With the increasing availability of rich, longitudinal, real-world clinical data recorded in electronic health records (EHRs) for millions of patients, there is a growing interest in leveraging these records to improve the understanding of human health and disease and translate these insights into clinical applications. However, there is also a need to consider the limitations of these data due to various biases and to understand the impact of missing information. Recognizing and addressing these limitations can inform the design and interpretation of EHR-based informatics studies that avoid confusing or incorrect conclusions, particularly when applied to population or precision medicine. Here we discuss key considerations in the design, implementation and interpretation of EHR-based informatics studies, drawing from examples in the literature across hypothesis generation, hypothesis testing and machine learning applications. We outline the growing opportunities for EHR-based informatics studies, including association studies and predictive modeling, enabled by evolving AI capabilities—while addressing limitations and potential pitfalls to avoid.

随着电子健康记录 (EHR) 中记录的数百万患者的丰富、纵向、真实临床数据的可用性不断增加，人们越来越有兴趣利用这些记录来增进对人类健康和疾病的了解，并将这些见解转化为临床应用。然而，还需要考虑由于各种偏差而导致这些数据的局限性，并了解缺失信息的影响。认识并解决这些局限性可以为基于 EHR 的信息学研究的设计和解释提供信息，从而避免令人困惑或不正确的结论，特别是在应用于人口或精准医学时。在这里，我们讨论了基于 EHR 的信息学研究的设计、实施和解释的关键考虑因素，并借鉴了假设生成、假设检验和机器学习应用领域文献中的示例。我们概述了基于 EHR 的信息学研究不断增长的机会，包括通过不断发展的人工智能功能实现的关联研究和预测建模，同时解决需要避免的限制和潜在陷阱。