This qualitative study explores the features and contexts of clinical decision support system use, providing insights into barriers and facilitators. It emphasizes coproduction with general practitioners, clear clinical pathways, and adequate training to improve CDSS implementation.
Proposed Intervention
Various interventions have been proposed for preventing diagnostic errors, with clinical decision support systems (CDSS) standing out as effective. Studies demonstrate that CDSS can significantly reduce misdiagnosis and delayed diagnosis, particularly in rare disease cases.
Conclusion
Diagnostic errors, including missed, wrong, and delayed diagnoses, pose significant risks to patient well-being. Limited research on diagnostic errors necessitates effective interventions. This study recommends the implementation of CDSS, supported by evidence indicating its efficacy in reducing diagnostic errors and ensuring patient safety.
References
Abimanyi-Ochom, J., Bohingamu Mudiyanselage, S., Catchpool, M., Firipis, M., Wanni Arachchige Dona, S., & Watts, J. J. (2019). Strategies to reduce diagnostic errors: A systematic review. BMC Medical Informatics and Decision Making, 19(1), 1-14. [https://doi.org/10.1186/s12911-019-0901-1]
Fernandes, M., Vieira, S. M., Leite, F., Palos, C., Finkelstein, S., & Sousa, J. M. (2020). Clinical decision support systems for triage in the emergency department using intelligent systems: A review. Artificial Intelligence in Medicine, 102, 101762. [https://doi.org/10.1016/j.artmed.2019.101762]
Ford, E., Edelman, N., Somers, L., Shrewsbury, D., Lopez Levy, M., Van Marwijk, H., Curcin, V., & Porat, T. (2021). Barriers and facilitators to the adoption of electronic clinical decision support systems: A qualitative interview study with UK general practitioners. BMC Medical Informatics and Decision Making, 21(1), 1-13. [https://doi.org/10.1186/s12911-021-01557-z]
Ronicke, S., Hirsch, M. C., Türk, E., Larionov, K., Tientcheu, D., & Wagner, A. D. (2019). Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study. Orphanet Journal of Rare Diseases, 14(1), 1-12. [https://doi.org/10.1186/s13023-019-1040-6]
NURS FPX 8030 Assessment 3 Critical Appraisal of Evidence-Based Literature
This paper reviews the contributions of intelligent clinical decision support systems to emergency department care. The study underscores the benefits of these systems in triage improvement, critical care prediction, and reduced misdiagnosis, supporting the potential of CDSS in reducing diagnostic errors.
Ford, E., et al. (2021). Barriers and facilitators to the adoption of electronic clinical decision support systems: a qualitative interview study with UK general practitioners. BMC Medical Informatics and Decision Making, 21(1), 1-13. [https://doi.org/10.1186/s12911-021-01557-z]
This qualitative study explores the features and contexts of clinical decision support system use, providing insights into barriers and facilitators. It emphasizes coproduction with general practitioners, clear clinical pathways, and adequate training to improve CDSS implementation.
Proposed Intervention
Various interventions have been proposed for preventing diagnostic errors, with clinical decision support systems (CDSS) standing out as effective. Studies demonstrate that CDSS can significantly reduce misdiagnosis and delayed diagnosis, particularly in rare disease cases.
Conclusion
Diagnostic errors, including missed, wrong, and delayed diagnoses, pose significant risks to patient well-being. Limited research on diagnostic errors necessitates effective interventions. This study recommends the implementation of CDSS, supported by evidence indicating its efficacy in reducing diagnostic errors and ensuring patient safety.
References
Abimanyi-Ochom, J., Bohingamu Mudiyanselage, S., Catchpool, M., Firipis, M., Wanni Arachchige Dona, S., & Watts, J. J. (2019). Strategies to reduce diagnostic errors: A systematic review. BMC Medical Informatics and Decision Making, 19(1), 1-14. [https://doi.org/10.1186/s12911-019-0901-1]
Fernandes, M., Vieira, S. M., Leite, F., Palos, C., Finkelstein, S., & Sousa, J. M. (2020). Clinical decision support systems for triage in the emergency department using intelligent systems: A review. Artificial Intelligence in Medicine, 102, 101762. [https://doi.org/10.1016/j.artmed.2019.101762]
Ford, E., Edelman, N., Somers, L., Shrewsbury, D., Lopez Levy, M., Van Marwijk, H., Curcin, V., & Porat, T. (2021). Barriers and facilitators to the adoption of electronic clinical decision support systems: A qualitative interview study with UK general practitioners. BMC Medical Informatics and Decision Making, 21(1), 1-13. [https://doi.org/10.1186/s12911-021-01557-z]
Ronicke, S., Hirsch, M. C., Türk, E., Larionov, K., Tientcheu, D., & Wagner, A. D. (2019). Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study. Orphanet Journal of Rare Diseases, 14(1), 1-12. [https://doi.org/10.1186/s13023-019-1040-6]
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