scholarly journals Fall Risk Assessment Tools

2019 ◽  
Vol 13 (4) ◽  
pp. 200-204
Author(s):  
Shyh Poh Teo
Keyword(s):  
Risk Assessment ◽  
Fall Risk ◽  
Hospital Costs ◽  
Hospital Setting ◽  
Assessment Tools ◽  
Screening Tools ◽  
Admission Diagnosis ◽  
Fall Risk Assessment ◽  
Psychological Harm ◽  
Fall Rates

Falls in hospital are common and have serious consequences for patients, including physical and psychological harm, increase length of stay and hospital costs. A systematic approach is required to report and identify factors contributing to in-hospital falls and develop interventions to reduce inpatient fall rates. Different hospital settings have different fall rates and characteristics depending on type of hospital service and admission diagnosis. Screening tools were developed to assess fall risk but are usually insensitive to be useful in reducing falls. There is also a need for prospective validation in each hospital setting to ensure accuracy, resulting in a move away from using such scoring tools. A recommended approach for fall risk assessment is given, which integrates the process for outpatient settings and inpatients.

scholarly journals Novel Approach to Inpatient Fall Risk Prediction and Its Cross-Site Validation Using Time-Variant Data (Preprint)

2018 ◽  
Author(s):  
Insook Cho ◽  
Eun-Hee Boo ◽  
Eunja Chung ◽  
David W. Bates ◽  
Patricia Dykes
Keyword(s):  
Risk Assessment ◽  
Error Rate ◽  
Fall Risk ◽  
Assessment Tool ◽  
Assessment Tools ◽  
Fall Risk Assessment ◽  
C Statistic ◽  
Risk Assessment Tools ◽  
Hospital Days ◽  
Cross Site

BACKGROUND Electronic medical records (EMRs) contain a considerable amount of information about patients. The rapid adoption of EMRs and the integration of nursing data into clinical repositories have made large quantities of clinical data available for both clinical practice and research. OBJECTIVE In this study, we aimed to investigate whether readily available longitudinal EMR data including nursing records could be utilized to compute the risk of inpatient falls and to assess their accuracy compared with existing fall risk assessment tools. METHODS We used 2 study cohorts from 2 tertiary hospitals, located near Seoul, South Korea, with different EMR systems. The modeling cohort included 14,307 admissions (122,179 hospital days), and the validation cohort comprised 21,172 admissions (175,592 hospital days) from each of 6 nursing units. A probabilistic Bayesian network model was used, and patient data were divided into windows with a length of 24 hours. In addition, data on existing fall risk assessment tools, nursing processes, Korean Patient Classification System groups, and medications and administration data were used as model parameters. Model evaluation metrics were averaged using 10-fold cross-validation. RESULTS The initial model showed an error rate of 11.7% and a spherical payoff of 0.91 with a c-statistic of 0.96, which represent far superior performance compared with that for the existing fall risk assessment tool (c-statistic=0.69). The cross-site validation revealed an error rate of 4.87% and a spherical payoff of 0.96 with a c-statistic of 0.99 compared with a c-statistic of 0.65 for the existing fall risk assessment tool. The calibration curves for the model displayed more reliable results than those for the fall risk assessment tools alone. In addition, nursing intervention data showed potential contributions to reducing the variance in the fall rate as did the risk factors of individual patients. CONCLUSIONS A risk prediction model that considers longitudinal EMR data including nursing interventions can improve the ability to identify individual patients likely to fall.

Comparison of two fall-risk assessment tools used in a long-term care facility

2020 ◽  
Vol 33 (3) ◽  
pp. 237-246
Author(s):  
Anat Glass ◽  
Gad Mendelson ◽  
Merav Ben Natan
Keyword(s):  
Risk Assessment ◽  
Older Adult ◽  
Fall Risk ◽  
Long Term Care ◽  
Care Facility ◽  
Assessment Tools ◽  
Term Care ◽  
Fall Risk Assessment ◽  
Content Type

PurposeThe purpose of this paper is to compare the ability of the Morse Fall Scale (MFS) and Farmer's fall-risk assessment tool (FFAT) to identify correlations between risk factors and falls among older adult long-term care (LTC) facility residents.Design/methodology/approachThis was a correlational retrospective study. 200 medical records of older adults hospitalized in a LTC facility in central Israel, from January 2017 to January 2018, were examined.FindingsOf all the residents, 75% and 99.5% of the residents were identified as having a high fall risk according to the MFS and FFAT, respectively. Only 12.5% of residents actually fell. MFS score was weakly correlated with actual falls (odds ratio = 1.035). It was also found that all fallers fell during their first week at the facility.Research limitations/implicationsFuture research should explore the ability of the tools to capture changes in the fall risk by repeat assessments, as this has not been examined in the present study.Practical implicationsThe MFS and FFAT tool may have little value in assessing fall risk in older adult LTC facility residents. Therefore, nurses should perform a clinical evaluation of each individual patient. In addition, nurses should place a particular emphasis on fall risk and prevention during the first week following admission.Originality/valueThe findings of the present study raise doubts regarding the utility of the common practice of assessing fall risk in older adult LTC facility residents using the tools MFS and the FFAT, thus emphasizing the need to adopt a different approach.

Relationship between malnutrition and different fall risk assessment tools in a geriatric in-patient unit

2019 ◽  
Vol 32 (7) ◽  
pp. 1279-1287 ◽  
Author(s):  
Nermien Naim Adly ◽  
Wafaa Mostafa Abd-El-Gawad ◽  
Rania Mohammed Abou-Hashem
Keyword(s):  
Risk Assessment ◽  
Fall Risk ◽  
Assessment Tools ◽  
Fall Risk Assessment ◽  
Risk Assessment Tools

Evaluation of three fall-risk assessment tools in an acute care setting

2007 ◽  
Vol 60 (4) ◽  
pp. 427-435 ◽  
Author(s):  
Emily Ang Neo Kim ◽  
Siti Zubaidah Mordiffi ◽  
Wong Hwee Bee ◽  
Kamala Devi ◽  
David Evans
Keyword(s):  
Risk Assessment ◽  
Acute Care ◽  
Fall Risk ◽  
Care Setting ◽  
Assessment Tools ◽  
Acute Care Setting ◽  
Fall Risk Assessment ◽  
Risk Assessment Tools
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