BENEFIT

描述(由申请人提供)：这项多阶段SBIR研究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。在65岁以上的人中，跌倒的比例高达30%，在80岁以上的人中，跌倒的比例高达40%。跌倒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%的人失去了行走能力。与跌倒相关的伤害是老年人与伤害相关死亡的主要原因。据估计，在美国，跌倒每年造成的成本超过280亿美元。拟议的工具将确保识别出跌倒风险增加的个人，并为其提供适当的干预措施，以减少跌倒事件的发生。避免伤害性跌倒将减少疼痛和痛苦，并将降低患者护理成本。贝叶斯信念网络是实现更快和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和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，用于基于在给定设置中可用的患者风险因素数据来初始化、调整和优化跌倒风险评估。建议的方法利用了两个重要和现有的资产：(1)关于跌倒风险因素和跌倒患病率统计的广泛文献；(2)特定机构的患者跌倒风险因素和跌倒结果数据(这些数据可用于培训和适应新的评估工具，从而获得针对特定环境的准确的、临床相关的估计)。这两项数据资产将被有效利用，以关闭证据和实践之间的环路。第一阶段工作的成功证明了福利工具的可行性和有效性。该福利工具的准确度远远超过了目前的评估工具。一项对第一阶段900名患者样本的回顾分析估计，使用拟议的工具将实现近200万美元的成本节约。

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.