Comparison of Two Different Models to Predict Fall Risk in Hospitalized Patients

Carroll, Chad; Arnold, Lea Ann; Eberlein, Bill; Westenberger, Christa; Colfer, Kathryn; Naidech, Andrew M.; Ramsey, Kristin; Sturgeon, Cord

doi:10.1016/j.jcjq.2021.09.009

Cited by 5 publications

(6 citation statements)

References 10 publications

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“…Future research might focus on developing a prediction model using data already available in the EHR and/or requiring minimal additional data collection. Developed prediction models show promising initial results [41][42][43][44]. A model that re-uses EHR data would diminish the workload of nurses and make this work process more efficient.…”

Section: Discussionmentioning

confidence: 99%

External validation of the Johns Hopkins Fall Risk Assessment Tool in older Dutch hospitalized patients

et al. 2022

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Key summary points Aim To assess the JHFRAT performance in a large sample of Dutch older inpatients, including its trend over time. Findings Among 17,263 older hospitalized patients, inpatient falls (2.5%) were identified by searching free text and the problem list. JHFRAT and its subcategories were significantly associated with inpatient falls, but JHFRAT showed low discrimination between fallers and non-fallers and over-prediction in the calibration. Message Falling is a serious adverse event and this paper showed that improvements in fall-risk assessment for older inpatients are warranted to improve efficiency. Supplementary Information The online version contains supplementary material available at 10.1007/s41999-022-00719-0.

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Section: Discussionmentioning

confidence: 99%

External validation of the Johns Hopkins Fall Risk Assessment Tool in older Dutch hospitalized patients

et al. 2022

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“…The ROF Model has been configured at our institution and collecting data in the background since October 2019. We recently validated the accuracy of the ROF Model as part of a quality improvement project 5 …”

Section: Methodsmentioning

confidence: 99%

Fall Risk Prediction Models During the Initial COVID-19 Surge

Arnold

Carroll

Eberlein

et al. 2023

CIN: Computers, Informatics, Nursing

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During the first COVID surge, multiple changes in nurse staffing and workflows were made to support care delivery in a resource-constrained environment. We hypothesized that there was a higher rate of inpatient falls during the COVID surge. Furthermore, we predicted that an automated predictive analytic algorithm would perform as well as the Johns Hopkins Fall Risk Assessment. A retrospective review of falls for 3 months before and the first 3 months of the first COVID surge was conducted. We determined the total number of falls and the overall fall rate and examined the distribution of scores and accuracy of fall predictive models for both groups. There was a statistically significant increase in fall rate during the first 3 months of the COVID surge compared with the 3 prior months (2.48/1000 patient-days vs 1.89/1000 patient-days respectively; P = .041). The Johns Hopkins instrument had a greater sensitivity of 78.9% compared with 57.0% for the predictive analytic model. Specificity and accuracy of the predictive analytic model were higher than the Johns Hopkins instrument (71.3% vs 54.1% and 71.2% vs 54.3%, respectively). These findings suggest that the automated predictive analytic model could be used in a resource-constrained environment to accurately classify patients' risk of fall.

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“…Moreover, falls may be mitigated if potential fallers were monitored with targeted interventions applied. 16 On average, fall events can add between 6 and 12 hospital days per admission 17 and cost $14 000 18 increasing to $34 294 19 for serious fall-related injury. The Centers for Medicare & Medicaid Services do not reimburse hospitals for additional costs associated with patient falls, 14 negatively impacting hospital reimbursement.…”

Section: Available Knowledgementioning

confidence: 99%

“…Moreover, fallers have longer lengths of stay and are more likely to be admitted to long-term care facilities postdischarge 12. Carroll and colleagues16 found approximately a third of hospital falls are preventable. Moreover, falls may be mitigated if potential fallers were monitored with targeted interventions applied 16.…”

Section: Available Knowledgementioning

confidence: 99%

“…Carroll and colleagues16 found approximately a third of hospital falls are preventable. Moreover, falls may be mitigated if potential fallers were monitored with targeted interventions applied 16. On average, fall events can add between 6 and 12 hospital days per admission17 and cost $14 00018 increasing to $34 29419 for serious fall-related injury.…”

Section: Available Knowledgementioning

confidence: 99%

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Remote Patient Monitoring Improves Patient Falls and Reduces Harm

Zimbro,

Bridges,

Bunn

et al. 2023

Journal of Nursing Care Quality

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Background: Minimizing patient falls and fall-related injuries within organizational constraints is a high priority for nurse leaders. The Centers for Medicare & Medicaid Services do not reimburse hospitals for fall-related expenditures. In-person sitters are used to prevent falls but are resource intensive and costly. Remote patient monitoring (RPM) may offer alternatives to in-person sitters to reduce fall-related harm. Purpose: The efficacy of RPM to reduce patient falls and fall-related injuries was explored. Methods: Electronic health record data were extracted from a 13-hospital integrated health care system. Incidence rate ratios were used to analyze the impact of RPM technology on falls and fall-related injuries. Results: When used in conjunction with standard fall precautions, RPM reduced falls 33.7% and fall-related injuries 47.4%. Fall-related expenditures decreased $304 400 with a combined estimated savings systemwide of $2 089 600 annually. Conclusions: RPM technology minimized falls and associated harm and improved patient safety, positively impacting hospital expenditures.

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Comparison of Two Different Models to Predict Fall Risk in Hospitalized Patients

Cited by 5 publications

References 10 publications

External validation of the Johns Hopkins Fall Risk Assessment Tool in older Dutch hospitalized patients

External validation of the Johns Hopkins Fall Risk Assessment Tool in older Dutch hospitalized patients

Fall Risk Prediction Models During the Initial COVID-19 Surge

Remote Patient Monitoring Improves Patient Falls and Reduces Harm

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