Empirical Studies of the Development and Diffusion of Medical Technologies

描述：技术变革是医疗保健市场的一个中心特征，在过去的几十年里，它彻底改变了许多疾病的治疗方法。尽管突破性的科学进步是医疗创新步伐的重要驱动力，但市场激励--如专利制度提供的激励--往往是潜在技术成功地从实验室过渡到市场的关键决定因素。这个项目的主要贡献是开发新的数据(目标1和3)和新的实证方法(目标2和4)，以调查专利如何塑造医学创新的发展和传播。具体地说，我们应用这些新数据和新方法来估计两个相关参数：专利对新技术发展的激励程度，以及现有技术专利对后续创新的阻碍程度。专利在吸引研究投资方面越有效，申请更长或更广泛专利的理由就越充分。另一方面，专利在阻碍后续创新方面的成本越大，这种情况就越弱。评估专利的这些成本和收益是优化政策设计的关键投入。为了估计第一个参数-更强大的专利保护前景如何影响研究投资-我们基于以下想法开发了一种新的经验方法(目标2)：因为制药公司在开始临床试验之前提交专利，较短的临床试验授予公司更长的有效专利期限。因此，如果更长的专利期鼓励更多的研究投资，我们应该看到更高水平的研究投资用于需要较短临床试验的患者组的治疗。我们将这种方法应用于一个新开发的数据集，该数据集衡量了癌症患者治疗方面的研究投资(在目标1中开发)。为了估计第二个参数--现有技术的专利如何影响后续研究投资--我们开发了一种新的经验方法(目标4)。我们方法背后的关键思想是利用两个事实：第一，专利申请是准随机分配给美国专利商标局的专利审查员的；第二，专利审查员对任何给定申请授予专利的可能性不同。我们将这种方法应用于一个新开发的数据集，该数据集衡量了与人类基因组相关的研究投资(在目标3中开发)，并量化了人类基因专利阻碍后续医学创新的程度。新的经验方法(目标2和目标4)将应用于两类特定的医疗技术--癌症治疗和基于基因的诊断和治疗--但每种方法都可以应用于其他情况。此外，新的数据构建方法(目标1和目标3)--基于文本的临床试验登记列表匹配，以及研究投资与相关患者群体的网状ICD匹配--也可以应用于其他情况。

DESCRIPTION: Technological change is a central feature of health care markets and over the past few decades has revolutionized the treatment of many medical conditions. Although breakthrough scientific advances are an important driver of the pace of medical innovation, market incentives - such as those provided by the patent system - are often a critical determinant of which potential technologies successfully make the transition 'from the lab to the market.' The key contribution of this project is to develop new data (Aims 1 and 3) and new empirical methods (Aims 2 and 4) to investigate how patents shape the development and diffusion of medical innovations. Specifically, we apply these new data and new methods to estimate two relevant parameters: the extent to which patents provide incentives for the development of new technologies, and the extent to which patents on existing technologies hinder subsequent innovation. The more effective patents are in inducing research investments, the stronger the case for longer or broader patents. On the other hand, the larger the costs of patents in terms of hindering subsequent innovation, the weaker is this case. Evaluating these costs and benefits of patents is a key input into optimal policy design. To estimate the first parameter - how the prospect of stronger patent protection affects research investments - we develop a new empirical method (Aim 2) based on the following idea: because pharmaceutical firms file patents prior to starting clinical trials, shorter clinical trials grant firms longer efective patent terms. Hence, if longer patent terms encourage more research investments, we should see higher levels of research investments on treatments for patient groups that require shorter clinical trials. We apply this method to a newly developed data set measuring research investments on treatments for cancer patients (developed in Aim 1). To estimate the second parameter - how patents on existing technologies affect subsequent research investments - we develop a new empirical method (Aim 4). The key idea behind our approach is to take advantage of two facts: first, patent applications are quasi- randomly assigned to patent examiners at the US Patent and Trademark Office; and second, patent examiners differ in their likelihood of awarding a patent to any given application. We apply this method to a newly- developed data set measuring research investments related to the human genome (developed in Aim 3) and quantify the extent to which patents on human genes have hindered subsequent medical innovation. The new empirical methods (Aims 2 and 4) will be applied to two particular classes of medical technologies - cancer treatments and gene-based diagnostics and treatments - but each method can be applied in other contexts. In addition, the new data construction methods (Aims 1 and 3) - text-based matching with clinical trial enrollment lists, and MeSH-ICD matching of research investments to the relevant groups of patients - can also be applied in other contexts.