This study was structured according to the recommendations of the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) 2025 statement (Checklist 1).

The primary objective of this trial is to evaluate whether implementation of an AI-SaMD as a stand-alone clinical decision-support tool in routine general-ward care reduces the incidence of IHCA compared with usual care based on conventional EWSs, especially among adult patients identified as high-risk (target cohort).

Secondary objectives include evaluating the effect of the intervention on all-cause in-hospital mortality, hospital and intensive care unit (ICU) length of stay (LOS), time to unplanned ICU transfer after the first alert, and neurologic outcome at discharge among patients who experienced IHCA. A further objective is to characterize the clinical actions (eg, critical care consultation, antibiotic escalation, early ICU transfer) that may mediate any observed outcome benefit in order to translate algorithmic risk prediction into action-anchored clinical utility.

The primary estimand will follow a treatment-policy strategy: the population-level effect of being admitted to a cluster operating in the postimplementation (AI-SaMD-guided) phase versus the preimplementation (usual care) phase, regardless of individual clinician adherence to AI-SaMD alerts, with the outcome variable defined as the binary occurrence of general-ward IHCA during hospitalization.

This study is designed as a pragmatic, multicenter SW-CRT [26]. The unit of randomization is the hospital (cluster); the 4 participating hospitals were randomized to sequentially transition from control to intervention according to a prespecified schedule: preimplementation, transitional (washout), and postimplementation periods (Figure 1). This SW-CRT design allows each cluster to serve as its own control, accounting for intercluster variability. The total study period is 1 year to ensure coverage of all seasonal variations.

The detailed study design is presented in Figure 2. Intervention effectiveness will be evaluated by comparing patient outcomes between the AI-SaMD-guided (postimplementation) and usual care (preimplementation) phases. Within this overall comparison, the primary analysis will focus on the target cohort, defined as patients who exceed the AI-SaMD alert threshold at least once during general-ward hospitalization. This cohort aligns with the intended use of AI-SaMD (DeepCARS), which is designed to identify patients at high risk of clinical deterioration, and with the clinical estimand of interest, as an RRS is by definition intended to identify patients at risk of deterioration and facilitate intervention [3,19,21-23,25,27]. Patients who do not exceed the alert threshold will be defined as the nontarget cohort. In prior studies, these non-high-risk patients demonstrated a very low incidence of the primary outcome [25,28-34]; the target cohort therefore represents the population in which the intervention is intended to act and in which a treatment effect can meaningfully be detected, supporting its designation as the primary analysis population. To validate the restriction of primary analysis to the target cohort, baseline characteristics, alert distribution, and patient outcomes will also be compared between the target and nontarget cohorts, as well as between the pre- and postimplementation phases, in a secondary analysis.

No predefined criteria exist for discontinuing or modifying the intervention, as the AI-SaMD will operate passively within routine workflows and poses minimal risk.

Patients admitted during the preimplementation period who remain hospitalized during the postimplementation period will be considered crossover cases and analyzed separately [26].

Adherence may be limited by delayed alert acknowledgment, noncompliance with alerts, patient refusal, alert fatigue, and staffing constraints [36,41-45].

To address these challenges, each participating hospital will undertake structured preimplementation preparation before AI-SaMD deployment. This will include (1) early coordination with hospital IT teams to enable seamless integration of the AI-SaMD into existing EMR and clinical workflows and (2) preimplementation education programs for relevant HCPs—including rapid response team members, ward nurses, and attending physicians—to standardize the interpretation of AI-SaMD outputs and the corresponding alert response procedures.

Ongoing education throughout the trial will further reinforce appropriate use and timely response to alerts. However, because the AI-SaMD-guided intervention is nonmandatory in order to preserve physician autonomy, full compliance cannot be guaranteed and will be evaluated in a secondary analysis.

All usual clinical practices are permitted. The AI-SaMD provides advisory alerts only and does not mandate clinical decisions.

No patient or public involvement was incorporated into the design or conduct of this trial, given the cluster-level intervention, waiver of individual consent, and exclusive reliance on routinely collected data.

The primary outcome is the incidence of general-ward cardiac arrest, defined according to the in-hospital Utstein guidelines as cessation of cardiac activity with the absence of pulse, unresponsiveness, and apnea requiring resuscitation [46]. Secondary outcomes include all-cause in-hospital mortality, hospital and ICU LOS, time to unplanned ICU transfer (UIT) after the first alert, and Cerebral Performance Category (CPC) at discharge among general-ward patients with cardiac arrest. UIT is defined as ICU transfer within 24 hours for clinical deterioration, excluding planned postoperative ICU admissions [47].

This study poses minimal risk, as the intervention (AI-SaMD) functions as an additional safety measure without altering usual care. Adverse events are expected to be rare and likely reflect underlying illness rather than the intervention. Adverse events will be recorded in the case report form (CRF) and reviewed during interim analyses by the Data and Safety Monitoring Board (DSMB) and institutional review board (IRB) at each participating site.

In addition, the conduct of this trial will be reported with the routine mortality and cardiac arrest conferences held at each of the 4 participating hospitals. If any case is judged at such a conference to raise concern about potential harm related to the intervention—for example, workflow disruption associated with the intervention—the conference participants may independently notify the site principal investigator (PI) and the DSMB chair at any time by telephone and email, using the contact details provided in the DSMB charter (Multimedia Appendix 1). Similarly, any notable increase in mortality or IHCA at a participating site that is considered at such a conference to be associated with AI-SaMD implementation may also be reported through the same channel at any time.

The study schedule is shown in Table 2.

The required sample size is estimated to detect a reduction in general ward cardiac arrest among patients identified as high risk by the AI-SaMD (target cohort). The baseline incidence in the control group is assumed to be 6.70% based on prior studies [23,25]. Assuming a 40% relative risk reduction (RRR), consistent with previous trials in comparable high-risk populations, the expected incidence in the intervention group is set at 4.00% [25,28,29,48].

With a 2-sided α of .05, 80% power, an intracluster correlation coefficient (ICC) of 0.01 (based on a meta-analysis of RRS studies) [49], and 350 patients per cluster-period (derived from prior institutional data) [21-23], the required sample size is calculated at 7002 patients in the target cohort [50]. Assuming a 10% high-risk alert rate and 5% dropout, total enrollment is estimated at 72,695 [21-23], including 65,693 patients in the nontarget cohort.

No specific recruitment strategy is required, as this study implements hospital-level intervention and includes all general ward admissions eligible for screening.

The study protocol was approved by the IRBs of all 4 participating hospitals: Inha University Hospital (INHAUH 2024-09-034; approved January 20, 2025), Hallym University Dongtan Sacred Heart Hospital (HDT 2024-10-006; approved March 24, 2025), Konyang University Hospital (KYUH 2024-12-004; approved December 18, 2024), and The Catholic University of Korea Incheon St. Mary’s Hospital (OC24DIDS0141; approved February 17, 2025). The requirement for informed consent was independently waived by the IRBs of all 4 participating hospitals.

Important protocol amendments will be promptly communicated to relevant parties. The principal investigator will submit revised protocols to the IRBs at all sites for approval before implementation. Approved amendments will be updated in the trial registry.

This study requests the waiving of consent from individual patients for the following three reasons: (1) this study poses minimal risk, as the AI-SaMD supplements standard care by providing nonmandatory screening recommendations without altering clinical workflows or physician autonomy; (2) the waiver will not adversely affect participants’ rights or welfare because the intervention does not restrict access to standard care; and (3) obtaining individual consent is not feasible in a large-scale hospital-based study and may introduce selection bias, as the study targets the entire inpatient population. Additional consent provisions are not applicable, as no additional participant data or biological specimens will be collected.

This study will ensure strict confidentiality throughout the data collection, analysis, and publication. All personal identifiers will be deidentified in accordance with the HIPAA requirements. Records will be retained for 3 years after completion of the trial, in accordance with the Korean Bioethics and Safety Act, and then securely disposed of. No secondary use beyond the study objectives is permitted. Ancillary and posttrial care is not applicable, as the study covers the entire general-ward hospitalization period.

The VITAL-MD trial will provide the first large-scale, multicenter randomized evidence on whether the deployment of an AI-EWS, used as a stand-alone clinical decision-support device—without algorithm-specific human curation or mandated workflow modification—improves patient-centered outcomes among general-ward patients in external validation environments. Building on the demonstrated diagnostic superiority of DeepCARS over conventional EWS in retrospective and prospective observational studies [19,21-24] and on the suggestive but nonrandomized signal from the recent NRCT [25], we hypothesize that the intervention period will be associated with a reduction in ward IHCA and in in-hospital mortality compared with the control group in which routine conventional EWS-based care (eg, NEWS2 and MEWS) is used. We additionally anticipate a reduction in the rate of late or missed escalations of care, an increase in proactive interventions (eg, critical care consultation, antibiotic escalation, early ICU transfer), and no clinically meaningful increase in alert burden.

This trial directly addresses the 2 principal limitations of the existing AI-EWS evidence base for ward-level deterioration. First, the 2 prior multicenter cluster RCTs for ward deterioration—the negative CoMET trial and the positive CONCERN trial, currently representing the highest-level randomized evidence in this field—were both conducted within their internal development environments [14,15]. Because AI model performance frequently drops substantially when transferred to external settings due to dataset shifts [59], whether these divergent outcomes generalize beyond their developing institutions remains unestablished. To our knowledge, VITAL-MD will be the first multicenter randomized trial to evaluate the patient outcome effect of an AI-EWS in true external validation environments. Second, previous interventional studies bundled algorithms with specific implementation infrastructures, such as off-site nurse curation in the Advance Alert Monitor deployment [28]. By deploying DeepCARS as a stand-alone device without algorithm-specific human curation or mandated workflow modification, VITAL-MD isolates the pure effect of the AI-SaMD itself from the surrounding human and workflow scaffolding. Furthermore, establishing causal outcome benefits for rapid response mechanisms has historically been challenging. Comprehensive Cochrane systematic reviews and the landmark Medical Early Response, Intervention, and Therapy (MERIT) trial demonstrated little or no significant reduction in unexpected mortality from conventional RRS and EWS interventions [8,60]. Despite this, major societies such as the AHA and SCCM continuously and strongly recommend hospital-wide surveillance systems [4,5]. VITAL-MD aims to bridge this critical evidence gap by generating definitive causal evidence that replacing the inaccurate afferent limb with a highly accurate AI-EWS can validate the clinical rationale behind these ongoing recommendations. Evaluating the intervention directly against usual clinical practice and the standard of care is strongly emphasized by major guidelines as the most rigorous benchmark for establishing clinical utility [11]. The SW-CRT design robustly controls for secular trends and between-cluster confounding, evaluating this real-world standard across a massive cohort of more than 70,000 anticipated admissions.

Furthermore, this study seeks to propose a novel paradigm for understanding and evaluating AI outputs, pivoting the focus from algorithmic “explainability” to clinical “actionability.” The prevailing approach to mitigating the “black-box” nature of deep learning models involves post hoc mathematical interpretability techniques, such as Shapley Additive Explanations (SHAP) values. However, emerging literature clearly indicates that such mathematical explanations fail to translate into tangible clinical actions. As highlighted by Bienefeld et al [61], mathematical feature weightings do not provide clinical actionability and are frequently perceived by clinicians as unhelpful in acute care settings. We will therefore investigate the specific clinical mechanisms driving outcome improvements by prospectively analyzing lead times to targeted clinical interventions (eg, critical care consultation, antibiotic escalation, and early ICU consultation) and identifying which clinical subgroups benefit most from actions prompted by AI alerts. Providing HCPs with practical, action-oriented guidelines on how to respond to AI alerts will directly maximize the real-world utility of the AI-SaMD.

Several limitations warrant explicit acknowledgment. First, while this study provides causal evidence for one specific algorithm, clinical efficacy is not a universal characteristic of all AI-EWS platforms. Emerging literature demonstrates that commercial medical AI devices exhibit vast disparities in their real-world performance [16]. Recent multicenter evaluations have shown that sensitivities, positive predictive values, and false-alarm burdens range widely across AI products even when evaluating the exact same clinical deterioration targets [9,62,63]. Consequently, the patient-centered benefits observed in this trial apply exclusively to the implemented AI-SaMD and do not validate the universal clinical utility of commercial early warning algorithms. Second, while all general ward admissions are included in the analysis, the primary evaluation focuses predominantly on the “target cohort,” patients who exceed the high-risk alert threshold. Although restricting the primary analysis to this subset limits inferences about the overall unselected hospital population, this targeted methodology perfectly aligns with the algorithm’s intended clinical use as a medical device to identify and trigger responses for high-risk patients. Furthermore, this approach is consistent with prior AI-EWS evaluations, such as the studies by Escobar et al [28] and Winslow et al [29], justifying its analytical validity for evaluating algorithmic rapid response interventions. Third, the study is conducted exclusively in tertiary academic hospitals within South Korea. The lack of racial and geographic diversity limits the direct generalizability of the findings to health care systems in other countries or nontertiary settings. Fourth, the planned number of randomized clusters was scaled down to 4 sites, which not only decreases statistical efficiency but also means the between-cluster variance is imprecisely estimated; we address the resulting inferential limitations through the stepped-wedge structure and prespecified small-sample corrections [26,54]. Finally, the passive, nonmandatory nature of the AI intervention might lead to variable compliance and alert fatigue among clinicians over time. Potential cross-contamination during transition periods will be carefully monitored via strict washout period definitions and prespecified sensitivity analyses.

If the trial demonstrates a beneficial effect of DeepCARS on patient-centered outcomes, it will set a methodological benchmark for AI-EWS evaluation, complementing prior multicenter cluster RCTs by being the first conducted in external validation environments as a stand-alone device. For clinical practice, it will provide direct evidence on whether an AI-EWS can improve outcomes in resource-limited or workforce-constrained settings where additional curating personnel are not feasible. For regulatory science, it will inform the ongoing debate on whether AI-SaMDs—currently approved primarily based on retrospective diagnostic performance—should also be required to demonstrate prospective effects on patient-centered outcomes before or shortly after market authorization [13].

The VITAL-MD trial is designed to generate the first multicenter randomized evidence on whether an AI-EWS, deployed as stand-alone software as a medical device in external validation environments, can improve patient-centered outcomes for general-ward patients. By directly comparing the intervention with usual care and prospectively characterizing the clinical actions that mediate any observed benefit, the trial seeks to advance AI-SaMD evaluation from retrospective diagnostic accuracy toward prospective, action-oriented clinical utility, with anticipated implications for clinical practice, hospital policy, and regulatory science.