Clinical Decision Support Implementation in Health Information Systems Across Radiology Departments
DOI:
https://doi.org/10.59992/IJSR.2023.v2n11p15Keywords:
Healthcare, Artificial Intelligence, Data Privacy, Sustainable Development, Technology IntegrationAbstract
Introduction: Understanding the economic landscape is pivotal for gauging the feasibility and sustainability of Clinical Decision Support (CDS) integration across radiology, nursing, and laboratory departments. This review will delve into the economic considerations associated with CDS implementation, shedding light on clinical impact and cost-effectiveness.
Methods: The systematic review employed a robust methodology, combining controlled vocabulary and free-text keywords in a comprehensive search across multiple databases. The inclusion criteria encompassed original research articles, systematic reviews, and meta-analyses in English, focusing on Clinical Decision Support (CDS) implementation in radiology, nursing, and laboratory departments within Health Information Systems. The two-step screening process, detailed data extraction, and methodological quality assessment were conducted with rigor by two reviewers, resolving discrepancies through discussion or consultation with a third reviewer.
Results: The systematic review incorporated seven intervention studies spanning radiology, nursing, and laboratory departments within Health Information Systems (HIS). Findings revealed a broad range of sample sizes, from 152 to 805 participants, showcasing the diversity of healthcare professionals involved. Across these studies, CDS interventions demonstrated substantial positive impacts, particularly in radiology with a risk ratio of 1.75 (95% CI: 1.42-2.10) for improved diagnostic accuracy, in nursing with a 58% risk reduction in medication errors (95% CI: 0.30-0.58), and in laboratory services with a 65% lower risk of unnecessary tests (95% CI: 0.24-0.51). These consistent themes highlight the effectiveness of CDS interventions but underscore the need for ongoing customization to meet department-specific needs.
Conclusions: The systematic review underscores the significant positive impact of Clinical Decision Support (CDS) implementation across radiology, nursing, and laboratory departments within Health Information Systems, as evidenced by improved diagnostic precision, medication management, and laboratory efficiency, while emphasizing the importance of continuous customization to address department-specific nuances.
References
1. Zakaria, N. and S.A.M. Yusof, Understanding technology and people issues in hospital information system (HIS) adoption: Case study of a tertiary hospital in Malaysia. Journal of infection and public health, 2016. 9(6): p. 774-780.
2. Sutton, R.T., et al., An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ digital medicine, 2020. 3(1): p. 17.
3. Romero-Brufau, S., et al., A lesson in implementation: a pre-post study of providers’ experience with artificial intelligence-based clinical decision support. International journal of medical informatics, 2020. 137: p. 104072.
4. Kao, D., et al., Clinical decision support may link multiple domains to improve patient care. JMIR Medical Informatics, 2020. 8(10): p. e20265.
5. Berkel, A., Integration of clinical decision support systems in Dutch radiology departments. 2019, University of Twente.
6. Dunn Lopez, K., et al., Integrative review of clinical decision support for registered nurses in acute care settings. Journal of the American Medical Informatics Association, 2017. 24(2): p. 441-450.
7. Flores, E., et al., Clinical decision support systems: a step forward in establishing the clinical laboratory as a decision maker hub. Computational and Structural Biotechnology Journal, 2023. 22: p. 2731.
8. Sen, A., et al., Clinical decision support: Converging toward an integrated architecture. Journal of biomedical informatics, 2012. 45(5): p. 1009.
9. Jayaratne, M., et al., A data integration platform for patient-centered e-healthcare and clinical decision support. Future Generation Computer Systems, 2019. 92: p. 996-1008.
10. Sorace, J., et al., Integrating pathology and radiology disciplines: an emerging opportunity? BMC medicine, 2012. 10(1): p. 1-6.
11. Prenestini, A., M. Sartirana, and F. Lega, Involving clinicians in management: assessing views of doctors and nurses on hybrid professionalism in clinical directorates. BMC Health Services Research, 2021. 21: p. 1-11.
12. Sperl-Hillen, J.M., et al., Clinical decision support directed to primary care patients and providers reduces cardiovascular risk: a randomized trial. Journal of the American Medical Informatics Association, 2018. 25(9): p. 1137-1146.
13. McGinn, T.G., et al., Efficacy of an evidence-based clinical decision support in primary care practices: a randomized clinical trial. JAMA Internal Medicine, 2013. 173(17): p. 1584-1591.
14. Karsh, B.-T., Clinical practice improvement and redesign: how the change in workflow can be supported by clinical decision support. 2009.
15. Kharbanda, A.B., et al., Effect of clinical decision support on diagnostic imaging for pediatric appendicitis: a cluster randomized trial. JAMA Network Open, 2021. 4(2): p. e2036344-e2036344.
16. Khan, S., et al., Improving provider adoption with adaptive clinical decision support surveillance: an observational study. JMIR human factors, 2019. 6(1): p. e10245.
17. Castaneda, C., et al., Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine. Journal of clinical bioinformatics, 2015. 5(1): p. 1-16.
18. Raja, A.S., et al., Effect of computerized clinical decision support on the use and yield of CT pulmonary angiography in the emergency department. Radiology, 2012. 262(2): p. 468-474.
19. Goldberg, H.S., et al., Use of a remote clinical decision support service for a multicenter trial to implement prediction rules for children with minor blunt head trauma. International journal of medical informatics, 2016. 87: p. 101-110.
20. Musen, M.A., B. Middleton, and R.A. Greenes, Clinical decision-support systems, in Biomedical informatics: computer applications in health care and biomedicine. 2021, Springer. p. 795840.
21. Kharbanda, A.B., et al., Implementation of electronic clinical decision support for pediatric appendicitis. Pediatrics, 2016. 137(5).
22. Lu, M.T., et al., Radiologist point-of-care clinical decision support and adherence to guidelines for incidental lung nodules. Journal of the American College of Radiology, 2016. 13(2): p. 156-162.
23. Lopez, K.D., et al., Conducting a representative national randomized control trial of tailored clinical decision support for nurses remotely: Methods and implications. Contemporary clinical trials, 2022. 118: p. 106712.
24. Austrian, J., et al., Applying A/B testing to clinical decision support: rapid randomized controlled trials. Journal of Medical Internet Research, 2021. 23(4): p. e16651.
25. Hughes, A.E. and R. Jackups Jr, Clinical decision support for laboratory testing. Clinical chemistry, 2022. 68(3): p. 402-412.
26. Gold, R., et al., Effect of clinical decision support at community health centers on the risk of cardiovascular disease: a cluster randomized clinical trial. JAMA network Open, 2022. 5(2): p. e2146519e2146519.
