Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes

Abstract

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this paper, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (e.g., natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope the studies described in this paper help readers: (a) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (b) identify gaps in the literature that are in need of exploration.

Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature

“…In our earlier reviews, we described the data science models in projects that focused on particular clinical problems such as patient falls, nosocomial infections, and pressure injuries. 15,16 We noted that the variables included in most statistical models were similar (i.e., demographics, diagnoses, laboratories), and the major data science models (i.e., supervised machine learning) were also a commonality across the spectrum of clinical problems we considered. What remained unclear to us at the conclusion of these reviews was the extent to which the data science models that were developed had been used in actual episodes of care or incorporated into health information systems and CDS.…”

Development of a Predictive Model for Hospital-Acquired Pressure Injuries

“…Reported sensitivity for predicting HAPI occurrences ranges between 0.48 and 0.87, specificity between 0.66 and 0.95, and area under the receiver operating characteristic curve between 0.76 and 0.86. [15][16][17][18][19][20] One of the main limitations is that these models do not take into account the temporal dimension and the evolution of the different parameters during the hospital stay. In this study, we aimed to develop, deploy, and validate in an operational clinical setting a new artificial intelligence-based and time-aware predictive model for the early detection of patients at risk of HAPI that is tailored to the inpatient population.…”

“…Many HAPI prediction models have been developed in the past few years with moderate to good predictive performance. Reported sensitivity for predicting HAPI occurrences ranges between 0.48 and 0.87, specificity between 0.66 and 0.95, and area under the receiver operating characteristic curve between 0.76 and 0.86 15–20 . One of the main limitations is that these models do not take into account the temporal dimension and the evolution of the different parameters during the hospital stay.…”