Mazumdar Soumya, Konings Paul, Hewett Michael, Bagheri Nasser, McRae Ian, Del Fante Peter
Australian Primary Healthcare Research Institute, Australian National University, Australian Capital Territory.
Aust N Z J Public Health. 2014 Dec;38(6):548-52. doi: 10.1111/1753-6405.12262. Epub 2014 Oct 12.
General practitioner (GP) practices in Australia are increasingly storing patient information in electronic databases. These practice databases can be accessed by clinical audit software to generate reports that inform clinical or population health decision making and public health surveillance. Many audit software applications also have the capacity to generate de-identified patient unit record data. However, the de-identified nature of the extracted data means that these records often lack geographic information. Without spatial references, it is impossible to build maps reflecting the spatial distribution of patients with particular conditions and needs. Links to socioeconomic, demographic, environmental or other geographically based information are also not possible. In some cases, relatively coarse geographies such as postcode are available, but these are of limited use and researchers cannot undertake precision spatial analyses such as calculating travel times.
We describe a method that allows researchers to implement meaningful mapping and spatial epidemiological analyses of practice level patient data while preserving privacy.
This solution has been piloted in a diabetes risk research project in the patient population of a practice in Adelaide.
The method offers researchers a powerful means of analysing geographic clinic data in a privacy-protected manner.
澳大利亚的全科医生诊所越来越多地将患者信息存储在电子数据库中。临床审计软件可以访问这些诊所数据库，以生成报告，为临床或人群健康决策以及公共卫生监测提供信息。许多审计软件应用程序还能够生成去识别化的患者个体记录数据。然而，提取数据的去识别化性质意味着这些记录通常缺乏地理信息。没有空间参考，就无法绘制反映特定病情和需求患者空间分布的地图。与社会经济、人口统计、环境或其他基于地理的信息建立联系也不可能。在某些情况下，可以获得相对粗略的地理信息，如邮政编码，但这些用途有限，研究人员无法进行精确的空间分析，如计算出行时间。
我们描述了一种方法，该方法允许研究人员在保护隐私的同时，对诊所层面的患者数据进行有意义的绘图和空间流行病学分析。
该解决方案已在阿德莱德一家诊所的患者群体中进行了糖尿病风险研究项目的试点。
该方法为研究人员提供了一种以隐私保护方式分析地理临床数据的强大手段。
