2019, Volume 12, Issue 2, pp 160 – 167

Designing Data Elements and Minimum Data Set (MDS) for Creating the Registry of Patients with Gestational Diabetes Mellitus

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Authors and Affiliations

Corresponding Author: Esmat Mirbagheri, School of Health Management and Information Sciences, Iran University of Medical Sciences, Tehran, Iran, Tel.: +98 9171876641; Fax: +98 21 88883334, E-mail: Mirbagheri.e@iums.ac.ir

Abstract

The prevalence of gestational diabetes mellitus (GDM) is increasing in Iran. Collection of patients’ data is commonly conducted through using medical records. However, for providing a structured reporting based on the information needs, a minimum data set is a fast, inexpensive, and suitable method. For exchanging high-quality data between different healthcare centers and health monitoring organization, the data are required to be uniformly collected and registered. The present study aims at designing an MDS for creating the registry of GDM. The present study is an applied one, conducted in two stages, with a qualitative Delphi method in 2018. In the first stage of the study, it was attempted to extract the data elements of mothers with GDM, through reviewing the related studies and collecting these patients’ data from the medical records. Then, based on the results of the first stage, a questionnaire including demographic, clinical, and pharmaceutical data was distributed among 20 individuals including gynecologists, pharmacists, nurses, and midwives. The validity of the questionnaire was examined by a team of experts and its reliability was examined by using Cronbach’s alpha. Data analysis was conducted using descriptive statistics (frequency, percentage, and mean) and excel. An MDS of gestational diabetes mellitus was developed. This MDS divided into three categories: administrative, clinical, and pharmaceutical with 4, 18, and 2 sections and 35, 199, and 12 data elements, respectively. Determining the minimum data sets of GDM will be an effective step toward integrating and improving data management of patients with GDM. Moreover, it will be possible to store and retrieve the data related to these patients.

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About this article

PMC ID: 6685309
PubMed ID: 31406518
DOI: 10.25122/jml-2019-0011

Article Publishing Date (print): Apr-Jun 2019
Available Online: 

Journal information

ISSN Printing: 1844-122X
ISSN Online: 1844-3117
Journal Title: Journal of Medicine and Life

Copyright License: Open Access

This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use and redistribution provided that the original author and source are credited.


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