BMC Pregnancy and Childbirth (Jul 2024)

Designing a core data set for benign hysterectomy registration system and its implementation in a referral teaching hospital in Northwest Iran

  • Fatemeh Moghadami Asl,
  • Elham Maserat,
  • Maryam Vaezi,
  • Zeinab Mohammadzadeh

DOI
https://doi.org/10.1186/s12884-024-06656-z
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 10

Abstract

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Abstract Background and aims Although minimally invasive hysterectomy offers advantages, abdominal hysterectomy remains the predominant surgical method. Creating a standardized dataset and establishing a hysterectomy registry system present opportunities for early interventions in reducing volume and selecting benign hysterectomy methods. This research aims to develop a dataset for designing benign hysterectomy registration system. Methods Between April and September 2020, a qualitative study was carried out to create a data set for enrolling patients who were candidate for hysterectomy. At this stage, the research team conducted an information needs assessment, relevant data element identification, registry software development, and field testing; Subsequently, a web-based application was designed. In June 2023the registry software was evaluated using data extracted from medical records of patients admitted at Al-Zahra Hospital in Tabriz, Iran. Results During two months, 40 patients with benign hysterectomy were successfully registered. The final dataset for the hysterectomy patient registry comprise 11 main groups, 27 subclasses, and a total of 91 Data elements. Mandatory data and essential reports were defined. Furthermore, a web-based registry system designed and evaluated based on data set and various scenarios. Conclusion Creating a hysterectomy registration system is the initial stride toward identifying and registering hysterectomy candidate patients. this system capture information about the procedure techniques, and associated complications. In Iran, this registry can serve as a valuable resource for assessing the quality of care delivered and the distribution of clinical measures.

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