Methodological Challenges in Examining the Impact of Healthcare Predictive Analytics on Nursing-Sensitive Patient Outcomes

Abstract

The expansion of real-time analytic abilities within current electronic health records has led to innovations in predictive modeling and clinical decision support systems. However, the ability of these systems to influence patient outcomes is currently unknown. Even though nurses are the largest profession within the healthcare workforce, little research has been performed to explore the impact of clinical decision support on their decisions and the patient outcomes associated with them. A scoping literature review explored the impact clinical decision support systems containing healthcare predictive analytics have on four nursing-sensitive patient outcomes (pressure ulcers, failure to rescue, falls, and infections). While many articles discussed variable selection and predictive model development/validation, only four articles examined the impact on patient outcomes. The novelty of predictive analytics and the inherent methodological challenges in studying clinical decision support impact are likely responsible for this paucity of literature. Major methodological challenges include (1) multilevel nature of intervention, (2) treatment fidelity, and (3) adequacy of clinicians' subsequent behavior. There is currently insufficient evidence to demonstrate efficacy of healthcare predictive analytics-enhanced clinical decision support systems on nursing-sensitive patient outcomes. Innovative research methods and a greater emphasis on studying this phenomenon are needed.

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