Our objective was to identify birth hospitalization severe maternal morbidity (SMM) diagnoses that were also coded during prior encounters and, thus, potentially falsely carried forward as de novo SMM events. This retrospective cohort study included pregnant patients with births between 2016 and 2020. We applied the SMM algorithm to the birth hospitalization and encounters occurring prepregnancy, antepartum, and postpartum. The primary outcome was the rate of SMM diagnoses recorded during the birth hospitalization that were also coded on previous encounters. There were 1,380 (1.8%) birthing patients with SMM. Of patients with SMM codes at the birth hospitalization, 19.0% had the same SMM code during a prior encounter. Certain SMM events may be prone to carry-forward errors and may not signify a de novo birth hospitalization event.

中文翻译：

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医院出院代码和高估分娩住院期间严重产妇发病率。

我们的目标是识别出生住院严重孕产妇发病 (SMM) 诊断，这些诊断也在之前的遭遇中被编码，因此可能被错误地转为新的 SMM 事件。这项回顾性队列研究包括 2016 年至 2020 年期间出生的怀孕患者。我们将 SMM 算法应用于分娩住院以及孕前、产前和产后发生的情况。主要结果是在分娩住院期间记录的 SMM 诊断率，这些诊断率也根据之前的遭遇进行编码。有 1,380 名 (1.8%) 分娩患者患有 SMM。在出生住院时具有 SMM 代码的患者中，19.0% 在之前就诊时具有相同的 SMM 代码。某些 SMM 事件可能容易出现结转错误，并且可能并不意味着新生出生住院事件。