BENEFIT

益处

基本信息

批准号：
8454000
负责人：
Brian R Clark
金额：
$ 49.95万
依托单位：
BARRON ASSOCIATES, INC.
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-02-01 至 2014-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8454000
关键词：
Accreditation Acute Adoption Adult Aged, 80 and over Algorithms Assisted Living Facilities Beds Belief Boxing Caring Cessation of life Chronic Care Closed head injuries Cost Savings Data Databases Devices Elderly Ensure Environment Environmental Risk Factor Equilibrium Etiology Exercise Feedback Fracture Goals Health Hip Fractures Home Care Services Hospitals Individual Injury Institution Institutional Policy Internet Intervention Joint Commission on Accreditation of Healthcare Organizations Lead Literature Logistic Regressions Marketing Measures Medicare Methodology Methods Metric Modeling Nurses Nursing Homes Occupational Therapy Online Systems Outcome Outcome Assessment Pain Patient Care Patients Phase Phase Transition Physical Restraint Physical therapy Play Policies Prevalence Preventive Preventive Intervention Probability Procedures Provider Publishing Reporting Research Research Personnel Risk Risk Assessment Risk Estimate Risk Factors Role Sampling Small Business Innovation Research Grant Solutions Stimulus System Tablets Techniques Technology Testing Time Training Trauma Uncertainty Work base clinical practice clinically relevant cohort cost design digital fall risk falls health care service organization high risk improved instrument laptop long bone mortality programs prototype public health relevance restraint screening statistics tool usability virtual

项目摘要

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.

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 岁以上人群中高达 30% 的人会发生跌倒，80 岁以上人群中高达 40% 的人会跌倒。跌倒使每个人都面临危险的闭合性头部损伤和长骨骨折的风险。据报道，髋部骨折后一年内的死亡率高达 27%，另有 22% 的人丧失行走能力。跌倒相关伤害是老年人伤害相关死亡的主要原因。据估计，在美国，跌倒每年会造成超过 280 亿美元的损失。拟议的工具将确保识别出跌倒风险较高的个人，并为其提供适当的干预措施，以减少跌倒发生。避免跌倒受伤将减少疼痛和痛苦，并降低患者护理成本。贝叶斯信念网络是实现更快、更准确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且客观的跌倒风险随机评估。跌倒风险评估工具将提供通用算法，用于根据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。所提出的方法利用了两个重要且现有的资产：（1）关于跌倒风险因素和跌倒流行率统计的广泛文献； (2) 机构特定的患者跌倒风险因素和跌倒结果数据（这些数据可用于训练和调整新的评估工具，从而针对特定环境获得准确的、临床相关的估计）。这两种数据资产将被有效利用，以形成证据与实践之间的闭环。第一阶段工作的成功证明了 BENEFIT 工具的可行性和有效性。 BENEFIT 工具的准确性大幅超过了当前的评估工具。对第一阶段 900 名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。