utilized with a structured Likert-scale questionnaire being administered to 319 professionals in the healthcare setting, which included public, private, and teaching hospitals. The statistical tests of the dataset included Normality (Kolmogorov-Smirnov, Shapiro-Wilk), Reliability (Cronbach Alpha > 0.7), and KMO = 0.874, p < 0.001 (all valid). Independent Samples t-test, One-way ANOVA, Kruskal-Wallis, and Chi-Square were used to perform inferential analyses to test the differences between groups. Dependent variables, Pearson Correlation, and Multiple Regression Analysis were used to evaluate relationships between variables to identify predictors of the efficiency of ICU capacity planning. Results: The constructs showed high standards of reliability (α = 0.91) and validity, which indicated the instrument to be robust. The data were normally distributed (p > 0.05), allowing the use of parametric tests. The inferential analyses did not exhibit any significant differences according to gender, education, and the type of institution (p < 0.05). Correlation showed a high positive relationship between Digital Health and Tele-ICU (r = 0.68–0.77) and Remote Monitoring and ICU Capacity Planning (r = 0.68–0.77). The outcome of regression models indicated that these three predictors, when combined, could uniquely account for 69.4% of the variation (R² = 0.694) in ICU capacity optimization, with all three coefficients corresponding to them significant (p < 0.001). Conclusion: The paper concludes with the finding that digital transformation in healthcare is a formal facilitator of effective ICU care. Removing the barriers between Tele-ICU systems, remote patient monitoring, and digital health systems improves the accuracy of planning, the use of resources, and clinical responsiveness. It is proposed that policymakers and hospital authorities should value the importance of digital infrastructure investment, employee education, and interoperability as crucial factors that would facilitate sustainable critical-care results. To demonstrate real-time effects of digital interventions in intensive care, future studies should be extended by means of longitudinal and outcome-based clinical studies that rely on tangible results.