Algorithms for Prediction of Clinical Deterioration on the General Wards: A Scoping Review

All of the studies that compared their proposed model with one of various warning systems (eg, EWS, National Early Warning Score [NEWS], Modified Early Warning Score [MEWS]) showed superior performance (based on AUROC and reported predictive values). In 17 studies, the authors reported their model as more useful or superior to the EWS.^{20-23,26-28,34,36-41} Four studies reported real-time detection of deterioration before regular EWS,^20,26,42 and three studies reported positive effects on patient-related outcomes.^26,35 Four negative effects were noted on the controllability, validity, and potential limitations.^27,42

There were 26 positive effects on the clinical process mentioned, 7 of which pointed out the effects of earlier, predictive alarming. Algorithms with higher PPVs reported greater rates of actionable alarms, less alarm fatigue, and improved workflow.^{21,22,24-27,30,32,33,35-38,40} Potential alarm fatigue was named as a barrier.^27,42 Smoother scoring instead of binned categories was mentioned positively.^24,26In the infrastructure domain, very few items were reported. The increased need for education on the used techniques was reported once as a negative effect.³⁴ One of the positive infrastructural effects noted was more efficient planning and use of resources.^24,37,40We identified 57 facilitators and 48 barriers for the clinical implementation and use of real-time predictive analytics (Appendix Figure). In the Technology domain, there were 18 facilitators and 20 barriers cited, and in the Organization domain, 25 and 14, respectively. They were equally present in the Professional and Physiology domains (6 vs 5, 8 vs 9).

Of the 38 remarks in the Technology domain, difficulty with implementation in daily practice was a commonly cited barrier.^22,24,40,42 Difficulties included creating real-time data feeds out of the EMR, though there were mentions of some successful examples.^25,27,36 Difficulty in the interpretability of AI was also considered a potential barrier.^{30,32,33,35,39,41} There were remarks as to the applicability of the prolonged prediction horizon because of the associated decoupling from the clinical view.^39,42

Conservative attitudes toward new technologies and inadequate knowledge were mentioned as barriers.³⁹ Repeated remarks were made on the difficulty of interpreting and responding to a predicted escalation, as the clinical pattern might not be recognizable at such an early stage. On the other hand, it is expected that less invasive countermeasures would be adequate to avert further escalation. Earlier recognition of possible escalations also raised potential ethical questions, such as when to discuss palliative care.²⁴

The heterogeneity of the general ward population and the relatively low prevalence of deterioration were mentioned as barriers.^24,30,38,41 There were also concerns that not all escalations are preventable and that some patient outcomes may not be modifiable.^24,38

Many investigators expected reductions in false alarms and associated alarm fatigue (reflected as higher PPVs). Furthermore, they expected workflow to improve and workload to decrease.^{21,23,27,31,33,35,38,41} Despite the capacity of modern EMRs to store large amounts of patient data, some investigators felt improvements to real-time access, data quality and validity, and data density are needed to ensure valid associated predictions.^{21,22,24,32,37}

As the complexity and comorbidity of hospitalized adults grow, predicting clinical deterioration is becoming more important. With an ever-increasing amount of available patient data, real-time algorithms can predict the patient’s clinical course with increasing accuracy, positively affecting outcomes.^4,21,25,43 The studies identified in this scoping review, as measured by higher AUROC scores and improved PPVs, show that predictive algorithms can outperform more conventional EWS, enable earlier and more efficient alarming, and be successfully implemented on the general wards. However, formal meta-analysis was made infeasible by differences in populations, use of different endpoint definitions, cut-off points, time-horizons to prediction, and other methodologic heterogeneity.