Effectiveness of nursing care provided for fall prevention: Survival analysis of nursing records in a tertiary hospital

Exploring nurses' clinical judgment concerning the relative importance of fall risk factors: A mixed method approach using the Q Methodology

BACKGROUND

Nurses are pivotal in averting patient falls through their assessment of cues presented by patients and their environments, rendering clinical judgments regarding the risk of falling, and implementing tailored interventions. Despite the intricate cognitive processes entailed in nurses' judgment, no prior studies have explored their approach to assessing the risk of falling.

OBJECTIVE

This study aimed to examine how nurses judge the risk of falling among patients with different conditions, whether there are differences in the importance of risk factors as judged by nurses, how they justify their judgments, and what attributes of the nurses influence their judgments.

DESIGN

A mixed method approach using the Q Methodology was employed.

SETTING(S)

Three public and private hospitals in Japan.

PARTICIPANTS

Eighteen nurses participated in the study.

METHODS

Participants were tasked with ranking 36 patient scenarios, each featuring a distinct set of fall risk factors. Subsequently, post-sorting interviews were conducted to gather insights into their typical approach to assessing fall risk and the rationale behind their ranking decisions. A by-person principal component factor extraction was employed to examine differences in the rankings of the scenarios. The interview data were analyzed descriptively to elucidate the reasons behind these discrepancies.

RESULTS

Nurses engage in complex cognitive manipulations when evaluating the risk of patient falls, drawing extensively from their wealth of experience while utilizing assessment tools to support their judgments. In essence, nurses identify patients' tendency to act alone without calling a nurse, impaired gait and cognition, sedative use, drains, and limited information sharing among healthcare professionals as key fall risks. In addition, nurses vary in the importance they attribute to certain risk factors, leading to the discrimination of three distinct judgment profiles. One group of nurses judges patients with cognitive impairment and acting alone as high risk. Another group of nurses considers patients with unstable gait and acting alone as high risk. The last group of nurses sees patients wearing slippers as high risk. The post-sorting interviews revealed that their judgments are closely related to the healthcare context and patient population.

CONCLUSIONS

Nurses operate within diverse contexts, wherein they interact with patients of varying characteristics, collaborate with professionals from diverse disciplines, and have access to varying levels of human and physical resources. This nuanced understanding empowers the formulation of judgments that are finely attuned to the specific context at hand.

STUDY REGISTRATION

Not registered.

Retrospective Investigation and Research on Fall Events Among Hospitalized Patients in the Rehabilitation Department

Objective

We investigated the clinical characteristics, fall outcomes, and related factors of falls in patients who were hospitalized in the rehabilitation department, and explored strategies to reduce the incidence of falls and prevent falls in patients.

Methods

Data from 60 patients who fell in the rehabilitation department between 2016 and 2021 were analyzed for clinical characteristics, associated factors, incidence of falls, injuries, and patient demographics. Under the random stratified sampling method, 60 patients who did not fall during the same period were selected as the control group, and relevant data was collected. Measurement data were compared using an independent sample t-test. Enumeration data were compared using chi-squared (χ2) test was employed to compare these data between the two groups. Non-parametric data were analyzed using the Mann-Whitney U-test. Factors potentially influencing falls were scrutinized through both univariate and binary logistic regression analyses.

Results

The median annual incidence of falls among patients who were hospitalized in the rehabilitation department was 0.04%, while the overall fall injury rate was 60%. Falls were most prevalent within 30 days of hospitalization (71.67%). The most common fall-related condition was craniocerebral disease (83.33%). The incidents of falls location of fall were mainly reported in nearby areas of rehabilitation ward (70%). Most accidents occurred between 7:00 a.m.-12:00 p.m. and 3:01 p.m.-6:00 p.m. (63.33%), and dyskinesia was the most common cause of falls (71.67%). There were 39 patients (65.00%) with Barthel Index (BI) scores ranging between 40-60.

Conclusion

Patients in the rehabilitation department had a greater incidence of falls and fall injuries. Within 30 days of admission, patients with moderately dependent craniocerebral disorders and dyskinesia frequently experienced falls during typical daytime shifts in areas characterized by endemic conditions.

Pilot testing of nurse led multimodal intervention for falls prevention

Patient fall-risk awareness, self-efficacy, and engagement in fall-prevention activities are important aspects of fall-prevention. The current pilot study examined the effect of a nurse-led multimodal intervention on hospitalized adults' levels of fall risk awareness, self-efficacy, and engagement in fall prevention. Sixty hospitalized older adults in an acute care setting in Florida participated in the study. There were significant differences [pre-test (M = 19.02, SD=1.152) and post-test (M = 21.71, SD=0.527); t (17.355) = 58, p≤.001] on level of fall risk awareness in fall prevention. There were no significant findings for fall self-efficacy and engagement. Study findings suggested that the higher the fall self-efficacy, the higher the engagement. Future research is needed to examine self-efficacy and engagement for fall prevention in larger diverse cohorts of hospitalized older adults.

Clinical Impact of an Analytic Tool for Predicting the Fall Risk in Inpatients: Controlled Interrupted Time Series

BACKGROUND

Patient falls are a common cause of harm in acute-care hospitals worldwide. They are a difficult, complex, and common problem requiring a great deal of nurses' time, attention, and effort in practice. The recent rapid expansion of health care predictive analytic applications and the growing availability of electronic health record (EHR) data have resulted in the development of machine learning models that predict adverse events. However, the clinical impact of these models in terms of patient outcomes and clinicians' responses is undetermined.

OBJECTIVE

The purpose of this study was to determine the impact of an electronic analytic tool for predicting fall risk on patient outcomes and nurses' responses.

METHODS

A controlled interrupted time series (ITS) experiment was conducted in 12 medical-surgical nursing units at a public hospital between May 2017 and April 2019. In six of the units, the patients' fall risk was assessed using the St. Thomas' Risk Assessment Tool in Falling Elderly Inpatients (STRATIFY) system (control units), while in the other six, a predictive model for inpatient fall risks was implemented using routinely obtained data from the hospital's EHR system (intervention units). The primary outcome was the rate of patient falls; secondary outcomes included the rate of falls with injury and analysis of process metrics (nursing interventions that are designed to mitigate the risk of fall).

RESULTS

During the study period, there were 42,476 admissions, of which 707 were for falls and 134 for fall injuries. Allowing for differences in the patients' characteristics and baseline process metrics, the number of patients with falls differed between the control (n=382) and intervention (n=325) units. The mean fall rate increased from 1.95 to 2.11 in control units and decreased from 1.92 to 1.79 in intervention units. A separate ITS analysis revealed that the immediate reduction was 29.73% in the intervention group (z=-2.06, P=.039) and 16.58% in the control group (z=-1.28, P=.20), but there was no ongoing effect. The injury rate did not differ significantly between the two groups (0.42 vs 0.31, z=1.50, P=.134). Among the process metrics, the risk-targeted interventions increased significantly over time in the intervention group.

CONCLUSIONS

This early-stage clinical evaluation revealed that implementation of an analytic tool for predicting fall risk may to contribute to an awareness of fall risk, leading to positive changes in nurses' interventions over time.

TRIAL REGISTRATION

Clinical Research Information Service (CRIS), Republic of Korea KCT0005286; https://cris.nih.go.kr/cris/search/detailSearch.do/16984.