Vol. 10, Issue 1 (2021)

Author(s):
Chenlep Yakha Konyak, Teisovi Angami and VK Vidyarthi

Abstract:
Medication errors pose significant risks to patient safety and healthcare quality, underscoring the importance of effective preventive measures. This study employs machine learning techniques to investigate the factors influencing medication error rates across diverse pharmacy settings. By leveraging a large, realistic dataset, the research aims to uncover insights that can inform targeted interventions and enhance patient care. Medication errors encompass mistakes in prescribing, dispensing, or administering medications, potentially leading to adverse drug events with serious health consequences. Through the application of advanced models like Random Forest, this study identifies critical factors affecting medication error rates. The findings are intended to empower healthcare providers with actionable insights for optimizing medication management processes and improving patient outcomes.