Automated Clinical Epidemiology Studies (ACES) platform for complex epidemiology study designs and diverse databases

收集的医疗数据来自医疗机构的电子医疗记录、医疗保险记录和管理记录。这些数据库越来越多地用于研究。它们被用来产生关于疾病原因的想法，评估保健服务政策，临床审计和疾病监测以及寻找药物的不良影响。除了这些益处之外，常规数据库还有助于了解在随机对照试验（RCT）中观察到的药物效应是否也在真实的世界环境中观察到，特别是在特征与RCT研究中不同的人群中。尽管常规收集的医疗保健数据库的好处，但在利用它们进行研究方面存在许多挑战。一些挑战是由于难以以允许复杂研究设计的方式提取数据。数据提取在时间、成本、精力和专业知识方面是昂贵且繁琐的。部分原因是数据库规模庞大，结构各异，数据范围广泛。提取中的一些困难是由于探索这些数据库所需的研究设计的复杂性，因为数据不是为研究目的收集的，因此具有许多固有的偏倚。此外，任何提取都需要临床、流行病学和技术专门知识来查询这些数据库。这些问题可能导致许多人为错误，并可能导致数据不准确和可重复。我们与计算机科学家，临床医生和方法学家合作，开发了一个自动临床流行病学研究（ACES）平台，用于从一个来自一般实践的医疗记录数据库（健康改善网络数据库）中提取准确和可重复的流行病学研究数据。该平台可以在几分钟到几小时内完成数据提取，而以前手动完成需要几周到几个月的时间。在过去的12个月里，该平台已经启动了许多研究。现在我们已经开发了这样一个平台，在这个研究项目中，我们的目标是将这个平台扩展到：1）复杂的流行病学研究设计和2）具有不同结构和编码系统的数据库。对于复杂的研究设计，我们将开发和评估一个用于链接母亲和婴儿数据库的平台，另一个用于药物作用研究（药物流行病学研究）。药物流行病学研究有助于了解药物的有益和有害影响。在开发药物流行病学研究自动化平台的过程中，我们还将审查并在必要时开发更准确地估计药物效果的方法。我们一直在与其他国家的机构进行对话，将我们的ACES平台扩展到他们的数据库，这些数据库具有不同的结构和编码系统，并评估这是否有效。如果我们做到了这一点，那么我们就可以同时研究不同国家的多个数据库。最后，我们还将评估拥有这种自动数据提取系统的风险。例如，可以在一天内进行许多研究，并且只报告显示积极结果的研究。我们将通过与相关利益攸关方讨论来确定这些问题，并就如何最好地避免这种情况提出一套建议。

Routinely collected health care data are derived from electronic medical records, health insurance records and administration records in healthcare organisations. These databases are increasingly being used for research. They have been used to generate ideas about causes of illness, evaluation of health service policies, clinical audits and surveillance of diseases and looking for adverse effects of medications. Beyond these benefits routine databases are also useful to find out if the effects of drugs that are observed in Randomised Controlled Trials (RCT) are also observed in real world setting, especially in groups of people whose characteristics are different to those in the RCT studies. Despite the benefits of routinely collected healthcare databases there are numerous challenges in utilising them for research. Some of the challenges are due to difficulty in extracting data in a way that allows complex study designs. Data extraction is expensive and tedious in terms of time, cost, effort and expertise. This is partly because the databases are huge in size, vary in structure and have wide range of data. Some of the difficulty in extraction is due to complexity of study designs needed to probe these databases, because the data was not collected for research purposes and therefore have numerous inherent biases. Furthermore any extraction needs clinical, epidemiological and technical expertise to interrogate these databases. These issues can lead to many human induced errors and can result in data that are not accurate and reproducible.Working with computer scientists, clinicians and methodologists we have developed an Automated Clinical Epidemiology Studies (ACES) platform for extracting data that are accurate and reproducible for epidemiological studies in one database of medical records from general practices (The Health Improvement Network database). The platform enables to complete data extraction within minutes to hours which previously took weeks to months when done manually. The platform has already enabled numerous studies in the last 12 months. Now that we have developed such a platform, in this research programme, we aim to extend this platform to; 1) complex epidemiological study designs and 2) databases that have different structure and coding systems. For complex study designs we will develop and evaluate one platform for linked mothers and babies databases and another for studies of the effects of drugs (pharmaco-epidemiological studies). Pharmaco-epidemiological studies help with understanding the beneficial and harmful effects of medications. In the process of developing the automated platform for pharmaco-epidemiological studies we will also review and where necessary develop methodologies to estimate the effects of medications more accurately. We have been in conversation with institutions in other countries to extend our ACES platform to their databases, which have different structure and coding systems, and evaluate if this works. If we achieve this then we could research multiple databases across different countries for one question simultaneously. Finally we will also assess the risks of having such an automated data extraction system. For example, it is possible to conduct numerous studies within a day and only report ones that are showing positive results. We will identify such issues by discussing with relevant stakeholders and produce a set of recommendations on how best to avoid such situations.