Table 3.

In-Hospital Monitoring

Best practices	Description
AI/ML algorithms track cardiovascular status from in-hospital monitoring	Development of in-hospital electronic monitoring which is integrated with other technologies may predict events such as cardiac arrest, heart failure, AF, and stroke.
AI/ML algorithms may identify conditions such as sepsis, hemorrhage, delirium, and overall clinical deterioration	AI/ML-based algorithms may provide early warning for many types of clinical deterioration, each of which may need different integrated workflows.
AI/ML algorithms reduce alarm fatigue among staff	Alarm fatigue is a major issue in ICU settings. AI/ML algorithms may reduce excessive alarms that result from current rule-based systems.
AI/ML algorithms improve allocation of services and resources	Use of AI/ML of in-hospital data streams may improve allocation of resources.
AI/ML algorithms for in-hospital use assist in procedures	Procedures may be improved by AI/ML methods, such as robotic surgery.
Gaps and challenges	Description
Translation performance of predictive AI/ML algorithms across centers	AI/ML-based alerting algorithms exhibit robust performance when tested across institutions and places that reflect differences in clinical settings or study designs.
Identification of patients, conditions where monitoring may improve outcomes	It is unclear which patients benefit from automated alerting systems, and if that affects disparities in in-hospital outcomes.
Evaluation of the effect of alarms across conditions and patient groups	Limited evaluation has been performed on the effect of false positive triggers and their reduction on clinician workload and health system cost.
Acceleration and scaling annotation of in-hospital monitoring data	Because the annotation of in-hospital monitoring data is labor intensive, and complicated by noise and artifacts, the limited availability of large, well-labeled datasets hampers progress. Open-source data sets may be noise free and not representative. New techniques (eg, semisupervised ML) may be effective.
Real-time operation of alert triggering AI/ML algorithms, across hospital settings	Few hospitals have pipelines that integrate physiological monitoring with other systems, which may widen the gap between safety net and high cost among hospitals.

AF indicates atrial fibrillation; AI, artificial intelligence; ICU, intensive care unit; and ML, machine learning.