Clinical Surveillance Technologies in Nonintensive Care Unit Hospital Settings: Systematic Review and Bayesian Network Meta-Analysis of Randomized Trials

Why clinicians should know about it

• Picked for Health Informatics (paper of the day, 25 September 2026): Systematic review and Bayesian NMA of electronic surveillance technologies

Abstract

BACKGROUND: Failure to recognize clinical deterioration in hospitalized patients has prompted the development of rule-based electronic surveillance (RB-ES), predictive model-based electronic surveillance (PM-ES), and continuous physiologic monitoring (CPM). However, their comparative effects on patient-centered outcomes remain uncertain. OBJECTIVE: The aim of this study is to compare the clinical effects of emergent intensive RB-ES, PM-ES, CPM, and local standard care in nonintensive care unit hospital settings. METHODS: We searched PubMed, Embase via online, the Cochrane Central Register of Controlled Trials via the Cochrane Library, and the Web of Science Core Collection from inception through February 28, 2026. Targeted supplementary surveillance, including trial-registry follow-up, backward and forward citation tracking, and known-item searches for newly available reports of registered trials, continued through July 22, 2026. Randomized, cluster-randomized, randomized crossover, and stepped-wedge trials were eligible. Interventions were classified according to their principal randomized function rather than their commercial or algorithmic labels. The primary outcomes were all-cause in-hospital or up-to-30-day mortality, and unplanned or emergent intensive care unit transfers. Bayesian random-effects network meta-analyses were performed using study-level adjusted relative effects. Secondary outcomes were evaluated using construct-specific pairwise meta-analyses. Risk of bias was assessed using the appropriate RoB 2 tool, and confidence in the network estimates was manually evaluated using the CINeMA (Confidence in Network Meta-Analysis) framework. Trial sequential analysis was retained as an exploratory supplementary analysis. RESULTS: The review included 28 independent trials. Nine met strict digital-surveillance criteria; 7 contributed to at least 1 network, while 2 contributed only to sensitivity analyses because of outcome-definition or zero-event limitations. Six trials with 13,716 observations contributed to the mortality network. Compared with standard care, odds ratios were 0.91 (95% credible interval [CrI] 0.35-2.30) for RB-ES, 1.22 (95% CrI 0.61-2.31) for PM-ES, and 0.70 (95% CrI 0.36-1.29) for CPM. Six trials with 13,441 observations contributed to the intensive care unit transfer network. Corresponding odds ratios were 1.20 (95% CrI 0.56-2.59), 0.95 (95% CrI 0.58-1.52), and 0.70 (95% CrI 0.37-1.28), respectively. Expanded rule-based surveillance-response systems did not clearly reduce cardiac arrest or cardiopulmonary resuscitation (OR 0.94, 95% CI 0.77-1.14). CPM (ratio of means 0.91, 95% CI 0.76-1.09) and predictive model-based surveillance (ratio of means 0.99, 95% CI 0.47-2.08) showed no clear effect on hospital length of stay. Trial sequential analyses were inconclusive. Confidence in all network comparisons was very low. CONCLUSIONS: Current randomized evidence does not establish a reliable clinical-effectiveness hierarchy among RB-ES, PM-ES, and CPM. Continuous monitoring showed directionally favorable but imprecise estimates for several outcomes. Surveillance strategies should therefore not be selected solely according to algorithm class or predictive complexity; their clinical effects may also depend on the target population, background monitoring, workflow integration, alert presentation, and clinical response pathway.

Abstract as published, via PubMed.