scholarly journals Opportunities and obstacles using a clinical decision support system for maternal care in Burkina Faso

Author(s):  
Sidagna Alphonse Zakane ◽  
Lars L Gustafsson ◽  
Ali Sie ◽  
Göran Tomson ◽  
Svetla Loukanova ◽  
...  

AbstractObjective:  Maternal and neonatal mortality is high in sub-Saharan Africa. To support Healthcare Workers (HCWs), a computerized decision support system (CDSS) was piloted at six rural maternal care units in Burkina Faso. During the two years of the study period, it was apparent from reports that the CDSS was not used regularly in clinical practice. This study aimed to explore the reasons why HCWs failed to use the CDSS.Methods: A workshop, organised as group discussions and a plenary session, was performed with 13 participants to understand their experience with the CDSS and suggest improvements if pertinent. Workshop transcripts were analysed thematically. Socio-demographic and usage patterns of the CDSS were examined by a questionnaire and analysed descriptively.Results: The participants reported that the contextual basic conditions for using the CDSS were not fulfilled. These included unreliable power supply, none user-friendly partograph, the CDSS was not integrated with workflow and staff lacked motivational incentives. Despite these limitations, the HCWs reported learning benefits from guidance and alerts in the CDSS. Using the CDSS enabled them to discover problems earlier as they learned to focus on symptoms to prevent harmful situations.Conclusion: The CDSS was not tailored to the needs and context of the users. The HCWs, defined their needs and suggested how the CDSS should be re-designed. This suggests that the successful and regular usage of any CDSS in rural settings requires the involvement of users throughout the construction and pilot-testing phases and not only during the early prototype design period.

Agronomie ◽  
2002 ◽  
Vol 22 (7-8) ◽  
pp. 839-846 ◽  
Author(s):  
Bernard Vanlauwe ◽  
Cheryl A. Palm ◽  
Herbert K. Murwira ◽  
Roel Merckx

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1309-P
Author(s):  
JACQUELYN R. GIBBS ◽  
KIMBERLY BERGER ◽  
MERCEDES FALCIGLIA

2020 ◽  
Vol 16 (3) ◽  
pp. 262-269
Author(s):  
Tahere Talebi Azad Boni ◽  
Haleh Ayatollahi ◽  
Mostafa Langarizadeh

Background: One of the greatest challenges in the field of medicine is the increasing burden of chronic diseases, such as diabetes. Diabetes may cause several complications, such as kidney failure which is followed by hemodialysis and an increasing risk of cardiovascular diseases. Objective: The purpose of this research was to develop a clinical decision support system for assessing the risk of cardiovascular diseases in diabetic patients undergoing hemodialysis by using a fuzzy logic approach. Methods: This study was conducted in 2018. Initially, the views of physicians on the importance of assessment parameters were determined by using a questionnaire. The face and content validity of the questionnaire was approved by the experts in the field of medicine. The reliability of the questionnaire was calculated by using the test-retest method (r = 0.89). This system was designed and implemented by using MATLAB software. Then, it was evaluated by using the medical records of diabetic patients undergoing hemodialysis (n=208). Results: According to the physicians' point of view, the most important parameters for assessing the risk of cardiovascular diseases were glomerular filtration, duration of diabetes, age, blood pressure, type of diabetes, body mass index, smoking, and C reactive protein. The system was designed and the evaluation results showed that the values of sensitivity, accuracy, and validity were 85%, 92% and 90%, respectively. The K-value was 0.62. Conclusion: The results of the system were largely similar to the patients’ records and showed that the designed system can be used to help physicians to assess the risk of cardiovascular diseases and to improve the quality of care services for diabetic patients undergoing hemodialysis. By predicting the risk of the disease and classifying patients in different risk groups, it is possible to provide them with better care plans.


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