A knowledge-based message tailoring system

摘要 医疗保健组织拥有丰富的关于护理质量和结果的数据，但缺乏可推广的策略， 将他们的数据用于工作以提高性能。向医疗保健提供临床性能反馈 专业人士是一种广泛使用的绩效改进策略，但有关其使用的证据显示了一种模式 在几十年的试验中，心理学理论在临床设计中未得到充分利用 绩效反馈，但它提供了强大的解释机制，以改善认知处理， 反馈信息的影响。基于知识的消息裁剪系统可以用理论推理 知识和临床性能数据来预测最佳反馈消息格式和内容，同时 提供消息设计原理的解释。本提案的研究目标是开发和 评估用于临床性能反馈的基于知识的消息定制系统。拟议工作 将在抗菌药物管理的卫生领域进行，这是一个明确的全球领域， 向临床医生提供的反馈通常用于促进行为改变的重要性。的具体目标 建议的项目是：1）开发一个知识库，用于基于理论的性能信息裁剪 反馈，2）创建抗菌药物管理的信息定制系统，以及3）评估 原型消息定制系统与医疗保健专业人员。通过实现这些目标，候选人 将获得研究经验，并提高他对发展和评估的知识， 临床环境中的知识系统。NLM K01奖项创造的培训机会将 使候选人能够提高他在本体开发，知识工程， 认知研究，并在密歇根大学的研究社区发展合作。的 奖项将最终帮助候选人实现他的长期目标，即转化现有的知识， 信息裁剪成可计算的形式，以进行有关有效性的研究， 临床表现反馈和其他形式的临床建议。

Abstract Healthcare organizations are rich in data about care quality and outcomes, but lack generalizable strategies for putting their data to work to improve performance. Giving clinical performance feedback to healthcare professionals is a widely used performance improvement strategy, but evidence about its use shows a pattern of mixed effects over decades of trials. Psychological theory is underutilized in the design of clinical performance feedback, yet it offers robust explanatory mechanisms to improve the cognitive processing and impact of feedback messages. A knowledge-based message tailoring system could reason with theoretical knowledge and clinical performance data to predict optimal feedback message formats and content, while offering explanations of the message design rationale. The research goal of this proposal is to develop and evaluate a knowledge-based message tailoring system for clinical performance feedback. The proposed work will be carried out in the health domain of antimicrobial stewardship, a well-defined domain of global importance in which feedback to clinicians is routinely used to promote behavior change. The specific aims of the proposed project are 1) Develop a knowledge base for theory-based message tailoring of performance feedback, 2) Create a message tailoring system for antimicrobial stewardship, and 3) Evaluate the function of the prototype message tailoring system with healthcare professionals. By achieving these aims, the candidate will gain research experience and enhance his knowledge about the development and evaluation of knowledge-based systems in clinical settings. The training opportunities created by the NLM K01 award will enable the candidate to enhance his knowledge in ontology development, knowledge engineering, and cognitive studies, and to develop collaborations in the research community at the University of Michigan. The award will ultimately help the candidate to achieve his long-term goal of transforming existing knowledge about message tailoring into computable forms for the purpose of conducting research about the effectiveness of clinical performance feedback and other forms of clinical advice.