SBIR Phase II: DATA-DRIVEN DECISION SUPPORT FOR EFFICIENT PATIENT PROGRESSION

小企业创新研究（SBIR）第二阶段的更广泛的影响/商业潜力是减少急诊室（艾德）拥挤问题加剧所造成的患者伤害和经济负担。 艾德拥挤对高紧急度患者的患者安全是一种威胁，并且在许多情况下与可避免的发病率和死亡率相关。 与此同时，急诊科面临着有效管理大量非紧急就诊的低敏度患者的挑战。所提出的电子健康记录（EHR）集成技术部署了新颖的机器学习算法，可以在患者护理路径中的可操作时间点预测临床事件。 这些决策支持工具将艾德描述为一个流动系统，将集中于艾德流入和医院流出的根本原因。这一新的远见有望实现创新的医院运营模式，加快患者进展（最大限度地减少患者等待），提高患者安全，并直接转化为可衡量的成本节约和/或创收。SBIR第二阶段项目将带来更高的产量和可扩展的决策支持技术，同时提升数据支持的科学和工程在医疗保健领域的价值。 拟议的项目目标是通过推进支撑我们的决策支持技术的数据科学、决策科学和运筹学来大大减少艾德拥挤。这项新技术基于数据标准化、特征选择和监督机器学习方法的新颖组合，以预测临床事件，从而推动明确的行动来优化医院资源。 这包括一个决策支持工具，在艾德分流（流入），以预测关键事件的风险，使安全分离的服务流急性病和非紧急病人。一个辅助工具在医院出院（流出）时发挥作用，以预测预期的出院时间，从而在全医院范围内对加快出院所需的资源进行优先排序。这释放了下游容量，并消除了造成长时间等待的主要艾德流出瓶颈。拟议的技术是创新的设计，可扩展，但适应医院的个别患者群体，运营目标和风险承受能力。预计该技术将进一步推进与供应商的学习，了解如何使用新的预测和解释性信息来支持决策。 II期的具体目标是通过以下方式继续开发该技术：（1）将反馈整合到预测输出中，（2）开发性能监测能力，以及（3）推动基于系统的患者进展管理。

The broader impact/commercial potential of the Small Business Innovation Research (SBIR) Phase II is to reduce the patient harm and financial burden created by the intensifying problem of emergency department (ED) crowding. ED crowding is a threat to patient safety for high acuity patients and has been associated with avoidable morbidity and mortality across many conditions. Concurrently, EDs are challenged to efficiently manage large volumes of low-acuity patients visiting with non-emergency conditions. The proposed electronic health record (EHR) integrated technology deploys novel machine learning algorithms that predict clinical events at actionable time-points in patients care pathways. Characterizing the ED as a flow system, these decision support tools will concentrate on the root causes that exist at ED inflow and hospital outflow. This new foresight is expected to enable innovative hospital operational models that expedite patient progression (minimize patient waiting), improve patient safety, and directly translate to measurable cost-savings and/or revenue generation. The SBIR Phase II project should result in higher yield and scalable decision support technology while promoting the value of data-enabled science and engineering in healthcare. The proposed project objective is to greatly reduce ED crowding by advancing the data-science, decision-science, and operations research that underpins our decision support technology. This new technology is based upon a novel combination of data normalization, feature selection, and supervised machine learning methods to predict clinical events that drives clear action to optimize hospital resources. This includes a decision support tool that functions at ED triage (inflow) to predict risk of critical events to empower safe separation of service streams for acutely ill and non-urgent patients. A complementary tool functions near hospital discharge (outflow) to predict expected discharge time enabling hospital-wide prioritization of resources required to expedite discharge. This unlocks downstream capacity and removes a major ED outflow bottleneck that creates prolonged waiting. The proposed technology is innovative by design to be scaled, yet adaptive to hospitals individual patient populations, operational objectives, and risk tolerances. The technology is expected to further advance learning with providers about how to consume new predictive and explanatory information for decision support. The specific Phase II objectives are to continue the development of the technology by: (1) integrating feedback into prediction output, (2) developing performance monitoring capabilities, and (3) driving systems-based management of patient progression.