BENEFIT

描述（由申请人提供）：这项多阶段SBIR研究工作的总体目标是开发一种手持式跌倒风险评估仪器，供健康和老年护理提供者使用。福尔斯在65岁以上的人中发生率高达30%，在80岁以上的人中高达40%。福尔斯使每个人都面临着危险的闭合性头部损伤和长骨骨折的风险。据报道，髋部骨折后一年的死亡率高达27%，另有22%的人失去行走能力。跌倒相关的伤害是老年人伤害相关死亡的主要原因。据估计，在美国，福尔斯每年造成的费用超过280亿美元。拟议的工具将确保识别出跌倒风险增加的个人，并提供适当的干预措施，以减少跌倒的发生。避免伤害性福尔斯跌倒将导致疼痛和痛苦的减少，并将降低患者护理的成本。贝叶斯信念网络是实现更快、更准确的跌倒评估的关键技术;贝叶斯方法允许将不同的信息合并为统一、客观的跌倒风险随机评估。跌倒风险评估工具将提供一种通用算法，用于根据给定环境中可用的患者风险因素数据初始化、调整和优化跌倒风险评估。所提出的方法利用了两个重要的和现存的资产：（1）关于跌倒风险因素和跌倒患病率统计数据的广泛文献;以及（2）机构特定的患者跌倒风险因素和跌倒结局数据（这些数据可用于训练和调整新的评估工具，允许针对特定设置获得准确的临床相关估计）。这两个数据资产将被有效地用于关闭证据和实践之间的循环。第一阶段工作的成功证明了效益工具的可行性和有效性。惠益工具的准确性大大超过目前的评估工具。对第一阶段900例患者样本的回顾性分析估计，使用拟议的仪器将节省近200万美元的成本。

DESCRIPTION (provided by applicant): The overall goal of this multi-phase SBIR research effort is to develop a handheld fall risk assessment instrument for use by health and elder care providers. Falls occur in up to 30% of those over the age of 65 and up to 40% for people over the age of 80. Falls place each individual at risk for dangerous closed head injury and long bone fractures. The mortality rate at one year following a hip fracture has been reported to be as high as 27%, with another 22% losing the ability to ambulate. Fall-related injuries are the leading cause of injury-related deaths among elderly adults. Falls are estimated to incur costs of over $28 billion annually in the U.S. The proposed instrument will ensure that individuals at increased risk for falling are identified and provided with appropriate interventions to reduce fal occurrences. Avoidance of injurious falls will result in reduced pain and suffering and will lower the costs of patient care. Bayesian belief networks are the key technology that will enable faster and more accurate fall assessments; the Bayesian methodology allows the merging of disparate information into a unified and objective stochastic assessment of fall risk. The fall risk assessment tool will furnish a universal algorithm for initializing, adapting, and optimizing fall isk assessments based on the patient risk factor data that are available in a given setting. The proposed approach leverages two important and extant assets: (1) an extensive literature on fall risk factors and fall prevalence statistics; and (2) institution-specific patient fall risk factor nd fall outcomes data (these data can be used to train and adapt the new assessment tool, allowing accurate, clinically relevant estimates to be obtained for particular settings). These two data assets will be used effectively to close the loop between evidence and practice. The successful Phase I work demonstrated both the feasibility and the efficacy of the BENEFIT instrument. The BENEFIT instrument's accuracy surpassed that of current assessment tools by a wide margin. A retrospective analysis of a 900 patient sample in Phase I estimated that use of the proposed instrument would have realized a cost savings of nearly $2 million.