The Cumulative Complexity Model and Repeat Falls
Authors: Stevenson Carl W. , Leis Meghan M. 
Published: May 2018

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 C. Stevenson and K. Payne (2017) 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
2 = .175, adjusted R
2 = .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
2 = .17, adjusted R
2 = .16.

Implications for Case Managers:

Two examples of the how to use these tools are located in the “Discussion” section of the article:

The use of our Excel approach suggested that macro will allow the case manager to predict the probability of future falls and demonstrate patients' response to interventions.

The Pareto tool will help prevent future falls by assisting in the identification of the major contributing variables so that they can be addressed before they turn into obstacles to progression of care.

The identification of these data trends and major contributing factors will empower the inpatient case manager to influence the improvement in delivery of care and build effective and efficient individualized plans of care based on the specific risk factors involved.