Requests for Compensation in Cases Involving Patients' Falls in Healthcare Settings: A Retrospective Analysis

Organizational factors associated with less use of restraints in older adults with dementia in acute care hospitals: A scoping review

OBJECTIVE

Dementia affects more than 55 million people worldwide. Use of restraints for hospitalized older adults with dementia is a social issue that should be addressed systematically and should not depend on the characteristics of nurses. This study reviewed the literature on organizational factors associated with reducing use of restraints in older adults with dementia admitted to acute care hospitals.

METHODS

A scoping review was performed. Three databases were searched for papers that met our eligibility criteria. Factors related to restraint reduction were extracted, and results were deduced. Through inductive analysis, subthemes were categorized according to similarities and differences, which were then integrated into broader themes.

RESULTS

Sixteen studies were eligible for inclusion. The prevalence of restraints ranged from 5.1% to 80.0% depending on how the meaning of restraint was interpreted. The most common indications for restraints were history of falls and fall risk. Interdisciplinary screening for restraints was associated with reduced prevalence of restraints, with a 0.18-fold (confidence interval [CI]: 0.12-0.24) reduction through use of a restraint decision flowchart and a 0.76-fold (CI: 0.63-0.92) reduction through consultation with a psychiatrist. Interdisciplinary members included nurses, physicians, clinical psychologists, pharmacists, respiratory therapists, and therapists.

CONCLUSIONS

Research is needed to introduce and develop an interdisciplinary restraint decision-making system and to test its effectiveness. Important factors in implementing alternatives to restraints are the harmful effects of restraints, expertise in dementia, regular education on alternative methods, an inpatient environment that ensures patient safety, and the development of human resources.

Validation and Improvement of the Saga Fall Risk Model: A Multicenter Retrospective Observational Study

Purpose

We conducted a pilot study in an acute care hospital and developed the Saga Fall Risk Model 2 (SFRM2), a fall prediction model comprising eight items: Bedriddenness rank, age, sex, emergency admission, admission to the neurosurgery department, history of falls, independence of eating, and use of hypnotics. The external validation results from the two hospitals showed that the area under the curve (AUC) of SFRM2 may be lower in other facilities. This study aimed to validate the accuracy of SFRM2 using data from eight hospitals, including chronic care hospitals, and adjust the coefficients to improve the accuracy of SFRM2 and validate it.

Patients and Methods

This study included all patients aged ≥20 years admitted to eight hospitals, including chronic care, acute care, and tertiary hospitals, from April 1, 2018, to March 31, 2021. In-hospital falls were used as the outcome, and the AUC and shrinkage coefficient of SFRM2 were calculated. Additionally, SFRM2.1, which was modified from the coefficients of SFRM2 using logistic regression with the eight items comprising SFRM2, was developed using two-thirds of the data randomly selected from the entire population, and its accuracy was validated using the remaining one-third portion of the data.

Results

Of the 124,521 inpatients analyzed, 2,986 (2.4%) experienced falls during hospitalization. The median age of all inpatients was 71 years, and 53.2% were men. The AUC of SFRM2 was 0.687 (95% confidence interval [CI]:0.678-0.697), and the shrinkage coefficient was 0.996. SFRM2.1 was created using 81,790 patients, and its accuracy was validated using the remaining 42,731 patients. The AUC of SFRM2.1 was 0.745 (95% CI: 0.731-0.758).

Conclusion

SFRM2 showed good accuracy in predicting falls even on validating in diverse populations with significantly different backgrounds. Furthermore, the accuracy can be improved by adjusting the coefficients while keeping the model's parameters fixed.