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

常规收集的医疗保健数据来自医疗机构的电子病历、健康保险记录和管理记录。这些数据库越来越多地被用于研究。它们被用来产生关于疾病原因的想法、评价保健服务政策、临床审计和疾病监测以及寻找药物的不良影响。除了这些好处之外，常规数据库还有助于发现在随机对照试验（RCT）中观察到的药物效果是否也在现实环境中观察到，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有很多好处，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一种允许复杂研究设计的方式提取数据。从时间、成本、精力和专业知识的角度来看，数据提取既昂贵又乏味。部分原因是数据库规模庞大，结构各异，数据范围广泛。提取的一些困难是由于调查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，因此有许多固有的偏差。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不可重复。与计算机科学家、临床医生和方法学家合作，我们开发了一个自动临床流行病学研究（ACES）平台，用于从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，而以前手工提取需要几周到几个月的时间。在过去的12个月里，该平台已经实现了许多研究。既然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)流行病学研究设计复杂；2)数据库结构和编码系统不同。对于复杂的研究设计，我们将开发和评估一个用于关联母亲和婴儿数据库的平台，以及另一个用于药物效应研究的平台（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作用。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，以将我们的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为一个问题研究不同国家的多个数据库。最后，我们还将评估拥有这样一个自动数据提取系统的风险。例如，可以在一天内进行大量研究，只报告那些显示出积极结果的研究。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此类情况提出一套建议。

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.