Assessing the concordance and accuracy between hospital discharge data, electronic health records, and register books for diagnosis of inpatient admissions of miscarriage: A retrospective linked data study

J Obstet Gynaecol Res. 2021 Jun;47(6):1987-1996. doi: 10.1111/jog.14785. Epub 2021 May 1.

Abstract

Background: Despite the high prevalence of miscarriage, there are few studies which assess the concordance of a diagnosis of miscarriage in routinely collected health databases.

Objectives: To determine agreement and accuracy for the diagnosis of miscarriage between electronic health records (EHR), the Hospital Inpatient-Enquiry (HIPE) system, and hospital register books in Ireland.

Methods: This is a retrospective study comparing agreement of diagnosis of miscarriage between three hospital data sources from January to June 2017. All inpatient admissions for miscarriage were reviewed from a single, tertiary maternity hospital in Ireland. Kappa, sensitivity, specificity, positive and negative predictive value were calculated.

Results: In this retrospective concordance study, EHR records confirmed 96.2% diagnosis of miscarriage of HIPE records, and 95.1% of register books records. A total of 95 records were not recorded in the register books but were recorded in HIPE and EHR. This study found a considerable variability when comparing definitions of type of miscarriage (i.e., missed miscarriage, incomplete, and complete) between the three data sources.

Conclusion: Although this study found a high concordance in inpatient admissions for miscarriage between EHR, HIPE, and register books, a considerable discrepancy was found when classifying miscarriage between the three data sources.

Keywords: data accuracy; electronic health records; epidemiology and statistics; inpatients; pregnancy loss.

MeSH terms

  • Abortion, Spontaneous* / diagnosis
  • Abortion, Spontaneous* / epidemiology
  • Books
  • Electronic Health Records
  • Female
  • Humans
  • Inpatients
  • Ireland / epidemiology
  • Patient Discharge
  • Pregnancy
  • Retrospective Studies
  • Semantic Web*