AOHC Encore 2023-102 Pillar by Pillar: The Evolution of the Denver Fire Department Wellness Program

Video Transcription

Video Summary

The video discusses the wellness programs implemented by the Denver Fire Department. It features individuals such as Eric Tade, Dr. Casey Stonemerger, Dr. Laura Stewart, and Dr. Elisa Coble. These programs focus on employee wellness, risk management, injury prevention, and integration with primary healthcare providers. The department has collaborated with various stakeholders including citizens, civil service, the Department of Safety, the firefighter union, philanthropy organizations, and health insurance providers. The program has evolved over time and includes initiatives for recruits, incumbent members, and behavioral health and resiliency support. The goal of the program is to promote employee wellness, performance, and longevity within the department.<br /><br />The video highlights the various components of the wellness program, such as unlimited sessions with clinicians, the Recess program for breaks and mindfulness exercises, and wellness screenings that assess physical and mental health. These screenings have resulted in early detection of health issues and successful returns to work. The program has a high participation rate of 87% and is supported by partnerships with healthcare providers and insurance companies.<br /><br />The video also discusses ongoing efforts to collect and analyze data from the program to continuously improve its effectiveness. There is a Q&A session at the end where questions about program costs, employee feedback, and cardiovascular health are addressed.<br /><br />Overall, the video emphasizes the comprehensive nature of the firefighter wellness program and its positive outcomes in terms of early detection, employee participation, and partnerships with stakeholders.

Keywords

wellness programs

Denver Fire Department

employee wellness

risk management

collaboration

healthcare providers

wellness screenings

early detection

high participation rate

data analysis