Managing Patient Falls in Psychiatric Inpatient Units: Part 2

Simplifying and Testing the Psychometric Psychiatric Patients' Fall Risk Scale: An Analysis of One-Year Admissions

This study aimed to simplify the number of items evaluated by fall risk assessment scales for psychiatric patients, conduct associated reliability, validity, and receiver operating characteristic analyses, and determine fall predictors for psychiatric patients. This methodological study was conducted in a hospital specializing in psychiatry, using data from 1101 patients who were hospitalized in 2018. This fall risk assessment scale was modified by the hospital for use in psychiatric patients. The mean age of the sample population was 44.88 (SD = 12.05) years, and the mean duration of hospital stay was 44.04 (SD = 48.14) days. Men comprised 66% of the study population, and women were 34%. Item reduction, psychometric testing for validity and reliability, and receiver operating characteristic analyses were conducted. Logistic regressions were used to analyze fall predictors, including “having anti-epileptic drugs”, “need for walking aids”, and “having experienced fall occurrence within one year”. This study successfully reduced the number of items assessed by the previous scale. The optimal cutoff point was reduced, and the sensitivity and accuracy of the newly revised scale were good. Three fall predictors for psychiatric patients were identified. The revised scale can facilitate the rapid and accurate identification of high-risk, fall-prone psychiatric patients by psychiatric nurses. Hospital information screening should include each patient’s fall history.

Exploring the Relationship Between Timed Up and Go Test Times and Fall History in an Inpatient Geriatric Psychiatry Unit: A Retrospective Case-Control Study

BACKGROUND AND PURPOSE

Inpatient geriatric psychiatry units have the highest fall rates in the acute care setting and most falls in this population occur during the mobility tasks of transfers and ambulation. The Timed Up and Go (TUG) test includes these 2 specific functional tasks and has been used to predict falls in other geriatric populations but has never been tested in an inpatient geriatric psychiatry unit. The purpose of this study was to determine whether the TUG time measurements of inpatient geriatric psychiatry patients were associated with falling.

METHODS

The study was a retrospective chart review using a case-control design. The sample was obtained from patients admitted to 1 inpatient geriatric psychiatry unit during the 4-month study period.

RESULTS

The total sample size was N = 62 and included older adults with (N = 29; "fallers") and without (N = 33; "nonfallers") a history of falls in the 6 months prior to admission. The mean age of fallers (M = 75.8, SD = 9.6) was not significantly different from the age of nonfallers (M = 74.0, SD = 7.6), P = .424. Both groups had higher proportions of female subjects; nonfallers were 75.8% (n = 25) female and fallers were 69.0% (n = 20) female. Most nonfallers (84.8%) completed the TUG testing without an assistive device, while most fallers (48.3%) used a walker. A significant difference was found between the TUG times of nonfallers and fallers, U = 737.00, z = 3.65, P < .001, r = 0.46. Fallers took longer to complete the TUG test (median = 26.5) than nonfallers (median = 13.6). The TUG time explanatory variable was statistically significant, P = .002. Increasing TUG times were associated with an increased likelihood of patient falls (odds ratio = 1.10). The optimal TUG cutoff score was 16.5 seconds, with 79.3% sensitivity and 72.7% specificity.

CONCLUSIONS

The TUG time measurement was found to be associated with falling. A cutoff time of 16.5 seconds is recommended to identify nonfallers from fallers in the inpatient geriatric psychiatry setting.

The Cumulative Complexity Model and Repeat Falls: A Quality Improvement Project

PURPOSE OF PROJECT

The purpose of this article is to demonstrate the effectiveness of the Cumulative Complexity Model as a framework to build an Excel tool and a Pareto tool that will enable inpatient case managers to predict the increased risk for and prevent repeat falls. The Excel tool is based on work explained in a previous article by and uses a macro to analyze the factors causing the repeat falls and then calculate the probability of it happening again. This enables the case manager to identify trends in how the patient is transitioning toward goals of care and identify problems before they become barriers to the smooth transition to other levels of care. Thus, the case manager will save the facility money by avoiding unneeded days of care and avoiding the costs that result from rendering medical care for the patient who has fallen.

PRIMARY PRACTICE SETTINGS

In July 2015, a group of nurses at a small Veterans Health Administration Hospital in the Northwest collaborated to find ways to reverse a trend of increasing falls and repeat falls.

METHODOLOGY AND SAMPLE

A retrospective chart review of all falls and repeat falls (N = 73) that happened between January 2013 and July 2015 was used to generate a list of top 11 contributing variables that enabled evaluation of the data. A bundle of 3 interventions was instituted in October 2015: (1) development of a dedicated charge nurse/resource nurse, (2) use of a standardized method of rounding, and (3) use of a noncontact patient monitoring system ("virtual nurses"). Falls pre- and postimplementation (N = 109) were analyzed using linear and logistic regression analyses. Data were entered into an Excel sheet and analyzed to identify the major contributing factors to falls and repeat falls and to identify trends. These data were also evaluated to find out whether length of stay and nurse workload contributed to falls.

RESULTS

Fifteen months after implementation of the aforementioned interventions, falls on the unit went down from 30 aggregate falls in 2015 to 17 aggregate falls in 2016. Repeat falls in 2015 went from 9 repeat falls after admission to the unit down to 2 repeat falls in 2016. Each additional extrinsic variable that was present added an additional 1.43 to the odds ratio (OR) for a fall. Similarly, each additional intrinsic variable present added 2.08 to the OR for a fall. The linear regression of length of stay and falls demonstrated that 17.5% of falls correlated with length of stay, F(1,36) = 7.63, p = .009, R = .175, adjusted R = .152. Workload correlated with work 17% of the time, as measured by using ward days of care, F(1,100) = 20.84, p = .00001, R = .17, adjusted R = .16.

IMPLICATIONS FOR CASE MANAGERS

Two examples of the how to use these tools are located in the "Discussion" section of the article.