SGER: Econometric Models for Impacts of Technology in Healthcare

医疗保健是美国经济的主要组成部分，在这一领域的新技术的研究和开发工作是巨大的。不幸的是，很少有工具可以预测新产品的经济影响，包括(1)新技术准备部署时的市场性质和(2)新技术在整个医疗保健系统中的系统影响。在本项目中，我们开发了新的计量经济模型，用于评估技术（特别是无线传感技术）在医疗保健领域的影响。我们假设，整个医疗保健部门可以被建模为一组松散联合的参与者（代理人），这些参与者（代理人）在接受和使用特定技术的程度方面达到平衡点。例如，保险公司对事物的看法显然与医生不同，而技术供应商、患者和监管机构对新技术的风险和机遇都有不同的看法，但最终，基于价值体系的隐性整合，实现了平衡。我们的目标是开发计量经济模型，将这种平衡描述为新技术提供的功能。这项研究将产生新的计量经济模型，以确定(1)新技术，特别是无线传感技术的潜在经济价值，以及(2)可能加速、延迟或限制新技术在实际使用中的接受程度的关键经济因素。我们将在两个案例研究中探讨建模问题、挑战和机遇，一个涉及无线运动捕捉设备评估帕金森患者的生理状态，另一个涉及评估持续血糖监测（CGM）在1型糖尿病患者中的作用。

Healthcare is a major component of the U.S. economy, and tremendous research and development efforts are directed toward new technologies in this arena. Unfortunately, few tools exist for predicting the economic impact of new products, including (1) the nature of the marketplace at the time new technologies are ready to be deployed and (2) the systemic implications of new technology within the healthcare system overall. In this project, we develop new econometric models for assessing impacts of technology, particularly wireless sensing technology, in healthcare. We hypothesize that the health care sector overall can be modeled as a loosely confederated set of participants (agents) that arrive at equilibrium points in terms of the acceptance and degree of use of particular technologies. For example, insurers clearly see things differently than doctors, while technology suppliers, patients, and regulatory bodies all have different perspectives on risks and opportunities for new technology, yet, in the end, a balance is achieved based on the implicit integration of value systems. Our goal is to develop econometric models that characterize this balance as a function of capabilities provided by new technology.This research will produce new econometric models for determining (1) the potential economic value of new technologies, especially wireless sensing technologies, and (2) the critical economic factors that might either expedite, delay or limit the acceptance of new technologies in actual use. We will explore modeling issues, challenges, and opportunities in two case studies, one relating to wireless motion capture devices assessing the physiological state of Parkinson's patients, and the other for assessing the role of Continuous Glucose Monitoring (CGM) in Type 1 diabetics.