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

Carl W Stevenson1, Meghan M Leis

  • 1Carl W. Stevenson, BSN, RN, has 25 years of experience as an RN in cardiac care, medical-surgical nursing, and quality improvement through participation in local and national VA committees. He has been conducting research with patients who have congestive heart failure, falls, and sepsis for the past 8 years. Meghan M. Leis, BSN, RN, has 4 years of experience as an RN in medical-surgical nursing. She has served on unit-level quality improvement projects for patient falls and pressure ulcers for the past 2 years. She is interested in improving the care of our veterans.

Professional Case Management
|May 31, 2018
PubMed
Summary
This summary is machine-generated.

This study developed tools to predict and prevent repeat patient falls, reducing falls by 43% and repeat falls by 78%. These tools empower case managers to identify risks and improve patient transitions, saving healthcare costs.

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Area of Science:

  • Healthcare Management
  • Patient Safety
  • Data Analytics

Background:

  • Increasing trends in patient falls and repeat falls were observed at a Veterans Health Administration Hospital.
  • A collaboration of nurses aimed to reverse this trend through innovative interventions.

Purpose of the Study:

  • To demonstrate the effectiveness of the Cumulative Complexity Model in developing predictive tools for inpatient falls.
  • To enable case managers to predict and prevent repeat falls, thereby reducing healthcare costs.

Main Methods:

  • A retrospective chart review identified 11 key variables contributing to falls.
  • Three interventions were implemented: dedicated charge nurse, standardized rounding, and noncontact patient monitoring.
  • Data analysis included linear and logistic regression to evaluate intervention impact and contributing factors like length of stay and workload.

Main Results:

  • Aggregate falls decreased from 30 in 2015 to 17 in 2016.
  • Repeat falls reduced from 9 in 2015 to 2 in 2016.
  • Each additional intrinsic variable increased the odds ratio for a fall by 2.08, and each extrinsic variable by 1.43. Length of stay and workload correlated with falls.

Conclusions:

  • The developed Excel and Pareto tools, based on the Cumulative Complexity Model, are effective in predicting and preventing repeat falls.
  • Case managers can utilize these tools to identify patient transition trends and potential barriers, optimizing care.
  • Successful implementation led to significant reductions in falls and repeat falls, demonstrating improved patient safety and cost savings.