SHB: Collaborative Research:Context-aware, dynamic, smart checklists: key cyber-infrastructure for systems delivering quality health care

本项目将开发和评估一个原型系统，为医疗保健流程的执行者提供在线流程监测和指导。医学研究所的一份报告估计，美国医院每年有近10万人死于可预防的错误，随后的报告表明，许多这些错误是由于医院流程的复杂性造成的。 该项目将演示如何使用上下文信息（回顾性、当前和前瞻性）为流程执行者提供及时的信息，以减少错误，提供即将发生的危险的快速警告，并改善结果。将开发积累和表示历史数据的技术，并将其纳入基于概况的分析技术，以评估概率并支持进行细粒度的流程区分。这些能力将为基于证据的持续流程改进提供强大的技术基础。项目技术将首先使用流程模型驱动的模拟生成的合成事件流进行评估，然后由护理学生使用患者人体模型进行人体模拟，最后由医疗专业人员在模拟临床环境中进行评估。要检查的过程包括输血、化疗、药物管理和患者身份验证。早期社区成功应用医疗检查表和最近的实验结果与积极的过程指导在医院急诊科是积极的迹象表明，这里提出的方法有一个很好的机会获得认可和改善医疗结果。 此外，这里开发的技术虽然专门针对医疗保健进行了评估，但也将适用于越来越多地在社会广泛领域使用的人力密集型系统。经过验证的医疗流程和拟议的原型将成为教育医疗专业人员了解当前最佳做法的有效框架。该项目还将教育计算机科学和工程专业的学生了解医疗领域所带来的挑战，鼓励这一新兴跨学科领域的新一代专家。它的社会吸引力也将有助于吸引聪明，充满活力的少数民族和女性学生，从而有助于扩大代表性不足的群体在STEM中的参与，特别是在计算机科学和工程领域。

This project will develop and evaluate a prototype system to provide on-line process monitoring and guidance to the performers of health care processes. An Institute of Medicine report has estimated that nearly 100,000 people per year die in US hospitals from preventable errors, and subsequent reports have suggested that many of these errors arise due to the complexity of hospital processes. The project will demonstrate how contextual information (retrospective, current, and prospective) can be used to provide process performers with timely information that could reduce errors, provide expedited warnings of impending hazards, and improve outcomes. Techniques to accumulate and represent historical data will be developed and will feed into profile-based analysis techniques that will evaluate probabilities and support making fine-grained process distinctions. These capabilities will provide a strong technological foundation for evidence-based, continuous process improvement.Project technologies will be evaluated first using synthetic event streams generated by process model driven simulations, then by human simulations with nursing students using patient mannequins, and finally with medical professionals in simulated clinical settings. Processes to be examined include blood transfusion, chemotherapy, medication administration, and patient identification verification. Early community success in applying medical checklists and recent experimental results with proactive process guidance in a hospital emergency department are positive indications that the approaches proposed here have an excellent chance of gaining acceptance and improving medical outcomes. Moreover, the technologies developed here, although evaluated specifically for health care, will also apply to human-intensive systems increasingly employed in a wide range of domains in society. The validated medical processes and the proposed prototype will be an effective framework for educating medical professionals in current best practices. This project will also educate computer science and engineering students about the challenges posed by the medical domain, encouraging a new generation of specialists in this emerging interdisciplinary field. Its societal appeal will also help engage bright, energetic minority and female students thereby helping broaden the participation of underrepresented groups in STEM generally and in computer science and engineering particularly.