Ferrara Maria, Gentili Elisabetta, Belvederi Murri Martino, Zese Riccardo, Alberti Marco, Franchini Giorgia, Domenicano Ilaria, Folesani Federica, Sorio Cristina, Benini Lorenzo, Carozza Paola, Little Julian, Grassi Luigi
Institute of Psychiatry, Department of Neuroscience and Rehabilitation, University of Ferrara, Ferrara, Italy.
Integrated Department of Mental Health and Pathological Addictions, Ferrara Local Health Trust, Ferrara, Italy.
JMIR Med Inform. 2023 Aug 9;11:e45523. doi: 10.2196/45523.
The immediate use of data exported from electronic health records (EHRs) for research is often limited by the necessity to transform data elements into an actual data set.
This paper describes the methodology for establishing a data set that originated from an EHR registry that included clinical, health service, and sociodemographic information.
The Extract, Transform, Load process was applied to raw data collected at the Integrated Department of Mental Health and Pathological Addictions in Ferrara, Italy, from 1925 to February 18, 2021, to build the new, anonymized Ferrara-Psychiatry (FEPSY) database. Information collected before the first EHR was implemented (ie, in 1991) was excluded. An unsupervised cluster analysis was performed to identify patient subgroups to support the proof of concept.
The FEPSY database included 3,861,432 records on 46,222 patients. Since 1991, each year, a median of 1404 (IQR 1117.5-1757.7) patients had newly accessed care, and a median of 7300 (IQR 6109.5-9397.5) patients were actively receiving care. Among 38,022 patients with a mental disorder, 2 clusters were identified; the first predominantly included male patients who were aged 25 to 34 years at first presentation and were living with their parents, and the second predominantly included female patients who were aged 35 to 44 years and were living with their own families.
The process for building the FEPSY database proved to be robust and replicable with similar health care data, even when they were not originally conceived for research purposes. The FEPSY database will enable future in-depth analyses regarding the epidemiology and social determinants of mental disorders, access to mental health care, and resource utilization.
从电子健康记录（EHR）导出的数据用于研究时，往往因需要将数据元素转换为实际数据集而受到限制。
本文描述了建立一个源自EHR注册库的数据集的方法，该注册库包含临床、卫生服务和社会人口统计学信息。
采用提取、转换、加载过程，对意大利费拉拉心理健康与病理性成瘾综合科于1925年至2021年2月18日收集的原始数据进行处理，以构建新的匿名化费拉拉精神病学（FEPSY）数据库。排除在第一个EHR实施之前（即1991年）收集的信息。进行无监督聚类分析以识别患者亚组，以支持概念验证。
FEPSY数据库包含46222名患者的3861432条记录。自1991年以来，每年新接受治疗的患者中位数为1404名（四分位间距1117.5 - 1757.7），正在接受治疗的患者中位数为7300名（四分位间距6109.5 - 9397.5）。在38022名患有精神障碍的患者中，识别出2个聚类；第一个聚类主要包括首次就诊时年龄在25至34岁且与父母同住的男性患者，第二个聚类主要包括年龄在35至44岁且与自己家人同住的女性患者。
构建FEPSY数据库的过程证明是稳健的，并且可以用类似的医疗保健数据进行复制，即使这些数据最初并非为研究目的而设计。FEPSY数据库将有助于未来对精神障碍的流行病学和社会决定因素、获得心理健康护理的情况以及资源利用进行深入分析。
