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工具，供健康和老年护理提供者使用。65岁以上老人跌倒的比例高达30%，80岁以上老人跌倒的比例高达40%。跌倒使每个人都有发生闭合性头部损伤和长骨骨折的危险。据报道，髋部骨折一年后的死亡率高达27%，另有22%的患者失去行走能力。与跌倒有关的伤害是老年人受伤相关死亡的主要原因。据估计，美国每年因跌倒造成的损失超过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到跌倒风险的统一和客观的随机评估中。跌倒风险评估工具将提供一种通用算法，用于初始化、调整和优化跌倒风险评估，该评估基于给定环境中可用的患者风险因素数据。提出的方法利用了两个重要的现存资产：(1)关于跌倒危险因素和跌倒患病率统计的大量文献；(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调整新的评估工具，以便在特定情况下获得准确的、与临床相关的估计）。这两种数据资产将有效地用于闭合证据与实践之间的循环。第一阶段的成功工作证明了BENEFIT工具的可行性和有效性。BENEFIT工具的准确性大大超过了目前的评估工具。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.