Department of Informatics, University of Sussex, Brighton, UK.
J Am Med Inform Assoc. 2014 Mar-Apr;21(2):292-8. doi: 10.1136/amiajnl-2013-001847. Epub 2013 Nov 22.
UK primary care databases, which contain diagnostic, demographic and prescribing information for millions of patients geographically representative of the UK, represent a significant resource for health services and clinical research. They can be used to identify patients with a specified disease or condition (phenotyping) and to investigate patterns of diagnosis and symptoms. Currently, extracting such information manually is time-consuming and requires considerable expertise. In order to exploit more fully the potential of these large and complex databases, our interdisciplinary team developed generic methods allowing access to different types of user.
Using the Clinical Practice Research Datalink database, we have developed an online user-focused system (TrialViz), which enables users interactively to select suitable medical general practices based on two criteria: suitability of the patient base for the intended study (phenotyping) and measures of data quality.
An end-to-end system, underpinned by an innovative search algorithm, allows the user to extract information in near real-time via an intuitive query interface and to explore this information using interactive visualization tools. A usability evaluation of this system produced positive results.
We present the challenges and results in the development of TrialViz and our plans for its extension for wider applications of clinical research.
Our fast search algorithms and simple query algorithms represent a significant advance for users of clinical research databases.
英国初级保健数据库包含了数百万名患者的诊断、人口统计学和处方信息，这些信息在地理位置上代表了英国，是医疗服务和临床研究的重要资源。它们可用于识别特定疾病或病症的患者（表型分析），并调查诊断和症状模式。目前，手动提取此类信息既耗时又需要大量专业知识。为了更充分地利用这些大型和复杂数据库的潜力，我们的跨学科团队开发了通用方法，允许不同类型的用户访问。
使用临床实践研究数据链接数据库，我们开发了一个面向用户的在线系统（TrialViz），该系统允许用户根据两个标准交互式地选择合适的医疗常规实践：患者群体适合预期研究（表型分析）和数据质量措施。
一个端到端系统，由创新的搜索算法提供支持，允许用户通过直观的查询界面实时提取信息，并使用交互式可视化工具探索这些信息。对该系统的可用性评估产生了积极的结果。
我们介绍了在开发 TrialViz 方面的挑战和结果，以及我们计划将其扩展到更广泛的临床研究应用的计划。
我们的快速搜索算法和简单查询算法代表了临床研究数据库用户的重大进步。
