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 However, methods to identify patient risk for hospital-acquired conditions are often outdated and crude.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

Predicted Influences of Artificial Intelligence on the Domains of Nursing: Scoping Review

“…In the hospital sector, AIHTs such as predictive algorithms and CDSSs have been shown to improve decision making and nursing activities (eg, documentation), allowing more time for patient care [69][70][71][72][73][74][75][76]. For example, one study noted that a predictive algorithm used for the identification of nursing diagnoses reduced the time spent on decision making from 35.5 min to 19.8 min [61].…”

“…The results of the review found numerous implications for nursing administration. Some articles discussed the use of AIHTs to assist nurses with automated analysis of patient data [69][70][71][72][73][74][75][76]. Other administration applications of AIHTs included scheduling nursing tasks [66], reducing documentation burden [80], and assisting nurses with triaging patients through AIHT computer systems [14], which could potentially aid in streamlining workflow processes and improving the efficiency and accuracy of patient care provided.…”

Predicted Influences of Artificial Intelligence on Nursing Education: Scoping Review

“…Curricular revisions are also delineated in the literature for graduate-level nursing courses to integrate more advanced AI content on topics such as informatics, ethics, privacy, research, and engineering concepts [ 5 , 28 , 39 , 48 , 49 ]. In one article, authors noted that smart homes are expected to influence graduate nursing curricula as they grow in popularity [ 28 ].…”

“…Changes are also suggested for courses at the doctoral level to provide more in-depth opportunities for nurses to develop competencies in predictive modeling, biostatistical programming, data management, risk adjustment, multivariable regression, ML, governance of big data, and cyberthreats [ 5 , 39 ]. Two universities in the United States have strategically incorporated data science into the core curriculum for their nursing doctoral program [ 5 ].…”

“…See Multimedia Appendix 2 for further details. The types of emerging AIHTs discussed in the literature that have influenced or are predicted to influence nursing education included the following: virtual avatar apps (ie, chatbots) [7], smart homes [28], predictive analytics [27,39,40], virtual or augmented reality devices [41], and robots [26,[42][43][44][45]. An overview of these emerging AIHTs and their current or predicted influences on nursing education is provided in Multimedia Appendix 2.…”