Enhanced injury surveillance using real-time reporting among healthcare workers

医护人员经常暴露在危害中，对他们自己造成广泛的后果， 病人和组织。在2018年劳工统计局的一份报告中，医疗保健仍然是 与其他行业相比，非致命性职业伤害率最高。肌肉骨骼损伤暴力 福尔斯和针刺是最常见的。医疗保健系统必须采用有效的和用户- 友好的监督，为预防伤害分配资源。然而，有大量证据表明， 报告的伤害率大大低估了真正的风险，现有的伤害监测系统没有 捕获影响健康和绩效的其他不利结果。其他主观健康投诉（SHC）， 例如压力和疲劳，是生病和缺勤的主要原因。此外，近距离失误 长期以来一直被整合到高风险行业的伤害监测中，如航空，核电， 军事，但医疗保健尚未系统地评估这些重要的前哨事件的发生率， 伤害预防。最后，尽管联邦法律和机构政策促进员工安全， 由于时间限制，症状自我管理，同伴压力，感知 伤害的“常态”和对报复的恐惧。关于未遂事件发生率、伤害和时间趋势的有效数据如下： 这对于确定高风险区域和部署资源以减轻危害至关重要。本研究将探讨 医疗保健和社会援助部门以及肌肉骨骼健康和健康的跨部门计划 工作设计和福祉，通过解决目前的伤害监测方法的弱点，使用 积极主动的方法。我们的长期目标是改善医疗伤害监测系统， 减少工伤。我们的具体目标是：将被动伤害报告与主动伤害报告相结合 报告以改进对工人伤害的检测;利用基于单元的被动和主动风险度量 工伤报告，以持续监测工作环境;并建立一个预警系统， 将更准确地触发干预措施，以减少和防止工人受伤的风险。随机 选定的患者护理提供者将在移动的应用程序上口头记录伤害、未遂事故和SHC， 800个两周周期。使用先进的统计方法（例如机器学习）和数据可视化，我们将 聚集伤害数据和基于单元的度量以产生伤害风险识别和预测模型; 开发一个数字热图，在现实世界的环境中近实时地识别高风险单位，并提供医院 向领导者发出通知，并制定针对特定风险的缓解策略。我们以R21 OH 010035为基础 该项目建立了主动伤害监测的可行性，并记录了其优于目前的 监视实践。为了响应PAR-18-812，我们的项目可以将当前的实践转向更严格的 伤害监测和预防的一体化。

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.