I-Corps: Decision Support System For Risk Reduction in Health Care Facilities

这个I-Corps项目的更广泛的影响/商业潜力是改善长期护理设施中的患者结局。随着美国的老龄化，对老年人的有效护理的需求变得更加迫切，超过50000个设施在任何时候都要照顾超过350万名患者。该I-Corps项目可以导致采用新的决策技术，该技术结合了独特的预测算法，以帮助长期护理机构的管理团队最大限度地利用当前收集的安全和临床数据中的信息。通过采用这项新技术，管理团队将能够预测可能存在缺陷的地方，优先考虑与特定患者结果相关的关键驱动因素，并预防不良事件。I-Corps的这一项目使用独特的预测建模算法，帮助客户发现创新，该算法可以识别特定患者治疗结果的关键驱动因素，并为每个驱动因素生成有意义的基准，管理团队可以使用这些基准来指导质量改进工作。该算法确定设施在每个关键驱动程序上与每个基准的关系，并产生持续改进所需的优先行动列表。该算法提供了一个稳定的框架，是不敏感的数据缺陷，并不允许这些缺陷分散决策从真实的优先级的改进。该算法是在广泛研究和实践的基础上开发的，这些研究和实践将Rasch模型和客观标准制定过程应用于不同的环境，包括健康，心理和交通。这些应用中最广泛的是为长期护理设施和医院开发严格的风险和防御性评估，以及为航空业的乘客体验开发差异化算法。

The broader impact/commercial potential of this I-Corps project is the improvement of patient outcomes in long-term care facilities. As America ages, the need for effective care of the elderly becomes more pressing, with more than 50000 facilities caring for more than 3.5 million patients at any one time. This I-Corps project can lead to the adoption of the new decision-making technology that incorporates unique predictive algorithms to help management teams in long-term care facilities maximize the information from their currently collected safety and clinical data. By adopting this new technology, management teams will be able to predict where there may be deficiencies, prioritize key drivers associated with specific patient outcomes, and prevent adverse events. The new technology can be adopted as a cloud based information and data delivery system.This I-Corps project supports customer discovery of an innovation using a unique predictive modeling algorithm that identifies key drivers of specific patient outcomes and produces meaningful benchmarks for each driver that management teams can use to guide quality improvement efforts. The algorithm determines where a facility is on each key driver in relation to each benchmark and produces a prioritized list of actions needed for continuous improvement. The algorithm provides a stable framework for improvement that is not sensitive to data imperfections and does not allow those imperfections to distract decision-making from real priorities. The algorithm was developed based on extensive research and practice of applying the Rasch model and objective standard-setting process in different settings, including health, psychology and transportation. The most extensive of these applications were the development of rigorous risk and defensibility assessments for long-term care facilities and hospitals, and the development of differentiation algorithms for passenger experience in the airline industry.