Catherine C Quatman-Yates1, David Wisner1, Mark Weade1, Mindy Gabriel1, Jessica M Wiseman1, Elizabeth Sheridan1, Jennifer H Garvin1, John F P Bridges1, Heena P Santry1, Ashish R Panchal1, Soledad Fernandez1, Carmen E Quatman1. 1. School of Health and Rehabilitation Sciences, The Ohio State University, Columbus, Ohio (CCQ-Y); Chronic Brain Injury Program, The Ohio State University, Columbus, Ohio (CCQ-Y); Sports Medicine Research Institute, The Ohio State University, Columbus, Ohio (CCQ-Y); Upper Arlington Fire Division, Upper Arlington, Ohio (DW, MW); Westerville Division of Fire, Westerville, Ohio (MG); Department of Orthopaedics, The Ohio State University, Columbus, Ohio (JMW, ES, CEQ); Health Information Management and Systems, The Ohio State University, Columbus, Ohio (JHG); Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio (JHG, JFPB, SF); Center for Surgical Health Assessment, Research, and Policy (SHARP), The Ohio State University Wexner Medical Center, Columbus, Ohio (JFPB, HPS, CEQ); Department of Surgery, The Ohio State University, Columbus, Ohio (HPS); Department of Emergency Surgery, The Ohio State University, Columbus, Ohio (ARP); Center for EMS, The Ohio State University, Columbus, Ohio (ARP).
Abstract
Background: Getting effective fall prevention into the homes of medically and physically vulnerable individuals is a critical public health challenge. Community paramedicine is emerging globally as a new model of care that allows emergency medical service units to evaluate and treat patients in non-emergency contexts for prevention efforts and chronic care management. The promise of community paramedicine as a delivery system for fall prevention that scales to community-level improvements in outcomes is compelling but untested.Objective: To study the impact of a community paramedic program's optimization of a fall prevention system entailing a clinical pathway and learning health system (called Community-FIT) on community-level fall-related emergency medical service utilization rates. Methods: We used an implementation science framework and quality improvement methods to design and optimize a fall prevention model of care that can be embedded within community paramedic operations. The model was implemented and optimized in an emergency medical service agency servicing a Midwestern city in the United States (∼35,000 residents). Primary outcome measures included relative risk reduction in the number of community-level fall-related 9-1-1 calls and fall-related hospital transports. Interrupted time series analysis was used to evaluate relative risk reduction from a 12-month baseline period (September 2016 - August 2017) to a 12-month post-implementation period (September 2018-August 2019). Results: Community paramedic home visits increased from 25 in 2017, to 236 in 2018, to 517 in 2019, indicating a large increase in the number of households that benefited from the efforts. A relative risk reduction of 0.66 (95% [CI] 0.53, 0.76) in the number of fall calls and 0.63 (95% [CI] 0.46, 0.75) in the number of fall-related calls resulting in transports to the hospital were observed.Conclusions: Community-FIT may offer a powerful mechanism for community paramedics to reduce fall-related 9-1-1 calls and transports to hospitals that can be implemented in emergency medical agencies across the country.
Background: Getting effective fall prevention into the homes of medically and physically vulnerable individuals is a critical public health challenge. Community paramedicine is emerging globally as a new model of care that allows emergency medical service units to evaluate and treat patients in non-emergency contexts for prevention efforts and chronic care management. The promise of community paramedicine as a delivery system for fall prevention that scales to community-level improvements in outcomes is compelling but untested.Objective: To study the impact of a community paramedic program's optimization of a fall prevention system entailing a clinical pathway and learning health system (called Community-FIT) on community-level fall-related emergency medical service utilization rates. Methods: We used an implementation science framework and quality improvement methods to design and optimize a fall prevention model of care that can be embedded within community paramedic operations. The model was implemented and optimized in an emergency medical service agency servicing a Midwestern city in the United States (∼35,000 residents). Primary outcome measures included relative risk reduction in the number of community-level fall-related 9-1-1 calls and fall-related hospital transports. Interrupted time series analysis was used to evaluate relative risk reduction from a 12-month baseline period (September 2016 - August 2017) to a 12-month post-implementation period (September 2018-August 2019). Results: Community paramedic home visits increased from 25 in 2017, to 236 in 2018, to 517 in 2019, indicating a large increase in the number of households that benefited from the efforts. A relative risk reduction of 0.66 (95% [CI] 0.53, 0.76) in the number of fall calls and 0.63 (95% [CI] 0.46, 0.75) in the number of fall-related calls resulting in transports to the hospital were observed.Conclusions: Community-FIT may offer a powerful mechanism for community paramedics to reduce fall-related 9-1-1 calls and transports to hospitals that can be implemented in emergency medical agencies across the country.
Entities:
Keywords:
community paramedicine; fall prevention; injury prevention; older adult falls
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