PDF(Pubmed)
Abstract：
UNASSIGNED: To achieve scientific goals, researchers often require integration of data from a primary electronic health record (EHR) system and one or more ancillary EHR systems used during the same patient care encounter. Although studies have demonstrated approaches for linking patient identity records across different EHR systems, little is known about linking patient encounter records across primary and ancillary EHR systems.
UNASSIGNED: We compared a patients-first approach versus an encounters-first approach for linking patient encounter records across multiple EHR systems.
UNASSIGNED: We conducted a retrospective observational study of 348,904 patients with 533,283 encounters from 2010 to 2020 across our institution\'s primary EHR system and an ancillary EHR system used in perioperative settings. For the patients-first approach and the encounters-first approach, we measured the number of patient and encounter links created as well as runtime.
UNASSIGNED: While the patients-first approach linked 43% of patients and 49% of encounters, the encounters-first approach linked 98% of patients and 100% of encounters. The encounters-first approach was 20 times faster than the patients-first approach for linking patients and 33% slower for linking encounters.
UNASSIGNED: Findings suggest that common patient and encounter identifiers shared among EHR systems via automated interfaces may be clinically useful but not \"research-ready\" and thus require an encounters-first linkage approach to enable secondary use for scientific purposes. Based on our search, this study is among the first to demonstrate approaches for linking patient encounters across multiple EHR systems. Enterprise data warehouse for research efforts elsewhere may benefit from an encounters-first approach.
UNASSIGNED: We compared a patients-first approach versus an encounters-first approach for linking patient encounter records across multiple EHR systems.
UNASSIGNED: We conducted a retrospective observational study of 348,904 patients with 533,283 encounters from 2010 to 2020 across our institution\'s primary EHR system and an ancillary EHR system used in perioperative settings. For the patients-first approach and the encounters-first approach, we measured the number of patient and encounter links created as well as runtime.
UNASSIGNED: While the patients-first approach linked 43% of patients and 49% of encounters, the encounters-first approach linked 98% of patients and 100% of encounters. The encounters-first approach was 20 times faster than the patients-first approach for linking patients and 33% slower for linking encounters.
UNASSIGNED: Findings suggest that common patient and encounter identifiers shared among EHR systems via automated interfaces may be clinically useful but not \"research-ready\" and thus require an encounters-first linkage approach to enable secondary use for scientific purposes. Based on our search, this study is among the first to demonstrate approaches for linking patient encounters across multiple EHR systems. Enterprise data warehouse for research efforts elsewhere may benefit from an encounters-first approach.
摘要：
■为了实现科学目标，研究人员通常需要整合主要电子健康记录（EHR）系统和同一患者护理期间使用的一个或多个辅助EHR系统的数据。尽管研究已经证明了在不同的EHR系统中连接患者身份记录的方法，关于在主要和辅助EHR系统之间链接患者就诊记录的情况知之甚少。
我们比较了在多个EHR系统中连接患者相遇记录的患者优先方法与相遇优先方法。
我们在2010年至2020年期间对348,904例患者进行了回顾性观察性研究，其中533,283例患者在我们机构的主要EHR系统和围手术期使用的辅助EHR系统中遇到。对于患者优先的方法和遭遇优先的方法，我们测量了创建的患者和遇到链接的数量以及运行时间。
■虽然患者优先的方法将43％的患者和49％的遭遇联系起来，相遇优先的方法将98%的患者和100%的相遇联系在一起.在连接患者方面，相遇优先方法比患者优先方法快20倍，在连接相遇方面慢33%。
研究结果表明，通过自动接口在EHR系统之间共享的常见患者和相遇标识符可能在临床上有用，但不是“研究就绪”，因此需要相遇-优先链接方法来实现用于科学目的的二次使用。根据我们的搜索,这项研究是首次展示跨多个EHR系统连接患者遭遇的方法之一.用于其他地方的研究工作的企业数据仓库可能会受益于相遇优先的方法。
我们比较了在多个EHR系统中连接患者相遇记录的患者优先方法与相遇优先方法。
我们在2010年至2020年期间对348,904例患者进行了回顾性观察性研究，其中533,283例患者在我们机构的主要EHR系统和围手术期使用的辅助EHR系统中遇到。对于患者优先的方法和遭遇优先的方法，我们测量了创建的患者和遇到链接的数量以及运行时间。
■虽然患者优先的方法将43％的患者和49％的遭遇联系起来，相遇优先的方法将98%的患者和100%的相遇联系在一起.在连接患者方面，相遇优先方法比患者优先方法快20倍，在连接相遇方面慢33%。
研究结果表明，通过自动接口在EHR系统之间共享的常见患者和相遇标识符可能在临床上有用，但不是“研究就绪”，因此需要相遇-优先链接方法来实现用于科学目的的二次使用。根据我们的搜索,这项研究是首次展示跨多个EHR系统连接患者遭遇的方法之一.用于其他地方的研究工作的企业数据仓库可能会受益于相遇优先的方法。
