Establishing a consensus-based definition of air medical transport need for rural patients after injury
建立基于共识的农村患者受伤后航空医疗运输需求定义
基本信息
- 批准号:10811026
- 负责人:
- 金额:$ 25.35万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-09-22 至 2025-03-31
- 项目状态:未结题
- 来源:
- 关键词:AirAlgorithmsAmbulancesBlood TransfusionCaringConsensusDataDecision MakingDisparityEmergency MedicineEmergency medical serviceEmergency responseFaceFeedbackFeesFinancial HardshipFutureGeographyGoalsGuidelinesHealthcare SystemsHelicopterHospitalsHourInjuryInterventionLifeLiteratureMeasuresMedicalMedical emergencyMethodologyMethodsMinorOnline SystemsOutcomeOutcome AssessmentPatient-Focused OutcomesPatientsPatternPersonsPhysiologyPre-hospitalization careRegistriesResearchResourcesRiskRuralRural PopulationSafetyStandardizationSurveysSystemTimeTraumaTrauma patientTriageValidationVariantWorkcare deliverycostdata registrydisparity reductionevidence baseexperiencefirst responderhigh riskimprovedimproved outcomeinjuredinnovationmeetingsmortality riskmultidisciplinarynovelpatient orientedpatient safetypatient subsetspreservationprospectiverecruitrural Americarural Americansrural arearural patientssevere injurytooltrauma caretrauma centersunderserved rural area
项目摘要
PROJECT ABSTRACT
Patients injured in rural America are more likely to die than their urban counterparts. This is due in part to poor
access to specialized care such as regional trauma centers, and rural patients are more likely to be taken to a
non-trauma center as a result. For rural patients, air medical transport (AMT) by helicopter is the only way to
get timely access to life-saving trauma care because of poor geographic access to trauma centers. AMT brings
life-saving care otherwise unavailable from ground ambulances to the patient even before reaching a trauma
center and gets the patient to definitive care at a trauma center faster. AMT when used appropriately can
mitigate this under-triage and save lives after injury. In remote rural regions, use of AMT may also preserve
local emergency response resources for other patients. Unnecessary use of AMT – over-triage – occurs in up
to 60% of helicopter flights for patients with only minor injuries and is also more common in rural areas. A
major driver of this inaccurate triage for AMT is the lack of a standardize definition of what patients and/or
circumstances warrant the use of AMT, reflected in significant variation in the measures used in prior literature
for appropriate AMT. This makes it impossible to compared or build on prior work, representing a critical barrier
to improving air medical triage of rural patients. Inaccurate AMT triage disproportionally impacts patients in
underserved rural areas and has serious negative consequences: Under-triage of rural patients increases the
risk of death by not transporting them to a facility that can treat their injuries. Over-triage, unnecessary AMT,
increases cost, makes this scarce resource unavailable for other patients that need it, and increases risk to the
patient and the crew. AMT crashes are a serious safety issue with a third of crashes experiencing a fatality
compared to just 1% of ground ambulance crashes, an avoidable risk if the flight is unnecessary. Further, AMT
fees are over $10,000 per transport, creating a significant financial burden for patients and healthcare systems.
This proposal seeks to develop the first definition of AMT need after injury through consensus from multiple
diverse perspectives with experts in prehospital trauma care and regionalized care delivery. This project will
then validate the definition using actual patient outcomes. In Aim 1 we will use web-based real-time modified
Delphi methods to develop a consensus definition of criteria that warrant AMT after trauma. In Aim 2 we will
assess the multiple aspects of validity for our consensus definition of AMT need. We will solicit feedback from
rural EMS professionals to assess face validity and refine the definition. We will then assess criterion validity
by applying the definition to patients in a state trauma registry to assess outcomes among those that do and do
not meet the definition. This proposal is foundational to developing evidence-based air medical triage
guidelines and will inform future work using this consensus definition to prospectively validate, implement, and
pilot an air medical triage algorithm. Successful completion will improve outcomes and trauma care value,
reducing the disparities after injury in rural America by getting the right patients the right care at the right time.
项目摘要
在美国农村受伤的病人比城市病人更容易死亡。这部分是由于穷人
获得专门的护理,如区域创伤中心,农村患者更有可能被带到一个
非创伤中心对于农村患者,直升机空中医疗运输(AMT)是唯一的方式,
及时获得挽救生命的创伤护理,因为创伤中心的地理位置很差。AMT带来
即使在到达创伤前,地面救护车也无法为患者提供救生护理
中心,并让病人在创伤中心更快地得到明确的治疗。AMT如果使用得当,
减轻这种分类不足的情况,并在受伤后挽救生命。在偏远的农村地区,使用AMT也可以保护
其他患者的当地应急响应资源。不必要地使用AMT -过度分流-发生在
直升机飞行中只有60%的患者受轻伤,在农村地区也更常见。一
这种不准确的AMT分诊的主要驱动因素是缺乏什么患者和/或
在某些情况下需要使用AMT,这反映在先前文献中使用的措施存在显着差异
适当的AMT。这使得无法比较或建立在之前的工作基础上,这是一个关键障碍
改善农村病人的空中医疗分诊。不准确的AMT分诊会对患者造成不良影响,
农村地区服务不足,并产生严重的负面后果:农村患者分诊不足增加了
没有将他们运送到可以治疗他们受伤的设施,就会有死亡的风险。过度分流不必要的AMT
增加了成本,使这种稀缺资源无法用于其他需要它的患者,并增加了患者的风险。
病人和船员。AMT碰撞是一个严重的安全问题,三分之一的碰撞导致死亡
相比之下,只有1%的地面救护车坠毁,如果飞行是不必要的,这是一个可以避免的风险。此外,AMT
每次运输的费用超过10 000美元,给病人和医疗保健系统造成了巨大的经济负担。
该提案旨在通过多方共识,制定伤害后AMT需求的第一个定义。
与院前创伤护理和区域化护理提供专家的不同观点。该项目将
然后使用实际的患者结果来验证定义。在目标1中,我们将使用基于Web的实时修改
采用德尔菲法对创伤后AMT的标准进行一致性定义。在目标2中,
评估我们对AMT需求的共识定义的有效性的多个方面。我们将征求以下方面的反馈意见:
农村EMS专业人员评估表面效度和完善的定义。然后,我们将评估标准有效性
通过将该定义应用于州创伤登记处的患者,以评估那些做和做的结果,
不符合定义。这一建议是发展循证空中医疗分诊的基础
指导方针,并将告知未来的工作,使用这一共识的定义,以前瞻性地验证,实施,
进行空中医疗分类成功完成将改善结果和创伤护理价值,
通过在正确的时间为正确的病人提供正确的护理来减少美国农村地区受伤后的差异。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Joshua B Brown其他文献
Shock Index As a Predictor of Earlier Risk of Vaso-Occlusive Crisis in Trauma Patients with Sickle Cell Disease
- DOI:
10.1182/blood-2024-203619 - 发表时间:
2024-11-05 - 期刊:
- 影响因子:
- 作者:
Ektha Parchuri;Rida Ashraf;Aqsa Owais;Gabrielle Lapping-Carr;Joshua B Brown - 通讯作者:
Joshua B Brown
The Impact of Multi-System Trauma in Patients with Sickle Cell Disease
- DOI:
10.1182/blood-2022-159851 - 发表时间:
2022-11-15 - 期刊:
- 影响因子:
- 作者:
Ektha Parchuri;Danielle Gruen;Francis X Guyette;Charles L Jonassaint;Joshua B Brown;Laura Decastro - 通讯作者:
Laura Decastro
Joshua B Brown的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
相似海外基金
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
- 批准号:
EP/Y029089/1 - 财政年份:2024
- 资助金额:
$ 25.35万 - 项目类别:
Research Grant
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
- 批准号:
2337776 - 财政年份:2024
- 资助金额:
$ 25.35万 - 项目类别:
Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
- 批准号:
2338816 - 财政年份:2024
- 资助金额:
$ 25.35万 - 项目类别:
Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
- 批准号:
2338846 - 财政年份:2024
- 资助金额:
$ 25.35万 - 项目类别:
Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
- 批准号:
2348261 - 财政年份:2024
- 资助金额:
$ 25.35万 - 项目类别:
Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
- 批准号:
2348346 - 财政年份:2024
- 资助金额:
$ 25.35万 - 项目类别:
Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
- 批准号:
2348457 - 财政年份:2024
- 资助金额:
$ 25.35万 - 项目类别:
Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
- 批准号:
2404989 - 财政年份:2024
- 资助金额:
$ 25.35万 - 项目类别:
Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
- 批准号:
2339310 - 财政年份:2024
- 资助金额:
$ 25.35万 - 项目类别:
Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
- 批准号:
2339669 - 财政年份:2024
- 资助金额:
$ 25.35万 - 项目类别:
Continuing Grant