The future of postoperative vital sign monitoring in general wards: improving patient safety through continuous artificial intelligence-enabled alert formation and reduction

Aasvang, Eske K.; Meyhoff, Christian S.

doi:10.1097/aco.0000000000001319

Cited by 7 publications

(1 citation statement)

References 36 publications

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“…Although many systems are generating similar data, the addition of the remote monitoring service and the interactions the clinical staff have with the data generate a uniquely labelled data set. To date, few statistical and machine learning models incorporate information on clinical activity, an addition that can significantly improve prediction accuracy [26]. Our unique data potentially allows us to develop more advanced decision support processes, including personalisation of multi-parameter escalation criteria in deterioration.…”

Section: Implications Of Work and Future Directionsmentioning

confidence: 99%

Health in a Virtual Environment (HIVE): A Novel Continuous Remote Monitoring Service for Inpatient Management

Bowles,

Trentino,

Lloyd

et al. 2024

Healthcare

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The aim of this study was to describe the implementation of a novel 50-bed continuous remote monitoring service for high-risk acute inpatients treated in non-critical wards, known as Health in a Virtual Environment (HIVE). We report the initial results, presenting the number and type of patients connected to the service, and assess key outcomes from this cohort. This was a prospective, observational study of characteristics and outcomes of patients connected to the HIVE continuous monitoring service at a major tertiary hospital and a smaller public hospital in Western Australia between January 2021 and June 2023. In the first two and a half years following implementation, 7541 patients were connected to HIVE for a total of 331,118 h. Overall, these patients had a median length of stay of 5 days (IQR 2, 10), 11.0% (n = 833) had an intensive care unit admission, 22.4% (n = 1691) had an all-cause emergency readmission within 28 days from hospital discharge, and 2.2% (n = 167) died in hospital. Conclusions: Our initial results show promise, demonstrating that this innovative approach to inpatient care can be successfully implemented to monitor high-risk patients in medical and surgical wards. Future studies will investigate the effectiveness of the program by comparing patients receiving HIVE supported care to comparable patients receiving routine care.

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Section: Implications Of Work and Future Directionsmentioning

confidence: 99%

Health in a Virtual Environment (HIVE): A Novel Continuous Remote Monitoring Service for Inpatient Management

Bowles,

Trentino,

Lloyd

et al. 2024

Healthcare

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Prognostic value of heart rate variability for risk of serious adverse events in continuously monitored hospital patients

Aagaard,

Olsen,

Rasmussen

et al. 2024

J Clin Monit Comput

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Technological advances allow continuous vital sign monitoring at the general ward, but traditional vital signs alone may not predict serious adverse events (SAE). This study investigated continuous heart rate variability (HRV) monitoring’s predictive value for SAEs in acute medical and major surgical patients. Data was collected from four prospective observational studies and two randomized controlled trials using a single-lead ECG. The primary outcome was any SAE, secondary outcomes included all-cause mortality and specific non-fatal SAE groups, all within 30 days. Subgroup analyses of medical and surgical patients were performed. The primary analysis compared the last 24 h preceding an SAE with the last 24 h of measurements in patients without an SAE. The area under a receiver operating characteristics curve (AUROC) quantified predictive performance, interpretated as low prognostic ability (0.5–0.7), moderate prognostic ability (0.7–0.9), or high prognostic ability (> 0.9). Of 1402 assessed patients, 923 were analysed, with 297 (32%) experiencing at least one SAE. The best performing threshold had an AUROC of 0.67 (95% confidence interval (CI) 0.63–0.71) for predicting cardiovascular SAEs. In the surgical subgroup, the best performing threshold had an AUROC of 0.70 (95% CI 0.60–0.81) for neurologic SAE prediction. In the medical subgroup, thresholds for all-cause mortality, cardiovascular, infectious, and neurologic SAEs had moderate prognostic ability, and the best performing threshold had an AUROC of 0.85 (95% CI 0.76–0.95) for predicting neurologic SAEs. Predicting SAEs based on the accumulated time below thresholds for individual continuously measured HRV parameters demonstrated overall low prognostic ability in high-risk hospitalized patients. Certain HRV thresholds had moderate prognostic ability for prediction of specific SAEs in the medical subgroup.

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Role-reversal simulation training to enhance performance and reduce stress of endovascular scrub nurses in the operating room

Skov,

Lawaetz,

Konge

et al. 2024

Current Problems in Surgery

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The future of postoperative vital sign monitoring in general wards: improving patient safety through continuous artificial intelligence-enabled alert formation and reduction

Cited by 7 publications

References 36 publications

Health in a Virtual Environment (HIVE): A Novel Continuous Remote Monitoring Service for Inpatient Management

Health in a Virtual Environment (HIVE): A Novel Continuous Remote Monitoring Service for Inpatient Management

Prognostic value of heart rate variability for risk of serious adverse events in continuously monitored hospital patients

Role-reversal simulation training to enhance performance and reduce stress of endovascular scrub nurses in the operating room

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