Enhanced injury surveillance using real-time reporting among healthcare workers

医护人员持续暴露在危险中，给自己带来广泛的后果， 病人和组织。在2018年劳工统计局的一份报告中，医疗保健继续拥有 相对于其他行业，非致命性职业伤害的比率最高。肌肉骨骼损伤，暴力， 跌倒和针刺是最常见的。至关重要的是，医疗保健系统必须采用有效和用户- 友好监督，为预防伤害分配资源。然而，有大量证据表明， 报告的伤害率严重低估了真实的风险，而现有的伤害监测系统没有 捕捉影响健康和绩效的其他不良后果。其他主观健康投诉(SHCS)， 如压力和疲劳，是生病和缺勤的主要原因。此外，险些发生的事故 长期以来一直被纳入航空、核电和医疗等高风险行业的伤害监测 军事，但医疗保健尚未系统地评估这些重要哨兵事件的发生率作为 预防伤害的方法。最后，尽管联邦法律和机构政策促进了员工的安全，但员工 由于时间限制、症状自我管理、同伴压力、感知到的原因，不愿报告受伤 “正常”的伤害，以及对报复的恐惧。关于险些发生的事故、伤害和时间趋势的有效数据是 对于识别高风险地区和部署资源以减轻伤害至关重要。这项研究将解决 医疗保健和社会援助部门与肌肉骨骼健康和健康跨部门计划 工作设计和幸福感，通过使用 积极主动的做法。我们的长期目标是改善医疗伤害监测系统，并显著 减少工伤事故。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；利用基于单位的被动和主动风险指标 伤害报告，以持续监测工作环境；并开发早期预警系统， 将更准确地触发干预措施，以降低和预防工伤风险。随机 选定的患者护理提供者将在移动应用程序上口头记录受伤、险些失手和SHCS 800个两周的周期。使用先进的统计方法(例如机器学习)和数据可视化，我们将 汇总伤害数据和基于单位的度量，以产生伤害风险识别和预测模型； 开发数字热图，在真实环境中近乎实时地识别高风险单元，并为医院提供 向领导者发出通知，并制定针对风险的缓解策略。我们在R21OH010035的基础上 项目，确立了主动伤害监测的可行性，并证明了其相对于当前 监视实践。作为对PAR-18-812的响应，我们的项目可以将当前的实践转变为更严格的 伤害监测与预防相结合。

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.