Improving Prediction of Fall Risk Using Electronic Health Record Data With Various Types and Sources at Multiple Times

Abstract

Inpatient falls are among the most common adverse events threatening patient safety. Although many studies have developed predictive models for fall risk, there are some drawbacks. First, most previous studies have relied on an incident-reporting system alone to identify fall events. Thus, it has been found that falls are more likely to be underreported. Second, there has been a controversy on how to select accurate representative values for patient status data across multiple times and various data sources in electronic health records. Given this background, this study used nurses' progress notes as a complementary data source to detect fall events. In addition, we developed criteria including coverage, currency, and granularity in order to integrate electronic health records data documented at multiple times in various data types and sources. Based on this methodology, we developed three models, logistic regression, Cox proportional hazard regression, and decision tree, to predict risk of patient falls and evaluate the predictive performance of these models by comparing the results to results from the Hendrich II Fall Risk Model. The findings of this study will be used in a clinical decision support system to predict risk of falling and provide evidence-based tailored recommendations in the future.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

“…For the topic of falls, we identified 24 studies that met inclusion criteria. Of these studies, eight used a retrospective cohort design 40 41 42 43 44 45 46 47 ; seven used a prospective cohort design 48 49 50 51 52 53 54 ; six were secondary analyses of research data obtained from prospective, retrospective, and cross-sectional studies 55 56 57 58 59 60 ; one used mixed methods wherein data from a public dataset were used in conjunction with measurements collected from sensors 61 ; and one was a meta-analysis of prospective cohort and observational studies. 62 Ten of the studies used health records as a source of data but in two of these studies, 44 47 it was not clear whether the records were electronic when they were obtained.…”

“…Adults with chronic diseases of all types were included, but three of the studies included adults with specific conditions. The conditions were postpolio syndrome, 51 neurology, neurosurgery, hematology, oncology, 52 and neurology. 53 Most studies were conducted in the United States, but studies were also completed in Italy, 49 England, 43 Japan, 44 51 Poland, 59 and South Korea.…”

“…53 Most studies were conducted in the United States, but studies were also completed in Italy, 49 England, 43 Japan, 44 51 Poland, 59 and South Korea. 52 The 11 studies included in the meta-analysis 62 were conducted in seven countries. Sample sizes ranged from 42 to 275,940 observations.…”

“…In several studies, the fall was self-reported, 49 51 57 60 63 but if it occurred while the participant was in an inpatient setting or being tracked in an outpatient setting, it was often documented in medical records, registries, or administrative databases used to track adverse events. 40 41 42 43 44 45 46 47 48 50 52 56 These differences in measuring the outcome are important, in that predictive models may then be more or less accurate, merely because of the accuracy or inaccuracy of the outcome measurement. The types of predictors across studies were quite consistent, nevertheless.…”

Testing of Reliability and Validity of the Peninsula Health Falls Risk Assessment Tool (PHFRAT) in Acute Care: A Cross-Sectional Study

“…Falls are likely being underreported, thus affecting the analysis and reliability of the study. 42 , 43 …”