Factors Affecting Decision Support System: Knowledge, Training, Ease of Use
DOI:
https://doi.org/10.31933/dijdbm.v4i6.2077Keywords:
Decision Support System, Knowledge, Training, Ease of UseAbstract
In the rapidly changing landscape of modern organizations, the ability to make informed and timely decisions is crucial. Decision Support Systems (DSS) have emerged as vital tools to aid decision-makers by providing valuable insights and assistance in the decision-making process. This journal explores the critical factors influencing the adoption and effectiveness of DSS, with a specific focus on the variables of knowledge, training, and ease of use. The research employed the research library method, conducting a comprehensive review of existing literature to advance understanding of the factors influencing DSS. The results and discussions highlight the significance of knowledge, training, and ease of use in the successful utilization of DSS. Users' computer proficiency, familiarity with similar systems, training, and system intuitiveness all contribute to the effectiveness and acceptance of DSS. Tailoring DSS to individual needs and knowledge levels can enhance their ability to support decision-making in various contexts.
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Young, A.S., et al. (2004). A network-based system to improve care for schizophrenia: the Medical Informatics Network Tool (MINT). Journal of the American Medical Informatics Association, 11(5), 358-367.
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Zaidi, S.T.R., and Marriott, J.L. (2021). Barriers and facilitators to adoption of a web-based antibiotic decision support system. Southern Medical Review, 5(2), 42-50.
Zheng, K., et al. (2005). Understanding technology adoption in clinical care: Clinician adoption behavior of a point-of-care reminder system. International Journal of Medical Informatics, 74(7-8), 535-543.
Buenestado, D., et al. (2013). Evaluating Acceptance and User Experience of a Guideline-based Clinical Decision Support System Execution Platform. Journal of Medical Systems, 37(2), 9910.21.
Cho, S.H., et al. (2010). Effectiveness of A Computer-Assisted Asthma Management Program on Physician Adherence to Guidelines. Journal of Asthma, 47(6), 680-686.
Demonchy, E., et al. (2014). Impact of a computerized decision support system on compliance with guidelines on antibiotics prescribed for urinary tract infections in emergency departments: a multicentre prospective before-and-after controlled interventional study. Journal of Antimicrobial Chemotherapy, 69(10), 2857-2863.
Kortteisto, T., et al. (2012). Clinical decision support must be useful, functional is not enough: a qualitative study of computer-based clinical decision support in primary care. BMC Health Services Research, 12.
Leslie, S.J., et al. (2006). Clinical decision support software for management of chronic heart failure: development and evaluation. Computers in Biology and Medicine, 36(5), 495-506.
Lesselroth, B.J., et al. (2011). Addressing the sociotechnical drivers of quality improvement: a case study of post-operative DVT prophylaxis computerised decision support. BMJ Quality & Safety, 20(5), 381-389.
Lomotan, E.A., et al. (2012). Evaluating the use of a computerized clinical decision support system for asthma by pediatric pulmonologists. International Journal of Medical Informatics, 81(3), 157-165.
Peiris, D.P., et al. (2009). An electronic clinical decision support tool to assist primary care providers in cardiovascular disease risk management: development and mixed methods evaluation. Journal of Medical Internet Research, 11(4), e51.
Peleg, M., et al. (2009). Using multi-perspective methodologies to study users' interactions with the prototype front end of a guideline-based decision support system for diabetic foot care. International Journal of Medical Informatics, 78(7), 482-493.
Trivedi, M.H., et al. (2009). Barriers to implementation of a computerized decision support system for depression: an observational report on lessons learned in "real world" clinical settings. BMC Medical Informatics and Decision Making, 9(6).
Varonen, H., T. Kortteisto, and M. Kaila (2008). What may help or hinder the implementation of computerized decision support systems (CDSSs): a focus group study with physicians. Family Practice, 25(3), 162-167.
Wess, M.L., et al. (2011). Usability of an atrial fibrillation anticoagulation decision-support tool. Journal of Primary Care & Community Health, 2(2), 100-106.
Young, A.S., et al. (2004). A network-based system to improve care for schizophrenia: the Medical Informatics Network Tool (MINT). Journal of the American Medical Informatics Association, 11(5), 358-367.
Zaidi, S.T.R. and K.A. Thursky (2013). Using formative evaluation to improve uptake of a web-based tool to support antimicrobial stewardship. Journal of Clinical Pharmacy and Therapeutics, 38(6), 490-497.
Zaidi, S.T.R., and Marriott, J.L. (2021). Barriers and facilitators to adoption of a web-based antibiotic decision support system. Southern Medical Review, 5(2), 42-50.
Zheng, K., et al. (2005). Understanding technology adoption in clinical care: Clinician adoption behavior of a point-of-care reminder system. International Journal of Medical Informatics, 74(7-8), 535-543.
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2023-10-31
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