Enhancing Medication Safety with ML-Enhanced Decision Support Systems: A Comparative Analysis of Prescription Error Detection

F Rahman; Lalnunthari

doi:10.25163/angiotherapy.899870

Angiogenesis, Inflammation & Therapeutics | Online ISSN 2207-872X

RESEARCH ARTICLE (Open Access)

Previous Next Contents Vol 8 (9)

Enhancing Medication Safety with ML-Enhanced Decision Support Systems: A Comparative Analysis of Prescription Error Detection

F Rahman ¹*, Lalnunthari ¹

+ Author Affiliations

Journal of Angiotherapy 8 (9) 1-6 https://doi.org/10.25163/angiotherapy.899870

Submitted: 10 July 2024 Revised: 29 August 2024 Published: 02 September 2024

Abstract

Background: Medication prescription errors are a global issue, leading to significant morbidity and mortality. Traditional rule-based Medical Decision Support Systems (MDSS) are often ineffective, generating numerous false alerts and failing to detect all potential errors. This study assesses a new anomaly detection system integrated with Electronic Health Records (EHR) to improve the accuracy and utility of medication error warnings. Methods: Anomalous prescription detection was implemented alongside an existing MDSS in a real-world inpatient setting over 18 months. The new system utilized Machine Learning (ML) combined with a rule-based MDSS to analyze historical EHR data. It aimed to identify and flag high-risk prescriptions through real-time anomaly detection. The performance of this hybrid system was compared against traditional MDSS and multicriteria query (MQ) methods. A clinical pharmacist reviewed 415 patients (3401 prescriptions) to validate the effectiveness of the system, assessing notifications for accuracy, clinical relevance, and practicality. Results: The ML-enhanced MDSS demonstrated superior performance compared to traditional systems. It achieved a 76% interception rate for prescriptions needing pharmacist review and a precision rate of 75%. The hybrid system outperformed traditional MDSS and MQ methods, with areas under the ROC and PRC curves of 0.84 and 0.79, respectively, compared to 0.66 and 0.57 for MDSS and 0.7 and 0.58 for MQ approaches. Conclusion: Integrating ML with rule-based MDSS significantly improves the detection of high-risk medication prescriptions, reducing false alerts and enhancing accuracy. This hybrid approach offers a more effective tool for identifying potential medication errors and improving patient safety in inpatient settings.

Keywords: Machine Learning, Medical Decision Support System, Electronic Health Record, High-risk prescriptions

References

Cornuault, L., Mouchel, V., Phan Thi, T. T., Beaussier, H., Bézie, Y., & Corny, J. (2018). Identification of variables influencing pharmaceutical interventions to improve medication review efficiency. International Journal of Clinical Pharmacy, 40, 1175-1179.

Elliott, R. A., Camacho, E., Jankovic, D., Sculpher, M. J., & Faria, R. (2021). Economic analysis of the prevalence and clinical and economic burden of medication error in England. BMJ Quality & Safety, 30(2), 96-105.

Elshayib, M., & Pawola, L. (2020). Computerized provider order entry–related medication errors among hospitalized patients: An integrative review. Health Informatics Journal, 26(4), 2834-2859.

Gates, P. J., Hardie, R. A., Raban, M. Z., Li, L., & Westbrook, J. I. (2021). How effective are electronic medication systems in reducing medication error rates and associated harm among hospital inpatients? A systematic review and meta-analysis. Journal of the American Medical Informatics Association, 28(1), 167-176.

Karande, S., Marraro, G. A., & Spada, C. (2021). Minimizing medical errors to improve patient safety: An essential mission ahead. Journal of postgraduate medicine, 67(1), 1-3.

Lexow, M., Wernecke, K., Sultzer, R., Bertsche, T., & Schiek, S. (2022). Determine the impact of a structured pharmacist-led medication review-a controlled intervention study to optimise medication safety for residents in long-term care facilities. BMC geriatrics, 22(1), 307.

Madhavi, M., Sasirooba, T., & Kumar, G. K. (2023). Hiding Sensitive Medical Data Using Simple and Pre-Large Rain Optimization Algorithm through Data Removal for E-Health System. Journal of Internet Services and Information Security, 13, 177-192.

Malathi, K., Shruthi, S. N., Madhumitha, N., Sreelakshmi, S., Sathya, U., & Sangeetha, P. M. (2024). Medical Data Integration and Interoperability through Remote Monitoring of Healthcare Devices. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), 15(2), 60-72.

Mohamed, K. N. R., Nijaguna, G. S., Pushpa, L. N., & Zameer, A. A. (2024). A Comprehensive Approach to a Hybrid Blockchain Framework for Multimedia Data Processing and Analysis in IoT-Healthcare. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications (JoWUA), 15(2), 94-108. https://doi.org/10.58346/JOWUA.2024.I2.007

Mohandas, R., Veena, S., Kirubasri, G., Mary, I. T. B., & Udayakumar, R. (2024). Federated Learning with Homomorphic Encryption for Ensuring Privacy in Medical Data. Indian Journal of Information Sources and Services, 14(2), 17-23. https://doi.org/10.51983/ijiss-2024.14.2.03

Newman-Toker, D. E., Nassery, N., Schaffer, A. C., Yu-Moe, C. W., Clemens, G. D., Wang, Z., & Siegal, D. (2024). Burden of serious harms from diagnostic error in the USA. BMJ Quality & Safety, 33(2), 109-120.

Nguyen, T. L., Leguelinel-Blache, G., Kinowski, J. M., Roux-Marson, C., Rougier, M., Spence, J., & Landais, P. (2017). Improving medication safety: development and impact of a multivariate model-based strategy to target high-risk patients. PloS one, 12(2), e0171995.

Olakotan, O. O., & Mohd Yusof, M. (2021). The appropriateness of clinical decision support systems alerts in supporting clinical workflows: A systematic review. Health Informatics Journal, 27(2), 14604582211007536.

Sindhusaranya, B., Yamini, R., Manimekalai, M. A. P., & Geetha, K. (2023). Federated Learning and Blockchain-Enabled Privacy-Preserving Healthcare 5.0 System: A Comprehensive Approach to Fraud Prevention and Security in IoMT. Journal of Internet Services and Information Security, 13(4), 199-209.

Suclupe, S., Martinez-Zapata, M. J., Mancebo, J., Font-Vaquer, A., Castillo-Masa, A. M., Viñolas, I., & Robleda, G. (2020). Medication errors in prescription and administration in critically ill patients. Journal of advanced nursing, 76(5), 1192-1200.

Surendar, A., Veerappan, S., Sadulla, S., & Arvinth, N. (2024). Lung cancer segmentation and detection using KMP algorithm. Onkologia i Radioterapia, 18(4).

Swen, J. J., van der Wouden, C. H., Manson, L. E., Abdullah-Koolmees, H., Blagec, K., Blagus, T., & Rodríguez-González, C. J. (2023). A 12-gene pharmacogenetic panel to prevent adverse drug reactions: an open-label, multicentre, controlled, cluster-randomised crossover implementation study. The Lancet, 401(10374), 347-356.

Tekkesin, A. I. (2019). Artificial intelligence in healthcare: past, present and future. Anatol J Cardiol, 22(Suppl 2), 8-9.

PDF
Abstract
Export Citation

View Dimensions

View Plumx

View Altmetric

Save

0
Citation

312
View

Share