TY - GEN
T1 - Electronic Prescription Service for Improved Healthcare Delivery
AU - Hai, Tao
AU - Li, Shaoyi
AU - Adedayo, Afolake O.
AU - Zadeh, Shirin Abolfath
AU - Cai, Jiuping
AU - Iwendi, Celestine
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - The aim of this research paper is to explore the potential of machine learning techniques in predicting the utilization of the Electronic Prescription Service (EPS) and Electronic Repeat Dispensing (eRD) items to categorize General Practitioner (GP) practices based on their usage patterns. The study utilized raw data related to dispensaries, EPS, and eRD acquired from the National Health Service online medical database. To achieve this objective, exploratory data analysis was conducted on the dataset, which was then split into a training set and a testing set. Various machine learning algorithms, including linear regression, decision tree regression, and random forest regression, were applied to the training set to develop a predictive model. The models were evaluated using measurements such as the “Score”, “Mean Squared Error (MSE)”, “Mean Absolute Error (MAE)”, “Sqrt Mean Absolute Error (MAE)” and “Coefficient of determination (R^2)”. The study found that the machine learning models developed were effective in predicting EPS utilisation and could categorize GP practices based on their usage patterns. This categorization could help identify high-utilization practices, leading to more efficient resource allocation and ultimately improved healthcare delivery. The results also indicate the potential for machine learning techniques to predict the utilization of other healthcare services and could pave the way for more personalized and targeted healthcare services in the future.
AB - The aim of this research paper is to explore the potential of machine learning techniques in predicting the utilization of the Electronic Prescription Service (EPS) and Electronic Repeat Dispensing (eRD) items to categorize General Practitioner (GP) practices based on their usage patterns. The study utilized raw data related to dispensaries, EPS, and eRD acquired from the National Health Service online medical database. To achieve this objective, exploratory data analysis was conducted on the dataset, which was then split into a training set and a testing set. Various machine learning algorithms, including linear regression, decision tree regression, and random forest regression, were applied to the training set to develop a predictive model. The models were evaluated using measurements such as the “Score”, “Mean Squared Error (MSE)”, “Mean Absolute Error (MAE)”, “Sqrt Mean Absolute Error (MAE)” and “Coefficient of determination (R^2)”. The study found that the machine learning models developed were effective in predicting EPS utilisation and could categorize GP practices based on their usage patterns. This categorization could help identify high-utilization practices, leading to more efficient resource allocation and ultimately improved healthcare delivery. The results also indicate the potential for machine learning techniques to predict the utilization of other healthcare services and could pave the way for more personalized and targeted healthcare services in the future.
KW - Electronic Prescription Service (EPS)
KW - Electronic Repeat Dispensing (eRD)
KW - Exploratory data analysis
KW - Machine learning
KW - National Health Service (NHS)
KW - Regression models
KW - Web applications
KW - ePrescribing systems
UR - https://www.scopus.com/pages/publications/85174519434
U2 - 10.1007/978-3-031-37164-6_12
DO - 10.1007/978-3-031-37164-6_12
M3 - Conference contribution
AN - SCOPUS:85174519434
SN - 9783031371639
T3 - Lecture Notes in Networks and Systems
SP - 161
EP - 173
BT - Proceedings of ICACTCE'23—The International Conference on Advances in Communication Technology and Computer Engineering - New Artificial Intelligence and the Internet of Things Based Perspective and Solutions
A2 - Iwendi, Celestine
A2 - Boulouard, Zakaria
A2 - Kryvinska, Natalia
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Advances in Communication Technology and Computer Engineering, ICACTCE 2023
Y2 - 24 February 2023 through 25 February 2023
ER -