Evaluation of Sensor Technology to Detect Fall Risk and Prevent Falls in Acute Care.

Background: Sensor technology that dynamically identifies hospitalized patients' fall risk and detects and alerts nurses of high-risk patients' early exits out of bed has potential for reducing fall rates and preventing patient harm. During Phase 1 (August 2014-January 2015) of a previously reported performance improvement project, an innovative depth sensor was evaluated on two inpatient medical units to study fall characteristics. In Phase 2 (April 2015-January 2016), a combined depth and bed sensor system designed to assign patient fall probability, detect patient bed exits, and subsequently prevent falls was evaluated.

Anatomy of Inpatient Falls: Examining Fall Events Captured by Depth-Sensor Technology.

Background: Sensor technology offers a new way to identify patient movement, detect falls, and automatically alert health care staff when falls occur. The information gained from analyzing actual fall events can be beneficial in developing individualized fall prevention strategies, informing nursing staff about the nature of falls, and identifying opportunities to make the patient care environment safer.

Methods: A six-month performance improvement pilot was conducted at Barnes-Jewish Hospital (St.

Contemporary issues in adult tracheostomy management.

Airway assessment and management is one of the primary responsibilities of nurses caring for the critically ill adult. Common types of artificial airways, such as endotracheal and tracheostomy tubes, require clinicians to have a complete understanding of indications, complications, and clinical applications. Although tracheostomy tubes are used to a lesser extent than endotracheal tubes in most ICUs, their use typically requires additional education and training because of the complexity and diversity of available tubes, care of surgical site, and other related nursing care issues.

Determinants of outcome for patients admitted to a long-term ventilator unit.

Background: This prospective cohort study was done to identify determinants of successful weaning from mechanical ventilation among patients admitted to the 10-bed long-term ventilator unit (LTVU) of a teaching hospital.

Methods: Prospective patient surveillance and data collection were done on 472 patients admitted to the LTVU over a 4-year period (January 1996 to December 1999).

Results: Multiple logistic regression analysis showed that the absence of home mechanical ventilation at the time of hospital admission, absence of intensive care unit (ICU) readmission, and admission to the LTVU from a nonmedical service were independently associated with successful weaning.