Clinical Decision Support System (CDSS) for Demand Management in the Healthcare Supply: A Case Study of Impacts Status (Risks) During Pregnancy

ahmed shihab; Hussein Ali  Salah; Safa Bhar  Layeb

doi:10.31642/JoKMC/2018/120106

Authors

ahmed shihab Department of Basic Sciences, College of Nursing, University of Baghdad, IRAQ https://orcid.org/0000-0001-8156-047X
Hussein Ali Salah Department of Computer Systems, Technical Institute- Suwaira, Middle Technical University
Safa Bhar Layeb LR-OASIS, National Engineering School of Tunis, University of Tunis El Manar

DOI:

https://doi.org/10.31642/JoKMC/2018/120106

Keywords:

Clinical Decision Support Systems, C4.5 Algorithm Decision Trees, Digital Healthcare., Electronic Health Records, High-Risk Pregnancy

Abstract

In recent years, the medical industry has showed interest in reorganizing its operations to accommodate technological advancements and incorporating decision support systems into normal clinical procedures. Clinical Decision Support Systems (CDSS) connect observations and knowledge of health in order to encourage physicians to make health-related decisions in order to improve health care. The goal of this study is to examine the problems with Clinical Decision Support Systems (CDSS) and to concentrate on their utility in improving clinical practice. This article contained a case study of CDSS implementation for High-Risk Pregnancy (HRPCDSS) and a description of the conditions for a successful CDSS implementation. We will provide a Clinical Decision Support System (HRPCDSS) that will aid doctors in estimating the probability of an illness, and this process enhances their ability to offer therapeutic advice. In this study that eliminates the risk factors that community midwife’s/lady health visitors encounter in providing standardized/effective healthcare services to mothers and children. In this study, we suggest using the C4.5 algorithm decision trees to identify these diseases and compare their effectiveness and correction rates.

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References

[1] A. De Ramón Fernández, D. Ruiz Fernández, and M. T. Prieto Sánchez, “A decision support system for predicting the treatment of ectopic pregnancies,” Int. J. Med. Inf., vol. 129, pp. 198–204, 2019.

[2] G. Wang et al., “A Novel Decision Aid Improves Quality of Reproductive Decision-Making and Pregnancy Knowledge for Women with Inflammatory Bowel Disease,” Dig. Dis. Sci., vol. 67, no. 9, pp. 4303–4314, 2022

[3] R. T. Sutton, D. Pincock, D. C. Baumgart, D. C. Sadowski, R. N. Fedorak, and K. I. Kroeker, "An overview of clinical decision support systems: benefits, risks, and strategies for success," NPJ digital medicine, vol. 3, no. 1, p. 17, 2020.

[4] H. A. Wahabi et al., “Systematic review and meta-analysis of the effectiveness of pre-pregnancy care for women with diabetes for improving maternal and perinatal outcomes,” PloS One, vol. 15, no. 8, p. e0237571, 2020.

[5] I. Banerjee et al., "Development and performance of the pulmonary embolism result forecast model (PERFORM) for computed tomography clinical decision support," JAMA network open, vol. 2, no. 8, pp. e198719-e198719, 2019.

[6] S. Mohan et al., “A cluster randomized controlled trial of an electronic decision-support system to enhance antenatal care services in pregnancy at primary healthcare level in Telangana, India: trial protocol,” BMC Pregnancy Childbirth, vol. 23, no. 1, p. 72, 2023.

[7] S. Nazari Nezhad, M. H. Zahedi, and E. Farahani, “Detecting diseases in medical prescriptions using data mining methods,” BioData Min., vol. 15, no. 1, p. 29, Nov. 2022.

[8] J. Carter, J. Sandall, A. H. Shennan, and R. M. Tribe, “Mobile phone apps for clinical decision support in pregnancy: a scoping review,” BMC Med. Inform. Decis. Mak., vol. 19, no. 1, p. 219, 2019.

[9] S. Gupta, S. N. Singh, and P. K. Jain, “Utilising predictive analytics for decision-making and improving healthcare services in public maternal healthcare database,” Int. J. Reason.-Based Intell. Syst., vol. 13, no. 2, p. 85, 2021.

[10] M. Muhindo et al., “Implementation of a newborn clinical decision support software (NoviGuide) in a Rural District Hospital in Eastern Uganda: feasibility and acceptability study,” JMIR MHealth UHealth, vol. 9, no. 2, p. e23737, 2021, 2024.