STTR Phase II: Self-Health Management Informatics Platform: Improving Patient Engagement in Care Delivery

这个小企业技术转让（STTR）第二阶段项目的更广泛的影响/商业潜力是一个以患者为中心的参与过程，为实现提高护理质量和结果、控制医疗成本和利用率以及提高患者满意度的三重目标提供了一个可扩展的方法。目前，全国医疗保健费用估计为3.2万亿美元。在患者能够参与到自己的健康中来之前，通过药物进行药理干预的进步将无法实现最佳的护理结果，医疗保健成本和利用率将保持在高位。让患者参与与动机指标相一致的自我管理活动，不仅可以改善患者的体验和满意度，还有助于收集大数据，将动机与行为治疗联系起来，从而对改善人口健康和提高个性化精度至关重要。 这个小企业技术转移（STTR）第二阶段项目是一个以患者为中心的参与过程MISA（测量-集成-共享-行动）。虽然已建立良好的行为模型，如计划行为理论（TPB）和信息动机行为技能（IMB）已被报道显示临床疗效，本研究的新奇是应用结构方程模型来开发定量行为模型。定量行为建模是重要的，因为它允许在拟合优度和统计功效方面对模型进行严格评估。此外，这种模型为获得专利的SIPPA分析引擎提供了预测与患者用户的动机指标一致的软件服务的基础，以便有效地使他们参与慢性病的自我健康管理，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持和更广泛的影响审查标准。

The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase II project is a patient-centric engagement process that offers the best chance for enabling a scalable approach towards achieving the triple aim of improving care quality and outcomes, controlling health care costs and utilization, and increasing patient satisfaction. Currently the national healthcare cost is estimated at $3.2 trillion. Until patients could be engaged to take ownership of their health, advancement in pharmacological interventions through medications will not be able to achieve optimal care outcomes, and the healthcare cost and utilization will remain high. Engaging patients in self-management activities that are in alignment with the motivation indicators not only improves patient experience and satisfaction, but contributes to collecting big data for linking motivation to behavioral therapy --- resulting in an effect essential to improving population health and advancing personalized precision. This Small Business Technology Transfer (STTR) Phase II project is a patient-centric engagement process MISA (Measure-Integrate-Share-Act). While well-established behavior models such as the Theory of Planned Behavior (TPB) and Information-Motivation-Behavioral Skill (IMB) have been reported to show clinical efficacy, the novelty of this research is the application of Structure Equation Modeling to develop a quantitative behavior model. Quantitative behavior modeling is significant because it allows a rigorous assessment of the model in terms of goodness of fit and statistical power. Furthermore, such model provides the patented SIPPA analytic engine a basis to predict the software services that are in alignment with the motivation indicators of a patient users in order to effectively engage them in self-health management of chronic conditions, and to promote behavioral change for healthy lifestyle.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.