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Reconfiguring the patient room as a fall protection strategy to increase patient stability during ambulation

重新配置病房作为跌倒保护策略，以提高患者行走期间的稳定性

基本信息

批准号：
10488188
负责人：
Bob Wong
金额：
$ 40万
依托单位：
UNIVERSITY OF UTAH
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-30 至 2024-09-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10488188
关键词：
Reconfiguring patient room fall protection

项目摘要

Despite decades of research into patient falls, falls and the injuries incurred continue to be a serious threat to patient safety. Fall rates continue to be unacceptably high. Advances in computational modeling and simulation will provide insight for designing a room that maximizes patient safety during ambulation. The purpose of this project is to increase the safety of a hospital room for patient mobility between the bed and the bathroom, using innovative simulation strategies and patient-centric design. An innovative simulation environment will be built to enable rapid assessment of room layout and fixture positioning and patient stability. The results from multiple simulations, informed by patient biomechanics and stakeholder input, will be used to fabricate an enhanced fidelity prototype room layout that will be tested by elderly participants at risk of falling and reviewed and updated with additional input from other relevant stakeholders. A final room prototype will be built and tested. A 3D augmented reality (AR) holographic application will be created to improve the translation of information about room design and patient safety. Results will be shared and disseminated for implementation. Hospital rooms are multi-functional rooms that should satisfy patient needs along with others who provide care or housekeeping. New healthcare equipment and laws, records of medical errors, infection control and injuries due to patient falls necessitate safer and more innovative hospital rooms. A more human-centered perspective can be used for room designs to meet everyone’s needs. What we propose will depart significantly from the status quo by formulating all these user needs as one multi- objective optimization problem. We will consider different constraints regarding patients and clinical care. Our method will combine, for the first time, new sampling-based motion planner ideas from biomechanically derived learning. Similar approaches have been highly successful in enabling robots to learn task constraints and imitate motion in unpredictable environments. This approach will serve as the model in simulation to predict the maximally safe environment for patient mobility. Our adapted framework will consist of a learning phase, the application of innovative cost metrics and constraints on the optimization problem, and will result in a spatial plan for the room and a motion plan for the patient and nurse. This safe patient room, once developed, will be always available, and not subject to caregiver error as is the case with existing surveillance technologies. Healthcare industry must demand safe rooms when planning and contracting for new facilities and renovation. Our configuration will have application to hospitals, nursing homes and extended care. Once incorporated into hospital units, the results of this research will have a profound impact on patient fall rate and reducing injuries from falls. The expected results from this research will direct future hospital fall intervention research and positively affect patients by reducing fall-related injuries, thereby resulting in substantial cost savings to the healthcare system.

尽管对患者福尔斯进行了数十年的研究，但福尔斯和所引起的损伤仍然是对患者健康的严重威胁。患者安全。下降率仍然高得令人无法接受。计算建模与仿真研究进展将为设计一个能最大限度地提高病人在住院期间安全性的房间提供见解。这样做的目的该项目是为了增加医院房间的安全性，使病人在床和浴室之间移动，使用创新的模拟策略和以患者为中心的设计。将建立一个创新的模拟环境，能够快速评估房间布局和夹具定位以及患者稳定性。结果来自多个根据患者生物力学和利益相关者的输入进行模拟， Fidelity原型房间布局将由有跌倒风险的老年参与者进行测试，并进行审查和更新其他相关利益攸关方的补充投入。最终的房间原型将被建造和测试。的3D 增强现实（AR）全息应用程序将被创建，以改善信息的翻译，房间设计和病人安全。将分享和传播成果以供执行。病房是多功能的房间，应满足病人的需要沿着其他人谁提供照顾或家务。新的医疗设备和法律，医疗差错记录，感染控制和由于患者福尔斯跌倒而造成的伤害需要更安全和更创新的医院房间。一个更加以人为本的可用于房间设计，以满足每个人的需求。我们的建议将大大偏离现状，将所有这些用户需求制定为一个多方面的需求，目标优化问题我们将考虑有关病人和临床护理的不同限制。我们方法将联合收割机，第一次，新的采样为基础的运动规划的想法，从生物力学推导学习类似的方法在使机器人学习任务约束和模仿方面非常成功在不可预知的环境中运动。这种方法将作为模拟中的模型来预测为患者提供最安全的移动环境。我们的适应框架将包括一个学习阶段，创新的成本指标和约束的优化问题的应用，并将导致空间规划以及病人和护士的运动计划。这个安全的病房，一旦开发，将始终可用，而不是受到照顾者的错误，因为是利用现有的监控技术。医疗保健行业在规划时必须要求安全室，承包新设施和翻新。我们的配置将应用于医院，疗养院和长期护理。一旦纳入医院单位，这一研究成果将产生深远影响患者跌倒率和减少福尔斯伤害。本研究的预期结果将指导未来的研究医院跌倒干预研究并通过减少跌倒相关伤害对患者产生积极影响，从而产生为医疗保健系统节省大量成本。