The Use of Physical Therapy ICU Assessments to Predict Discharge Home.

OBJECTIVES: To establish cutoff values for making recommendations for discharge to the home setting using standardized physical therapy assessments. DESIGN: Retrospective study. SETTING: Five ICUs at a large academic medical center. PATIENTS: 1,203 ICU patients. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: The Functional Status Score for the ICU and the ICU Mobility Scale were collected during the initial physical therapy assessment, at ICU discharge, and prior to hospital discharge. The Activity Measure for Post-Acute Care-Inpatient Mobility Short Form "6 clicks" was only collected during the initial physical therapy assessment. Receiver Operating Characteristic curves were used to determine a potential cutoff value for discharge home. The Receiver Operating Characteristic was adjusted for ICU and hospital length of stay along with mobility status prior to hospital admission. Cutoff values were then determined by using Youden's Index. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated based on these cut off values. The Functional Status Score for the ICU at ICU discharge was the best predictor of a discharge to the home setting in patients who had an ICU admission. The area under the curve for the Functional Status Score for the ICU at ICU discharge was 0.80. A Functional Status Score for the ICU score at ICU discharge of 19 or higher predicted discharge to home with a sensitivity of 82.9% and specificity of 73.6% CONCLUSIONS:: The Functional Status Score for the ICU at ICU discharge provided the best accuracy for making a timely recommendation for discharge home in patients who had an ICU admission.

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. METHODS: Fall detection depth sensors remained in place on two medicine units; bed sensors used to detect patient bed exits were added on only one of the medicine units. Fall rates and fall with injury rates were evaluated on both units. RESULTS: During Phase 2, the designated evaluation unit had 14 falls, for a fall rate of 2.22 per 1,000 patient-days-a 54.1% reduction compared with the Phase 1 fall rate. The difference in rates from Phase 1 to Phase 2 was statistically significant (z = 2.20; p = 0.0297). The comparison medicine unit had 30 falls-a fall rate of 4.69 per 1,000 patient-days, representing a 57.9% increase as compared with Phase 1. CONCLUSION: A fall detection sensor system affords a level of surveillance that standard fall alert systems do not have. Fall prevention remains a complex issue, but sensor technology is a viable fall prevention option.

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. Louis) to assess the ability of a depth-sensor system to capture inpatient fall events within patient hospital rooms. Depth sensors were installed on two inpatient medicine units with a history of high fall rates. The depth sensors captured actual fall events on video. Video clips were reviewed and analyzed to identify the characteristics of patient falls, staff response times, and environmental conditions contributing to falls. RESULTS: A total of 16 falls involving 13 patients were recorded by depth sensors. Six of the 13 patients who fell were classified as high risk on the basis of the hospital's fall rating tool. Common contributing factors included difficulty rising from their bed, weakened lower extremities, and unsteady or slow gait. Eleven of the falls involved patients reaching for objects in their path in an effort to achieve stability. Nurses had less than two minutes from the time a patient began to exit a bed to the time a fall occurred. Patients expressed few complaints with depth sensors installed in rooms. CONCLUSION: Fall-detection sensor systems offer valuable data for analyzing the nature of patient falls, with the potential promise of prescribing specific fall interventions for patients and to identify staff development opportunities. Hospitals should understand these devices' benefits and limitations and how they affect nursing practice.

APTA Geriatrics' Guiding Principles for Best Practices in Geriatric Physical Therapy: An Executive Summary.

Geriatric physical therapy requires a unique skill set and knowledge to provide best practice care. The skill set requires clinicians to recognize the significance of the continuum of aging from optimal to pathology-influenced aging and how psychosocial, environmental, behavioral, accessibility, and economic factors affect this aging continuum, optimal health, and wellness. Employing this distinctive skill set while also utilizing evidence-based practice, acknowledging the variability observed in older adults, and utilizing interwoven care systems that impact outcomes are key characteristics of best practice. The Academy of Geriatric Physical Therapy developed best practice guidelines consisting of 6 principles needed to ensure patients receive the care that they deserve. Best practice principles include person-centered care, anti-ageist beliefs, holistic assessment using sound outcome measures, evidence-based interventions, physical activity promotion, and interprofessional collaborative practice. This executive summary presents these principles along with suggested action steps for each element of best practice. The aims are to encourage individual self-assessment, promote improvement in practice on an individual and facility/system level, increase communication and collaboration with other health care providers about global best practices for older adults, and to further target education, resources, and advocacy toward achieving best practice on a larger scale.

Implementation of an Interdisciplinary AACN Early Mobility Protocol.

BACKGROUND: Increasing mobility in the intensive care unit is an important part of the ABCDEF bundle. Objective To examine the impact of an interdisciplinary mobility protocol in 7 specialty intensive care units that previously implemented other bundle components. METHODS: A staggered quality improvement project using the American Association of Critical-Care Nurses mobility protocol was conducted. In phase 1, data were collected on patients with intensive care unit stays of 24 hours or more for 2 months before and 2 months after protocol implementation. In phase 2, data were collected on a random sample of 20% of patients with an intensive care unit stay of 3 days or more for 2 months before and 12 months after protocol implementation. RESULTS: The study population consisted of 1266 patients before and 1420 patients after implementation in phase 1 and 258 patients before and 1681 patients after implementation in phase 2. In phase 1, the mean (SD) mobility level increased in all intensive care units, from 1.45 (1.03) before to 1.64 (1.03) after implementation (P < .001). Mean (SD) ICU Mobility Scale scores increased on initial evaluation from 4.4 (2.8) to 5.0 (2.8) (P = .01) and at intensive care unit discharge from 6.4 (2.5) to 6.8 (2.3) (P = .04). Complications occurred in 0.2% of patients mobilized. In phase 2, 84% of patients had out-of-bed activity after implementation. The time to achieve mobility levels 2 to 4 decreased (P = .05). Intensive care unit length of stay decreased significantly in both phases. CONCLUSIONS: Implementing the American Association of Critical-Care early mobility protocol in intensive care units with ABCDEF components in place can increase mobility levels, decrease length of stay, and decrease delirium with minimal complications.