Clinical Decision Support System Adoption & Implementation in African Health Systems: A SWOT Analysis. (Preprint)

2021 ◽  
Author(s):  
Victoria Oluwafunmilola Kolawole

BACKGROUND The clinical decision support system (CDSS) has been an important achievement of health technology in the 21st century. In developed countries, it has transformed the way health services are being delivered and has shown to be a tool that reduces medical errors and misdiagnoses in Healthcare. However, CDSS remains underutilized in developing countries in Africa. OBJECTIVE This study aims to review the literature to improve our understanding of the “strengths, weaknesses, opportunities and threats (SWOT)” associated with CDSS implementation in African health systems. METHODS This study included a literature review conducted in PubMed with a total of 19 articles between the year 2010 to date (past 10years) reviewed for key themes and categorized into one of 4 possible areas within the SWOT analysis. RESULTS Articles reviewed showed common strengths of efficiency at the workplace, Improved healthcare quality, benefits in developed countries, good examples of evidence-based decision making. unreliable electric power supply, inconsistent Internet connectivity, clinician's limited computer skills, and lack of enough published evidence of benefits in developing countries are listed as a weakness. The opportunities are high demand for evidence-based practice in healthcare, a strong demand for quality healthcare, growing interest to use modern technologies. The common threats identified are government policy, political instability, low funding and resistance of use by providers. CONCLUSIONS There’s the need to work on the technical, organizational and financial barriers to ensure high adoption and implementation of the CDSS in African Health systems. Also, the lag on the knowledge available on its impact in developing countries must be worked on by supporting more studies to add to the body of knowledge.

2020 ◽  
pp. 50-57
Author(s):  
Ali Кhusein ◽  
Urquhart A

The application of the Clinical Decision Support Systems (CDSS) in the process of facilitating the activity of the evidence-centred treatment project effect enhances the quality of the healthcare services. The main purpose of this article is to define and illustrate the basis of the processes of the evidencecentred decision support tracking at the two thousand AMIA symposium spring. The analysis has been done on the basis of protocol issues when capturing the evidence-centred practices in machine interpretation and repositories for supporting and developing the CDSS for evidence-centred treatment. As a result, the research recommendations are based on five areas: capturing literature-centered and practice-centred evidence in the interpretation of machine knowledge and bases; creating maintainable methodological and technical elements for computer-centred decision support CDSS; assessing the medical costs and effects for clinical decision support system and the manner in which the systems affect the organizational best practices; disseminating and identifying the works based on work-flow sensitivity approach for the system and creating the public policy which will effectively provide the incentives meant to implement CDSS to enhance the quality of healthcare services. The paper is concluded with an assumption of evidence-based medicine aspect being strong. However, future research is still recommended in CDSS to potentially realize more defined benefits of the systems.


Author(s):  
Manju Priya Sundaramurthy

Most of the developing countries face major problems in providing quality healthcare. It is very essential to move the health stream to a higher level with more effective. Though medical care is improving, due to the enormous amount of data, making the decisions is more complex. The technology already links patients, providers, and customers in many ways that are converting the patient experience and delivery of care. This chapter reveals the importance of healthcare by using CDSS along with IoT. By combining connected devices with CDSS will help the clinicians to take decisions immediately for any disease. It provides an efficient, effective quality measurement and enhancement because of its ability to get the data of any patient at any time anywhere.


2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 19676-19676
Author(s):  
J. Van Erps ◽  
M. Aapro ◽  
K. MacDonald ◽  
P. Soubeyran ◽  
M. Muenzberg ◽  
...  

19676 Background: The 2006 European Organisation for Research and Treatment of Cancer (EORTC) guidelines for erythropoietic proteins in cancer-related anemia provide the most up-to-date assessment of the evidence base. To promote clinicians’ adoption of evidence- based (EB) practice guidelines (EBPGs), it is critical to bring guidelines to the point of care. RESPOND is an EB clinical decision support system (CDSS) based on the EORTC guidelines. CDSSs are seldom validated. We describe the methodologies of two studies being conducted to validate RESPOND. Methods. Study 1: descriptive design - accuracy and content validity. Five experts are asked to rate the accuracy of algorithms derived from the guidelines; the objective being an intraclass correlation coefficient =0.90 for each of 27 algorithmic sets. Study 2: hybrid matched pre-post design - concurrent and discriminant validity. Two patient cohorts (n=33 each) matched by type of cancer and similarity of chemotherapy regimen ie, sample 1 (4 months prospective data after RESPOND [post]) and sample 2 (4 months retrospective data prior to RESPOND [pre]) were used to test concurrent validity (congruence scores [CS] of sample 1) and discriminant validity (difference between sample 2 and sample 1 CS). A score is calculated for each patient to quantify the extent to which treatment and outcomes are congruent with the EORTC EBPG. Table 1 . Calculation of the congruence score ESA, erythropoiesis-stimulating agent; Hb, hemoglobin, Q1W, once weekly, Q3W, once every three weeks Conclusions. These studies will provide the necessary validation for RESPOND as an evidence-based clinical support tool. [Table: see text] [Table: see text]


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