STTR Phase I: Data-Driven Decision Support Services for Emergency Department Operations

STTR 第一阶段：面向急诊科运营的数据驱动决策支持服务

• 批准号: 1448898
• 负责人: Kenneth Lopiano
• 金额: $ 22.5万
• 依托单位: Roundtable Analytics LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-01-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1448898&HistoricalAwards=false
• 关键词: STTR; Phase; Data; Driven; Decision

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project is very significant. Suboptimal operational decision-making in emergency departments leads to inefficiencies that result in extreme patient wait-times, the diversion of ambulances to other emergency departments, wasted resources and patients who either leave before being treated or against medical advice. By connecting modern analytical approaches, including statistical modeling and systems engineering methods, to real-time data routinely collected in emergency departments, the proposed project promises to result in a tremendously valuable analytics platform that will assist emergency departments in making dozens of operational and staffing decisions each day. This informed decision-making will not only improve emergency department efficiency, it will lead to both healthier and more satisfied patients and simultaneous dramatic increases in revenue and profit. The technology proposed will have the potential to add significant value to the approximately 5,000 emergency departments in the U.S., often on the order of millions of dollars annually. Hospitals and health systems now realize the value of effective analytics, and the analytics platform proposed here will be an obvious investment for any emergency department whose goal is to provide the best care to its patients at lower costs.The proposed project promises to yield a decision-support platform upon which emergency departments will base their decisions each day. Substantial investments by hospitals and health systems on information technology, and in particular, electronic health records, have set the stage for evidence-based, data-driven decisions. These decisions will effectively leverage real-time data along with analytical methods such as statistical forecasting and event-simulation modeling. In particular, this proposed project will create a software platform, based on these analytical methods and linking to real-time data sources, tailored to emergency departments. This project will involve 1) understanding the capacity and real-time availability of data in emergency rooms, 2) developing a statistical and simulation modeling platform that maximizes the potential of these data, in real-time, and specifically reflects emergency departments, and 3) ultimately ensuring that actionable insights are delivered in a timely and intuitive manner to key stakeholders. These actionable insights that derive from the data and sophisticated methods must be delivered to the right decision-maker at the right time and in the right format, but will then have the capacity to substantially improve both the quality and efficiency of care-delivery in an emergency department.

这个小企业技术转让（STTR）第一阶段项目的更广泛的影响/商业潜力非常重要。急诊部门的次优运营决策导致效率低下，导致患者等待时间过长，救护车分流到其他急诊部门，浪费资源，患者在接受治疗前离开或违背医疗建议。通过将包括统计建模和系统工程方法在内的现代分析方法与急诊部门定期收集的实时数据相连接，拟议的项目有望产生一个非常有价值的分析平台，帮助急诊部门每天做出数十项运营和人员配置决策。这种明智的决策不仅会提高急诊科的效率，还会使患者更健康、更满意，同时大幅增加收入和利润。这项技术将有可能为美国大约5,000个急诊部门增加重要价值，通常是每年数百万美元的数量级。医院和卫生系统现在已经意识到有效分析的价值，这里提出的分析平台将是任何急诊科的一项明显投资，其目标是以较低的成本为患者提供最好的护理。拟议的项目有望产生一个决策支持平台，急诊科每天都将根据该平台做出决策。医院和卫生系统对信息技术的大量投资，特别是电子健康记录，为基于证据、数据驱动的决策奠定了基础。这些决策将有效地利用实时数据沿着分析方法，如统计预测和事件模拟建模。特别是，这个拟议的项目将创建一个软件平台，基于这些分析方法，并连接到实时数据源，为紧急部门量身定制。该项目将涉及1）了解急诊室数据的容量和实时可用性，2）开发一个统计和模拟建模平台，最大限度地发挥这些数据的潜力，实时，并具体反映急诊部门，3）最终确保以及时和直观的方式向关键利益相关者提供可操作的见解。这些来自数据和复杂方法的可操作见解必须在正确的时间以正确的格式提供给正确的决策者，然后才有能力大幅提高急诊科护理服务的质量和效率。