STSS - RARE DISEASES INFORMATICS PROGRAM (RDIP) SUPPORT

目前，在美国大约有7,000-10,000种已知的罕见疾病影响着人们，但只有几百种(不到5%)有治疗方法，更没有治愈的。美国法律(1983年的《孤儿药物法》(ODA)、2002年的《罕见病法》(RDA))将一种罕见疾病定义为在美国影响少于20万人的疾病或状况。大多数罕见疾病影响的患者比这少得多，大多数罕见疾病只影响几百到数千名患者。然而，总的来说，罕见疾病并不罕见，据估计，美国总共有多达2500万至3000万患者受到影响，这使得罕见疾病成为一个重大的公共卫生考虑因素。 信息学在罕见病研究支持和患者护理中发挥着重要作用。鉴于不同的罕见疾病数量众多，每一种疾病只影响一小部分患者，评估广泛的罕见疾病研究和医疗保健图景是具有挑战性的。为了更好地了解罕见疾病患者正在进行的研究、治疗和医疗保健利用以及诊断过程，NCATS ORDR正在寻求创建一个针对罕见疾病的分析平台，该平台可以使从不同来源(如医学文献、NIH数据库和医疗保健系统数据)收集、集成和分析数据的方法更加高效和协调。这可能包括建立信息学平台基础设施，开发机器学习工具和方法来识别、收集、整合和分析此类数据，并将研究结果传播给罕见疾病研究和临床社区，以帮助确定和告知罕见疾病的研究重点。最初，建立信息学基础设施框架和执行较小的试点项目将是优先事项，更大的目标是在这一经验的基础上，随着时间的推移在更广泛的范围内更好地了解罕见疾病研究和医疗保健。

Currently, there are about 7,000-10,000 known rare diseases that affect people in the US, yet only a few hundred (less than 5%) have a treatment and even fewer a cure. A rare disease is defined in US law (Orphan Drug Act (ODA) of 1983, Rare Diseases Act (RDA) of 2002) as a disease or condition that affects fewer than 200,000 people in the US. Most rare diseases affect far fewer patients than this, with most rare diseases affecting only a few hundreds to thousands of patients. Collectively, however, rare diseases are not rare at all, and in total are estimated to affect as many as 25-30 million patients in the US, making rare diseases a large public health consideration. Informatics plays an important role in rare disease research support and patient care. Given the large number of different rare diseases, each of which affects only a small number of patients, assessing the broad rare diseases research and healthcare landscape is challenging. In order to better understand the ongoing research, treatment and healthcare utilization, and diagnostic journey of patients with rare diseases, NCATS ORDR is seeking to create a rare disease-specific analytics platform that could enable more efficient and coordinated approaches to data collection, integration and analysis from diverse sources, such as the medical literature, NIH databases and healthcare systems data. This could include establishing an informatics platform infrastructure, development of machine-learning tools and methods to identify, collect, integrate and analyze such data, and dissemination of the findings to the rare diseases research and clinical communities to help identify and inform rare diseases research priorities. Initially, building an informatics infrastructure framework and performing smaller pilot projects would be prioritized, with the larger goal of building on this experience to gain greater understanding of rare diseases research and healthcare over a broader scope over time.