Development and Validation of the Iranian Neonatal Prematurity Minimum Data Set (IMSPIMDS): A Systematic Review Using Focus Group Discussion and the Delphi Technique

Abstract

Background: Information systems help to collect information about patients. The minimum data set (MDS) provides the basis for decision-making. Objectives: This study was conducted to determine the comprehensive national MDS for prematurity information management system (IMSPIMDS) in Iran Methods: This research is a cross-sectional study with three steps, including systematic review, focus group discussion, and the Delphi technique. A systematic review was conducted in relevant databases. Then, a focus group discussion was used to classify the extracted data elements by contributing specialists in various fields. Finally, MDSs were chosen through the decision Delphi technique in two rounds. Collected data were analyzed using SPSS 26. Results: In total, 233 data elements were included in the Delphi survey. The data elements based on the experts’ opinions, were classified into two main categories, including maternal and newborn. The final data elements for maternal and newborn categories were 107 and 126. Conclusions: The existence of a national MDS as the core of the premature newborn surveillance program is essential and leads to appropriate decisions. We developed and internally validated an MDS for prematurity studies. This study generated new knowledge to enable healthcare system professionals to collect relevant and meaningful data. The use of this standardized approach can help benchmark clinical practice and target improvements worldwide.

Keywords

• delphi
• prematurity
• maternal
• minimum data set
• neonatal and newborn
• surveillance