Nursing Staff Characteristics on Resident Outcomes in Nursing Homes

Shin, Juh Hyun

doi:10.1097/jnr.0000000000000269

Cited by 21 publications

(36 citation statements)

References 26 publications

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“…Five ML algorithms predicted the HPRD of the secretary general, inconsistent with previous research [10]. The logistic regression, SVM linear, and SVM sigmoid also supported the HPRD of CNAs as a predictor of falls, supported in previous research [40][41][42] but inconsistent with other research [43,44].…”

Section: Discussioncontrasting

confidence: 70%

“…Psychiatric medication was a very strong predictor of falls, consistent with previous research [6,7,9,10,[31][32][33], but inconsistent with a couple of studies [34,35]. The proportion of residents with urinary incontinence was an important factor in falls, supported in previous research [9][10][11][12], but not supported in other studies [7,32]. Results confirmed that the incidence of resident falls relates to increased psychiatric medication and urinary incontinence [9,12,36,37].…”

Section: Discussionmentioning

confidence: 45%

“…Remarkably, administrative staff was an important factor in resident falls using ML analysis. Very limited study [10] reported the stability of administrative staff related to resident falls. A scientific and sound interdisciplinary approach among NH staff requires teamwork among nurses and administrative staff, including shared governance [45].…”

Section: Discussionmentioning

confidence: 99%

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Application of Machine Learning Methods in Nursing Home Research: Using Fall-Related Data in Nursing Homes

Lee

Ahn

Shin

et al. 2020

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Background: To compare machine learning (ML) methods beyond traditional regression analysis using a real dataset on nurse staffing and falls in nursing homes. Methods:We used a total of 6 ML algorithms to compare the superiority of ML over traditional regression methods. We applied three representative ML algorithms-random forest (RF), logistics regression, support vector machine (SVM): linear, polynomial, radial, and sigmoid-to the preprocessed dataset to develop a prediction model (N = 60). We used an accuracy measure to evaluate prediction models.Results: RF was the most accurate model (0.883), followed by the logistic regression model (0.867).Using RF procedures, we identified a total of 6 variables as predictors of falls including hours per resident day of administrative staff, proportion of nursing home residents with psychiatric medications, aggressive behaviors and cognitive dysfunction, urinary incontinence, current number of residents in each nursing home, and the maximum capacity of each nursing home. Conclusions:The appropriate choice of prediction model is quite challenging for nursing researchers.For effective fall management, researchers should consider organizational characteristics as well as personal factors. The examination of related factors on falls is quite meaningful in that preventing falls contributes to improving the quality of life and care of residents and decreases health care costs. BackgroundThe most frequently reported adverse event among nursing home (NH) residents, falls are associated with increased morbidity and mortality, and with reduced functioning [1-3]. The U.S. fall prevalence is 1.7 falls per resident yearly in NHs [1]; about 50% of NH residents in developed countries fall eachyear [4]. Accurate prediction of factors associated with a fall in NHs is important because nurses, health care professionals, researchers in practice, administrative staff, researchers, and politicians address fall issues. These stakeholders develop targeted fall-prevention management and assess residents based on the factors associated with a fall [5]. Prior studies identified a number of risk factors for falls, including age, sex, visual deficits, psychotropic medications, cognitive dysfunction [6][7][8], range of motion, urinary incontinence [9-12], hours per residents day (HPRD) of registered

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Section: Discussioncontrasting

confidence: 70%

Section: Discussionmentioning

confidence: 45%

Section: Discussionmentioning

confidence: 99%

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Application of Machine Learning Methods in Nursing Home Research: Using Fall-Related Data in Nursing Homes

Lee

Ahn

Shin

et al. 2020

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“…67 Positive effects of qualified nurses on care quality have also been reported for longterm care settings. [68][69][70] Reflecting this evidence and in compliance with the claims of relevant German guidelines and standards, 41,71 we cautiously interpret our findings in the way that registered nurses have a clearer conceptualization and more comprehensive understanding of pain management-without neglecting the essential contribution of nursing assistants and additional staff. These preliminary results seem to support the need for large-scale staff education and training in pain management of the nursing team including registered nurses and unregistered staff in order to achieve sustainable improvements of pain outcomes and associated phenomena in NH.…”

Section: Discussionmentioning

confidence: 61%

<p>Nursing Education Intervention Effects on Pain Intensity of Nursing Home Residents with Different Levels of Cognitive Impairment: A Cluster-Randomized Controlled Trial</p>

Kutschar¹,

Berger²,

Brandauer³

et al. 2020

JPR

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Purpose: Pain management in nursing homes is challenging and pain prevalence remains high. The objective of this study was to improve the pain situation of nursing home residents following a nursing-related educational intervention within a cluster-randomized controlled trial (2016-2018). Participants: Clusters were nursing homes from one nursing home operator in Bavaria, Germany. Nursing home residents who were permanently registered in the facilities, at least 60 years of age, and who themselves or their legal guardians provided informed consent were included. Intervention: In addition to the implementation of pain nurses and pain care assistants, staff of the intervention group received an educational intervention in pain management, containing classroom (quality circles) and web-based training for nurses. Methods: Based on the Mini-Mental State Examination (MMSE), residents were either interviewed (MMSE 10-30) using self-report instruments or observed (MMSE 0-9) by proxy assessment. The primary outcome in residents able to self-report was maximum pain intensity according to Brief Pain Inventory (BPI); in those not able to self-report treatmentrelevant pain above cutoff (≥2) on the Pain Assessment in Advanced Dementia (PAINAD). Results: Out of 20 randomly selected clusters, 9 nursing homes from the control, and 6 nursing homes from the intervention group participated. Multilevel linear (n=347 residents, MMSE 10-30) and logistic regression (n=222 residents, MMSE 0-9) analyses were conducted. Maximum pain intensity was higher after intervention (B=1.32, p<0.01), decreased with a better quality of life (B=−0.07, p<0.001), and was lower when dementia diagnoses were present (B=−1.12, p<0.01). PAINAD scores before and after intervention did not differ significantly (OR=0.89, p=0.724), but chances to exhibit treatment-related pain were higher with decreasing MMSE (OR=0.94, p<0.05). Conclusion: While no significant positive intervention effect was measured, findings suggest nurses' raised awareness towards pain management. Overall results indicate that large-scale educational interventions seem to be less effective in complex nursing home settings without also including specific individual-based intervention measures.

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“…These stakeholders develop targeted fall-prevention management, and assess residents based on factors associated with a fall [ 5 ]. Prior studies identified a number of risk factors for falls; including age, sex, visual deficits, psychotropic medications, cognitive dysfunction [ 6 , 7 , 8 ], range of motion, urinary incontinence [ 9 , 10 , 11 ], hours per resident day (HPRD) of registered nurses (RNs), and skill mix [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Methods in Nursing Home Research

Lee

Ahn

Shin

et al. 2020

IJERPH

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Background: A machine learning (ML) system is able to construct algorithms to continue improving predictions and generate automated knowledge through data-driven predictors or decisions. Objective: The purpose of this study was to compare six ML methods (random forest (RF), logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM) of predicting falls in nursing homes (NHs). Methods: We applied three representative six-ML algorithms to the preprocessed dataset to develop a prediction model (N = 60). We used an accuracy measure to evaluate prediction models. Results: RF was the most accurate model (0.883), followed by the logistic regression model, SVM linear, and polynomial SVM (0.867). Conclusions: RF was a powerful algorithm to discern predictors of falls in NHs. For effective fall management, researchers should consider organizational characteristics as well as personal factors. Recommendations for Future Research: To confirm the superiority of ML in NH research, future studies are required to discern additional potential factors using newly introduced ML methods.

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Nursing Staff Characteristics on Resident Outcomes in Nursing Homes

Cited by 21 publications

References 26 publications

Application of Machine Learning Methods in Nursing Home Research: Using Fall-Related Data in Nursing Homes

Application of Machine Learning Methods in Nursing Home Research: Using Fall-Related Data in Nursing Homes

<p>Nursing Education Intervention Effects on Pain Intensity of Nursing Home Residents with Different Levels of Cognitive Impairment: A Cluster-Randomized Controlled Trial</p>

Application of Machine Learning Methods in Nursing Home Research

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