TY - GEN
T1 - Gauging the Gaps for Decision Support - Data integration in the Hospital Information Systems with Machine Learning
AU - Wang, William Yu Chung
AU - Jiang, Philip Hong Wei
AU - Tiong, Thye Goh
AU - Hsieh, Chih Chia
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/5/15
Y1 - 2022/5/15
N2 - It has been trendy to embedded machine learning techniques in enhancing decision supports in the organisations. The essential expectation for getting accurate prediction and estimation via such tool is the integrated systems and data quality - accuracy, completeness, consistency, timeliness, validity, and uniqueness. As suggested by the literature, however, hospital information systems are fragmented, and various departments implement various expert systems from different vendors due to the nature of medical complexity. Therefore, this paper proposes a conceptual framework that explains how data could be integrated from the separated systems for clinical decision support with a context of emergency department and how machine learning systems can be placed in the architecture of the completed hospital information systems.
AB - It has been trendy to embedded machine learning techniques in enhancing decision supports in the organisations. The essential expectation for getting accurate prediction and estimation via such tool is the integrated systems and data quality - accuracy, completeness, consistency, timeliness, validity, and uniqueness. As suggested by the literature, however, hospital information systems are fragmented, and various departments implement various expert systems from different vendors due to the nature of medical complexity. Therefore, this paper proposes a conceptual framework that explains how data could be integrated from the separated systems for clinical decision support with a context of emergency department and how machine learning systems can be placed in the architecture of the completed hospital information systems.
UR - http://www.scopus.com/inward/record.url?scp=85140001395&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85140001395&partnerID=8YFLogxK
U2 - 10.1145/3545729.3545742
DO - 10.1145/3545729.3545742
M3 - Conference contribution
AN - SCOPUS:85140001395
T3 - ACM International Conference Proceeding Series
SP - 43
EP - 47
BT - ICMHI 2022 - 2022 6th International Conference on Medical and Health Informatics
PB - Association for Computing Machinery
T2 - 6th International Conference on Medical and Health Informatics, ICMHI 2022
Y2 - 12 May 2022 through 15 May 2022
ER -