BACKGROUND:The clinical deterioration of patients in general hospital wards is an important safety issue. Aggregate-weighted early warning systems (EWSs) may not detect risk until patients present with acute decline. PURPOSE:We aimed to compare the prognostic test accuracy and clinical workloads generated by EWSs using statistical modeling (multivariable regression or machine learning) versus aggregate-weighted tools.DATA SOURCES: We searched PubMed and CINAHL using terms that described clinical deterioration and use of an advanced EWS. STUDY SELECTION:The outcome was clinical deterioration (intensive care unit transfer or death) of adult patients on general hospital wards. We included studies published from January 1, 2012 to September 15, 2018.DATA EXTRACTION: Following 2015 PRIMSA systematic review protocol guidelines; 2015 TRIPOD criteria for predictive model evaluation; and the Cochrane Collaboration guidelines, we reported model performance, adjusted positive predictive value (PPV), and conducted simulations of workup-to-detection ratios.DATA SYNTHESIS: Of 285 articles, six studies reported the model performance of advanced EWSs, and five were of high quality. All EWSs using statistical modeling identified at-risk patients with greater precision than aggregate-weighted EWSs (mean AUC 0.80 vs 0.73). EWSs using statistical modeling generated 4.9 alerts to find one true positive case versus 7.1 alerts in aggregateweighted EWSs; a nearly 50% relative workload increase for aggregate-weighted EWSs.CONCLUSIONS: Compared with aggregate-weighted tools, EWSs using statistical modeling consistently demonstrated superior prognostic performance and generated less workload to identify and treat one true positive case. A standardized approach to reporting EWS model performance is needed, including outcome definitions, pretest probability, observed and adjusted PPV, and workupto-detection ratio.

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Nurse leaders are dually responsible for resource stewardship and the delivery of high-quality care. However, methods to identify patient risk for hospital-acquired conditions are often outdated and crude. Although hospitals and health systems have begun to use data science and artificial intelligence in physician-led projects, these innovative methods have not seen adoption in nursing. We propose the Petri dish model, a theoretical hybrid model, which combines population ecology theory and human factors theory to explain the cost/benefit dynamics influencing the slow adoption of data science for hospital-based nursing. The proliferation of nurse-led data science in health systems may be facing several barriers: a scarcity of doctorally prepared nurse scientists with expertise in data science; internal structural inertia; an unaligned national “precision health” strategy; and a federal reimbursement landscape, which constrains—but does not negate the hard dollar business case. Nurse executives have several options: deferring adoption, outsourcing services, and investing in internal infrastructure to develop and implement risk models. The latter offers the best performing models. Progress in nurse-led data science work has been sluggish. Balanced partnerships with physician experts and organizational stakeholders are needed, as is a balanced PhD-DNP research-practice collaboration model.