TY - GEN
T1 - A System Approach to Detect Medical Errors in Operational Data in Hospitals
AU - Aldoihi, Saad
AU - Alblalaihid, Khalid
AU - Alzemaia, Fozah
AU - Almoajel, Alia
AU - Hammami, Omar
AU - Alwablely, Shatha
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - Medical errors represent a significant challenge in healthcare systems worldwide, leading to increased patient morbidity, mortality, and healthcare costs. Early detection and prevention of such errors in hospital operational data can significantly improve patient safety and overall healthcare quality. This paper proposes a novel, data-driven approach to model a healthcare system for detecting medical errors using advanced machine learning techniques. We leverage electronic health records (EHR) and other hospital operational data sources to develop a comprehensive framework that can automatically identify potential errors in real-time. The model aims to identify patterns and anomalies in the data to detect potential errors and provide insights for process improvement. The proposed model can help healthcare providers to proactively monitor and address medical errors, thereby reducing the risk of harm to patients.
AB - Medical errors represent a significant challenge in healthcare systems worldwide, leading to increased patient morbidity, mortality, and healthcare costs. Early detection and prevention of such errors in hospital operational data can significantly improve patient safety and overall healthcare quality. This paper proposes a novel, data-driven approach to model a healthcare system for detecting medical errors using advanced machine learning techniques. We leverage electronic health records (EHR) and other hospital operational data sources to develop a comprehensive framework that can automatically identify potential errors in real-time. The model aims to identify patterns and anomalies in the data to detect potential errors and provide insights for process improvement. The proposed model can help healthcare providers to proactively monitor and address medical errors, thereby reducing the risk of harm to patients.
KW - Data Mining
KW - Healthcare Artificial Intelligence
KW - Healthcare System Modelling
KW - Medical Error Detection
KW - complex System Modeling
U2 - 10.1109/AICCSA59173.2023.10479263
DO - 10.1109/AICCSA59173.2023.10479263
M3 - Conference contribution
AN - SCOPUS:85190118041
T3 - Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA
BT - 2023 20th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2023 - Proceedings
PB - IEEE Computer Society
T2 - 20th ACS/IEEE International Conference on Computer Systems and Applications, AICCSA 2023
Y2 - 4 December 2023 through 7 December 2023
ER -