Fall Prediction and Prevention in Inpatient Open-Cubicle Wards
Care Continuum
Care Process & Redesign
National Healthcare Innovation and Productivity Medals
Agency for Integrated Care - Intermediate and Long Term Care
31 December 2024
Achieve a fall detection rate of ≥80% for bed-exit prediction, 67% manpower savings, and a 34% fall rate reduction in open. The system improved fall prevention and saved manpower hours.
Year Submitted: 2024
Published Date: 31 December 2024
Tags: Care Continuum, Care Process ＆ Redesign, Intermediate and Long Term Care ＆ Community Care, Nursing Home Care, Risk Management, Preventive Approach, Value Based Care, Safe Care
About this Content
Aims
Achieve a fall detection rate of ≥80% for bed-exit prediction, 67% manpower savings, and a 34% fall rate reduction in open cubicle wards.
Background
Falls in inpatient wards were common, with 60% occurring at night and 80% happening near the bedside. To address this, SACH adopted PreSAGE® bed-exit sensors.
Methods
Installed 20 PreSAGE® bed-exit sensors in open cubicles and 2 in isolation rooms. Conducted AI-based learning for alarm optimization and analyzed performance metrics.
Results
Achieved a 78% true positive detection rate, 96% manpower hour savings, and a 44% reduction in fall rates in open cubicles.
Conclusion
The system improved fall prevention and saved manpower hours. Future plans include expanding implementation to more wards and refining AI learning models.
Lessons Learnt
AI-based alarm optimization reduces false alarms. Involvement of caregivers and staff is crucial for successful implementation.
Keywords
Fall risk, bed sensor, PreSAGE, alarm, inpatient, fall incidents, bedside, rehabilitation
Innovators' Details
Innovators' Details
Healthcare Cluster(s) | Agency for Integrated Care - Intermediate and Long Term Care |
Organization(s) Involved | St. Andrew Community Hospital |
Platform(s) | National Healthcare Innovation and Productivity Medals |
Healthcare Professional Group(s) | Ancillary Care, Nursing, Medical |
Applicable Specialty or Discipline | Rehabilitation Therapy, Palliative Medicine, Geriatric Medicine |
Project Lead(s) | Adrian Tan |
Project Member(s) | Arlene Dergam Aleta |
Connect with this contributor!
Ms Chan Soo Sin - Soosin_chan@sach.org.sg
Project Attachment
152_SACH_NHIP_2024_Fall_Prediction_and_Prevention_in_Inpatient_Open_Cubicle_Wards.pdf