Beyond the Flowsheet: A Text Mining Study of Hospital-Acquired Falls (Preprint)

BACKGROUND Around 1 million patients fall in US hospitals annually, with an associated direct medical cost of $50 billion dollars. Substantial nationwide efforts to reduce hospital falls in the past decade have yet to produce sustained results. This may in part be due to limited understanding of fall risk factors and overreliance on limited data. OBJECTIVE The purpose of our study was to explore text patterns associated with patient falls in a previously understudied data source: registered nurses’ electronic health record progress notes. METHODS This study employed supervised and unsupervised text-mining methods using data from medical/surgical units in a large academic health center in North Florida between 2013 and 2015. The data corpus consisted of registered nurses’ progress notes for patient cases who fell during their hospitalization and patient controls who were at risk during the same period but did not fall. RESULTS The analytical sample comprised of 107,842 progress notes for 2,171 patients (734 fallers and 1,437 non-fallers who had registered nurses’ progress notes documented during their stay). Supervised text-mining with dictionary matching revealed significantly more frequent documentation of cognitive patient factors and environmental factors in fallers’ progress notes compared to non-fallers. Unsupervised text-mining through topic modelling highlighted text patterns indicative of workflow or communication factors. Predictive models for both supervised and unsupervised text-mining features were developed, with an F1 score ranging from 0.184-0.591. CONCLUSIONS Findings of this study indicate that registered nurses’ progress notes contain factors associated with risk of falling that may not be captured in structured data. These include environmental factors, cognitive patient factors, and factors related to documentation practices. The findings highlight previously under-examined risk factors for hospital-acquired falls and can be used for hypothesis generation for further clinical research to prevent falls and improve patient safety at the bedside.