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NURS FPX 8030 Assessment 3 Critical Appraisal of Evidence-Based Literature

NURS FPX 8030 Assessment 3 Critical Appraisal of Evidence-Based Literature

Name

Capella university

NURS-FPX 8030 Evidence-Based Practice Process for the Nursing Doctoral Learner

Prof. Name

Date

Critical Appraisal of Evidence-Based Literature on Diagnostic Errors

Diagnosing medical conditions stands as a primary responsibility for healthcare providers; however, errors in diagnosis, whether missed, incorrect, or delayed, can lead to adverse outcomes (Abimanyi-Ochom et al., 2019). The research on diagnostic errors has been limited due to challenges in defining, detecting, preventing, and discussing these errors. Additionally, measuring diagnostic errors effectively remains elusive, with scarce sources of valid and reliable data. Such errors contribute to high healthcare costs, resulting from negative health outcomes, income loss, decreased productivity, and, in extreme cases, loss of life (Abimanyi-Ochom et al., 2019). Trust in the healthcare system can be eroded, leading to dissatisfaction among patients and healthcare professionals. Thus, there is a pressing need for effective interventions to mitigate diagnostic errors in clinical settings.

PICOT Question

Among adult patients in acute or ambulatory care settings (P), a clinical decision support system in a hospital (I), compared with its absence (C), can enhance diagnostic processes to reduce diagnostic errors (O), within 24 months of implementation (T).

Critical Appraisal Tool

The JBI Checklist for Systematic Reviews will be employed as the critical appraisal tool for evaluating articles in this study. This tool ensures the methodological quality of the studies and assesses the extent to which bias has been addressed in their design, conduct, and analysis. Given that the selected studies are largely systematic reviews, the JBI Checklist is deemed appropriate for its ability to provide robust evidence across various research questions.

Annotated Bibliography

Abimanyi-Ochom, J., et al. (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]

This study explores communication and audit strategies to reduce diagnostic errors, emphasizing technology-based interventions like clinical decision support systems. The research recommends trigger algorithms, including computer-based systems and alerts, to prevent delays in diagnosis and improve accuracy.

Ronicke, S., et al. (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]

This study investigates the diagnostic decision support system Ada DX, showing its potential to suggest accurate rare disease diagnoses early in the course of cases. The Checklist for Case-Control Studies ensures the methodological quality of the study, supporting the use of clinical decision support systems in diagnostic improvement.

Fernandes, M., et al. (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]

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, encompassing 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

NURS FPX 8030 Assessment 3 Critical Appraisal of Evidence-Based Literature

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

Scott, I. A., & Crock, C. (2020). Diagnostic error: Incidence, impacts, causes, and preventive strategies. Medical Journal of Australia, 213(7), 302-305. https://doi.org/10.5694/mja2.50771

Soufi, M. D., Samad-Soltani, T., Vahdati, S. S., & Rezaei-Hachesu, P. (2018). Decision support system for triage management: A hybrid approach using rule-based reasoning and fuzzy logic. International Journal of Medical Informatics, 114, 35-44. https://doi.org/10.1016/j.ijmedinf.2018.03.008

Trinkley, K. E., Blakeslee, W. W., Matlock, D. D., Kao, D. P., Van Matre, A. G., Harrison, R., Larson, C. L., Kostman, N., Nelson, J. A., Lin, C. T., & Malone, D. C. (2019). Clinician preferences for computerized clinical decision support for medications in primary care: A focus group study. BMJ Health & Care Informatics, 26(1), 0. https://doi.org/10.1136/bmjhci-2019-000015

Willmen, T., Völkel, L., Ronicke, S., Hirsch, M. C., Kaufeld, J., Rychlik, R. P., & Wagner, A. D. (2021). Health economic benefits through the use of diagnostic support systems and expert knowledge. BMC Health Services Research, 21(1), 1-11. https://doi.org/10.1186/s12913-021-06926-y

NURS FPX 8030 Assessment 3 Critical Appraisal of Evidence-Based Literature