NURS-FPX 8030 Evidence-Based Practice Process for the Nursing Doctoral Learner ​​​​​​​Critical Appraisal of Evidence-Based Literature on Diagnostic Errors

The accurate diagnosis of medical conditions is a fundamental responsibility for healthcare providers. However, errors in diagnosis, including missed, incorrect, or delayed diagnoses, can lead to adverse outcomes (Abimanyi-Ochom et al., 2019). The research on diagnostic errors faces challenges in defining, detecting, preventing, and discussing these errors. Furthermore, effectively measuring diagnostic errors remains elusive, with limited sources of valid and reliable data. Such errors contribute to elevated healthcare costs, resulting from negative health outcomes, income loss, decreased productivity, and, in extreme cases, loss of life (Abimanyi-Ochom et al., 2019). Erosion of trust in the healthcare system can lead to dissatisfaction among patients and healthcare professionals. Therefore, there is a compelling need for effective interventions to mitigate diagnostic errors in clinical settings.

PICOT Question 

Among adult patients in acute or ambulatory care settings (P), the presence of 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]