Empirical research

What Can We Learn about Fall Risk Factors from EHR Nursing Notes A Text Mining Study

Authors: {'first_name': 'Ragnhildur I.', 'last_name': 'Bjarnadottir'},{'first_name': 'Robert J.', 'last_name': 'Lucero'}


Introduction: Hospital falls are a continuing clinical concern, with over one million falls occurring each year in the United States. Annually, hospital-acquired falls result in an estimated $34 billion in direct medical costs. Falls are considered largely preventable and, as a result, the Centers for Medicare and Medicaid Services have announced that fall-related injuries are no longer a reimbursable hospital cost. While policies and practices have been implemented to reduce falls, little sustained reduction has been achieved. Little empirical evidence supports the validity of published fall risk factors. While chart abstraction has been used to operationalize risk factors, few studies have examined registered nurses’ (RNs’) narrative notes as a source of actionable data. Therefore, the purpose of our study was to explore whether there is meaningful fall risk and prevention information in RNs’ electronic narrative notes.

Methods: This study utilized a natural language processing design. Data for this study were extracted from the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database. The date comprises deidentified EHR data associated with patients who stayed in critical care units between 2001 and 2012. Text mining procedures were performed on RN’s narrative notes following the traditional steps of knowledge discovery.

Results: The corpus of data extracted from MIMIC-III database was comprised of 1,046,053 RNs’ notes from 36,583 unique patients. We identified 3,972 notes (0.4 percent) representing 1,789 (5 percent) patients with explicit documentation related to fall risk/prevention. Around 10 percent of the notes (103,685) from 23,025 patients mentioned intrinsic (patient-related) factors that have been theoretically associated with risk of falling. An additional 1,322 notes (0.1 percent) from 692 patients (2 percent) mentioned extrinsic risk factors, related to organizational design and environment. Moreover, 7672 notes (0.7 percent) from 2,571 patients (7 percent) included information on interventions that could theoretically impact patient falls.

Conclusions: This exploratory study using a NLP approach revealed that meaningful information related to fall risk and prevention may be found in RNs’ narrative notes. In particular, RNs’ notes can contain information about clinical as well as environmental and organizational factors that could affect fall risk but are not explicitly recorded by the provider as a fall risk factors. In our study, potential fall risk factors were documented for more than half of the sample. Further research is needed to determine the predictive value of these factors.

Implications for Policy or Practice: This study highlights a potentially rich but understudied source of actionable fall risk data. Furthermore, the application of novel methods to identify quality and safety measures in RNs’ notes can facilitate inclusion of RNs’ voices in patient outcomes and health services research.

Keywords: text miningnursingpatient safetyhealth informaticsdata science 
DOI: http://doi.org/10.5334/egems.237

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