Information Requirements of Clinical Decision Support Systems for the Diagnosis and Prediction of Preeclampsia

Abstract

Background: Clinical decision support systems (CDSS) are valuable tools for diagnosing and predicting diseases. However, their effectiveness hinges on the quality of the information provided. Objectives: This study aimed to identify the information requirements for a CDSS designed to diagnose and predict preeclampsia. Methods: This applied study was conducted in 2024. A literature review was performed to identify relevant studies. Based on the findings, a questionnaire with a five-point Likert scale was developed and validated through the input of 22 experts in related fields. Data were analyzed using SPSS version 22, and the findings are presented in the text and tables. Results: Among 143 items identified, 115 were deemed essential for a CDSS to diagnose and predict preeclampsia. The information requirements were classified into eight main categories: Demographic information, medical history, laboratory data, pregnancy-related data, complications in other organs, medical examinations, warning signs, paraclinical data, and lifestyle. Conclusions: The findings of this study provide critical insights for developers of CDSS tailored to preeclampsia diagnosis and prediction. By addressing these information needs, such systems can significantly enhance the capabilities of women's health professionals, advancing timely diagnosis and prevention of preeclampsia.