An Exploration of the Association of Patient Characteristics and Pharmacological Treatments to Inpatient Falls Among Patients At-Risk for Falling During Hospitalization
2016
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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- Usage100
- Downloads91
- Abstract Views9
Lecture / Presentation Description
Introduction: In 2013, the total direct cost of fall injuries for people 65 and above was $34 billion after adjusting it to inflation (cdc.gov). Falls may be the most commonly reported incidents in the acute care setting, and the second most frequent cause of harm in the hospital (patientcarelink.org). Yet, most studies have been descriptive in nature with no attempt made to determine what factors actually predicted the occurrence of falls among hospitalized patients (Dupree & Musheno, 2014; Tzeng & Yin, 2008; Williams, Szekendi, & Stephen, 2014). The purpose of this study is to identify potential predictors of inpatient falls by comparing the characteristics of patients at high risk for falling during hospitalization who actually fell to those who did not fall.Methods: This observational study involves the analysis of retrospective data. A sample of all patients with a Morse Fall Scale of ≥45 were extracted from medical records data of patients hospitalized at Homestead Hospital from July 1st, 2014 to June 30th, 2015. Potential predictor variables consist of patient demographic characteristics, clinical characteristics, comorbidities, and medications administered during hospitalization and within 24 hours prior to a fall. The study results will be reported in aggregate. Descriptive and analytical statistics will be generated including frequencies, percentiles, comparisons based on measures of central tendency, cross-tabulation, univariate and multivariate logistic regression, etc.Results: The final sample size was 5357 patients with 150,000 medication data points. The study is currently in the analysis phase; completion of data analysis is expected by conference date.Conclusions: We expect that the results of this study will assist nurses and other clinicians to develop fall prevention programs that target subpopulations at highest risk for falling.
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