A knowledge-based message tailoring system

基于知识的消息定制系统

基本信息

批准号：
9765395
负责人：
Zachary Landis-Lewis
金额：
$ 17.78万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-10 至 2020-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9765395
关键词：
Address Agreement Award Behavior Caring Characteristics Clinical Clinical effectiveness Cognition Cognitive Collaborations Communication Communities Computer software Custom Data Development Engineering Evaluation Evidence based practice Feedback Future Goals Health Health Communication Health Professional Health system Healthcare Individual Intelligence Knowledge Laboratories Lead Mentored Research Scientist Development Award Methods Michigan Modeling Ontology Outcome Participant Patient Care Patient-Focused Outcomes Pattern Perception Performance Problem Solving Process Professional Role Psychological Theory Public Health Quality of Care Recording of previous events Research Specific qualifier value System Taxonomy Technology To specify Universities Variant Work antimicrobial base behavior change care outcomes clinical effect design evidence base experience health care quality health care service organization improved information display knowledge base ontology development preference prototype system architecture tailored messaging theories training opportunity

项目摘要

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.

抽象的医疗机构拥有丰富的有关护理质量和结果的数据，但缺乏通用的策略将他们的数据用于提高性能。向医疗保健提供临床表现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数十年试验的混合效应。心理学理论在临床设计中没有得到充分利用绩效反馈，但它提供了强大的解释机制来改善认知处理和反馈信息的影响。基于知识的消息剪裁系统可以用理论进行推理知识和临床表现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表现反馈的基于知识的消息定制系统。拟议的工作将在抗菌药物管理的健康领域进行，这是一个明确的全球范围常规使用向临床医生提供的反馈来促进行为改变的重要性。具体目标拟议的项目是 1) 开发基于理论的性能消息定制的知识库反馈，2) 创建抗菌药物管理信息定制系统，以及 3) 评估与医疗保健专业人员一起的原型消息定制系统。通过实现这些目标，候选人将获得研究经验并增强他对开发和评估的了解临床环境中基于知识的系统。 NLM K01 奖项创造的培训机会将使候选人能够增强本体开发、知识工程和认知研究，并在密歇根大学的研究界开展合作。这该奖项最终将帮助候选人实现其改变现有知识的长期目标将消息剪裁成可计算的形式，以便进行有效性研究临床表现反馈和其他形式的临床建议。