Ripe for Disruption? Adopting Nurse-Led Data Science and Artificial Intelligence to Predict and Reduce Hospital-Acquired Outcomes in the Learning Health System

Abstract

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 However, methods to identify patient risk for hospital-acquired conditions are often outdated and crude.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.