EAGER: Mutual Understanding Theory of Health Care Quality Frontiers

这一早期探索性研究补助金(AGER)奖为卫生保健质量科学的发展提供了资金，该科学可以为质量决定因素提供解释性理论，为卫生服务研究提供一个背景框架以了解什么是有效的，生成关键投入/产出相互作用的数学模型，并生成提供机构指导而不是成绩单的衡量标准。与任何科学一样，结构包括一种解释可观察现象、公理或第一原理的理论，这些原理产生可检验的假设和模型、相关本体论的识别和方法论，最重要的是有用。除了潜在地将医疗质量从一门艺术转变为一门科学的相当实质性的影响外，使用统一组件建模来开发我们的医疗质量相互理解理论的主要组件的数学模型，将影响数据挖掘技术、预测建模、随机和确定性系统的表征、贝叶斯学习和数据用户界面方法。如果成功，这项研究的结果将影响医疗质量和成本，改善患者结果，并为更广泛的服务科学领域做出贡献。服务业对美国经济的贡献越来越大，这引发了人们对支持服务创新的组织结构、如何衡量服务生产率、客户满意度之间的关系(包括营销和客观结果衡量标准)以及预测服务需求、服务能力和清点员工技能的适当方法的兴趣。这项工作将对这些领域中的每一个作出贡献。

This EArly Grant for Exploratory Research (EAGER) award provides funding for the development of a science of health care quality that can provide an explanatory theory of the determinants of quality, provide a contextualized framework for health service research to understand what works, generate a mathematical model of critical inputs/output interactions, and generate metrics that provide institutional guidance rather than report cards. As with any science, the structure includes a theory that explains observable phenomena, Axioms, or First Principles that generate testable hypotheses and models, identification of relevant ontology, and methodologies, and above all, be useful. Beside the rather substantial impact of potentially transforming health care quality from an art to a science, the use of Unified Component Modeling to develop mathematical models of the principle components of our Mutual Understanding Theory of Health Care Quality, will impact data mining techniques, predictive modeling, characterization of stochastic and deterministic systems, Bayesian learning and data-user interface methodologies.If successful, the results of this research will impact health care quality and cost, improve patient outcomes, and contribute to the broader field of service science. The increasing contribution of service industries to the American economy has opened interest in organizational structures that support service innovation, how to measure service productivity, the relationship between client satisfaction, including, marketing and objective outcome measures and appropriate methodologies for predicting service demand, service capacity, and inventorying employee skill-sets. This work will contribute to each of these areas.