Enhanced injury surveillance using real-time reporting among healthcare workers

Healthcare workers are consistently exposed to hazards with widespread consequences for themselves, patients, and the organization. In a 2018 Bureau of Labor Statistics report, healthcare continues to have one of the highest rates of non-fatal occupational injury relative to other industries. Musculoskeletal injuries, violence,

falls, and needle-sticks are most common. It is essential that healthcare systems employ effective and user- friendly surveillance to allocate resources for injury prevention. However, there is extensive evidence that reported injury rates significantly underestimate the true risk, and existing injury surveillance systems do not

capture other adverse outcomes that impact health and performance. Other subjective health complains (SHCs), such as stress and fatigue, are major reasons for sickness and absence from work. Furthermore, near-misses have long been integrated into injury surveillance of high-risk industries such as aviation, nuclear power, and the

military, but healthcare has yet to systematically assess the incidence of these important sentinel events as part of injury prevention. Finally, despite federal law and institutional policies promoting employee safety, employees are reluctant to report injuries due to time constraints, symptom self-management, peer pressure, perceived

`normalcy' of injury, and fear of reprisal. Valid data on the incidence of near-misses, injuries, and time trends are essential for identifying high-risk areas and deploying resources to mitigate harm. This study will address the Healthcare & Social Assistance sector and the cross-sector programs of Musculoskeletal Health and Healthy

Work Design & Well-Being by addressing the weaknesses in current injury surveillance methods using a proactive approach. Our long-term goal is to improve healthcare injury surveillance systems and significantly reduce work-related injuries. Our specific aims are to: integrate passive injury reporting with active injury

reporting to improve detection of worker harm; leverage unit-based risk metrics, passive and active injury reports to continuously monitor the work environment; and develop an early-warning system that will more accurately trigger interventions to decrease the risk of and prevent worker injuries. Randomly

selected patient care providers will verbally record injuries, near-misses, and SHCs on a mobile application for 800 two-week periods. Using advanced statistical methods (e.g. machine learning) and data visualization we will aggregate injury data and unit-based metrics to produce an injury risk identification and prediction model;

develop a digital heat map to identify high-risk units in near real-time, in a real-world setting, and provide hospital leaders with notifications with established risk-specific mitigation strategies. We build upon our R21OH010035 project, which established the feasibility of active injury surveillance and documented its superiority to current

surveillance practice. In response to PAR-18-812, our project can shift current practice toward a more rigorous integration of injury surveillance and prevention.